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A MThe document discusses threats to validity from information bias and selection bias in epidemiology studies. It explains key concepts like the difference between causal and error-based associations, internal and external validity, types of populations, and sources of information and selection bias. Specifically, it covers identifying sources of bias, understanding differential and non-differential misclassification and their implications, and strategies for minimizing biases in study design and analysis.
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3.5.2 selection bias
This document discusses selection bias, which occurs when there are systematic differences in the characteristics of those selected for a study compared to the overall population from which they were selected. It provides examples of selection bias in case-control and cohort studies. Selection bias can be introduced when the probability of being included in the study depends on both the exposure and outcome of interest. This distorts the association observed in the study sample compared to the true association in the population. Differential participation or loss to follow up based on exposure and outcome status are common causes of selection bias.
3.5 types of biases
The document discusses three main types of biases that can occur in studies: information bias, which arises from mismeasurement or misclassification of variables; selection bias, which comes from how study subjects are selected and factors influencing participation; and confounding bias, which is considered a form of bias where true causal associations exist in the population, unlike other biases. The document notes there is disagreement around whether confounding should be called a bias, and that approaches to handling confounding differ from information and selection biases.
3.6 comparing biases
Selection bias relates to how participants are recruited into a study and their likelihood of remaining in the study or dropping out, which can affect the exposure-outcome association seen in the study compared to the overall population. Information bias relates to how exposure and outcome data are collected once participants are enrolled, such as through self-report, study personnel, records, or a registry, and whether misclassification is differential between exposed and unexposed or diseased and non-diseased groups.
3.7 preventing biases
This document discusses strategies for preventing bias in studies. It describes preventing information bias by collecting objective exposure data before disease onset to address recall bias, blinding interviewers to disease status to prevent interviewer bias, and blinding data collectors to exposure status to prevent outcome identification bias. It also discusses preventing selection bias by designing studies such that exposure and outcome do not both affect study inclusion, attempting high response rates, and comparing participants and non-participants to evaluate potential biases.
5.4 strengths and challenges
This document outlines the strengths and challenges of cohort studies. The strengths include examining multiple health outcomes and exposures over time without binary classifications. Participants can change exposure levels. Exposures are measured before outcomes, allowing assessment of temporality. Challenges include loss to follow-up, expense of repeated data collection, long time to conduct, inefficiency for rare outcomes, reliance on historical data quality, ensuring blind outcome assessment, challenging exposure classification, requiring sufficient population exposure variation, and loss to follow-up potentially introducing bias if associated with outcomes or exposures.
3.5.1 information bias
Information bias can occur from flawed measurement of study variables, resulting in misclassification of subjects. There are two types of misclassification: non-differential, where the degree of misclassification is unrelated to exposure/outcome status, and differential, where the degree depends on status. Non-differential misclassification typically biases results towards the null, while differential can bias in either direction. Specific biases include recall bias, interviewer bias, observer bias, and respondent bias. Proper evaluation of measurement error and its direction is important to understand potential biases.
What is bias in statistics its definition and types
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From association to causation
This document discusses approaches for studying disease etiology, including ecological studies, case-control studies, and cohort studies. It explains that ecological studies examine the relationship between characteristics at a group level but cannot determine individual exposure. The document also discusses different types of causal relationships such as necessary and sufficient, necessary but not sufficient, and sufficient but not sufficient. Finally, it outlines nine guidelines for determining whether an observed association is likely causal, including temporal relationship, strength of association, dose-response relationship, replication of findings, biological plausibility, and specificity of association.
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3.5.2 selection bias
This document discusses selection bias, which occurs when there are systematic differences in the characteristics of those selected for a study compared to the overall population from which they were selected. It provides examples of selection bias in case-control and cohort studies. Selection bias can be introduced when the probability of being included in the study depends on both the exposure and outcome of interest. This distorts the association observed in the study sample compared to the true association in the population. Differential participation or loss to follow up based on exposure and outcome status are common causes of selection bias.
3.5 types of biases
The document discusses three main types of biases that can occur in studies: information bias, which arises from mismeasurement or misclassification of variables; selection bias, which comes from how study subjects are selected and factors influencing participation; and confounding bias, which is considered a form of bias where true causal associations exist in the population, unlike other biases. The document notes there is disagreement around whether confounding should be called a bias, and that approaches to handling confounding differ from information and selection biases.
3.6 comparing biases
Selection bias relates to how participants are recruited into a study and their likelihood of remaining in the study or dropping out, which can affect the exposure-outcome association seen in the study compared to the overall population. Information bias relates to how exposure and outcome data are collected once participants are enrolled, such as through self-report, study personnel, records, or a registry, and whether misclassification is differential between exposed and unexposed or diseased and non-diseased groups.
