RCPsych AGM10 - Quality of Preventive and Screening Care for those with Menta...Alex J Mitchell
This document summarizes a study that aimed to examine whether the quality of preventive care received by patients with mental health conditions differs from those without mental illness. The study reviewed 73 studies from 1980 to 2009 that compared preventive care in those with and without psychiatric illness. It found 27 comparisons that showed inferior preventive care for individuals with mental illness, while 10 suggested superior care and 24 were inconclusive. Inferior care was most common for those with schizophrenia in areas like osteoporosis screening, blood pressure monitoring, and mammography. The study concludes there is strong evidence that preventive care quality is often lower for those with mental illness compared to others.
This document presents a Bayesian nonparametric approach to test equating. It introduces a Bayesian nonparametric model that is applicable to all major equating designs and has advantages over previous equating models. The Bayesian model accounts for positive dependence between distributions of scores from two tests, unlike previous models. The Bayesian model is compared to previous equating models through analysis of famous data sets in equating literature. Classical equating models are shown to be special cases of the Bayesian model under certain prior distributions.
This document provides a guide to important concepts in biostatistics and epidemiology used in medical research. It defines different types of study designs including experimental, observational, cohort, case-control and cross-sectional studies. It also defines key terminology used in these fields such as bias, confounding, measures of central tendency, measures of association like relative risk and odds ratio, and measures used to describe the quality of diagnostic tests and measurements. The guide is intended to help readers understand concepts in research articles on medical topics.
The document provides an overview of analysis of variance (ANOVA). It discusses the basic idea of comparing variability within and between treatment groups. The hypotheses aim to determine if treatment means are equal. Notations are introduced for the number of treatments, sample sizes, sums, means, and variances. An example illustrates the calculations. The theory is based on a normal model, and the treatment sum of squares captures variability between means. Proofs show the expected value of the treatment sum of squares.
This study evaluated the outcomes of a pharmacist-provided diabetes medication therapy management program (MTMP) sponsored by an employer for its employees and dependents. The study found:
1) Patients experienced improved clinical outcomes including reductions in A1c, blood pressure, and hospitalizations/ER visits.
2) Economic outcomes improved with reductions in costs of physician visits, hospitalizations, and ER visits.
3) Humanistic outcomes were positive with high patient satisfaction and improved disease knowledge retention over 6 months.
The MTMP resulted in overall improved health, quality of life and cost savings for participants.
Asenapine was found to be superior to placebo in improving both positive and negative symptoms of schizophrenia, as measured by PANSS scores. It was well tolerated with minimal weight gain or prolactin elevation compared to risperidone. Risperidone only improved positive symptoms versus placebo and was associated with greater weight gain and increased prolactin levels. The study demonstrates the efficacy and tolerability of asenapine for treatment of acute schizophrenia.
This document discusses key concepts in clinical trial statistics including probability, hypothesis testing, and types of errors. It defines important terms like confidence intervals, statistical significance, and power. Larger sample sizes, higher effect sizes, and less variability between samples can increase a study's power to detect real effects and avoid type II errors. The relationship between p-values, effect sizes, sample sizes, and clinical significance is complex. Statistical significance does not always equal clinical importance.
RCPsych AGM10 - Quality of Preventive and Screening Care for those with Menta...Alex J Mitchell
This document summarizes a study that aimed to examine whether the quality of preventive care received by patients with mental health conditions differs from those without mental illness. The study reviewed 73 studies from 1980 to 2009 that compared preventive care in those with and without psychiatric illness. It found 27 comparisons that showed inferior preventive care for individuals with mental illness, while 10 suggested superior care and 24 were inconclusive. Inferior care was most common for those with schizophrenia in areas like osteoporosis screening, blood pressure monitoring, and mammography. The study concludes there is strong evidence that preventive care quality is often lower for those with mental illness compared to others.
This document presents a Bayesian nonparametric approach to test equating. It introduces a Bayesian nonparametric model that is applicable to all major equating designs and has advantages over previous equating models. The Bayesian model accounts for positive dependence between distributions of scores from two tests, unlike previous models. The Bayesian model is compared to previous equating models through analysis of famous data sets in equating literature. Classical equating models are shown to be special cases of the Bayesian model under certain prior distributions.
This document provides a guide to important concepts in biostatistics and epidemiology used in medical research. It defines different types of study designs including experimental, observational, cohort, case-control and cross-sectional studies. It also defines key terminology used in these fields such as bias, confounding, measures of central tendency, measures of association like relative risk and odds ratio, and measures used to describe the quality of diagnostic tests and measurements. The guide is intended to help readers understand concepts in research articles on medical topics.
