This document provides an overview of case control studies in epidemiology. It defines a case control study as a retrospective study that compares cases (people with a disease or condition) to controls (people without the disease or condition) to determine risk factors for the disease. The key steps outlined are: selection of cases and controls from the same study base; matching cases and controls on important characteristics; measuring exposure to suspected risk factors in the same way for both groups; and analyzing the data to compare exposure rates between cases and controls and estimate disease risk associated with exposure. Advantages are that case control studies are efficient for rare diseases, but limitations include potential for selection and recall bias.
This document discusses different types of study designs used in epidemiology. It begins by defining epidemiology and then outlines the objectives of the presentation which are to understand study design concepts, appropriately apply designs to research, and learn about advantages and disadvantages. Descriptive and analytical studies are introduced. Descriptive studies describe disease frequency and distribution without hypotheses, while analytical studies compare at least two groups to test hypotheses. Case-control and cohort studies are presented as common analytical designs. Case-control studies compare exposed vs unexposed groups among cases and controls to calculate odds ratios. Potential biases are discussed.
The document discusses different types of epidemiological studies, including descriptive studies like case reports and case series that focus on person, place and time to create hypotheses. Analytical studies like case-control and cohort studies are used to test hypotheses by being either observational or interventional. Randomized controlled trials are the gold standard for comparing new interventions. Observational analytical studies include cross-sectional, cohort and case-control designs, while interventional analytical studies are clinical trials. The appropriate study design depends on the research goals and objectives.
This document discusses case-control studies. It begins with an introduction and definition of case-control studies. It then covers the basic steps in conducting a case-control study, including estimating sample size, measures of association, and potential biases. Key points include that case-control studies are retrospective and compare exposures between cases and controls to determine associations with outcomes. Odds ratios are commonly used to measure associations while potential biases include recall and selection biases.
This document discusses various study designs used in epidemiology, including measures of disease occurrence such as prevalence and incidence. It defines prevalence as the total number of cases of a disease at a specified time, while incidence refers to the number of new cases that occur over a period of time. Cohort studies are described as following groups over time to compare rates of an outcome between those exposed and unexposed to a factor. Case-control studies select groups based on having or not having an outcome and look back to compare exposures. Biases such as selection, information and confounding are also outlined.
Screening tests are used to identify apparently healthy people who may be at higher risk for a disease, while diagnostic tests are used to confirm or rule out a diagnosis. Some key indications for screening include when a disease has a known natural history and treatment, an acceptable and accurate screening test exists, and screening can improve health outcomes. Screening tests are evaluated using metrics like sensitivity, specificity, and predictive values, which can be depicted in a 2x2 contingency table.
Measures of association like the relative risk (RR) and odds ratio (OR) quantify the strength between an exposure and disease. An RR or OR of 1 means no association, above 1 means positive association, and below 1 means negative association. The RR compares outcomes between exposed and unexposed groups in cohort studies, while the OR provides an estimate of the RR using case-control studies. Confidence intervals describe the precision of a point estimate, with a narrower interval indicating a more precise estimate. Interpreting if a 95% CI includes 1 determines if there is a statistically significant association.
Observational descriptive study: case report, case series & ecological studyPrabesh Ghimire
This document discusses different types of research designs, including observational and intervention designs. It focuses on non-intervention designs like case reports, case series, and cross-sectional studies. Case reports describe the occurrence, diagnosis, treatment and follow-up of an individual patient, especially unusual cases. Case series describe aspects of a disease or treatment by following a group of patients with common characteristics. Both case reports and case series are useful for generating hypotheses but have limitations due to lack of a control group.
This document provides an overview of case control studies in epidemiology. It defines a case control study as a retrospective study that compares cases (people with a disease or condition) to controls (people without the disease or condition) to determine risk factors for the disease. The key steps outlined are: selection of cases and controls from the same study base; matching cases and controls on important characteristics; measuring exposure to suspected risk factors in the same way for both groups; and analyzing the data to compare exposure rates between cases and controls and estimate disease risk associated with exposure. Advantages are that case control studies are efficient for rare diseases, but limitations include potential for selection and recall bias.
This document discusses different types of study designs used in epidemiology. It begins by defining epidemiology and then outlines the objectives of the presentation which are to understand study design concepts, appropriately apply designs to research, and learn about advantages and disadvantages. Descriptive and analytical studies are introduced. Descriptive studies describe disease frequency and distribution without hypotheses, while analytical studies compare at least two groups to test hypotheses. Case-control and cohort studies are presented as common analytical designs. Case-control studies compare exposed vs unexposed groups among cases and controls to calculate odds ratios. Potential biases are discussed.
The document discusses different types of epidemiological studies, including descriptive studies like case reports and case series that focus on person, place and time to create hypotheses. Analytical studies like case-control and cohort studies are used to test hypotheses by being either observational or interventional. Randomized controlled trials are the gold standard for comparing new interventions. Observational analytical studies include cross-sectional, cohort and case-control designs, while interventional analytical studies are clinical trials. The appropriate study design depends on the research goals and objectives.