3.7 preventing biases
This document discusses strategies for preventing bias in studies. It describes preventing information bias by collecting objective exposure data before disease onset to address recall bias, blinding interviewers to disease status to prevent interviewer bias, and blinding data collectors to exposure status to prevent outcome identification bias. It also discusses preventing selection bias by designing studies such that exposure and outcome do not both affect study inclusion, attempting high response rates, and comparing participants and non-participants to evaluate potential biases.
5.4 strengths and challenges
This document outlines the strengths and challenges of cohort studies. The strengths include examining multiple health outcomes and exposures over time without binary classifications. Participants can change exposure levels. Exposures are measured before outcomes, allowing assessment of temporality. Challenges include loss to follow-up, expense of repeated data collection, long time to conduct, inefficiency for rare outcomes, reliance on historical data quality, ensuring blind outcome assessment, challenging exposure classification, requiring sufficient population exposure variation, and loss to follow-up potentially introducing bias if associated with outcomes or exposures.
3.5.1 information bias
Information bias can occur from flawed measurement of study variables, resulting in misclassification of subjects. There are two types of misclassification: non-differential, where the degree of misclassification is unrelated to exposure/outcome status, and differential, where the degree depends on status. Non-differential misclassification typically biases results towards the null, while differential can bias in either direction. Specific biases include recall bias, interviewer bias, observer bias, and respondent bias. Proper evaluation of measurement error and its direction is important to understand potential biases.
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From association to causation
This document discusses approaches for studying disease etiology, including ecological studies, case-control studies, and cohort studies. It explains that ecological studies examine the relationship between characteristics at a group level but cannot determine individual exposure. The document also discusses different types of causal relationships such as necessary and sufficient, necessary but not sufficient, and sufficient but not sufficient. Finally, it outlines nine guidelines for determining whether an observed association is likely causal, including temporal relationship, strength of association, dose-response relationship, replication of findings, biological plausibility, and specificity of association.
Estimating risk ,pptx
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Bias and confounding
This document discusses bias and confounding in epidemiological studies. It defines bias as systematic error that results in incorrect estimation of the association between exposure and outcome. Selection bias and information bias are two common types of bias. Selection bias occurs when groups being compared differ systematically, while information bias results from misclassification of exposure or disease status. Confounding occurs when another exposure is associated with both the disease and exposure being studied. Methods for handling confounding include restriction, matching, randomization, stratification, and multivariate analysis.
Basics of Research and Bias
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1. It emphasizes that reliable evidence generally comes from multiple studies and research teams, and the totality of evidence matters most.
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4.3.1. controlling confounding matching
The document discusses various methods to control for confounding in epidemiological studies, including randomization, restriction, matching, and multivariate analysis. Randomization can reduce confounding by unknown factors, but residual confounding may remain if baseline characteristics are not equally distributed after randomization. Restriction controls confounding by only including subjects with specific levels of a confounding variable, but may limit the sample size and residual confounding could remain if restriction categories are too broad. Matching controls for confounding by selecting control subjects to have the same distribution of a confounding factor as exposed subjects, but has disadvantages such as increasing costs and sample size limitations.
Cohort study
This document provides an overview of cohort studies. It defines a cohort study as observing a group of people who share a common characteristic or experience over time to study the frequency of disease. The key features discussed include: identifying cohorts prior to disease, observing cohorts prospectively to study cause-effect relationships, minimizing attrition, and comparing exposed and non-exposed groups. The document also covers types of cohort studies, elements of cohort studies like follow-up, and strengths such as establishing causation but also weaknesses like loss to follow-up.
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Bias and confounder
This document discusses various types of bias and confounding that can occur in epidemiological studies, including:
1) Selection bias, information bias, and confounding can all introduce systematic error and invalidate study results. Selection bias occurs when individuals have different probabilities of being included based on exposure or outcome. Information bias is misclassification of exposure or outcome.
2) Specific types of biases discussed include recall bias, interviewer bias, observer bias, non-response bias, inappropriate control selection, incidence-prevalence bias, loss to follow up, migration bias, Berksonian bias, healthy worker effect, exposure-related bias, non-differential misclassification, and differential misclassification.
3) Bias can
Cross sectional studies
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Bias and errors
This document discusses various types of biases and errors that can occur in epidemiological studies, including random error, systematic error, random misclassification, bias, and confounding. It provides definitions and examples of these terms. Specific types of biases covered include selection bias, information bias, and confounding. Methods for controlling biases discussed include randomization, restriction, matching, stratification, standardization, and blinding.
