Randomized Clinical Trials and Clinical Research
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Randomized Clinical Trials (RCTs) are a gold standard in clinical research and drug development, providing a rigorous and unbiased approach to evaluating the safety and efficacy of medical interventions. Here's an overview of Randomized Clinical Trials, their key features, and their significance in advancing medical knowledge
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Randomized Clinical Trial
Randomized controlled trials RCTs are widely used in medical research to assess the effectiveness of medical interventions. RCTs are designed to reduce the potential for bias and increase the reliability of results by randomly assigning participants to either the treatment group or the control group. In an RCT, the treatment group receives the experimental intervention, while the control group receives either no intervention or the standard treatment. By comparing the outcomes of the two groups, researchers can determine whether the intervention is effective, less effective, or no different in effectiveness. There are various types of RCT designs, including simple RCT, cluster RCT, and factorial design. Blinding is a technique used to reduce bias in RCTs. The results of RCTs are widely used to inform clinical practice, health policy, and decision making. Atika Siddiqua | Hiba Ahmed | P. Aravind "Randomized Clinical Trial" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55088.pdf Paper URL: https://www.ijtsrd.com.com/other-scientific-research-area/other/55088/randomized-clinical-trial/atika-siddiqua
Randomization – From The Technical Front
Randomization is a fundamental concept in clinical research that refers to the process of randomly assigning participants to different study groups. Randomization is a crucial tool for reducing bias and ensuring that study results are unbiased and statistically valid.
Randomization involves assigning participants to study groups in a way that is not influenced by any factors that could potentially affect the outcome of the study. This is typically done using a computer-generated randomization sequence or a random number table.
There are several benefits to using randomization in clinical research. First, it helps to ensure that the study groups are comparable in terms of baseline characteristics, such as age, sex, and disease severity. This reduces the risk of bias and confounding factors that could impact study results.
Second, randomization helps to ensure that any observed differences between the study groups are due to the intervention being tested, rather than other factors. This is critical for establishing causality and determining whether a particular intervention is effective.
Finally, randomization helps to ensure that the results of the study are statistically valid. By randomly assigning participants to study groups, researchers can calculate the probability of obtaining the observed results by chance alone, which helps to determine the significance of the findings.
In summary, randomization is a critical tool for ensuring the validity and reliability of clinical research results. By reducing bias and ensuring that study groups are comparable, randomization helps to establish causality and determine the effectiveness of medical interventions.
Randomized Clinical Trials
In the pursuit of advancing medical knowledge and improving patient care, randomized clinical trials (RCTs) stand as the gold standard for rigorous and unbiased research. They are the backbone of evidence-based medicine, offering invaluable insights into the effectiveness and safety of medical interventions. Let's delve into the world of RCTs, exploring their significance, key principles, and their critical role in healthcare.
The Foundation of Evidence-Based Medicine
RCTs are the linchpin of evidence-based medicine, a paradigm that emphasizes clinical decisions based on empirical evidence and scientific inquiry. The fundamental premise of RCTs is to provide a structured and unbiased way to evaluate the efficacy and safety of medical treatments, interventions, or drugs.
Key Principles of Randomized Clinical Trials:
Randomization: Participants are randomly allocated into two or more groups, ensuring that each group is comparable at the outset. This minimizes the risk of bias in group assignment, enhancing the reliability of the results.
Control Group: RCTs typically include a control group that receives either a placebo or an existing standard treatment. The experimental group receives the new intervention under investigation.
Blinding: To minimize observer and participant bias, RCTs often employ blinding. Single-blind studies conceal information from either the participants or the investigators, while double-blind studies conceal information from both.
Outcomes and Endpoints: RCTs define specific outcomes or endpoints, such as disease progression, side effects, or mortality rates, to measure the intervention's impact.
clinical trials types and design
types of clinical trials , phases of clinical trials , clinical design, reference
3.conducting research effectively in a clinical setup with voice overs
Informative content on types of clinical study like experimental and non-experimental studies with examples which explains what kind of study yields specific results, when to consider hypothesis, how observational study differs from experimental etc.
Recommended
Randomized Clinical Trial
Randomized controlled trials RCTs are widely used in medical research to assess the effectiveness of medical interventions. RCTs are designed to reduce the potential for bias and increase the reliability of results by randomly assigning participants to either the treatment group or the control group. In an RCT, the treatment group receives the experimental intervention, while the control group receives either no intervention or the standard treatment. By comparing the outcomes of the two groups, researchers can determine whether the intervention is effective, less effective, or no different in effectiveness. There are various types of RCT designs, including simple RCT, cluster RCT, and factorial design. Blinding is a technique used to reduce bias in RCTs. The results of RCTs are widely used to inform clinical practice, health policy, and decision making. Atika Siddiqua | Hiba Ahmed | P. Aravind "Randomized Clinical Trial" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55088.pdf Paper URL: https://www.ijtsrd.com.com/other-scientific-research-area/other/55088/randomized-clinical-trial/atika-siddiqua
Randomization – From The Technical Front
Randomization is a fundamental concept in clinical research that refers to the process of randomly assigning participants to different study groups. Randomization is a crucial tool for reducing bias and ensuring that study results are unbiased and statistically valid.
Randomization involves assigning participants to study groups in a way that is not influenced by any factors that could potentially affect the outcome of the study. This is typically done using a computer-generated randomization sequence or a random number table.
There are several benefits to using randomization in clinical research. First, it helps to ensure that the study groups are comparable in terms of baseline characteristics, such as age, sex, and disease severity. This reduces the risk of bias and confounding factors that could impact study results.