The document provides an overview of analysis of variance (ANOVA). It discusses the basic idea of comparing variability within and between treatment groups. The hypotheses aim to determine if treatment means are equal. Notations are introduced for the number of treatments, sample sizes, sums, means, and variances. An example illustrates the calculations. The theory is based on a normal model, and the treatment sum of squares captures variability between means. Proofs show the expected value of the treatment sum of squares.
This study evaluated the outcomes of a pharmacist-provided diabetes medication therapy management program (MTMP) sponsored by an employer for its employees and dependents. The study found:
1) Patients experienced improved clinical outcomes including reductions in A1c, blood pressure, and hospitalizations/ER visits.
2) Economic outcomes improved with reductions in costs of physician visits, hospitalizations, and ER visits.
3) Humanistic outcomes were positive with high patient satisfaction and improved disease knowledge retention over 6 months.
The MTMP resulted in overall improved health, quality of life and cost savings for participants.
Asenapine was found to be superior to placebo in improving both positive and negative symptoms of schizophrenia, as measured by PANSS scores. It was well tolerated with minimal weight gain or prolactin elevation compared to risperidone. Risperidone only improved positive symptoms versus placebo and was associated with greater weight gain and increased prolactin levels. The study demonstrates the efficacy and tolerability of asenapine for treatment of acute schizophrenia.
This document discusses key concepts in clinical trial statistics including probability, hypothesis testing, and types of errors. It defines important terms like confidence intervals, statistical significance, and power. Larger sample sizes, higher effect sizes, and less variability between samples can increase a study's power to detect real effects and avoid type II errors. The relationship between p-values, effect sizes, sample sizes, and clinical significance is complex. Statistical significance does not always equal clinical importance.
This document discusses key concepts in determining sample size and statistical power for clinical trials. It provides examples of calculating sample sizes for dichotomous and continuous outcomes. The importance of choosing an appropriate primary outcome and estimating event rates in control and intervention groups is emphasized. Methods for randomization in clinical trials like simple randomization and blocked randomization are also covered.
This document provides information about health statistics in Malaysia. It notes that in 2009, the population of Malaysia was 27.9 million with 499,410 total births and 133,920 total deaths. This suggests the population in 2010 should be 28,265,490, though immigration and emigration may cause discrepancies. It also defines key health statistics terms like rates, ratios, proportions, and discusses calculating and adjusting rates.
This document discusses statistical guidelines and considerations for clinical trials from various organizations. It provides an overview of how statistics are described in ICH, WHO, Malaysian, and European guidelines. Key points covered include the importance of pre-specifying the statistical analysis plan before a trial begins, considerations for study design such as randomization and blinding, determining sample size, evaluating safety data, and reporting trial results according to the pre-specified plan.
8.Calculate samplesize for clinical trials (continuous outcome)Azmi Mohd Tamil
This document discusses calculating sample sizes for studies involving continuous outcome data from two independent groups. It provides the formula for calculating the standardized difference between groups given the clinically relevant difference and population standard deviation. It then shows how to use power, alpha level, and standardized difference to determine sample size using tables or software. Examples are provided to demonstrate calculating the combined standard deviation when two standard deviations are provided, and what to do if prior information is not available to determine sample size.
5. Calculate samplesize for case-control studiesAzmi Mohd Tamil
This document discusses sample size calculations for case-control studies. It provides an example comparing the rate of diabetes mellitus (DM) between patients with cataracts (cases) and those with normal vision (controls). Based on literature finding a 50% DM rate in cases and 8% in controls, the required sample size is 17 cases and 17 controls to detect this difference with 80% power and 5% type 1 error rate. Manual calculations and online calculators can both be used to determine sample size for case-control studies. Prior information is needed on exposure rates in cases and controls to perform these calculations.
This document discusses tools for calculating sample size, including the free PS2 program and StatCalc program. It explains that sample size calculation depends on study design, and directs the reader to other sections that describe how to calculate sample size for different study designs like cross-sectional, case-control, cohort, and clinical trials. References are provided for further information.
This document discusses different sampling techniques in SAS including systematic sampling from a known population, systematic sampling from an unknown population, random sampling with and without replacement, and permuted block randomization for clinical trials.
This document summarizes information about prescription drug costs and development. It discusses how medicines have transformed treatment for diseases like hepatitis C and cancer. Developing new treatments takes over 10 years and $2.6 billion on average, with only 12% of drug candidates being approved. Medicines help avoid expensive medical services and provide major savings to the healthcare system. While drug costs have risen, they account for a stable share of overall healthcare spending and are projected to grow in line with other healthcare costs. Many factors influence drug prices, including discounts, rebates, and competition from generics.
- A sample is a small group selected from a population to represent that population. Sampling provides benefits like being less time-consuming, less expensive, and allowing results to be repeated.
- There are two main types of samples: probability and non-probability. Probability samples include simple random, systematic, stratified, and cluster samples. Sample size is determined based on factors like the type of study, expected results, costs, and available resources.