6.5 strengths and challenges
Case-control studies have strengths such as the ability to assess multiple exposures and outcomes efficiently with lower costs than cohort studies. However, they also face challenges such as finding a control group that accurately represents the exposure experience of the study base and avoiding differential recall bias between cases and controls when using self-reported exposure data. Additionally, rare exposures may be inefficient to study and outcomes must be dichotomized as present or absent. Overall, ensuring an unbiased control group and accurate exposure data are two major issues that can introduce bias if not properly addressed in case-control study designs.
Bias in health research
Selection bias, information bias, and confounding are the three main types of bias that can occur in epidemiological studies. Selection bias results from the inappropriate selection of study participants and can be reduced through randomization and clearly defining eligibility criteria. Information bias occurs due to errors in measuring or classifying exposure, disease status, or other variables and can be reduced through blinding of outcome assessors. Confounding happens when a variable is associated with both the exposure and the outcome but is not on the causal pathway, distorting the exposure-outcome relationship.
7-Cohort Studies
Cohort studies follow groups of individuals who are exposed or unexposed to a particular risk factor over time. They allow researchers to compare outcomes like disease rates between the exposed and unexposed groups. The document outlines key steps in cohort studies including defining hypotheses, selecting exposed and unexposed populations, following participants over time to measure outcomes, and analyzing the results. It also discusses advantages like assessing temporal relationships and examining multiple predictors, as well as disadvantages like potential confounding and loss to follow-up impacting validity.
Cohort study
cohort study is clinical study design. particular form of longitudinal study that samples a cohort group of people. type of panel study.
cohort study represent fundamental designs of epidemiology in field of medicine, social science & psychology.
5.1 study design overview
This document provides an overview of different study designs used in epidemiology, including randomized controlled trials, observational cohort studies, case-control studies, cross-sectional studies, and ecological studies. It describes key aspects of each design such as how exposures and outcomes are measured over time to estimate disease measures and associations between exposures and outcomes while addressing issues like confounding. The goal of all designs is to capture how different exposures affect populations over time in order to make causal inferences about disease etiology.
Assessment of Bias
This document discusses assessing risk of bias in studies included in systematic reviews. It defines bias as systematic error that can vary in direction and magnitude. The Cochrane Risk of Bias tool is recommended for assessing bias in domains such as selection bias, performance bias, detection bias, attrition bias, and reporting bias. Assessments involve describing the risk of bias and making a judgment of low, high, or unclear risk. Summary assessments of risk of bias are made within and across studies.
Cross sectional design
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Observational study design
This document discusses different types of observational study designs used in epidemiology. It describes cohort studies, both prospective and retrospective, as well as case-control studies. Prospective cohort studies follow groups of subjects over time to observe outcomes based on exposures. Retrospective cohort studies use historical data to compare groups. Case-control studies compare cases with a disease to controls without the disease to identify risk factors. Cohort studies can precisely measure exposure-outcome relationships but are time-consuming and expensive. Observational studies are prone to biases like confounding, selection, information, and measurement bias.
3.4 types of validity
The document discusses two types of validity: internal validity and external validity. Internal validity refers to the extent to which a study's findings reflect the true causal effect in the target population. External validity refers to the extent to which findings from a study population can be generalized to an external population beyond the target population. A key point is that internal validity is a prerequisite for external validity - a study must first be internally valid before its findings can be generalized to other populations.
3.2 sourced of error
This document discusses sources of error in epidemiologic studies, including random error and systematic error (bias). Random error is due to chance and relates to precision, as estimates from random samples may differ from the true population value. Systematic error is a systematic difference between observed and true effects due to factors like measurement error. Studies can be precise but not valid (if biased) or precise and valid (if minimizing bias). The direction and magnitude of bias must be considered when interpreting study results.
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This document defines key terms used to estimate risk and potential for disease prevention. It discusses probability, risk, and odds, and how they differ. It then describes three main ways to estimate risk: absolute risk, relative risk, and odds ratio. Absolute risk is the probability of an outcome in a population without comparison. Relative risk compares risks between groups, such as exposed vs unexposed. Odds ratio specifically estimates the association between an exposure and outcome using case-control study designs. The document provides examples and interpretations for each term to demonstrate how to calculate and understand risk estimation.
