Case-control study is a variety of analytical studies. This is a brief presentation regarding history, design, issues, advantages - disadvantages and examples of Case-control study.
Bias, confounding and causality in p'coepidemiological researchsamthamby79
A brief description of three issues (Bias, Confounding and Causality) commonly encountered while performing pharmacoepidemiological research. A big THANK YOU to Mr. Strom and Mr. Kimmel.
Clinical study types and designs are terms which represent the way in which clinical trials are structured and formulated.
Since we all know that clinical research is an extremely complex topic and not everything can be explained in a simple way, here we’ll focus only on some of the most basic types of clinical study types and designs which involve human subjects or participants.
First of all, you should know that the most basic grouping of study designs is experimental (treatment) studies and observational studies.
As we can suppose from the names, in an observational study, researchers have less control over subjects and they’re just observing what happens to subjects, while in experimental studies, researchers are using different methods (such as randomization) to place subjects in separate groups. This gives experimental studies much more validity than observational studies.
In this guide, we’ll talk about the 2 possible types of studies, as well as different study designs within.
Case-control study is a variety of analytical studies. This is a brief presentation regarding history, design, issues, advantages - disadvantages and examples of Case-control study.
Bias, confounding and causality in p'coepidemiological researchsamthamby79
A brief description of three issues (Bias, Confounding and Causality) commonly encountered while performing pharmacoepidemiological research. A big THANK YOU to Mr. Strom and Mr. Kimmel.
Clinical study types and designs are terms which represent the way in which clinical trials are structured and formulated.
Since we all know that clinical research is an extremely complex topic and not everything can be explained in a simple way, here we’ll focus only on some of the most basic types of clinical study types and designs which involve human subjects or participants.
First of all, you should know that the most basic grouping of study designs is experimental (treatment) studies and observational studies.
As we can suppose from the names, in an observational study, researchers have less control over subjects and they’re just observing what happens to subjects, while in experimental studies, researchers are using different methods (such as randomization) to place subjects in separate groups. This gives experimental studies much more validity than observational studies.
In this guide, we’ll talk about the 2 possible types of studies, as well as different study designs within.
Pharmacoepidemiology is the study of effects of drugs in large numbers of people.
Epidemiologic Study Designs, Reasons to perform Pharmacoepidemiology studies, Users of pharmacoepidemiology and Role of Pharmacists & other Public Health Practitioners in Pharmacoepidemiology are discussed in this presentation.
various measures for the measurement of outcome such as incidence prevalence and other drug us measures are briefly discussed here with suitable examples and equations
Benefit-risk assessment is an integral part of FDA's regulatory review of marketing applications for new drugs and biologics. These assessments capture the Agency's evidence, uncertainties, and reasoning used to arrive at its final determination for specific regulatory decisions.
A sponsor in literal terms is defined as an individual or a company or an institution that takes the responsibility for the initiation, management and/or financing of a clinical study.
In case an investigator independently initiates and takes full responsibility for a trial, he/she automatically assumes the role of a sponsor.
INCLUSION AND EXCLUSION CRITERIA session ACRM.pptxACSRM
Outline:
1. What is a Systematic Review?
2. Hierarchy of Evidence in Research
3. Inclusion and Exclusion Criteria [IC/EC]
4. Rationale for IC/EC in a Systematic Review [SR]
5. Models/Frameworks used in Formulating IC/EC for a SR
6. Examples of Models/Frameworks for
7. Qualitative and Quantitative SR
8. Other Considerations for IC/EC
Its a paper presentation that tries to explore in detail, the ethical issues in research. The ethical issues presented cut across almost all the discipline; education, sociology, social science, humanities, e.t.c. In other words a multidisciplinary approach has been used to present these ethical issues in research.
Pharmacoepidemiology is the study of effects of drugs in large numbers of people.
Epidemiologic Study Designs, Reasons to perform Pharmacoepidemiology studies, Users of pharmacoepidemiology and Role of Pharmacists & other Public Health Practitioners in Pharmacoepidemiology are discussed in this presentation.
various measures for the measurement of outcome such as incidence prevalence and other drug us measures are briefly discussed here with suitable examples and equations
Benefit-risk assessment is an integral part of FDA's regulatory review of marketing applications for new drugs and biologics. These assessments capture the Agency's evidence, uncertainties, and reasoning used to arrive at its final determination for specific regulatory decisions.
A sponsor in literal terms is defined as an individual or a company or an institution that takes the responsibility for the initiation, management and/or financing of a clinical study.
In case an investigator independently initiates and takes full responsibility for a trial, he/she automatically assumes the role of a sponsor.