Second, randomization helps to ensure that any observed differences between the study groups are due to the intervention being tested, rather than other factors. This is critical for establishing causality and determining whether a particular intervention is effective.
Finally, randomization helps to ensure that the results of the study are statistically valid. By randomly assigning participants to study groups, researchers can calculate the probability of obtaining the observed results by chance alone, which helps to determine the significance of the findings.
In summary, randomization is a critical tool for ensuring the validity and reliability of clinical research results. By reducing bias and ensuring that study groups are comparable, randomization helps to establish causality and determine the effectiveness of medical interventions.
Randomized Clinical Trials
In the pursuit of advancing medical knowledge and improving patient care, randomized clinical trials (RCTs) stand as the gold standard for rigorous and unbiased research. They are the backbone of evidence-based medicine, offering invaluable insights into the effectiveness and safety of medical interventions. Let's delve into the world of RCTs, exploring their significance, key principles, and their critical role in healthcare.
The Foundation of Evidence-Based Medicine
RCTs are the linchpin of evidence-based medicine, a paradigm that emphasizes clinical decisions based on empirical evidence and scientific inquiry. The fundamental premise of RCTs is to provide a structured and unbiased way to evaluate the efficacy and safety of medical treatments, interventions, or drugs.
Key Principles of Randomized Clinical Trials:
Randomization: Participants are randomly allocated into two or more groups, ensuring that each group is comparable at the outset. This minimizes the risk of bias in group assignment, enhancing the reliability of the results.
Control Group: RCTs typically include a control group that receives either a placebo or an existing standard treatment. The experimental group receives the new intervention under investigation.
Blinding: To minimize observer and participant bias, RCTs often employ blinding. Single-blind studies conceal information from either the participants or the investigators, while double-blind studies conceal information from both.
Outcomes and Endpoints: RCTs define specific outcomes or endpoints, such as disease progression, side effects, or mortality rates, to measure the intervention's impact.
clinical trials types and design
types of clinical trials , phases of clinical trials , clinical design, reference
3.conducting research effectively in a clinical setup with voice overs
Informative content on types of clinical study like experimental and non-experimental studies with examples which explains what kind of study yields specific results, when to consider hypothesis, how observational study differs from experimental etc.
Role of Biostatistics in Clinical Trials
Biostatistics plays a pivotal role in the design, conduct, analysis, and interpretation of clinical trials. This field of statistics is indispensable in ensuring the scientific rigor and validity of clinical research. Here are key aspects of the role of biostatistics in clinical trials
Epidemiology designs for clinical trials - Pubrica
1. Clinical trial study design
2. Cohort Study design
3. Case-Control Studies
4. Cross-Sectional Studies
5. Ecological Studies
6. Randomized Clinical Trials
Continue Reading: https://bit.ly/3tDt6rH
Reference: https://pubrica.com/services/research-services/experimental-design/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Surrogate endpoints in global health research: still searching for killer app...
Surrogate endpoints in global health
research: still searching for killer apps
and silver bullets?
Bias in clinical research
This is an interesting ppt discussing in detail about bias in clinical research and how to overcome it...
Randomization in Clinical Trials.pptx
By preventing confounding from other circumstances, a successful clinical trial reduces the heterogeneity of the assessment and offers an objective assessment of the intervention.
With the exception of the intervention that each group receives, randomization ensures that every patient has an identical likelihood of obtaining any of the therapies being studied.
Available online at www.sciencedirect.comN u r s O u t l o o.docx
Available online at www.sciencedirect.com
N u r s O u t l o o k 6 0 ( 2 0 1 2 ) 1 8 2 e 1 9 0
www.nursingoutlook.org
Using meta-analyses for comparative effectiveness
research
Vicki S. Conn, PhD, RN, FAAN*, Todd M. Ruppar, PhD, RN, GCNS-BC,
Lorraine J. Phillips, PhD, RN, Jo-Ana D. Chase, MN, APRN-BC
Meta-Analysis Research Center, School of Nursing, University of Missouri, Columbia, MO
a r t i c l e i n f o
Article history:
Received 30 December 2011
Revised 16 April 2012
Accepted 22 April 2012
Keywords:
Comparative effectiveness
research
Meta-analysis
* Corresponding author: Dr. Vicki S. Conn, A
Center, S317 School of Nursing, University o
E-mail address: [email protected] (V.S.
0029-6554/$ - see front matter � 2012 Elsevi
doi:10.1016/j.outlook.2012.04.004
a b s t r a c t
Comparative effectiveness research seeks to identify the most effective inter-
ventions for particular patient populations. Meta-analysis is an especially
valuable form of comparative effectiveness research because it emphasizes the
magnitude of intervention effects rather than relying on tests of statistical
significance among primary studies. Overall effects can be calculated for diverse
clinical and patient-centered variables to determine the outcome patterns.
Moderator analyses compare intervention characteristics among primary
studies by determining whether effect sizes vary among studies with different
intervention characteristics. Intervention effectiveness can be linked to patient
characteristics to provide evidence for patient-centered care. Moderator anal-
yses often answer questions never posed by primary studies because neither
multiple intervention characteristics nor populations are compared in single
primary studies. Thus, meta-analyses provide unique contributions to knowl-
edge. Although meta-analysis is a powerful comparative effectiveness strategy,
methodological challenges and limitations in primary research must be
acknowledged to interpret findings.