Bias and confounding
This document discusses bias and confounding in epidemiological studies. It defines bias as systematic error that results in incorrect estimation of the association between exposure and outcome. Selection bias and information bias are two common types of bias. Selection bias occurs when groups being compared differ systematically, while information bias results from misclassification of exposure or disease status. Confounding occurs when another exposure is associated with both the disease and exposure being studied. Methods for handling confounding include restriction, matching, randomization, stratification, and multivariate analysis.
Basics of Research and Bias
This document discusses several key aspects of research and statistics:
1. It emphasizes that reliable evidence generally comes from multiple studies and research teams, and the totality of evidence matters most.
2. Statistics are useful for determining whether differences or associations are likely due to chance or represent real effects, but numbers can be easily manipulated.
3. Several types of biases and errors can influence research, decision making, memory, and social judgments. Rigorous methodology is important to produce high quality, accurate research and publications.
Case-Control Studies
this presentation has a simple explanation about the case and control studies for the researchers who are interested to know more information
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This document defines and describes different types of bias that can affect studies, including confounding, selection bias, and information bias. Bias can result in incorrect estimates of associations between exposures and outcomes. Selection bias occurs when subjects have different probabilities of being included based on exposure or outcome. Information bias results from poor measurement of variables. Confounding bias is due to the influence of a third variable, called a confounder, that is associated with both the exposure and outcome. The document provides examples and discusses approaches to control for different types of bias, such as matching, stratification, and adjustment in data analysis.
4.3.1. controlling confounding matching
The document discusses various methods to control for confounding in epidemiological studies, including randomization, restriction, matching, and multivariate analysis. Randomization can reduce confounding by unknown factors, but residual confounding may remain if baseline characteristics are not equally distributed after randomization. Restriction controls confounding by only including subjects with specific levels of a confounding variable, but may limit the sample size and residual confounding could remain if restriction categories are too broad. Matching controls for confounding by selecting control subjects to have the same distribution of a confounding factor as exposed subjects, but has disadvantages such as increasing costs and sample size limitations.
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This document provides an overview of cohort studies. It defines a cohort study as observing a group of people who share a common characteristic or experience over time to study the frequency of disease. The key features discussed include: identifying cohorts prior to disease, observing cohorts prospectively to study cause-effect relationships, minimizing attrition, and comparing exposed and non-exposed groups. The document also covers types of cohort studies, elements of cohort studies like follow-up, and strengths such as establishing causation but also weaknesses like loss to follow-up.
Bias and validity
This document discusses bias and validity in clinical research. It defines clinical epidemiology as the study of health-related states and events in populations to control health problems. It describes how epidemiologic studies compare outcomes like disease rates between exposed and unexposed groups. Validity is important, with internal validity indicating good construct free from bias/errors, and external validity showing generalizability. Bias and confounding can threaten validity and lead to erroneous associations if not avoided or controlled for.
Bias and confounder
This document discusses various types of bias and confounding that can occur in epidemiological studies, including:
1) Selection bias, information bias, and confounding can all introduce systematic error and invalidate study results. Selection bias occurs when individuals have different probabilities of being included based on exposure or outcome. Information bias is misclassification of exposure or outcome.
2) Specific types of biases discussed include recall bias, interviewer bias, observer bias, non-response bias, inappropriate control selection, incidence-prevalence bias, loss to follow up, migration bias, Berksonian bias, healthy worker effect, exposure-related bias, non-differential misclassification, and differential misclassification.
3) Bias can
Cross sectional studies
This document discusses cross-sectional study designs. It defines cross-sectional studies as a way to collect information about variables and estimate disease prevalence at a specific point in time. The document distinguishes between descriptive cross-sectional studies, which provide information about variables, and analytical cross-sectional studies, which examine relationships between predictors and outcomes. It provides an example of a hypothetical cross-sectional study on smoking and hypertension using a 2x2 table to calculate risk ratios and interpret the results.
Bias and errors
This document discusses various types of biases and errors that can occur in epidemiological studies, including random error, systematic error, random misclassification, bias, and confounding. It provides definitions and examples of these terms. Specific types of biases covered include selection bias, information bias, and confounding. Methods for controlling biases discussed include randomization, restriction, matching, stratification, standardization, and blinding.
6.5 strengths and challenges
Case-control studies have strengths such as the ability to assess multiple exposures and outcomes efficiently with lower costs than cohort studies. However, they also face challenges such as finding a control group that accurately represents the exposure experience of the study base and avoiding differential recall bias between cases and controls when using self-reported exposure data. Additionally, rare exposures may be inefficient to study and outcomes must be dichotomized as present or absent. Overall, ensuring an unbiased control group and accurate exposure data are two major issues that can introduce bias if not properly addressed in case-control study designs.