- Inferential statistics allow generalization from a sample to a population through hypothesis testing and significance tests. Tests include t-tests, F-tests, chi-squared tests, and correlation/regression to analyze relationships between variables. Significant results suggest differences are likely not due to chance
This document discusses various parametric tests used for hypothesis testing with quantitative data, including:
- One-sample t-test to compare a sample mean to a predefined value
- Two-sample t-test to compare means of two independent groups
- Paired t-test to compare means of two related/matched groups
- ANOVA tests to compare means of three or more groups, including one-way and two-way ANOVA
- Assumptions of parametric tests like normal distribution and additive effects are also outlined.
The binomial distribution describes the number of successes in a fixed number of binary trials. A binomial experiment has the following properties: it consists of n independent and identical trials, each resulting in either success or failure. The probability of success p is the same for each trial. The random variable X represents the number of successes, which follows a binomial distribution with parameters n and p. The mean of the binomial is np and the standard deviation is npq, where q is 1 - p. For large n, the binomial can be approximated by a normal distribution.
This document discusses statistical issues related to using patient-reported outcome (PRO) measures in clinical trials. It notes that PROs are often measured on an ordinal scale with skewed distributions and are subject to floor and ceiling effects. Standard analysis methods may not be appropriate. It also discusses challenges like regression to the mean, baseline adjustment, and evaluating change over time without a control group. While a multi-scale PRO like a health-related quality of life instrument can be used as a primary endpoint, its multidimensional nature raises issues around validation and methodology compared to single measures. Regulatory agencies provide guidance on properly using PROs, including multi-scale ones, as primary endpoints.
Here are some potential threats:
Internal validity threats:
- History effects (other events occurring simultaneously could influence outcomes)
- Maturation effects (participants naturally changing over time independent of treatment)
- Testing effects (pre-testing influencing post-test results)
- Instrumentation effects (changes in how outcomes are measured over time)
- Statistical regression (participants selected for extreme scores may regress to the mean)
- Selection biases (non-random assignment of participants to conditions)
- Mortality effects (differential dropout rates across conditions)
External validity threats:
- Interaction of selection and treatment (findings may not generalize to other populations)
- Reactive effects of experimental arrangements (results due
Basic Concepts of Standard Experimental Designs ( Statistics )Hasnat Israq
This document outlines key concepts in standard experimental design. It defines experimental design as assigning experimental units to treatment conditions to measure and compare treatment effects. Sample design selects units for measurement from a population. The document discusses necessary steps like replication and randomization. It presents linear statistical models including fixed, random, and mixed effects models. It also explains analysis of variance and standard designs like completely randomized design, randomized block design, and Latin square design, including their analysis of variance tables. The conclusion compares the efficiency of these standard designs.
This document provides an overview of probability theory, including key definitions, concepts, and calculations. It discusses:
1. Definitions of probability, including the frequency and subjective concepts. It also defines basic terminology like experiments, trials, outcomes, and events.
2. Methods of calculating probability, including classical and empirical approaches. It presents the classical probability formula.
3. Common probability distributions like the binomial distribution and normal distribution. It provides examples of calculating probabilities using these distributions.
4. Additional probability concepts like independent and conditional probability, random variables, and transformations to the standardized normal distribution.
5. The importance of the normal distribution in applications like medicine, sampling, and statistical significance testing. It
This document provides information on sample size estimation. It discusses the importance of sample size estimation and how the objective of the study determines whether sample size needs to be estimated. It provides examples of sample size calculation for pilot studies, estimation studies, and hypothesis testing studies. Formulas are presented for estimating parameters like prevalence, sensitivity and specificity as well as for testing differences between groups. The document emphasizes setting significance levels and power appropriately depending on the goals of the study.
Statistical Methods for Removing Selection Bias In Observational StudiesNathan Taback
The slide deck is from a talk I delivered at a Dana Farber / Harvard Cancer Center outcomes seminar. It presents an overview of currently available statistical methods to remove bias in observational studies.
Transitional Care for Pediatric Patients with Neuromuscular Diseases: A Healt...HTAi Bilbao 2012
Transitional Care for Pediatric Patients with Neuromuscular Diseases: A Health Technology Assessment
Jackie Tran, MD
University of Medicine and Dentistry of New Jersey, USA
HTAi 9th Annual Meeting, Bilbao
Integrated Care for a Patient Centered System
25 June, 2012
Health Technology Assessment (HTA) Report: Interventions to increase particip...HTAi Bilbao 2012
The document summarizes a Health Technology Assessment report on interventions to increase participation in organized cancer screening programs. It found that mail and phone recalls, as well as having a general practitioner's signature on the invitation letter, consistently increased participation. Fixed appointments also increased participation compared to open invitations. Self-sampling for HPV testing increased participation in non-responders relative to standard recall letters. The report evaluated interventions' efficacy, cost-effectiveness, organizational impact, and social/ethical issues.