Cite this article: Conn, V. S., Ruppar, T. M., Phillips, L. J., & Chase, J.-A. D. (2012, AUGUST). Using meta-
analyses for comparative effectiveness research. Nursing Outlook, 60(4), 182-190. doi:10.1016/
j.outlook.2012.04.004.
Despite remarkable scientific advances over recent
decades, the effectiveness of many health interven-
tions remains unclear. The Institute of Medicine noted
that evidence of effectiveness exists for less than half of
the interventions in use today.1 Scant evidence exists
comparing multiple possible interventions for the same
health problem.2 Newer or more costly interventions
may not be linked with better outcomes, and variations
in health care expenditure may be unrelated to changes
in health outcomes.3-5 The troubling lack of information
about interventions’ relative effectiveness led to
comparative effectiveness research (CER) initiatives.
ssociate Dean & Potter-B
f Missouri, Columbia, MO
Conn).
er Inc. All rights reserved
CER can be defined as research designed to discov.
Excelsior College PBH 321 Page 1 EXPERI MENTAL E.docx
Excelsior College PBH 321
Page 1
EXPERI MENTAL E PIDE MIOLOGICAL STUDIE S
Epidemiologic studies are either observational or experimental. Observational studies, including ecologic,
cross-sectional, cohort, and case-control designs, are considered “natural” experiments, but experimental
studies are considered true experiments. We will spend the next 2 modules discussing these designs.
Before we begin to discuss study designs, we need a brief introduction to a concept that we will spend more
time discussing in later modules -- bias. The definition of bias is:
“Deviation of results or inferences from the truth, or processes leading to such deviation. Any trend in the
collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are
systematically different from the truth.” (Last, J.M., A Dictionary of Epidemiology, 4th ed.)
Epidemiologists are naturally concerned whether the results of an epidemiologic study are biased, since many
important public health decisions are often drawn from epidemiologic research. The severity of the bias, that
is - how much it influences or distorts the results, is related to the study design as well as how information is
analyzed.
Experimental Studies
The defining feature of experimental studies is that the investigator assigns exposure to the study subjects.
Experimental studies most closely resemble controlled laboratory experiments and serve as models for the
conduct of observational studies, thus they are the “gold standard” of epidemiologic research. Experimental
studies have high validity (i.e., less bias), and can identify even very small effects. The most well known type of
experimental study is a randomized trial (sometimes referred to as a randomized controlled trial), where the
investigator randomly assigns exposure to the study subjects. In this type of study, the only expected
difference between the experimental and control groups is the outcome variable being studied.
Experimental designs like the randomized trial can assess both preventive interventions, where a prophylactic
agent is given to healthy or high-risk individual to prevent disease, or can assess effects of therapeutic
treatment, such as those given to diseased individuals to reduce their risk of disease recurrence, or to improve
their survival or quality of life.
Preventive intervention: Does tamoxifen lower the incidence of breast cancer in women with high risk profile
compared to high risk women not given tamoxifen?
Therapeutic intervention: Do combinations of two or three antiretroviral drugs prolong survival of AIDS
patients as well as regimens of single drugs?
The investigator can assign exposures (or allocate interventions) to either individuals or to an entire
community.
Individual-level assignment: Do women with stage I breast cancer given a lumpectomy alone survive as long
without recurrence of disease as women given a lumpec ...
THE CLINICAL AND ECONOMIC VALUE OF GENETIC SEQUENCING IN CANCER CARE
NGS-based diagnostic testing compared to single-marker genetic testing (SMGT), has the potential to improve testing efficiency and to identify more cancer patients who could benefit from targeted therapies, but the impact on outcomes and total costs of care is uncertain. Recent studies using simulation modeling informed with data from the Flatiron Health database, representing curated electronic health record-derived clinical information from 191 oncology practices, has shown only moderate cost effectiveness of NGS vs. SGMT for patients with advanced non-small cell lung cancer (aNSCLC). The data suggests, however, that efforts to increase the proportion of patients who receive targeted therapies would improve the cost-effectiveness of NGS. To effectively inform access and reimbursement policy decisions there is a need to examine the NGS value proposition from the perspective of all stakeholders.
Author(s) and affiliation(s): Lotte Steuten (Office of Health Economics, London, UK); Bernardo Goulart (Fred Hutchinson Cancer Research Center, Seattle, WA, US & Seattle Cancer Care Alliance, Seattle, WA, US); Neal J. Meropol (Flatiron Health, New York, NY, US & Case Western Reserve University, Cleveland, OH, US); Daryl Pritchard (Personalized Medicine Coalition, Washington, DC, US); and Scott D. Ramsey (Fred Hutchinson Cancer Research Center, Seattle, WA, US)
Event: ISPOR 2019
Location: New Orleans, LA, United States
Date: 20/05/2019
Causality Assessment of Adverse Drug Reactions
Causality assessment of adverse drug reactions (ADRs) is a process of evaluating the relationship between a drug and an observed adverse event. It aims to determine the likelihood that the drug caused the adverse event. Several methods and tools are used to assess causality, including:
Naranjo Algorithm: The Naranjo algorithm is a widely used tool that provides a systematic approach to assess causality. It consists of a series of questions to evaluate the temporal relationship, the presence of alternative causes, dechallenge and rechallenge information, and previous knowledge about the drug's association with the adverse event. The answers to these questions are scored, and the total score indicates the probability of a causal relationship.