This document discusses case-control studies. It begins with an introduction and definition of case-control studies. It then covers the basic steps in conducting a case-control study, including estimating sample size, measures of association, and potential biases. Key points include that case-control studies are retrospective and compare exposures between cases and controls to determine associations with outcomes. Odds ratios are commonly used to measure associations while potential biases include recall and selection biases.
This document discusses various study designs used in epidemiology, including measures of disease occurrence such as prevalence and incidence. It defines prevalence as the total number of cases of a disease at a specified time, while incidence refers to the number of new cases that occur over a period of time. Cohort studies are described as following groups over time to compare rates of an outcome between those exposed and unexposed to a factor. Case-control studies select groups based on having or not having an outcome and look back to compare exposures. Biases such as selection, information and confounding are also outlined.
Screening tests are used to identify apparently healthy people who may be at higher risk for a disease, while diagnostic tests are used to confirm or rule out a diagnosis. Some key indications for screening include when a disease has a known natural history and treatment, an acceptable and accurate screening test exists, and screening can improve health outcomes. Screening tests are evaluated using metrics like sensitivity, specificity, and predictive values, which can be depicted in a 2x2 contingency table.
Measures of association like the relative risk (RR) and odds ratio (OR) quantify the strength between an exposure and disease. An RR or OR of 1 means no association, above 1 means positive association, and below 1 means negative association. The RR compares outcomes between exposed and unexposed groups in cohort studies, while the OR provides an estimate of the RR using case-control studies. Confidence intervals describe the precision of a point estimate, with a narrower interval indicating a more precise estimate. Interpreting if a 95% CI includes 1 determines if there is a statistically significant association.
Observational descriptive study: case report, case series & ecological studyPrabesh Ghimire
This document discusses different types of research designs, including observational and intervention designs. It focuses on non-intervention designs like case reports, case series, and cross-sectional studies. Case reports describe the occurrence, diagnosis, treatment and follow-up of an individual patient, especially unusual cases. Case series describe aspects of a disease or treatment by following a group of patients with common characteristics. Both case reports and case series are useful for generating hypotheses but have limitations due to lack of a control group.
This document describes a case-control study design. It discusses how case-control studies proceed from the outcome (disease status) to exposure to identify risk factors. The document provides examples of research questions that can be addressed using a case-control design. It describes the key aspects of case-control studies including selection of cases and controls, ascertainment of exposure, and analysis to calculate measures of association like odds ratios. Several potential biases in case-control studies like selection bias, recall bias, and confounding are also discussed.
Epidemiology lecture 2 measuring disease frequencyINAAMUL HAQ
This document discusses measuring disease frequency in epidemiology. It defines key terms like incidence, prevalence, population at risk, and rates. Incidence refers to new cases in a specified time period, while prevalence looks at total current cases. Prevalence can be point prevalence (at a point in time), period prevalence (over a specified time period), or lifetime prevalence. The document provides examples of calculating prevalence from population data and discusses how prevalence is used to understand disease burden and plan health services.
Introduction to meta-analysis (1612_MA_workshop)Ahmed Negida
This document provides an overview of a meta-analysis workshop. It will introduce descriptive and inferential statistics, the concept of meta-analysis, and meta-analysis software and models. The workshop covers new topics like quality effects meta-analysis, heterogeneity models, and assessment of publication bias. It explains that simply averaging study results is incorrect, and meta-analysis statistically combines studies while weighting them by size and power to provide a single pooled effect estimate. Meta-analysis has advantages like larger power but must address heterogeneity and differences between studies.
This document discusses various types of errors and biases that can occur in epidemiological studies. It defines error as a phenomenon where a study's results do not reflect the true facts. There are two basic types of error: random error, which occurs by chance and makes observed values differ from true values; and systematic error or bias, which is due to factors in the study design that cause results to depart from the truth. Types of bias discussed include selection bias, information bias, and confounding. Strategies for controlling biases such as randomization, restriction, matching, and statistical modeling are also outlined.
The Presentation will take the reader through various ethical issues in biomedical research. It covers topics like The Nuremberg Code, Declaration of Helsinki, Declaration of Geneva, selected code and regulations that guide research with human subjects, etc.
Design of experiments is the most common Research design will wide reliability. It is mostly applicable in scientific lab type of research. This method is not applicable for descriptive research.
It involves both qualitative and quantitative data sets. The researchers can manipulate, control, replicate and randomize the experimental variables.
There are several types of experimental design depending on the selection of control, test and standard groups and their experimental setting.
The slides also show the guidelines regarding design of research proposal, Literature survey and important ethics in research. Guiding protocol to prepare a research and review article is also discussed.
The document provides an overview of basic statistics and research methodology, focusing on study designs. It discusses observational studies like cross-sectional, case-control and cohort studies as well as experimental studies like clinical trials. For each study design, it describes the key elements including temporal sequence, intervention, sampling methods, and how they differ from one another. It emphasizes the importance of selecting the appropriate study design based on the research question and highlights factors to consider like ability to determine causation, study of rare diseases, costs and time involved.