This document discusses key concepts in determining sample size and statistical power for clinical trials. It provides examples of calculating sample sizes for dichotomous and continuous outcomes. The importance of choosing an appropriate primary outcome and estimating event rates in control and intervention groups is emphasized. Methods for randomization in clinical trials like simple randomization and blocked randomization are also covered.
This document provides information about health statistics in Malaysia. It notes that in 2009, the population of Malaysia was 27.9 million with 499,410 total births and 133,920 total deaths. This suggests the population in 2010 should be 28,265,490, though immigration and emigration may cause discrepancies. It also defines key health statistics terms like rates, ratios, proportions, and discusses calculating and adjusting rates.
This document discusses statistical guidelines and considerations for clinical trials from various organizations. It provides an overview of how statistics are described in ICH, WHO, Malaysian, and European guidelines. Key points covered include the importance of pre-specifying the statistical analysis plan before a trial begins, considerations for study design such as randomization and blinding, determining sample size, evaluating safety data, and reporting trial results according to the pre-specified plan.
8.Calculate samplesize for clinical trials (continuous outcome)Azmi Mohd Tamil
This document discusses calculating sample sizes for studies involving continuous outcome data from two independent groups. It provides the formula for calculating the standardized difference between groups given the clinically relevant difference and population standard deviation. It then shows how to use power, alpha level, and standardized difference to determine sample size using tables or software. Examples are provided to demonstrate calculating the combined standard deviation when two standard deviations are provided, and what to do if prior information is not available to determine sample size.
5. Calculate samplesize for case-control studiesAzmi Mohd Tamil
This document discusses sample size calculations for case-control studies. It provides an example comparing the rate of diabetes mellitus (DM) between patients with cataracts (cases) and those with normal vision (controls). Based on literature finding a 50% DM rate in cases and 8% in controls, the required sample size is 17 cases and 17 controls to detect this difference with 80% power and 5% type 1 error rate. Manual calculations and online calculators can both be used to determine sample size for case-control studies. Prior information is needed on exposure rates in cases and controls to perform these calculations.
This document discusses tools for calculating sample size, including the free PS2 program and StatCalc program. It explains that sample size calculation depends on study design, and directs the reader to other sections that describe how to calculate sample size for different study designs like cross-sectional, case-control, cohort, and clinical trials. References are provided for further information.
This document discusses different sampling techniques in SAS including systematic sampling from a known population, systematic sampling from an unknown population, random sampling with and without replacement, and permuted block randomization for clinical trials.
This document summarizes information about prescription drug costs and development. It discusses how medicines have transformed treatment for diseases like hepatitis C and cancer. Developing new treatments takes over 10 years and $2.6 billion on average, with only 12% of drug candidates being approved. Medicines help avoid expensive medical services and provide major savings to the healthcare system. While drug costs have risen, they account for a stable share of overall healthcare spending and are projected to grow in line with other healthcare costs. Many factors influence drug prices, including discounts, rebates, and competition from generics.
- A sample is a small group selected from a population to represent that population. Sampling provides benefits like being less time-consuming, less expensive, and allowing results to be repeated.
- There are two main types of samples: probability and non-probability. Probability samples include simple random, systematic, stratified, and cluster samples. Sample size is determined based on factors like the type of study, expected results, costs, and available resources.
- Inferential statistics allow generalization from a sample to a population through hypothesis testing and significance tests. Tests include t-tests, F-tests, chi-squared tests, and correlation/regression to analyze relationships between variables. Significant results suggest differences are likely not due to chance
This document discusses various parametric tests used for hypothesis testing with quantitative data, including:
- One-sample t-test to compare a sample mean to a predefined value
- Two-sample t-test to compare means of two independent groups
- Paired t-test to compare means of two related/matched groups
- ANOVA tests to compare means of three or more groups, including one-way and two-way ANOVA
- Assumptions of parametric tests like normal distribution and additive effects are also outlined.
The binomial distribution describes the number of successes in a fixed number of binary trials. A binomial experiment has the following properties: it consists of n independent and identical trials, each resulting in either success or failure. The probability of success p is the same for each trial. The random variable X represents the number of successes, which follows a binomial distribution with parameters n and p. The mean of the binomial is np and the standard deviation is npq, where q is 1 - p. For large n, the binomial can be approximated by a normal distribution.