INCLUSION AND EXCLUSION CRITERIA session ACRM.pptxACSRM
Outline:
1. What is a Systematic Review?
2. Hierarchy of Evidence in Research
3. Inclusion and Exclusion Criteria [IC/EC]
4. Rationale for IC/EC in a Systematic Review [SR]
5. Models/Frameworks used in Formulating IC/EC for a SR
6. Examples of Models/Frameworks for
7. Qualitative and Quantitative SR
8. Other Considerations for IC/EC
Its a paper presentation that tries to explore in detail, the ethical issues in research. The ethical issues presented cut across almost all the discipline; education, sociology, social science, humanities, e.t.c. In other words a multidisciplinary approach has been used to present these ethical issues in research.
2.0 Introduction
2.1 Objectives
2.2 Meaning of Descriptive Statistics
2.3 Organisation of Data
2.3.1 Classification
2.3.1.1 Frequency Distribution can be with Ungrouped Data and Grouped Data
2.3.1.2 Types of Frequency Distribution
2.3.2 Tabulation
2.3.3 Graphical Presentation of Data
2.3.3.1 Cumulative Frequency Curve or Ogive
2.3.4 Diagrammatic Presentation of Data
2.4 Summarisation of Data
2.4.1 Measures of Central Tendency
2.4.2 Measures of Dispersion
2.4.3 Skewness and Kurtosis
2.4.4 Advantages and Disadvantages of Descriptive Statistics
2.5 Meaning of Inferential Statistics
2.5.1 Estimation
2.5.2 Point Estimation
2.5.3 Interval Estimation
2.6 Hypothesis Testing
2.6.1 Statement of Hypothesis
2.6.2 Level of Significance
2.6.3 One Tail and Two Tail Test
2.7 Errors in Hypothesis Testing
2.7.1 Type I Error
2.7.2 Type II Error
2.7.3 Power of a Test
2.8 General Procedure for Testing A Hypothesis
Systematic (non-random) error that results in an incorrect estimate of the association between exposure and risk of disease.
Can occur in all stages of a study
Not affected by study sample size
Difficult to adjust for afterwards, but can be reduced by adequate study design.
•Can never be totally avoided, but we must be aware of it and interpret our results accordingly
This is an easiest power-point slide you will get on topic Epidemiology. It’s basic of Epidemiology. This ppt includes difference between observational study & experimental study. Classification of Epidemiological study. You can read this & have an overview of Epidemiological study design in short. This power point will help you regarding understanding Epidemiological study. Including cohort study, case control study, descriptive study. This includes advantage & disadvantage of many studies of Epidemiological study design such ase cohort study, case control study, analytical study. It was our group presentation so we made with all our affords. I was the leader of our team I can assure you, you won’t get disappointment after studying this slides.
Error/Bais in Rsearch Methodology and pharmaceutical statisticsakashpharma19
Error/Bais in Rsearch Methodology and Pharmaceutical Statistics .
A biased estimate is
one which, on the average, does not equal the population parameter.
EpidemiologyUnit 3Bias, Error, Confounding and Effect Modification4hrs
Radha Maharjan
MN(WHD)
Contents
3.1 Bias and Error in Epidemiology
3.1.1 Bias (Researcher and Respondent)
Recall Bias
Information Bias ( sponsor bias, social desirability bias, acquiescence Bias)
Selection Bias
Confirmation Bias
The halo effect.
Contents
3.1.2 Error
Systematic Error
Random Error
Confounding & Effect Modification
Definition of Error
A measure of the estimated difference between the observed or calculated value of a quantity and its true value.
Random error or Chance
It is the by-chance error
It makes observed value different from the true value
May occur through sampling variability or random fluctuation of the event of interest due to
biological variability, sampling error and measurement error (not due to machine)
lack of precision in the measurement of an association
Biological variability:
The natural variability in a lab parameter due to physiologic differences among subjects and within the same subject over time.
Differences between subjects due to differences in diet, genetics or immune status.
Sampling error:
Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
Measurement error:
Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value.
Random error or Chance
Random error can never be completely eliminated since we can study only a sample of the population.
Random error can be reduced by
careful measurement of exposure and outcome
Proper selection of study
Taking larger sample- increase the size of the study.
Systematic error or Bias
Systematic error (or bias) occurs in epidemiology when results differ in a systematic manner from the true values.
Bias is any difference between the true value and observed value due to all causes other than random fluctuation and sampling variability.
This type of error is generally more insidious and hard to detect.
Systematic error or Bias
For example over-estimate of blood sugar of every subject by 0.05 mmol/l resulted from using inaccurate analyser.
The possible sources of systematic error are many and varied but the important biases are selection bias, measurement bias, confounding, information bias, recall (respondent) bias, etc..
Sources of error in epidemiological study
Common sources of error are
selection bias
absence or inadequacy of controls
unwarranted conclusions
improper interpretation of associations
mixing of non-comparable records
errors of measurement (intra-observer variation, inter-observer variation), etc.