Systematic and random errors can affect epidemiological studies. Random errors are due to chance and include individual biological variation, measurement error, and sampling error. Systematic errors, also called biases, are non-random and can distort study results. Selection bias occurs if study groups differ in characteristics unrelated to exposure that influence outcomes. Measurement bias happens if exposures or diseases are inaccurately classified. Confounding is present when a third factor is associated with both the exposure and outcome under investigation. Careful study design and analysis techniques can help reduce biases and errors to obtain more accurate results.
Researchers tested a new anti-anxiety medication on 200 people and a placebo on another 200 people. 64 of those on the medication and 92 of those on the placebo reported anxiety symptoms. The researchers want to determine if there is a statistically significant difference in reported anxiety between the two groups using a two-sample z-test with an alpha of 0.05. A two-sample z-test is used to compare differences between two sample proportions and determines if any observed difference is likely due to chance or not.
This document discusses measures of association used in epidemiology to quantify the strength of relationships between categorical variables. It defines relative risk, odds ratio, and attributable risk, and provides formulas for calculating each. Examples are given for how to calculate and interpret these measures using data from cohort and case-control studies. Relative risk reflects the likelihood of disease in the exposed group compared to unexposed. Attributable risk quantifies the excess risk among the exposed that can be attributed to the exposure.
This document discusses different types of epidemiologic study designs including descriptive studies, analytical studies, and experimental studies. It provides details on descriptive epidemiology, analytic epidemiology, and different types of observational and experimental study designs such as cohort studies, case-control studies, randomized controlled trials, and ecological studies. Key aspects of cohort and case-control study designs are outlined including their advantages and disadvantages. Potential sources of error and bias in epidemiologic studies are also reviewed.
This document discusses the history and principles of evidence-based medicine (EBM). It notes that while EBM has ancient origins, the modern concept was popularized in the 1970s by Archie Cochrane. EBM involves applying the best available evidence from scientific research to medical practice and decision making. Evidence is ranked based on the strength of the research design. Guidelines help regulate practice based on evidence, while individual decision making focuses on practitioners building their decisions from evidence. Randomized controlled trials provide the strongest evidence, while observational studies and descriptive research provide weaker evidence. Rigorous research requires strength, consistency and adherence to proper methodology.
Workshop for Family Medicine Residents at the University of Calgary on Evidence-Based Medicine, the PICO approach to critical appraisal, and the need for skepticism
When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results. ... The p-value is a number between 0 and 1 and interpreted in the following way: A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.
Rapid review of the different study designs of clinical research including case report, case series, case-control, respective cohort, prospective cohort, and clinical trials.
The document discusses methods for calculating sample sizes for various study designs, including measuring prevalence, cross-sectional studies, case-control studies, and clinical trials. It provides formulas and examples for calculating sample sizes needed to measure a dichotomous outcome and a continuous outcome. For measuring prevalence, the sample size depends on the expected prevalence rate, desired precision level, and confidence interval. For studies comparing two groups, the sample size depends on the event rates in each group and the desired power and significance level to detect a difference between groups.
XNN001 Introductory epidemiological concepts - Study designramseyr
This document provides an overview of key epidemiological concepts and study designs. It defines epidemiology and discusses why epidemiological data is collected through monitoring and surveillance and to identify relationships between exposures and disease. The main observational study designs covered are ecological, cross-sectional, case-control, cohort studies as well as randomized controlled trials. For each study design, the document outlines their structure, advantages and limitations.
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.
This document provides an overview of randomized controlled trials (RCTs), including their definition, characteristics, and critical appraisal. RCTs are prospective studies that randomly assign participants to experimental and control groups. The key characteristics discussed include randomization and allocation concealment to distribute confounding variables equally between groups, blinding to reduce bias, pre-specified outcomes, sample size calculation, and intention-to-treat analysis. Critical appraisal involves assessing the validity, relevance, and risk of bias in RCTs to determine the reliability and generalizability of results.
Observational analytical and interventional studiesAchyut Raj Pandey
This document provides an overview of different types of epidemiological study designs, including observational analytical studies like cohort and case-control studies, as well as experimental studies. It describes key aspects of cohort and case-control studies such as their designs, advantages, disadvantages, examples, and considerations for conducting them. Cohort studies follow groups over time from exposure to outcome, while case-control studies identify cases and controls and look back from outcome to exposure. Experimental studies actively alter variables to assess relationships between them.
Fundamentals of clinical research and experimental design, Prof. Usama M.Fouda umfrfouda
This document provides an overview of key concepts in clinical research and experimental design. It defines clinical research as investigations involving human subjects that aim to understand the causes, prevention, diagnosis or treatment of human disease. The document then classifies clinical studies as either observational (e.g. cross-sectional, cohort, case-control) or interventional (e.g. randomized controlled trials). It describes the purpose and methodology of several major study designs, highlights their relative strengths and weaknesses, and provides examples of studies using each design.
This document describes a case-control study design. It discusses how case-control studies proceed from the outcome (disease status) to exposure to identify risk factors. The document provides examples of research questions that can be addressed using a case-control design. It describes the key aspects of case-control studies including selection of cases and controls, ascertainment of exposure, and analysis to calculate measures of association like odds ratios. Several potential biases in case-control studies like selection bias, recall bias, and confounding are also discussed.