World Health Organization-Uppsala Monitoring Centre (WHO-UMC) System: The WHO-UMC system classifies adverse drug reactions into different categories based on the strength of the evidence suggesting a causal relationship. These categories include "certain," "probable/likely," "possible," "unlikely," and "unclassified." The assessment takes into account factors such as temporal relationship, dechallenge and rechallenge data, and consistency of the event with known drug effects.
Bradford Hill Criteria: The Bradford Hill criteria are a set of guidelines originally developed to assess causality in epidemiological studies. They are also applied in the field of pharmacovigilance to evaluate the association between drugs and adverse events. The criteria include factors such as strength of association, consistency, temporal relationship, dose-response relationship, biological plausibility, and experimental evidence.
Algorithm-Based Approaches: Various algorithms have been developed to assess causality based on predefined criteria and algorithms. Examples include the Roussel Uclaf Causality Assessment Method (RUCAM) for hepatotoxicity and the Drug-Induced Liver Injury Network (DILIN) Causality Assessment Tool.
Randomized controlled trial: Going for the Gold
Techniques and methodology of randomized control trials
38 www.e-enm.orgEndocrinol Metab 2016;3138-44httpdx.
38 www.e-enm.org
Endocrinol Metab 2016;31:38-44
http://dx.doi.org/10.3803/EnM.2016.31.1.38
pISSN 2093-596X · eISSN 2093-5978
Review
Article
How to Establish Clinical Prediction Models
Yong-ho Lee1, Heejung Bang2, Dae Jung Kim3
1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; 2Division of Biostatistics, Department
of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA; 3Department of Endocrinology
and Metabolism, Ajou University School of Medicine, Suwon, Korea
A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymp-
tomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education.
Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statisti-
cal analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model develop-
ment and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for de-
veloping and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection;
handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods
for developing clinical prediction models with comparable examples from real practice. After model development and vigorous
validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use
in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading
to active applications in real clinical practice.
Keywords: Clinical prediction model; Development; Validation; Clinical usefulness
INTRODUCTION
Hippocrates emphasized prognosis as a principal component of
medicine [1]. Nevertheless, current medical investigation
mostly focuses on etiological and therapeutic research, rather
than prognostic methods such as the development of clinical
prediction models. Numerous studies have investigated wheth-
er a single variable (e.g., biomarkers or novel clinicobiochemi-
cal parameters) can predict or is associated with certain out-
comes, whereas establishing clinical prediction models by in-
corporating multiple variables is rather complicated, as it re-
quires a multi-step and multivariable/multifactorial approach to
design and analysis [1].
Clinical prediction models can inform patients and their
physicians or other healthcare providers of the patient’s proba-
bility of having or developing a certain disease and help them
with associated decision-making (e.g., facilitating patient-doc-
tor communication based on more objective information). Ap-
Received: 9 January 2016, Revised: 14 ...
Role of Drug Regulatory agencies in Clinical Research.
Drug regulatory agencies play a pivotal role in overseeing and regulating clinical research to ensure the safety, efficacy, and quality of pharmaceutical products. Their primary responsibility is to protect public health by evaluating the data generated from clinical trials and determining whether a drug can be approved for marketing and use in patients. Here are some key roles of drug regulatory agencies in clinical research:
Approval and Oversight of Clinical Trials: Regulatory agencies review and approve clinical trial protocols, ensuring that they adhere to ethical and scientific standards. They assess the design, methodology, and objectives of trials to ensure patient safety and the reliability of data generated.
Regulatory Guidance and Standards: These agencies provide guidance and establish regulations governing the conduct of clinical trials, including Good Clinical Practice (GCP) guidelines, which outline standards for trial design, conduct, monitoring, and reporting.
Review and Evaluation of Data: Regulatory agencies review the data collected from clinical trials to assess the safety and efficacy of investigational drugs. They evaluate study results, adverse events, and other relevant information to make informed decisions about drug approval.
Drug Approval and Labeling: Based on the evaluation of clinical trial data, regulatory agencies decide whether to approve a drug for marketing and use. They also determine the appropriate labeling, including indications, dosages, contraindications, and warnings, to ensure safe and effective use by healthcare professionals and patients.
Post-Marketing Surveillance: Regulatory agencies continue to monitor the safety and effectiveness of approved drugs through post-marketing surveillance programs. They collect and analyze real-world data on adverse events and drug utilization to identify potential risks and take appropriate regulatory actions if safety concerns arise.
Enforcement of Regulations: Regulatory agencies enforce compliance with regulatory requirements and take enforcement actions against sponsors, investigators, or manufacturers who fail to adhere to ethical or regulatory standards in clinical research.
International Collaboration: Many regulatory agencies collaborate with counterparts in other countries to harmonize regulatory standards, exchange information, and streamline the drug approval process, facilitating global drug development and access to new therapies.
Data Privacy and consent management .. .
Data privacy and consent management are critical aspects of ensuring that individuals' personal information is handled responsibly and ethically, particularly in healthcare settings where sensitive medical data is involved. Data privacy refers to the protection of personal information from unauthorized access, use, or disclosure, while consent management involves obtaining and managing individuals' permissions for the collection, storage, and processing of their data.
In healthcare, patients entrust providers with their sensitive medical information, expecting that it will be kept confidential and used only for legitimate purposes related to their care. Robust data privacy measures include encryption, access controls, and anonymization techniques to safeguard patient data from unauthorized access or breaches. Additionally, healthcare organizations must adhere to regulatory standards such as HIPAA in the United States or GDPR in the European Union, which outline specific requirements for the protection of patient information and impose penalties for non-compliance.