Bias in health research
Selection bias, information bias, and confounding are the three main types of bias that can occur in epidemiological studies. Selection bias results from the inappropriate selection of study participants and can be reduced through randomization and clearly defining eligibility criteria. Information bias occurs due to errors in measuring or classifying exposure, disease status, or other variables and can be reduced through blinding of outcome assessors. Confounding happens when a variable is associated with both the exposure and the outcome but is not on the causal pathway, distorting the exposure-outcome relationship.
7-Cohort Studies
Cohort studies follow groups of individuals who are exposed or unexposed to a particular risk factor over time. They allow researchers to compare outcomes like disease rates between the exposed and unexposed groups. The document outlines key steps in cohort studies including defining hypotheses, selecting exposed and unexposed populations, following participants over time to measure outcomes, and analyzing the results. It also discusses advantages like assessing temporal relationships and examining multiple predictors, as well as disadvantages like potential confounding and loss to follow-up impacting validity.
Cohort study
cohort study is clinical study design. particular form of longitudinal study that samples a cohort group of people. type of panel study.
cohort study represent fundamental designs of epidemiology in field of medicine, social science & psychology.
5.1 study design overview
This document provides an overview of different study designs used in epidemiology, including randomized controlled trials, observational cohort studies, case-control studies, cross-sectional studies, and ecological studies. It describes key aspects of each design such as how exposures and outcomes are measured over time to estimate disease measures and associations between exposures and outcomes while addressing issues like confounding. The goal of all designs is to capture how different exposures affect populations over time in order to make causal inferences about disease etiology.
Assessment of Bias
This document discusses assessing risk of bias in studies included in systematic reviews. It defines bias as systematic error that can vary in direction and magnitude. The Cochrane Risk of Bias tool is recommended for assessing bias in domains such as selection bias, performance bias, detection bias, attrition bias, and reporting bias. Assessments involve describing the risk of bias and making a judgment of low, high, or unclear risk. Summary assessments of risk of bias are made within and across studies.
Cross sectional design
The document discusses different types of cross-sectional and cohort research designs used in marketing research. A cross-sectional design involves collecting information from a sample of a population once, while a multiple cross-sectional design collects information from different samples at different time points. Cohort analysis surveys the same group of respondents over time as they experience an event. An example case study tracks soft drink consumption in the US over 30 years in 10-year intervals by cohort.
Observational study design
This document discusses different types of observational study designs used in epidemiology. It describes cohort studies, both prospective and retrospective, as well as case-control studies. Prospective cohort studies follow groups of subjects over time to observe outcomes based on exposures. Retrospective cohort studies use historical data to compare groups. Case-control studies compare cases with a disease to controls without the disease to identify risk factors. Cohort studies can precisely measure exposure-outcome relationships but are time-consuming and expensive. Observational studies are prone to biases like confounding, selection, information, and measurement bias.
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This document discusses sources of error in epidemiologic studies, including random error and systematic error (bias). Random error is due to chance and relates to precision, as estimates from random samples may differ from the true population value. Systematic error is a systematic difference between observed and true effects due to factors like measurement error. Studies can be precise but not valid (if biased) or precise and valid (if minimizing bias). The direction and magnitude of bias must be considered when interpreting study results.
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4 Threats to validity from confounding bias and effect modification
This document discusses various approaches to understanding confounding in epidemiology, including:
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2. The "classical" approach based on a priori criteria of a confounder being associated with both the exposure and outcome but not an intermediate variable.
3. Collapsibility and data-based approaches which evaluate if crude and adjusted effect measures are equal, indicating no confounding.
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3.3 hierarchy of populations
This document outlines the hierarchy of populations in research studies, including:
1. The study population or sample, which is the population that participated in the study.
2. The actual population that met eligibility criteria and could have participated.
3. The target population, which is broader than the actual population and is what inferences are aimed to be made about.
4. One or more external populations, which are broader than the target population and may or may not overlap with it, and are what the investigator wants to generalize findings to.
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Etiologic research aims to establish causal relationships between determinants and disease outcomes. There are two main observational study designs for etiologic research: cohort studies and case-control studies. Cohort studies follow groups of individuals over time based on exposure to a determinant and compare disease outcome rates. Case-control studies identify cases of a disease and controls without the disease and compare past exposure to determinants. Both study designs are prone to biases like selection bias, information bias, and confounding, which can distort the true determinant-outcome relationship if not adequately addressed.