This document discusses statistical issues related to using patient-reported outcome (PRO) measures in clinical trials. It notes that PROs are often measured on an ordinal scale with skewed distributions and are subject to floor and ceiling effects. Standard analysis methods may not be appropriate. It also discusses challenges like regression to the mean, baseline adjustment, and evaluating change over time without a control group. While a multi-scale PRO like a health-related quality of life instrument can be used as a primary endpoint, its multidimensional nature raises issues around validation and methodology compared to single measures. Regulatory agencies provide guidance on properly using PROs, including multi-scale ones, as primary endpoints.
Here are some potential threats:
Internal validity threats:
- History effects (other events occurring simultaneously could influence outcomes)
- Maturation effects (participants naturally changing over time independent of treatment)
- Testing effects (pre-testing influencing post-test results)
- Instrumentation effects (changes in how outcomes are measured over time)
- Statistical regression (participants selected for extreme scores may regress to the mean)
- Selection biases (non-random assignment of participants to conditions)
- Mortality effects (differential dropout rates across conditions)
External validity threats:
- Interaction of selection and treatment (findings may not generalize to other populations)
- Reactive effects of experimental arrangements (results due
Basic Concepts of Standard Experimental Designs ( Statistics )Hasnat Israq
This document outlines key concepts in standard experimental design. It defines experimental design as assigning experimental units to treatment conditions to measure and compare treatment effects. Sample design selects units for measurement from a population. The document discusses necessary steps like replication and randomization. It presents linear statistical models including fixed, random, and mixed effects models. It also explains analysis of variance and standard designs like completely randomized design, randomized block design, and Latin square design, including their analysis of variance tables. The conclusion compares the efficiency of these standard designs.
This document provides an overview of probability theory, including key definitions, concepts, and calculations. It discusses:
1. Definitions of probability, including the frequency and subjective concepts. It also defines basic terminology like experiments, trials, outcomes, and events.
2. Methods of calculating probability, including classical and empirical approaches. It presents the classical probability formula.
3. Common probability distributions like the binomial distribution and normal distribution. It provides examples of calculating probabilities using these distributions.
4. Additional probability concepts like independent and conditional probability, random variables, and transformations to the standardized normal distribution.
5. The importance of the normal distribution in applications like medicine, sampling, and statistical significance testing. It
This document provides information on sample size estimation. It discusses the importance of sample size estimation and how the objective of the study determines whether sample size needs to be estimated. It provides examples of sample size calculation for pilot studies, estimation studies, and hypothesis testing studies. Formulas are presented for estimating parameters like prevalence, sensitivity and specificity as well as for testing differences between groups. The document emphasizes setting significance levels and power appropriately depending on the goals of the study.
Statistical Methods for Removing Selection Bias In Observational StudiesNathan Taback
The slide deck is from a talk I delivered at a Dana Farber / Harvard Cancer Center outcomes seminar. It presents an overview of currently available statistical methods to remove bias in observational studies.
Similar to Comparison of effect sizes associated with surrogate and final primary endpoints in randomised clinical trials. Cianti. (10)
Transitional Care for Pediatric Patients with Neuromuscular Diseases: A Healt...HTAi Bilbao 2012
Transitional Care for Pediatric Patients with Neuromuscular Diseases: A Health Technology Assessment
Jackie Tran, MD
University of Medicine and Dentistry of New Jersey, USA
HTAi 9th Annual Meeting, Bilbao
Integrated Care for a Patient Centered System
25 June, 2012
Health Technology Assessment (HTA) Report: Interventions to increase particip...HTAi Bilbao 2012
The document summarizes a Health Technology Assessment report on interventions to increase participation in organized cancer screening programs. It found that mail and phone recalls, as well as having a general practitioner's signature on the invitation letter, consistently increased participation. Fixed appointments also increased participation compared to open invitations. Self-sampling for HPV testing increased participation in non-responders relative to standard recall letters. The report evaluated interventions' efficacy, cost-effectiveness, organizational impact, and social/ethical issues.
The use of ‘colloquial evidence’ in HTA: the experience of NICE HTAi Bilbao 2012
The document summarizes a presentation given at the HTAi Annual Meeting about the National Institute for Health and Care Excellence's (NICE) use of "colloquial evidence" in developing clinical guidance. It defines colloquial evidence, explores how NICE utilizes it alongside scientific evidence at different stages of the guidance process, and discusses developing better methods for identifying and critically appraising colloquial evidence. The presentation aims to map NICE's use of colloquial evidence to an existing conceptual framework and identify variations across NICE centers.
Social values international programme: integrating research and policy to ens...HTAi Bilbao 2012
Social values international programme: integrating research and policy to ensure fair allocation of health care resources .
HTAi Conference 2012 Panel Session
Joint chairs
Professor Peter Littlejohns and Professor Albert Weale
Challenges in commissioning research on what works in integrated careHTAi Bilbao 2012
This document discusses challenges in commissioning research on integrated care and how new studies are tackling these challenges. Integrated care research is complex due to the interplay of context, mechanisms and outcomes, and difficulty tracking activity and costs across settings. New studies are using more robust methods like difference-in-difference analysis across multiple sites and person-linked data to better understand costs and impacts. They are also considering generalizability and using mixed methods to understand how micro-level integrated care can be supported at higher levels.