Epidemiology lecture 2 measuring disease frequencyINAAMUL HAQ
This document discusses measuring disease frequency in epidemiology. It defines key terms like incidence, prevalence, population at risk, and rates. Incidence refers to new cases in a specified time period, while prevalence looks at total current cases. Prevalence can be point prevalence (at a point in time), period prevalence (over a specified time period), or lifetime prevalence. The document provides examples of calculating prevalence from population data and discusses how prevalence is used to understand disease burden and plan health services.
Introduction to meta-analysis (1612_MA_workshop)Ahmed Negida
This document provides an overview of a meta-analysis workshop. It will introduce descriptive and inferential statistics, the concept of meta-analysis, and meta-analysis software and models. The workshop covers new topics like quality effects meta-analysis, heterogeneity models, and assessment of publication bias. It explains that simply averaging study results is incorrect, and meta-analysis statistically combines studies while weighting them by size and power to provide a single pooled effect estimate. Meta-analysis has advantages like larger power but must address heterogeneity and differences between studies.
This document discusses various types of errors and biases that can occur in epidemiological studies. It defines error as a phenomenon where a study's results do not reflect the true facts. There are two basic types of error: random error, which occurs by chance and makes observed values differ from true values; and systematic error or bias, which is due to factors in the study design that cause results to depart from the truth. Types of bias discussed include selection bias, information bias, and confounding. Strategies for controlling biases such as randomization, restriction, matching, and statistical modeling are also outlined.
The Presentation will take the reader through various ethical issues in biomedical research. It covers topics like The Nuremberg Code, Declaration of Helsinki, Declaration of Geneva, selected code and regulations that guide research with human subjects, etc.
Design of experiments is the most common Research design will wide reliability. It is mostly applicable in scientific lab type of research. This method is not applicable for descriptive research.
It involves both qualitative and quantitative data sets. The researchers can manipulate, control, replicate and randomize the experimental variables.
There are several types of experimental design depending on the selection of control, test and standard groups and their experimental setting.
The slides also show the guidelines regarding design of research proposal, Literature survey and important ethics in research. Guiding protocol to prepare a research and review article is also discussed.
The document provides an overview of basic statistics and research methodology, focusing on study designs. It discusses observational studies like cross-sectional, case-control and cohort studies as well as experimental studies like clinical trials. For each study design, it describes the key elements including temporal sequence, intervention, sampling methods, and how they differ from one another. It emphasizes the importance of selecting the appropriate study design based on the research question and highlights factors to consider like ability to determine causation, study of rare diseases, costs and time involved.
Systematic and random errors can affect epidemiological studies. Random errors are due to chance and include individual biological variation, measurement error, and sampling error. Systematic errors, also called biases, are non-random and can distort study results. Selection bias occurs if study groups differ in characteristics unrelated to exposure that influence outcomes. Measurement bias happens if exposures or diseases are inaccurately classified. Confounding is present when a third factor is associated with both the exposure and outcome under investigation. Careful study design and analysis techniques can help reduce biases and errors to obtain more accurate results.
Researchers tested a new anti-anxiety medication on 200 people and a placebo on another 200 people. 64 of those on the medication and 92 of those on the placebo reported anxiety symptoms. The researchers want to determine if there is a statistically significant difference in reported anxiety between the two groups using a two-sample z-test with an alpha of 0.05. A two-sample z-test is used to compare differences between two sample proportions and determines if any observed difference is likely due to chance or not.
This document discusses measures of association used in epidemiology to quantify the strength of relationships between categorical variables. It defines relative risk, odds ratio, and attributable risk, and provides formulas for calculating each. Examples are given for how to calculate and interpret these measures using data from cohort and case-control studies. Relative risk reflects the likelihood of disease in the exposed group compared to unexposed. Attributable risk quantifies the excess risk among the exposed that can be attributed to the exposure.
This document discusses different types of epidemiologic study designs including descriptive studies, analytical studies, and experimental studies. It provides details on descriptive epidemiology, analytic epidemiology, and different types of observational and experimental study designs such as cohort studies, case-control studies, randomized controlled trials, and ecological studies. Key aspects of cohort and case-control study designs are outlined including their advantages and disadvantages. Potential sources of error and bias in epidemiologic studies are also reviewed.
This document discusses the history and principles of evidence-based medicine (EBM). It notes that while EBM has ancient origins, the modern concept was popularized in the 1970s by Archie Cochrane. EBM involves applying the best available evidence from scientific research to medical practice and decision making. Evidence is ranked based on the strength of the research design. Guidelines help regulate practice based on evidence, while individual decision making focuses on practitioners building their decisions from evidence. Randomized controlled trials provide the strongest evidence, while observational studies and descriptive research provide weaker evidence. Rigorous research requires strength, consistency and adherence to proper methodology.
Workshop for Family Medicine Residents at the University of Calgary on Evidence-Based Medicine, the PICO approach to critical appraisal, and the need for skepticism
When you perform a hypothesis test in statistics, a p-value helps you determine the significance of your results. ... The p-value is a number between 0 and 1 and interpreted in the following way: A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.