Pharmacoepidemiology
Pharmacoepidemiology is defined as the study of drug utilization and effects in large populations. It aims to evaluate drug effectiveness, safety, and usage under realistic conditions using methods from clinical epidemiology. Common errors in pharmacoepidemiological studies include selection bias, information bias, confounding bias, and random error. Selection bias can occur from improper selection procedures for study groups or controls. Information bias results from incorrect or missing information that leads to misclassification. Confounding bias is caused by extraneous variables that influence the association between exposure and disease. Random error is due to chance from factors like individual variation or sampling error. Larger sample sizes can reduce random error but not eliminate it.
Study design of Epidemiology.pdf
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 statistics
Error/Bais in Rsearch Methodology and Pharmaceutical Statistics .
A biased estimate is
one which, on the average, does not equal the population parameter.
Bias and confounding
This document discusses bias and confounding in epidemiological studies. It defines bias as systematic error that results in incorrect estimation of exposure-outcome associations. Selection bias and information bias are two common types of bias. Confounding occurs when another exposure is associated with both the disease and exposure being studied, independently of the exposure-disease relationship. Methods to control for confounding include restriction, matching, randomization, stratification, and multivariate analysis at the design and analysis stages of a study.
Bias and Confounding
1. Bias and confounding can systematically skew epidemiological study results if not properly addressed. Bias results from errors in study design, data collection, analysis, or reporting. Confounding occurs when another variable is associated with both the exposure and outcome under study.
2. Common types of bias include selection bias, information bias, and surveillance bias. Selection bias results when study groups systematically differ. Information bias stems from misclassification of exposures or outcomes.
3. Confounding can be controlled for through restriction, matching, randomization, and statistical techniques like stratification and multivariate analysis. These methods help equalize the effects of extraneous variables to isolate the exposure-outcome association being studied.
Systematic error bias
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
Introduction to research
This document discusses key concepts related to the scientific method and research. It covers understanding the scientific method, the role of hypotheses in research studies, and strategies for formulating hypotheses. The scientific method involves choosing a question, identifying a hypothesis, making testable predictions, designing an experiment, collecting data, determining results, and assessing if results support the hypothesis. Well-formulated hypotheses are specific, testable, and help guide research by identifying what should be studied.
Steps and Principals of clinical research in Surgery.pptx
The document outlines the basic steps and principles of clinical research in surgery. It defines research and describes the inductive and deductive approaches. Research aims to advance medical knowledge and improve patient care. The main types of research are basic science, clinical, translational, and applied. Clinical research involves studying patients and health outcomes to identify areas for improvement. Key steps include developing objectives and protocols, obtaining ethical approval, collecting and analyzing data, and disseminating findings. Important principles are informed consent, safety, validity, transparency, and avoiding bias and plagiarism.
Psychology four major forms of research
Psychology is the scientific study of human behavior and mind. Psychologists conduct research using systematic methods to measure behavior objectively and reduce biases. There are four major forms of research in psychology: experimental research, correlation research, interviews, and surveys. Experimental research uses controlled techniques to test hypotheses, while correlational research examines relationships between variables without manipulation. Interviews and surveys assess behaviors, opinions, and feelings through direct questions.
Jan Hrabal: Evaluation of medical information quality #bcs2015
Talk given at the BOBCATSSS 2015 conference - http://www.bobcatsss2015.com/.
The paper deals with the concept of quality of health-related information in the internet environment. It brings definitions of indicators of medical information quality, which are set into the methodics for evaluation of medical information quality on Czech websites. The methodics is divided in two parts: one for non-expert sources in common online environment designed for laymen and one extended version designed for experts, which includes also criteria for evaluation of research papers and reviews.
This assessment addresses the following learning outcomes1.Apply co.docx
This assessment addresses the following learning outcomes:1.Apply common epidemiological concepts including incidence and prevalence of disease, mortality and survival and age standardisation.2. Recognise the role of exposure to biological, behavioural, social and environmental risks in disease patterns.3. Differentiate between different types of research designs, including observation and experimental and mixed methods designs. 4. Assess levels of evidence and make recommendations.5. Interpret data arising from surveillance and research studies, including rates and ratios.6. Understand the difference between association and causation, statistical and public health significance.7. Analyse the role of epidemiology in screening and prevention programs, and assess the sensitivity and specificity of programs.8. Critically evaluate epidemiological studies, including potential for bias, confounding and chance errors.9. Identify key health indicators and sources of data.
...