Building a portfolio of research findings for use by healthcare managers and ...HTAi Bilbao 2012
The document summarizes research conducted by the NIHR Health Services and Delivery Research programme on integrated care. It outlines several research projects funded through specific calls on integrated care between 2009-2011, including evaluations of case management initiatives, self-care support, and virtual wards. The research aims to identify healthcare managers' needs and generate evidence to improve services. The programme commissions applied health research to benefit the NHS based on both need and scientific merit.
This document summarizes a study exploring methods for assessing international use of UK-funded health technology assessments (HTAs). The study reviewed literature on impact assessment models and explored bibliometrics, website analytics, and citations in HTA reports. Bibliometric analysis found low international academic impact. Website analytics revealed most non-UK visits were for systematic reviews. Citations in HTA reports provided some evidence of international uptake. The study recommends a multidimensional model and further research using case studies to explore nature of HTA use internationally.
EVALUATION OF PSYCHOSOCIAL FACTORS INFLUENCING HEALTHCARE PROFESSIONAL ACCEPT...HTAi Bilbao 2012
EVALUATION OF PSYCHOSOCIAL FACTORS INFLUENCING HEALTHCARE PROFESSIONAL ACCEPTANCE OF TELEMONITORING FOR CHRONIC PATIENTS
Estibalitz Orruño1, Marie-Pierre Gagnon2-3, José Asua4, Eva Reviriego1
1 Basque Office for Health Technology Assessment (Osteba), Department of Health and Consumer Affairs, Basque Government, Vitoria-Gasteiz, Spain.
2 Faculty of Nursing Sciences, Université Laval, Québec, Canada.
3 Research Centre of the Centre Hospitalier Universitaire de Québec, Québec, Canada.
4 Direction of Knowledge Management and Evaluation, Department of Health and Consumer Affairs, Basque Government, Vitoria-Gasteiz, Spain.
The document discusses improving opportunities for patient and consumer engagement in health technology assessment (HTA) in Australia. It notes that while mechanisms exist for patient input, the overall HTA process is not well understood, advocacy groups are under-resourced, and the timeline for submissions is short. It proposes the formation of HTA_AUS, a coalition of interested parties including patient groups, government, industry and others, to address this issue and develop practical solutions like supporting patient submissions and extending deadlines. The coalition aims to increase awareness, education and support for patient engagement in HTA.
METHODS, MATHEMATICAL MODELS, DATA QUALITY ASSESSMENT AND RESULT INTERPRETATI...HTAi Bilbao 2012
METHODS, MATHEMATICAL MODELS, DATA QUALITY ASSESSMENT AND RESULT INTERPRETATION: SOLUTIONS DEVELOPED IN THE IFEDH FRAMEWORK
G. Zauner
dwh Simulation Services
Vienna , Austria
How to promote the prescription of evidence-based non-pharmacological treatme...HTAi Bilbao 2012
How to promote the prescription of evidence-based non-pharmacological treatments in France?
HTAi 2012, Bilbao
Clémence Thébaut, Olivier Scemama, Françoise Hamers, Catherine Rumeau-Pichon
Department of economic and public health evaluation
The application of Health Technology Assessment in the field of biologics: an...HTAi Bilbao 2012
This document provides a health technology assessment of etanercept for treating rheumatoid arthritis. It includes 7 chapters that evaluate the epidemiology and burden of RA, biologic drugs for RA including etanercept, the economic impacts, organizational implications, and ethical considerations of using etanercept. The assessment finds that etanercept improves quality of life when combined with methotrexate compared to methotrexate alone, and has a cost-effectiveness ratio of €25,130 per quality-adjusted life year gained, making it a reasonable treatment option.
Hospital-based HTA: does it impact on medical technologies’ expenditure and c...HTAi Bilbao 2012
This document summarizes a study on hospital-based health technology assessment (HB HTA) in Italy. The study aimed to assess the diffusion of HB HTA, its impact on hospital decisions, and potential factors influencing its effectiveness. A survey found that almost half of hospitals have an HTA commission. While few clear impacts were identified, some relationships were found between HTA characteristics and resource use. Future research should evaluate additional outcomes to better understand HB HTA's effects and inform its adoption.
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- Video recording of this lecture in English language: https://youtu.be/Pt1nA32sdHQ
- Video recording of this lecture in Arabic language: https://youtu.be/uFdc9F0rlP0
- 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
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
Comparison of effect sizes associated with surrogate and final primary endpoints in randomised clinical trials. Cianti.