Rapid review of the different study designs of clinical research including case report, case series, case-control, respective cohort, prospective cohort, and clinical trials.
The document discusses methods for calculating sample sizes for various study designs, including measuring prevalence, cross-sectional studies, case-control studies, and clinical trials. It provides formulas and examples for calculating sample sizes needed to measure a dichotomous outcome and a continuous outcome. For measuring prevalence, the sample size depends on the expected prevalence rate, desired precision level, and confidence interval. For studies comparing two groups, the sample size depends on the event rates in each group and the desired power and significance level to detect a difference between groups.
XNN001 Introductory epidemiological concepts - Study designramseyr
This document provides an overview of key epidemiological concepts and study designs. It defines epidemiology and discusses why epidemiological data is collected through monitoring and surveillance and to identify relationships between exposures and disease. The main observational study designs covered are ecological, cross-sectional, case-control, cohort studies as well as randomized controlled trials. For each study design, the document outlines their structure, advantages and limitations.
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.
This document provides an overview of randomized controlled trials (RCTs), including their definition, characteristics, and critical appraisal. RCTs are prospective studies that randomly assign participants to experimental and control groups. The key characteristics discussed include randomization and allocation concealment to distribute confounding variables equally between groups, blinding to reduce bias, pre-specified outcomes, sample size calculation, and intention-to-treat analysis. Critical appraisal involves assessing the validity, relevance, and risk of bias in RCTs to determine the reliability and generalizability of results.
Observational analytical and interventional studiesAchyut Raj Pandey
This document provides an overview of different types of epidemiological study designs, including observational analytical studies like cohort and case-control studies, as well as experimental studies. It describes key aspects of cohort and case-control studies such as their designs, advantages, disadvantages, examples, and considerations for conducting them. Cohort studies follow groups over time from exposure to outcome, while case-control studies identify cases and controls and look back from outcome to exposure. Experimental studies actively alter variables to assess relationships between them.
Fundamentals of clinical research and experimental design, Prof. Usama M.Fouda umfrfouda
This document provides an overview of key concepts in clinical research and experimental design. It defines clinical research as investigations involving human subjects that aim to understand the causes, prevention, diagnosis or treatment of human disease. The document then classifies clinical studies as either observational (e.g. cross-sectional, cohort, case-control) or interventional (e.g. randomized controlled trials). It describes the purpose and methodology of several major study designs, highlights their relative strengths and weaknesses, and provides examples of studies using each design.
This document discusses research design. It defines research design as the specific plan for conducting a study to translate a conceptual hypothesis into an operational one. Research design helps make decisions about how to complete the entire research process validly, objectively, accurately, and economically. The document then discusses classifications of study designs based on number of contacts with participants, reference period, and nature of investigation. It provides examples and advantages and disadvantages of descriptive studies like case reports, case series, and ecological studies as well as analytical studies like case-control and cohort studies. It also discusses experimental design, blind studies, and double-blind studies.
This document provides an overview of epidemiology study designs, including descriptive epidemiology, analytic epidemiology, experimental studies, and observational studies. It describes the key features and examples of analytic epidemiology, observational cohort studies, case-control studies, and cross-sectional studies. The purpose of analytic epidemiology is to identify and quantify relationships between exposures and health outcomes. Observational studies observe exposures under natural conditions rather than introducing an intervention.
This document provides an overview of clinical trials and the role of statistics within clinical research. It discusses how clinical trials allow researchers to systematically study medical treatments and make evidence-based inferences about a treatment's effects. The document outlines the key elements of clinical trials, such as using a control group, randomization, blinding, and informed consent. It also discusses the history of clinical trials and how they became the preferred method for medical research due to providing a standardized and ethical way to study human subjects compared to observational studies. Finally, the document addresses some of the ethical considerations around clinical trials, such as balancing patient welfare with gaining scientific knowledge.
This document discusses different types of research classification. It describes how research can be classified based on the type of data (qualitative vs quantitative), availability of data (primary vs secondary), research setting (public health, clinical, pre-clinical), study design (observational vs analytic vs experimental), and research method (philosophical, historical, survey, experimental, case study). Clinical trials are also discussed, including phases 0 through 4. Both qualitative and quantitative methods are important to scientific research.
This document provides an overview of pharmacoepidemiology presented by Aisha Siddiqui. It defines pharmacoepidemiology as the study of drug use and effects in large populations. The field evolved from the joining of clinical pharmacology and epidemiology to study adverse drug reactions. Various study designs are discussed, including observational studies like cross-sectional and cohort studies, as well as experimental controlled trials. Drug utilization studies specifically evaluate prescribing, dispensing, administration and outcomes of medication use. In summary, pharmacoepidemiology applies epidemiological methods to understand drug effects at a population level.
Evidence-based medicine involves integrating the best available research evidence with clinical expertise and patient values to deliver appropriate care. It uses techniques like randomized controlled trials, systematic reviews and meta-analyses to establish what treatments work best. While medicine has a long history of scientific inquiry, events like the thalidomide tragedy highlighted the need for a more evidence-based approach. Evidence-based medicine guides decisions at various levels of healthcare systems and allows clinicians to tailor treatment to individual patients based on the evidence.