Consent management plays a crucial role in ensuring that individuals have control over how their data is used. Patients should be informed about the purposes for which their data will be collected and processed, as well as any potential risks or benefits associated with its use. Obtaining informed consent involves providing individuals with clear and transparent information about their privacy rights and giving them the opportunity to consent to or decline the use of their data for specific purposes. Consent management systems help healthcare organizations track and manage patients' consent preferences, ensuring that data is used in accordance with their wishes and legal requirements.
Effective data privacy and consent management practices not only protect individuals' privacy rights but also foster trust and transparency in healthcare relationships. By implementing robust security measures, respecting patients' autonomy, and promoting informed decision-making, healthcare organizations can uphold the principles of data privacy and consent while leveraging data responsibly to improve patient care and outcomes.
More Related Content
Similar to Randomized Clinical Trials and Clinical Research
Role of Biostatistics in Clinical Trials
Biostatistics plays a pivotal role in the design, conduct, analysis, and interpretation of clinical trials. This field of statistics is indispensable in ensuring the scientific rigor and validity of clinical research. Here are key aspects of the role of biostatistics in clinical trials
Epidemiology designs for clinical trials - Pubrica
1. Clinical trial study design
2. Cohort Study design
3. Case-Control Studies
4. Cross-Sectional Studies
5. Ecological Studies
6. Randomized Clinical Trials
Continue Reading: https://bit.ly/3tDt6rH
Reference: https://pubrica.com/services/research-services/experimental-design/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Surrogate endpoints in global health research: still searching for killer app...
Surrogate endpoints in global health
research: still searching for killer apps
and silver bullets?
Bias in clinical research
This is an interesting ppt discussing in detail about bias in clinical research and how to overcome it...
Randomization in Clinical Trials.pptx
By preventing confounding from other circumstances, a successful clinical trial reduces the heterogeneity of the assessment and offers an objective assessment of the intervention.
With the exception of the intervention that each group receives, randomization ensures that every patient has an identical likelihood of obtaining any of the therapies being studied.
Available online at www.sciencedirect.comN u r s O u t l o o.docx
Available online at www.sciencedirect.com
N u r s O u t l o o k 6 0 ( 2 0 1 2 ) 1 8 2 e 1 9 0
www.nursingoutlook.org
Using meta-analyses for comparative effectiveness
research
Vicki S. Conn, PhD, RN, FAAN*, Todd M. Ruppar, PhD, RN, GCNS-BC,
Lorraine J. Phillips, PhD, RN, Jo-Ana D. Chase, MN, APRN-BC
Meta-Analysis Research Center, School of Nursing, University of Missouri, Columbia, MO
a r t i c l e i n f o
Article history:
Received 30 December 2011
Revised 16 April 2012
Accepted 22 April 2012
Keywords:
Comparative effectiveness
research
Meta-analysis
* Corresponding author: Dr. Vicki S. Conn, A
Center, S317 School of Nursing, University o
E-mail address: [email protected] (V.S.
0029-6554/$ - see front matter � 2012 Elsevi
doi:10.1016/j.outlook.2012.04.004
a b s t r a c t
Comparative effectiveness research seeks to identify the most effective inter-
ventions for particular patient populations. Meta-analysis is an especially
valuable form of comparative effectiveness research because it emphasizes the
magnitude of intervention effects rather than relying on tests of statistical
significance among primary studies. Overall effects can be calculated for diverse
clinical and patient-centered variables to determine the outcome patterns.
Moderator analyses compare intervention characteristics among primary
studies by determining whether effect sizes vary among studies with different
intervention characteristics. Intervention effectiveness can be linked to patient
characteristics to provide evidence for patient-centered care. Moderator anal-
yses often answer questions never posed by primary studies because neither
multiple intervention characteristics nor populations are compared in single
primary studies. Thus, meta-analyses provide unique contributions to knowl-
edge. Although meta-analysis is a powerful comparative effectiveness strategy,
methodological challenges and limitations in primary research must be
acknowledged to interpret findings.
Cite this article: Conn, V. S., Ruppar, T. M., Phillips, L. J., & Chase, J.-A. D. (2012, AUGUST). Using meta-
analyses for comparative effectiveness research. Nursing Outlook, 60(4), 182-190. doi:10.1016/
j.outlook.2012.04.004.
Despite remarkable scientific advances over recent
decades, the effectiveness of many health interven-
tions remains unclear. The Institute of Medicine noted
that evidence of effectiveness exists for less than half of
the interventions in use today.1 Scant evidence exists
comparing multiple possible interventions for the same
health problem.2 Newer or more costly interventions
may not be linked with better outcomes, and variations
in health care expenditure may be unrelated to changes
in health outcomes.3-5 The troubling lack of information
about interventions’ relative effectiveness led to
comparative effectiveness research (CER) initiatives.
ssociate Dean & Potter-B
f Missouri, Columbia, MO
Conn).
er Inc. All rights reserved
CER can be defined as research designed to discov.
Excelsior College PBH 321 Page 1 EXPERI MENTAL E.docx
Excelsior College PBH 321
Page 1
EXPERI MENTAL E PIDE MIOLOGICAL STUDIE S
Epidemiologic studies are either observational or experimental. Observational studies, including ecologic,
cross-sectional, cohort, and case-control designs, are considered “natural” experiments, but experimental
studies are considered true experiments. We will spend the next 2 modules discussing these designs.