1. Comparison of effect sizes associated with
surrogate and final primary endpoints in
randomised clinical trials
Ciani O., Garside R., Pavey T., Stein K., Taylor R.S.
1
2. Background
Classic Definition for surrogates
Disease-centered characteristics Patient-centered characteristics
Biomarkers Surrogate outcomes Final outcome
A characteristic that is A characteristic
objectively measured A biomarker that is that reflects how
and evaluated as an intended to substitute patient feels,
indicator of normal, and predict for a final functions or
pathogenic or outcome. survives.
pharmacologic
responses to a
therapeutic intervention.
Cardiovascular
e.g. LDL-cholesterol
Mortality
e.g. Intraocular pressure Loss of vision
3. Background
HTA-based Definition of surrogates
Disease-centered characteristics Patient-centered characteristics
Biomarkers Surrogate outcomes Final outcome
A characteristic that is A characteristic
objectively measured A biomarker - or clinical that reflects how
and evaluated as an or patient-relevant patient feels,
indicator of normal, outcome - that is functions or
pathogenic or intended to substitute survives.
pharmacologic and predict for a final
responses to a outcome, namely
therapeutic intervention. survival or HRQoL.
e.g. Rate of hip fracture Mortality/HRQoL
e.g. Event-free Survival Overall Survival
4. Objectives of the study
I. To study the association between primary endpoint
(surrogate vs final) and treatment effect estimates in
RCTs
II.To compare the risk of bias in trials reporting a
surrogate endpoint vs trials reporting a final primary
endpoint
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5. Methods
Study selection
Initial sample of abstracts (N = 639)
Initial sample of abstracts (N = 639)
Excluded (N = 55)
Excluded (N = 55)
NotRCTs (N = 17)
Not RCTs (N = 17)
Economicevaluation studies (N = 11)
Economic evaluation studies (N = 11)
Noninterventional treatment (N = 25)
Non interventional treatment (N = 25)
Secondaryanalysis (N = 2)
Secondary analysis (N = 2)
For outcomes classification (N = 584)
For outcomes classification (N = 584)
Composite mixed outcomes (N = 73)
Composite mixed outcomes (N = 73)
Eligible for the study (N = 511)
Eligible for the study (N = 511)
Matching procedure
Matching procedure
Surrogate outcomes based (N = 137)
Surrogate outcomes based (N = 137) Final outcomes based (N = 137)
Final outcomes based (N = 137)
Excluded (N = 36)
Excluded (N = 36)
Excluded (N = 53)
Excluded (N = 53) Compositemixed outcomes (N = 9)
Composite mixed outcomes (N = 9)
Equivalence/Non-inferioritystudy (N = 15)
Equivalence/Non-inferiority study (N = 15) Earlytermination (N = 1)
Early termination (N = 1)
UnpooledMuliti-arm (N = 33)
Unpooled Muliti-arm (N = 33) Equivalence/Non-inferioritystudy (N = 11)
Equivalence/Non-inferiority study (N = 11)
Noanalysable data (N = 5)
No analysable data (N = 5) UnpooledMuliti-arm (N = 11)
Unpooled Muliti-arm (N = 11)
Noanalysable data (N = 4)
No analysable data (N = 4)
Surrogate outcome trials (N = 84)
Surrogate outcome trials (N = 84) Final outcome trials (N = 101)
Final outcome trials (N = 101)
Binaryendpoint (N = 51) Binaryendpoint (N = 83)
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Binary endpoint (N = 51) Binary endpoint (N = 83)
6. Methods
Data extraction
Effect Size
Binary endpoints: n/N data for each arm
Continuous endpoints: SS, Mean, SD for each arm
TEs(95%CI) as reported by authors
Study characteristics: sample size, follow-up, type of intervention,
patient population, sponsor (i.e. FP, NFP and mixed), positive outcome in
favour of the new treatment
Risk of bias: adoption of the intention to treat (ITT) principle, adequate
randomized sequence generation and allocation concealment, double-
blind/placebo-control
Surrogate outcomes: type of surrogate (i.e. imaging, histo/biochemical,
instrumental, other), authors’ statement about validation and use of a
substitute outcome
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7. Methods
Data analyses
Primary Analysis
Random-effects meta-analysis
Binary endpoints: TEs expressed as ORs
Meta-regression models
Binary endpoints: Ratio of ORs (95%CI)
ROR > 1 → greater TEs of the surrogate endpoints
Adjustment for key trial characteristics
Sensitivity Analyses
Pooled Relative Risk Ratios estimate (RRR)
Combined continuous and binary endpoints ROR estimation
Within-pair comparison of differences in ln(OR)
Secondary Analysis