Pharmacoepidemiology is the study of the use and effects of drugs in large populations. It combines the fields of clinical pharmacology and epidemiology. Recent data shows that adverse drug reactions cause 100,000 deaths and 1.5 million hospitalizations in the US each year, yet 20-70% may be preventable. Pharmacoepidemiology aims to detect adverse drug reactions early through observational studies in order to educate healthcare providers and the public about safer medication use. Key study types include case series, case-control studies, cohort studies, cross-sectional studies, and experimental studies. Drug utilization studies also fall under pharmacoepidemiology and evaluate factors related to prescribing, dispensing, administering, and taking
Case-control studies compare subjects who have a condition (cases) to similar subjects who do not (controls) to identify factors that may contribute to a disease or risk factor. They are useful when experiments are impossible or unethical, and are a relatively inexpensive way to study rare diseases. Advantages include studying rare factors and providing quick, cheap results, but they are prone to bias and recall errors. A case-control study example identified smoking as linked to lung cancer by interviewing patients.
This document discusses different types of study designs used in medical research, including qualitative and quantitative methods. It covers observational studies like cohort and case-control studies, as well as experimental designs like randomized controlled trials. For each study type, it outlines their purpose, strengths, weaknesses and the types of research questions they can help answer. The goal is to help researchers choose the most appropriate design based on their specific research question and aims.
This document discusses different types of observational studies and experimental trials used in research methodology. It defines observational studies as those that involve collecting data without intervening or altering the course of events. The main types of observational studies covered are case-control studies, cohort studies, cross-sectional studies, and ecological studies. Experimental trials involve manipulating a variable and measuring the effects. Randomized controlled trials are described as the gold standard for determining causation. Key aspects of randomized controlled trial design and methodology are outlined.
1. A case control study compares exposures in people with a disease (cases) to people without the disease (controls) to determine if any exposures are associated with the disease.
2. Key features of case control studies include directionality from exposure to outcome, retrospective assessment of exposure, and sampling based on outcome status.
3. Potential biases include selection bias, recall/information bias, and confounding which must be addressed through careful study design and analysis.
This document discusses validity in epidemiological studies. It defines validity as the degree to which a study accurately measures what it aims to measure. Internal validity refers to minimizing errors in data collection, while external validity is the ability to generalize results to other settings and populations. Bias, confounding, and chance can threaten validity. Bias can occur in selection of participants or measurement. Confounding involves extraneous factors associated with both exposure and outcome. Larger sample sizes and longer studies reduce the impact of chance on validity. Assessing validity involves evaluating the study design and ensuring it limits threats to validity.
This document discusses different types of epidemiological study designs including analytic, observational, and interventional studies. It provides details on cohort and case-control study designs, including how to select cases and controls, measure exposures and outcomes, analyze results, and consider advantages and limitations. It also defines various measures of mortality such as crude death rate, age-specific mortality rate, and case fatality rate.
This document discusses various epidemiologic study designs used to identify and investigate risk factors for disease. Descriptive designs like case reports, case series, and cross-sectional studies measure disease frequency and exposure levels. Analytic designs like case-control and cohort studies attempt to specify disease causes. Case-control studies identify existing diseases and look back at previous exposures, while cohort studies follow subjects over time to compare disease incidence between exposed and unexposed groups. Experimental studies randomly allocate subjects to exposure groups to establish causality but have ethical and cost disadvantages compared to observational designs. The appropriate study design depends on factors like the question, resources, disease frequency, and data quality.
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نظرية التطور عند المسلمين (بروفيسور محمد علي البار
ويقدم فيها سردا تاريخيا لنظريات نشأة الخلق وخلق آدم وكيف ان نظرية التطور هي نظرية علمية وليس دينية لكن تم استغلالها لمحاربة الكنيسة
Ethical considerations in research during armed conflicts.pptxDr Ghaiath Hussein
My talk @AUBMC Salim El-Hoss Bioethics Webinar Series. In this webinar, we have discussed the following points:
1- How armed conflicts affect the planning and conduct of research?
2- What is ethically unique about research during armed conflicts?
3- How did my doctoral project approach these ethical issues both at the normative and the empirical levels?
4- What are the lessons learned from the conflicts in the middle east (Sudan, Syria, Yemen, etc.) and how do they differ from the situation in Ukraine?
Acknowledgement: This talk is based on my doctoral thesis (http://etheses.bham.ac.uk/8580/), which was fully funded by Wellcome Trust, UK.
Medically Assisted Dying in (MAiD) Ireland - Mapping the Ethical Terrain (May...Dr Ghaiath Hussein
This document outlines a presentation on mapping the ethical terrain of medically assisted dying (MAiD) in Ireland. It does not take a stance but provides a framework to guide conceptual discussion. It focuses on the decision, decision makers, and outcomes using Canada as an example country that has legalized MAiD. Key ethical questions are raised about patients' autonomy and consent, physicians' conflicting duties, and impacts on public perception and resource allocation. Data from Canada on MAiD providers and annual reported deaths is presented. The conclusion emphasizes the need for evidence from all stakeholders and learning from other jurisdictions' experiences before a decision is made.