The error can be minimised through
study design (by randomisation, restriction & matching) and
during analysis of the results (by stratification and statistical modelling) ..
Selection bias
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
3. Objectives
At the end of this session each student should be able
to;
Define error
Mention sources of error
Describe types of error
State the ways of minimizing error
4. Definition
Error
Defined as a mistake which can occur during the study.
Sources of error
• Selective survival
• Selective recall
• Incorrect classification of subjects with regard to their
disease and/or exposure status.
5. Types of error
Random error
Systematic error
A. random error
Define as that part of our experience that we cannot
predict.
From a statistical perspective, random error can also
be conceptualized as sampling variability .
6. The major strategies for reducing random error are:
Increase sample size
A larger sample, other things being equal, will yield
more precise estimates of population parameters .
Improve sampling procedures
A more refined sampling strategy .
Reduce measurement variability by using strict
measurement protocols, or averages of multiple
measurements.
7. Use more statistically efficient analytic methods
Statistical procedures vary in their efficiency, i.e., in the
degree of precision obtainable from a given sample size.
B. Systematic error (bias)
Is a difference between an observed value and the true
value due to all causes other than sampling variability.
It can arise from different sources, including factors
involved in the choice or recruitment of a study
population and factors involved in the definition and
measurement of study variables
8. Types of bias
I. Selection bias
Occurs as a result of errors in identifying the study
population.
Sources of selection bias
Sampling bias
Systematically excluding or over-representing certain
groups.
9. Allocation bias
Systematic differences in the way which subjects are
recruited into different groups for a study.
For example, a study may fail to use random
sampling, the first 20 patients who arrive at a clinic
are allocated to a new treatment, and the next 20
patients are allocated to an existing treatment.
However, the patients who arrive early may be better-
off, on the other hand the doctor may have asked to
see the most seriously ill patients first.
10. Minimizing selection bias
• Clear definition of study population
• Choose cases and controls from same population
• Selection of exposed and non-exposed without
knowing disease status.
11. II. Information bias (also called misclassification bias)
Is caused by systematic differences in data collection,
measurement or classification.
Sources of Information bias
Recall bias
People suffering from a disease may have spent more
time thinking of possible links between their past
behavior and their disease than no sufferers.
12. Interviewer bias.
Interviewers may phrase questions differently for
different subjects, or write down their own
interpretations of what subjects have said.
Follow-up bias
In studies that follow up subjects at intervals, people
from certain groups may tend to be lost to follow-up,
or a disproportionate number of exposed subjects may
be lost to follow-up compared with non-exposed
subjects.
13. Validity
Is the extent to which a measurement measures what it
is supposed to measure.
• Internal validity refers to absence of systematic error
that causes the study findings (parameter estimates) to
differ from the true values as defined in the study
objectives.
• External validity refers to the extent to which a study's
findings apply to populations other than the one that
was being investigate.
15. For example, a study designed to estimate the
prevalence of smoking in a population may select
subjects for interview in a number of locations.
If the interviews are only conducted on weekdays, the
study is likely to under-represent people who are in
full-time employment, and include a higher
proportion of those who are unemployed, off work or
mothers with children.
16. Recording bias.
Medical records may contain more information on
patients who are 'cases‘.
Minimizing information bias
• Standardise measurement instruments
• Administer instruments equally to cases and controls
(exposed/unexposed)
• Use multiple sources of information
– Questionnaires
– Case records
17. Confounding
Confounding occurs when a separate factor (or
factors) influences the risk of developing a disease,
other than the risk factor being studied.
To be a confounder, the factor has to be related to the
exposure, and it also has to be an independent risk
factor for the disease being studied. (Third variable
problem).
18. To be a confounding factor, 2 conditions must be met
For example, if a study assesses whether high alcohol
consumption is a risk factor for coronary heart disease,
smoking is a confounding factor (also called a
confounder) .
This is because smoking is known to be related to
alcohol consumption, and it is also a risk factor for
coronary heart disease.
20. Conclusion
Identification of possible bias is a difficult exercise
but is crucial to improve validity. Bias can’t usually
be totally eliminated.
It must be to keep it to a minimum, to identify those
biases that cannot be avoided, to assess their potential
impact and to take this into account when interpreting
the results.
21. References
Schoenbach, J. V. (2001). Sources of error.
Stewart, A. (2002). Basic statistics and epidemiology.
A practical guide.
22. Validity
Is the extent to which a measurement measures what it
is supposed to measure.
• Internal validity refers to absence of systematic
error that causes the study findings (parameter
estimates) to differ from the true values as
defined in the study objectives.
• External validity refers to the extent to which a study's
findings apply to populations other than the one that
was being investigate.