Before we begin to discuss study designs, we need a brief introduction to a concept that we will spend more
time discussing in later modules -- bias. The definition of bias is:
“Deviation of results or inferences from the truth, or processes leading to such deviation. Any trend in the
collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are
systematically different from the truth.” (Last, J.M., A Dictionary of Epidemiology, 4th ed.)
Epidemiologists are naturally concerned whether the results of an epidemiologic study are biased, since many
important public health decisions are often drawn from epidemiologic research. The severity of the bias, that
is - how much it influences or distorts the results, is related to the study design as well as how information is
analyzed.
Experimental Studies
The defining feature of experimental studies is that the investigator assigns exposure to the study subjects.
Experimental studies most closely resemble controlled laboratory experiments and serve as models for the
conduct of observational studies, thus they are the “gold standard” of epidemiologic research. Experimental
studies have high validity (i.e., less bias), and can identify even very small effects. The most well known type of
experimental study is a randomized trial (sometimes referred to as a randomized controlled trial), where the
investigator randomly assigns exposure to the study subjects. In this type of study, the only expected
difference between the experimental and control groups is the outcome variable being studied.
Experimental designs like the randomized trial can assess both preventive interventions, where a prophylactic
agent is given to healthy or high-risk individual to prevent disease, or can assess effects of therapeutic
treatment, such as those given to diseased individuals to reduce their risk of disease recurrence, or to improve
their survival or quality of life.
Preventive intervention: Does tamoxifen lower the incidence of breast cancer in women with high risk profile
compared to high risk women not given tamoxifen?
Therapeutic intervention: Do combinations of two or three antiretroviral drugs prolong survival of AIDS
patients as well as regimens of single drugs?
The investigator can assign exposures (or allocate interventions) to either individuals or to an entire
community.
Individual-level assignment: Do women with stage I breast cancer given a lumpectomy alone survive as long
without recurrence of disease as women given a lumpec ...
THE CLINICAL AND ECONOMIC VALUE OF GENETIC SEQUENCING IN CANCER CARE
NGS-based diagnostic testing compared to single-marker genetic testing (SMGT), has the potential to improve testing efficiency and to identify more cancer patients who could benefit from targeted therapies, but the impact on outcomes and total costs of care is uncertain. Recent studies using simulation modeling informed with data from the Flatiron Health database, representing curated electronic health record-derived clinical information from 191 oncology practices, has shown only moderate cost effectiveness of NGS vs. SGMT for patients with advanced non-small cell lung cancer (aNSCLC). The data suggests, however, that efforts to increase the proportion of patients who receive targeted therapies would improve the cost-effectiveness of NGS. To effectively inform access and reimbursement policy decisions there is a need to examine the NGS value proposition from the perspective of all stakeholders.
Author(s) and affiliation(s): Lotte Steuten (Office of Health Economics, London, UK); Bernardo Goulart (Fred Hutchinson Cancer Research Center, Seattle, WA, US & Seattle Cancer Care Alliance, Seattle, WA, US); Neal J. Meropol (Flatiron Health, New York, NY, US & Case Western Reserve University, Cleveland, OH, US); Daryl Pritchard (Personalized Medicine Coalition, Washington, DC, US); and Scott D. Ramsey (Fred Hutchinson Cancer Research Center, Seattle, WA, US)
Event: ISPOR 2019
Location: New Orleans, LA, United States
Date: 20/05/2019
Causality Assessment of Adverse Drug Reactions
Causality assessment of adverse drug reactions (ADRs) is a process of evaluating the relationship between a drug and an observed adverse event. It aims to determine the likelihood that the drug caused the adverse event. Several methods and tools are used to assess causality, including:
Naranjo Algorithm: The Naranjo algorithm is a widely used tool that provides a systematic approach to assess causality. It consists of a series of questions to evaluate the temporal relationship, the presence of alternative causes, dechallenge and rechallenge information, and previous knowledge about the drug's association with the adverse event. The answers to these questions are scored, and the total score indicates the probability of a causal relationship.
World Health Organization-Uppsala Monitoring Centre (WHO-UMC) System: The WHO-UMC system classifies adverse drug reactions into different categories based on the strength of the evidence suggesting a causal relationship. These categories include "certain," "probable/likely," "possible," "unlikely," and "unclassified." The assessment takes into account factors such as temporal relationship, dechallenge and rechallenge data, and consistency of the event with known drug effects.
Bradford Hill Criteria: The Bradford Hill criteria are a set of guidelines originally developed to assess causality in epidemiological studies. They are also applied in the field of pharmacovigilance to evaluate the association between drugs and adverse events. The criteria include factors such as strength of association, consistency, temporal relationship, dose-response relationship, biological plausibility, and experimental evidence.
Algorithm-Based Approaches: Various algorithms have been developed to assess causality based on predefined criteria and algorithms. Examples include the Roussel Uclaf Causality Assessment Method (RUCAM) for hepatotoxicity and the Drug-Induced Liver Injury Network (DILIN) Causality Assessment Tool.
Randomized controlled trial: Going for the Gold
Techniques and methodology of randomized control trials
38 www.e-enm.orgEndocrinol Metab 2016;3138-44httpdx.
38 www.e-enm.org
Endocrinol Metab 2016;31:38-44
http://dx.doi.org/10.3803/EnM.2016.31.1.38
pISSN 2093-596X · eISSN 2093-5978
Review
Article
How to Establish Clinical Prediction Models
Yong-ho Lee1, Heejung Bang2, Dae Jung Kim3
1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea; 2Division of Biostatistics, Department
of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA; 3Department of Endocrinology
and Metabolism, Ajou University School of Medicine, Suwon, Korea
A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymp-
tomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education.
Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statisti-
cal analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model develop-
ment and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for de-
veloping and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection;
handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods
for developing clinical prediction models with comparable examples from real practice. After model development and vigorous
validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use
in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading
to active applications in real clinical practice.
Keywords: Clinical prediction model; Development; Validation; Clinical usefulness
INTRODUCTION
Hippocrates emphasized prognosis as a principal component of
medicine [1]. Nevertheless, current medical investigation
mostly focuses on etiological and therapeutic research, rather
than prognostic methods such as the development of clinical
prediction models. Numerous studies have investigated wheth-
er a single variable (e.g., biomarkers or novel clinicobiochemi-
cal parameters) can predict or is associated with certain out-
comes, whereas establishing clinical prediction models by in-
corporating multiple variables is rather complicated, as it re-
quires a multi-step and multivariable/multifactorial approach to
design and analysis [1].
Clinical prediction models can inform patients and their
physicians or other healthcare providers of the patient’s proba-
bility of having or developing a certain disease and help them
with associated decision-making (e.g., facilitating patient-doc-
tor communication based on more objective information). Ap-
Received: 9 January 2016, Revised: 14 ...
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38 www.e-enm.orgEndocrinol Metab 2016;3138-44httpdx.
38 www.e-enm.orgEndocrinol Metab 2016;3138-44httpdx.
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Role of Drug Regulatory agencies in Clinical Research.
Drug regulatory agencies play a pivotal role in overseeing and regulating clinical research to ensure the safety, efficacy, and quality of pharmaceutical products. Their primary responsibility is to protect public health by evaluating the data generated from clinical trials and determining whether a drug can be approved for marketing and use in patients. Here are some key roles of drug regulatory agencies in clinical research:
Approval and Oversight of Clinical Trials: Regulatory agencies review and approve clinical trial protocols, ensuring that they adhere to ethical and scientific standards. They assess the design, methodology, and objectives of trials to ensure patient safety and the reliability of data generated.
Regulatory Guidance and Standards: These agencies provide guidance and establish regulations governing the conduct of clinical trials, including Good Clinical Practice (GCP) guidelines, which outline standards for trial design, conduct, monitoring, and reporting.
Review and Evaluation of Data: Regulatory agencies review the data collected from clinical trials to assess the safety and efficacy of investigational drugs. They evaluate study results, adverse events, and other relevant information to make informed decisions about drug approval.
Drug Approval and Labeling: Based on the evaluation of clinical trial data, regulatory agencies decide whether to approve a drug for marketing and use. They also determine the appropriate labeling, including indications, dosages, contraindications, and warnings, to ensure safe and effective use by healthcare professionals and patients.
Post-Marketing Surveillance: Regulatory agencies continue to monitor the safety and effectiveness of approved drugs through post-marketing surveillance programs. They collect and analyze real-world data on adverse events and drug utilization to identify potential risks and take appropriate regulatory actions if safety concerns arise.
Enforcement of Regulations: Regulatory agencies enforce compliance with regulatory requirements and take enforcement actions against sponsors, investigators, or manufacturers who fail to adhere to ethical or regulatory standards in clinical research.
International Collaboration: Many regulatory agencies collaborate with counterparts in other countries to harmonize regulatory standards, exchange information, and streamline the drug approval process, facilitating global drug development and access to new therapies.
Data Privacy and consent management .. .
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In healthcare, patients entrust providers with their sensitive medical information, expecting that it will be kept confidential and used only for legitimate purposes related to their care. Robust data privacy measures include encryption, access controls, and anonymization techniques to safeguard patient data from unauthorized access or breaches. Additionally, healthcare organizations must adhere to regulatory standards such as HIPAA in the United States or GDPR in the European Union, which outline specific requirements for the protection of patient information and impose penalties for non-compliance.
Consent management plays a crucial role in ensuring that individuals have control over how their data is used. Patients should be informed about the purposes for which their data will be collected and processed, as well as any potential risks or benefits associated with its use. Obtaining informed consent involves providing individuals with clear and transparent information about their privacy rights and giving them the opportunity to consent to or decline the use of their data for specific purposes. Consent management systems help healthcare organizations track and manage patients' consent preferences, ensuring that data is used in accordance with their wishes and legal requirements.
Effective data privacy and consent management practices not only protect individuals' privacy rights but also foster trust and transparency in healthcare relationships. By implementing robust security measures, respecting patients' autonomy, and promoting informed decision-making, healthcare organizations can uphold the principles of data privacy and consent while leveraging data responsibly to improve patient care and outcomes.
Clinical data security and Integrity in Clinical Research
Clinical data security and integrity are paramount in maintaining patient confidentiality, protecting sensitive medical information, and ensuring the reliability of healthcare data. Security measures encompass various strategies and technologies aimed at safeguarding clinical data from unauthorized access, breaches, or manipulation.
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Pharmacovigilance in Patriatics and Geriatrics
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Precision medicine, with its focus on tailoring medical treatment to the individual characteristics of each patient, has ushered in a new era in healthcare. Within this paradigm, clinical trials play a pivotal role in testing and validating targeted therapies. This article explores the importance of adopting patient-centric approaches in precision medicine trials and outlines strategies to enhance their success. By prioritizing patient engagement, leveraging digital technologies, and fostering collaborative partnerships, precision medicine trials can not only advance scientific understanding but also ensure that patient perspectives and experiences are integral to the research process.