Research or Not Research? This Is Not the Question for Public Health Emergencies
November 17, 2021 @ 4:00 pm - 5:00 pm EST
Speaker:
Ghaiath Hussein, Assistant Professor, Medical Ethics and Law, Trinity College Dublin, Ireland
About this Seminar:
Public health emergencies, whether natural or man-made, local or global, in peacetime or during armed conflicts are always associated with the need to collect data (and sometimes biological samples) about and from those affected by these emergencies. One of the central questions in the relevant literature is whether the activities that involve the collection of data and/or biological samples are considered ‘research’, with the subsequent endeavour to define what ‘research’ is and whether they should be submitted for ethical approval or not. In this seminar, I will argue that this is not the central question when it comes to research/public health/humanitarian ethics. Using the findings of a systematic review on the research conducted in Darfur and findings from a qualitative project that aimed at defining what constitutes ‘research’ in public health emergencies I will, alternatively, present what I refer to as the ‘ethical characterization’ of these research-like activities and how they can be ethically guided.
Medically assisted dying in (MAiD) Ireland - mapping the ethical terrainDr Ghaiath Hussein
This document provides an outline for a presentation on medically assisted dying (MAiD) in Ireland. It aims to establish an ethical framework for conceptual discussion of MAiD by considering: the decision, the decision makers, and the outcome. It does not endorse any viewpoint. The presentation raises several ethical questions around patient autonomy and consent, concepts of life and death, the role of healthcare providers, and impacts on community and public trust. Examples are provided from Canada, where MAiD is legal, to illustrate challenges in practice. The document stresses the need for evidence from all stakeholders and learning from other jurisdictions' experiences before legalizing MAiD in Ireland.
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Emotional and Behavioural Problems in Children - Counselling and Family Thera...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Cancer treatment has advanced significantly over the years, offering patients various options tailored to their specific type of cancer and stage of disease. Understanding the different types of cancer treatments can help patients make informed decisions about their care. In this ppt, we have listed most common forms of cancer treatment available today.
VEDANTA AIR AMBULANCE SERVICES IN REWA AT A COST-EFFECTIVE PRICE.pdfVedanta A
Air Ambulance Services In Rewa works in close coordination with ground-based emergency services, including local Emergency Medical Services, fire departments, and law enforcement agencies.
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Sectional dentures for microstomia patients.pptxSatvikaPrasad
Microstomia, characterized by an abnormally small oral aperture, presents significant challenges in prosthodontic treatment, including limited access for examination, difficulties in impression making, and challenges with prosthesis insertion and removal. To manage these issues, customized impression techniques using sectional trays and elastomeric materials are employed. Prostheses may be designed in segments or with flexible materials to facilitate handling. Minimally invasive procedures and the use of digital technologies can enhance patient comfort. Education and training for patients on prosthesis care and maintenance are crucial for compliance. Regular follow-up and a multidisciplinary approach, involving collaboration with other specialists, ensure comprehensive care and improved quality of life for microstomia patients.
English Drug and Alcohol Commissioners June 2024.pptxMatSouthwell1
Presentation made by Mat Southwell to the Harm Reduction Working Group of the English Drug and Alcohol Commissioners. Discuss stimulants, OAMT, NSP coverage and community-led approach to DCRs. Focussing on active drug user perspectives and interests
Digital Health in India_Health Informatics Trained Manpower _DrDevTaneja_15.0...DrDevTaneja1
Digital India will need a big trained army of Health Informatics educated & trained manpower in India.
Presently, generalist IT manpower does most of the work in the healthcare industry in India. Academic Health Informatics education is not readily available at school & health university level or IT education institutions in India.
We look into the evolution of health informatics and its applications in the healthcare industry.
HIMMS TIGER resources are available to assist Health Informatics education.
Indian Health universities, IT Education institutions, and the healthcare industry must proactively collaborate to start health informatics courses on a big scale. An advocacy push from various stakeholders is also needed for this goal.
Health informatics has huge employment potential and provides a big business opportunity for the healthcare industry. A big pool of trained health informatics manpower can lead to product & service innovations on a global scale in India.
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The story of Dr. Ranjit Jagtap's daughters is more than a tale of inherited responsibility; it's a narrative of passion, innovation, and unwavering commitment to a cause greater than oneself. In Poulami and Aditi Jagtap, we see the beautiful continuum of a father's dream and the limitless potential of compassion-driven healthcare.
Health Tech Market Intelligence Prelim Questions -Gokul Rangarajan
The Ultimate Guide to Setting up Market Research in Health Tech part -1
How to effectively start market research in the health tech industry by defining objectives, crafting problem statements, selecting methods, identifying data collection sources, and setting clear timelines. This guide covers all the preliminary steps needed to lay a strong foundation for your research.
This lays foundation of scoping research project what are the
Before embarking on a research project, especially one aimed at scoping and defining parameters like the one described for health tech IT, several crucial considerations should be addressed. Here’s a comprehensive guide covering key aspects to ensure a well-structured and successful research initiative:
1. Define Research Objectives and Scope
Clear Objectives: Define specific goals such as understanding market needs, identifying new opportunities, assessing risks, or refining pricing strategies.