Introduction of Research Integrity
Agricultural Research Research Ethics and Rural Development
TOPIC RESEARCH ETHICS AND RESEARCH INTEGRITY
Research techniques; samling and ethics elt
Advance Research Techniques; How to make samples Abdurrahman Abdalla .. كيف تؤخد العينة في طرق البحث المتقدم .. إعداد عبدالرحمن المهدي نصير جامعة الشرق الادنى - قبرص الشمالية
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Transparency7
This document contains slides from a lecture on public health given by Alasdair Cohen at UC Berkeley on October 17, 2016. The slides discuss various topics related to research quality including publication bias, p-hacking, negligent science, Cochrane reviews, transparent reporting, and the CONSORT checklist. The CONSORT checklist is explained in detail over several slides, outlining the key elements that should be reported in randomized controlled trials to improve transparency and replication.
Transparency6
This document summarizes the key points from a lecture on public health research methods. It discusses recommendations for organizing data analysis workflows, including using annotated text files for code, GitHub for version control and collaboration, and having others replicate analyses using different software. It also covers citation management options, ethics review boards, and some Berkeley-specific resources. The overall lecture provided an overview of best practices for conducting and documenting public health research.
Transparency5
This document summarizes a lecture on data de-identification and anonymization given by Alasdair Cohen at UC Berkeley on October 17. The lecture covered why data de-identification is important, definitions of direct identifiers and quasi-identifiers, issues around de-identification including an example of Governor Weld being re-identified, techniques to reduce de-identification risk such as suppression, generalization and swapping, estimating re-identification risk, and briefly mentioned data repositories and journal policies. The majority of the content in the lecture was sourced from a 2016 presentation by Simson Garfinkel of NIST on data de-identification issues.
Transparency4
This document summarizes a lecture about transparency and openness in research. It discusses the CONSORT statement, which was developed to standardize the reporting of randomized controlled trials. CONSORT began in the 1990s and has been revised over time, with the 2010 statement providing a checklist of essential items to include in trial reports. The lecture also notes that CONSORT has been expanded to provide guidelines for specifically reporting different types of trials and that hundreds of other reporting guidelines have been developed for other research areas as well.
Transparency3
This document summarizes a lecture on protocol registration and pre-specified analysis plans. It discusses the history of clinical trial registration requirements, highlighting milestones like the establishment of ClinicalTrials.gov. It also reviews reasons for registration like ethical principles from the Belmont Report. Additionally, it covers types of registration platforms, issues with registration alone, debates around registering observational studies, and templates for developing pre-analysis plans. The key takeaways are that registration and pre-specifying analysis plans can help reduce bias, while allowing flexibility like registering changes to protocols.
Transparency2
Alasdair Cohen gave a lecture on replication science at UC Berkeley on October 17, 2016 for a public health class. The lecture introduced the concept of replication by discussing two examples: a story about computer worms and how an Excel error impacted austerity policies. The remainder of the lecture covered the topic of replication more broadly.
Transparency1
This presentation outline covers issues with scientific research including publication bias, substandard science, p-hacking, and scientific misconduct. It then discusses approaches to address these problems such as replication science, pre-registration of study protocols and analysis plans, transparent reporting, and responsible data management. Tools for organizing research and ensuring ethical standards are also reviewed. The presentation aims to improve the reliability and integrity of scientific research.
5.3.5 causal inference in research
1. The document outlines steps for conducting causal inference research including defining a hypothesis, study design, drawing a directed acyclic graph (DAG) to identify confounders, analyzing data while controlling for confounders, and making scientific inferences.
2. An example is provided on estimating associations between deworming, hygienic latrines, finished floors, and soil-transmitted helminth infections using a cross-sectional survey design.
3. Results show the adjusted prevalence ratios for single and combined exposures on deworming and household characteristics on infection, including any additive interaction effects.
5.3.4 reporting em
Causal inference involves using directed acyclic graphs (DAGs) and the potential outcomes model to assess effect modification (EM) from a causal perspective. DAGs can help identify confounding and selection bias. The potential outcomes model distinguishes statistical from causal interaction and allows estimating average risk or risk differences to determine if causal interaction is present on the additive scale. Studies should minimize bias, control confounding, and quantify uncertainty to best assess causal EM.
5.3.2 sufficient cause em
This document discusses causal inference and effect modification. It outlines key concepts like directed acyclic graphs, confounding, and selection bias. It then explains the potential outcomes model for causal interaction using counterfactual types. These types represent what would happen to individuals under different exposure scenarios. The document shows that an additive interaction implies causal effect modification if certain assumptions hold, by deriving expected risks under different exposure combinations with and without interacting types.