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Raising Awareness and Education:
Patient advocacy groups are instrumental in raising awareness about precision medicine among their communities.
They provide education and resources to patients, caregivers, and the general public, promoting a better understanding of the benefits and implications of precision medicine.
Patient Empowerment:
Advocacy groups empower patients by providing them with information about their conditions and treatment options.
They help patients understand the importance of participating in precision medicine initiatives, including clinical trials and genetic testing.
Supporting Research and Development:
Patient advocacy groups often collaborate with researchers and industry stakeholders to support the development of targeted therapies and diagnostics.
By actively participating in research initiatives, advocacy groups contribute to the identification of genetic markers, biomarkers, and other factors that influence treatment response.
Influencing Policy and Regulation:
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They work to ensure that regulations promote patient access to personalized treatments and protect patient rights, privacy, and data security.
Fostering Collaboration:
Patient advocacy groups facilitate collaboration among patients, researchers, healthcare providers, and industry partners.
They create platforms for sharing information, experiences, and best practices, fostering a collaborative environment that accelerates progress in precision medicine.
Clinical Trial Recruitment:
Advocacy groups play a crucial role in recruiting patients for clinical trials related to precision medicine.
Their engagement helps ensure diverse and representative participant populations, leading to more robust and applicable research outcomes.
Feedback and Patient-Centric Approaches:
Advocacy groups provide valuable feedback on the patient experience, preferences, and priorities.
This patient-centric approach helps researchers and healthcare professionals tailor precision medicine strategies to better meet the needs and expectations of the individuals they serve.
Championing Access to Treatments:
Patient advocacy groups work to improve patient access to precision medicine treatments.
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Flu Vaccine Alert in Bangalore Karnataka
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
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Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
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4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Novas diretrizes da OMS para os cuidados perinatais de mais qualidade
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Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Charaka Samhita Sutra sthana Chapter 15 Upakalpaniyaadhyaya
Charaka Samhita Sutra sthana Chapter 15 Upakalpaniyaadhyaya
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Hemodialysis: Chapter 3, Dialysis Water Unit - Dr.Gawad
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
ANATOMY AND PHYSIOLOGY OF URINARY SYSTEM.pptx
Valuable Content of Human Anatomy and Physiology of Urinary system as per PCI Syllabus for Pharmacy and PharmD Students.
Physiology of Special Chemical Sensation of Taste
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
BENIGN PROSTATIC HYPERPLASIA.BPH. BPHpdf
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
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micro teaching on communication m.sc nursing.pdf
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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Randomized Clinical Trials and Clinical Research
- 1. Welcome RANDOMIZED CLINICAL TRIALS 24/12/2023 www.clinosol.com | follow us on social media @clinosolresearch 1 Sravani Yenneti M. Pharmacy Student ID: CLS_232/112023
- 2. Randomized Clinical Trials 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 2 INTRODUCTION: RCT’s are the experimental studies designed to assess the effectiveness of medical interventions. These are the studies in clinical research in which, participants are randomly (randomized) assigned to an experimental group or a control group. These studies measures the effectiveness of a new intervention or treatment.
- 3. Randomized Clinical Trials 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 3 PURPOSE OF RCT’S: The main purpose of RCT’s is to measure the effectiveness of a new intervention or treatment. These studies reduces bias and provides a rigorous tool to examine cause-effect relationships between an intervention and outcome. These studies plays a pivotal role in providing robust evidence for medical decision-making.
- 4. Randomized Clinical Trials 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 4 DESIGN OF RCT’S: • Randomization process in clinical trials ensures that subjects have an equal chance of being assigned to each group. • This design helps in differentiating between control and experimental groups, and discussing the role of blinding to minimize bias.
- 5. Randomized Clinical Trials RANDOMIZATION TECHNIQUES: 1. Simple : Complete randomness of assignment of treatment to a particular group. 2. Stratified : Treatment and control groups are balanced on important prognostic factors like gender, age etc., that can influence the outcome of the study. 3. Block : Subjects are divided into blocks and randomization is carried out in each blocks. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 5
- 6. Randomized Clinical Trials STATISTICAL ANALYSIS: Null hypotheses – the intervention will have no impact on the outcome measure. − Outcome will be similar in both the test and control groups Alternative hypotheses − intervention will have meaningful effect and statistically significant. Statistical method (e.g.); − Pre and post intervention differences = paired t-test − Mean differences pre and post between two group = independent test − Mean differences pre and post between more than two groups = ANOVA 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 6
- 7. Randomized Clinical Trials CHALLENGES IN RCT’S: Participant recruitment can be a significant challenge in RCT’s. Researches may struggle to enroll a sufficiently diverse and representative sample, impacting the generalizability of study findings. Maintaining participant engagement throughout the trial is crucial. High dropout rates can compromise the study’s internal validity and statistical power, affecting the reliability of results. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 7
- 8. Randomized Clinical Trials CONCLUSION: As the results of a randomized clinical trial can have a significant impact on human health and on the way that health care is provided, it is very important to understand the overall course of each individual research. Successful RCT’s can have a profound impact on clinical practise, shaping guidelines and improving patient outcomes. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 8
- 9. Randomized Clinical Trials REFERENCES: BJOG. Author manuscript; available in PMC 2018 Dec 1. Published in final edited form as: BJOG. 2018 Dec; 125(13): 1716. Published online 2018 Jun 19. doi: 10.1111/1471-0528.15199 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 9
- 10. ThankYou! www.clinosol.com (India | Canada) 9121151622/623/624 info@clinosol.com 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 10