Logistic regression model
OR of reporting result in favour of the new treatment
Risk of bias assessment
χ2 - test of methodological quality dimensions across groups
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8. Results
Study characteristics
Surrogate Final P-
Characteristics
outcomes (N = 84) outcomes (N = 101) value
Intervention, N(%) 0.33
Pharmaceuticals 49 (58) 61 (60)
Medical Devices 7 (8) 7 (7)
Surgical procedures 4 (5) 8 (8)
Health promotion activities 7 (8) 2 (2)
Other therapeutic technologies 17 (20) 23 (23)
Sponsor, N(%) 0.86
Profit 24 (29) 28 (28)
Not-for-Profit 49 (58) 57 (56)
Mixed 11 (12) 16 (16)
Sample size, Median (IQR) 371 (162-787) 741 (300-4731)
<0.001
Follow up, [days] Median (IQR) 255 (137-540) 180 (40-730) 0.73 8
*Chi-square test, Fisher exact test, Mann-Whitney U test
9. Results
Comparison of TEs – primary analysis
Method of Analysis Surrogate Final outcome
Adjusted^
(Nr of Surrogate trials vs. Nr of outcome Trials Trials ROR (95% CI)
ROR (95% CI)
Final Outcome trials) OR (95% CI) OR (95% CI)
Primary analysis
Binary outcomes 0.51 0.76 1.47 1.46
(51 vs. 83) (0.42 to 0.60) (0.70 to 0.82) (1.07 to 2.01) (1.05 to 2.04)
ORs = Odds ratios pooled using DerSimonian & Laird random effects meta-analyses. ROR: Relative Odds Ratio; ^Adjusted for trial-level
characteristics of clinical area of intervention, patient population, type of intervention, sponsor, journal, mean sample size and mean follow
up time
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10. Results
Comparison of TEs – sensitivity analyses
Method of Analysis Adjusted^
Surrogate Trials Final Trials ROR or RRR
(Nr Surrogate trials vs. ROR or RRR
RR (95% CI) RR (95% CI) (95% CI)
Nr Final Outcome trials) (95% CI)
Inclusion of risk ratios as
0.56 0.80 1.38 1.36
reported by authors
(0.48 to 0.65) (0.75 to 0.86) (1.12 to 1.71) (1.08 to 1.70)
(57 vs. 86)
Inclusion of continuous
0.46 0.68 1.44 1.48
outcomes
(0.39 to 0.54) (0.62 to 0.74) (0.83 to 2.49) (0.83 to 2.62)
(84 vs. 101)
Binary outcomes
0.48 0.68 1.38
matched-pairs -
(0.39 to 0.59) (0.61 to 0.77) (1.01 to 1.88)
(43 vs. 43)
RRR: Relative Risk Ratio; ^Adjusted for trial-level characteristics of clinical area of intervention, patient population, type of intervention,
sponsor, journal, mean sample size and mean follow up time 10
11. Results
Risk of bias
Surrogate Final outcomes
Risk of Bias Assessment, N(%) P-value
outcomes (N=84) (N=101)
ITT adoption 62 (74) 83 (82) 0.17
Adequate Randomization 54 (64) 65 (64) 0.99
sequence generation
Adequate Randomization 61 (73) 74 (73) 0.92
allocation concealment
Double Blinding/Placebo control 42 (50) 43 (43) 0.31
*Chi-square test
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12. Discussion and limitations
Between-trial comparison of treatment effects
Possible role of smaller trial sample size in surrogate outcome
trials
~40% ‘overestimation’ of TEs in surrogate outcomes trials
Consistent result across sensitivity analyses, confirmed by
secondary analyses
Findings not explained by methodological quality or other key
trial characteristics
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14. Main References
1. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints:
preferred definitions and conceptual framework. Clinical Pharmacology and
Therapeutics 2001; 69: 89–95.
2. Bucher, H. C. et al. Users' guides to the medical literature: XIX. Applying clinical trial
results. A. How to use an article measuring the effect of an intervention on surrogate
end points. Evidence-Based Medicine Working Group. JAMA, 1999: 282, 771-8.
3. Fleming TR, DeMets DL. Surrogate endpoints in clinical trials: Are we being misled?
Annals of Internal Medicine 1996; 125: 605–13.
4. Lassere M. The Biomarker-Surrogacy Evaluation Schema: a review of the
biomarker-surrogate literature and a proposal for a criterion-based, quantitative,
multidimensional hierarchical levels of evidence schema for evaluating the status of
biomarkers as surrogate endpoints .Statistical Methods in Medical Research 2007;
17: 303–340.
5. Taylor RS, Elston J. The use of surrogate outcomes in model-based cost-
effectiveness analyses: a survey of UK Health Technology Assessment reports.
Health Technol Assess 2009; 13(8).
6. Weir CJ, Walley RJ. Statistical evaluation of biomarkers as surrogate endpoints: a
literature review. Stat Med 2006; 25: 183-203.
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