5.2.3 dags for selection bias
This document discusses causal inference and outlines key concepts including:
1) Using directed acyclic graphs (DAGs) to identify confounding and selection bias
2) Different types of interaction and effect modification that can be modeled causally
3) Integrating causal concepts like DAGs, effect modification, and addressing biases into research by defining hypotheses, study design, analysis, and scientific inferences
5.1.2 counterfactual framework
1. The document discusses approaches for identifying confounding using directed acyclic graphs (DAGs). It outlines two main approaches: (1) removing direct effects and checking for common causes, and (2) checking for open back-door paths and blocking them.
2. An example applies these approaches to assess whether adjusting for prenatal care is sufficient to control for confounding between vitamin use and birth defects. It determines that additional adjustment is needed based on remaining connections in the DAG.
3. Traditional criteria for confounding may sometimes disagree with the DAG approach, and the DAG approach is more reliable as it can identify situations like conditioning on a collider that may bias results.
5.1.1 sufficient component cause model
This document outlines concepts related to causality in epidemiology. It discusses different models of causality including the sufficient-component cause model (Rothman's pies), counterfactual framework, Hill's criteria, and graphical models. The sufficient-component cause model depicts disease causation as occurring through combinations of component causes that are sufficient to result in the disease outcome. It illustrates concepts like necessary and component causes. The model contributes to understanding how the strength of associations can depend on the prevalence of other component causes in the population.
5.2.1 dags
This document discusses causal inference in epidemiology. It begins by defining what causal inference entails, including careful definition of estimation goals and a set of assumptions that link observed data to ideal causal data. Directed acyclic graphs (DAGs) are introduced as a way to visually depict causal assumptions and identify potential confounders. Conditioning on variables in DAGs is discussed, as are different DAG configurations like chains, forks, and colliders. Limitations of DAGs are noted. Non-parametric structural equation models provide a link between DAGs and counterfactual causal analysis.
4.4. effect modification
This document discusses effect modification and how it differs from confounding. It defines effect modification as a change in the magnitude of the effect of an exposure on an outcome according to levels of a third variable. Effect modification provides a more detailed description of the relationship between exposure and outcome, whereas confounding is a bias to be eliminated. The document contrasts effect modification and confounding, and provides examples to illustrate the concepts. It also discusses testing for effect modification using tests of homogeneity and how the interpretation of effect modification depends on the choice of effect measure.
4.5. logistic regression
This document describes using logistic regression to analyze data on smoking, matches use, and lung cancer while adjusting for potential confounding. It presents sample data stratified by smoking and matches use, then develops a logistic regression model with smoking and matches as predictors. The model indicates smoking significantly increases lung cancer risk but matches use does not modify this relationship. The document concludes by noting logistic regression can simultaneously adjust for multiple variables and derive coefficient estimates using maximum likelihood.
4.3.2. controlling confounding stratification
Stratification and multivariate analysis are methods used in epidemiologic studies to reduce confounding. Stratification involves dividing the data into subgroups based on potential confounding variables, and examining the relationship between exposure and outcome separately within each subgroup. The Mantel-Haenszel method provides a pooled estimate that averages the stratum-specific estimates to provide an overall effect estimate adjusted for confounding. Evaluating differences between adjusted and non-adjusted estimates helps determine if confounding is present.
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3.1 big picture
- 1. Threats to Validity: Information and Selection Bias 1. Understand the sources of association in epidemiology, i.e. causal vs. error 1. Understand the sources of error, i.e. random and systematic 2. Understand difference between precision and validity 3. Understand difference between internal and external validity 2. Know and understand the difference between external, target, actual, and study populations 1. Understand for which populations findings from epidemiologic studies provide inference and why 3. Understand the concept of information bias 1. Identify sources of information bias (e.g. interviewer bias, exposure or outcome identification, misclassification) 2. Understand the difference between differential and non-differential misclassification i. Understand the implications of differential and non-differential misclassification for your study findings in terms of direction of bias 4. Understand the concept of selection bias i. Identify sources of selection bias in both case-control and cohort studies 5. Identify strategies for minimizing bias 1
- 2. Threats to Validity: Information and Selection Bias 2
- 3. 3 Bias outline • Big picture • Sources of error • Hierarchy of populations • Types of validity • Biases – Information – Selection • Comparing biases • Preventing biases • Summary
- 4. Big picture • Sources of association in epidemiology – After analyzing epidemiologic data, you observe an association between exposure and disease – What are possible explanations for the association? 4
- 5. 5 Big picture • Causal association – Exposure causes disease – This is typically what is of interest in an epidemiologic study – While causal effect may be behind an observed association, other explanations must be considered as well – This unit is about considering other possible explanations