Scope Definition: Clearly outline the boundaries of the research in terms of geographical focus, target demographics (e.g., age, socio-economic status), and industry sectors (e.g., healthcare IT).
3. Review Existing Literature and Resources
Literature Review: Conduct a thorough review of existing research, market reports, and relevant literature to build foundational knowledge.
Gap Analysis: Identify gaps in existing knowledge or areas where further exploration is needed.
4. Select Research Methodology and Tools
Methodological Approach: Choose appropriate research methods such as surveys, interviews, focus groups, or data analytics.
Tools and Resources: Select tools like Google Forms for surveys, analytics platforms (e.g., SimilarWeb, Statista), and expert consultations.
5. Ethical Considerations and Compliance
Ethical Approval: Ensure compliance with ethical guidelines for research involving human subjects.
Data Privacy: Implement measures to protect participant confidentiality and adhere to data protection regulations (e.g., GDPR, HIPAA).
6. Budget and Resource Allocation
Resource Planning: Allocate resources including time, budget, and personnel required for each phase of the research.
Contingency Planning: Anticipate and plan for unforeseen challenges or adjustments to the research plan.
7. Develop Research Instruments
Survey Design: Create well-structured surveys using tools like Google Forms to gather quantitative data.
Interview and Focus Group Guides: Prepare detailed scripts and discussion points for qualitative data collection.
8. Sampling Strategy
Sampling Design: Define the sampling frame, size, and method (e.g., random sampling, stratified sampling) to ensure representation of target demographics.
Participant Recruitment: Plan recruitment strategies to reach and engage the intended participant groups effectively.
9. Data Collection and Analysis Plan
Data Collection: Implement methods for data gathering, ensuring consistency and validity.
Analysis Techniques: Decide on analytical approaches (e.g., statistical
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This is a presentation on the overview of the role of monitoring and evaluation in public health. It describes the various components and how a robust M&E system can possitively impact the results or effectiveness of a public health intervention.
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L5 rm cohort studies case control studies
1. Research Methodology and
Evidence Based Healthcare (EBHC481)
Cohort Studies
Case-Control Studies
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD1
Lecture 5
Level 8
Year 4
2. Research Methodology and
Evidence Based Healthcare (EBHC481)
Objectives
• Identify cohort design study
• Discuss types of cohort study
• Explain case-control study
• Discuss advantages of case-control study
• Explain difference between retrospective cohort study and a case-
control study
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD2
3. Research Methodology and
Evidence Based Healthcare (EBHC481)
Cohort Study Design
• Cohort studies are a type of medical research used to investigate
the causes of disease, establishing links between risk factors and
health outcomes.
• Cohort studies are usually forward-looking - that is, they are
"prospective" studies, or planned in advance and carried out over
a future period of time.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD3
4. Research Methodology and
Evidence Based Healthcare (EBHC481)
Types of Cohort Studies
• The simplest cohort design is prospective, i.e., following a group
forward in time, but a cohort study can also be 'retrospective'.
• In general, the descriptor, 'prospective' or 'retrospective',
indicates when the cohort is identified relative to the initiation of
the study.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD4
5. Research Methodology and
Evidence Based Healthcare (EBHC481)
1- Prospective cohort
Prospective cohort (may also be called a longitudinal or a concurrent
study)
• An investigator identifies the study population at the beginning of the
study and accompanies the subjects through time.
• In a prospective study, the investigator begins the study at the same
time as the first determination of exposure status of the
cohort. When proposing a prospective cohort study, the investigator
first identifies the characteristics of the group of people he/she wishes
to study.
• The investigator then determines the present case status of individuals,
selecting only non-cases to follow forward in time. Exposure status is
determined at the beginning of the study.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD5
6. Research Methodology and
Evidence Based Healthcare (EBHC481)
Prospective cohort
• Problems: loss to follow up; loss of funding support; continually improving
methods for detecting exposure (leading to greater misclassification than
would be expected in current practice)
• Examples: Nurses Health Study; National Health and Nutrition Examination
Study Follow up Study.
• Exposure was then measured who were followed over a period of time until
reaching the study endpoint.
• A member of the cohort reaches the endpoint either by dying, becoming a
case, or reaching the end of the study period.
• A subject can also be lost to follow-up over the course of the study. The
investigator progresses through time with the subjects in a prospective cohort
study.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD6
7. Research Methodology and
Evidence Based Healthcare (EBHC481)
2- Retrospective cohort study
Retrospective cohort study (historical cohort; non-concurrent
prospective cohort)
• An investigator accesses a historical roster of all exposed and non
exposed persons and then determines their current case/non-case
status.
• The investigator initiates the study when the disease is already
established in the cohort of individuals, long after the original
measurement of exposure.
• Doing a retrospective cohort study requires good data on exposure
status for both cases and non cases at a designated earlier time point.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD7
8. Research Methodology and
Evidence Based Healthcare (EBHC481)
Case-control study
• A case-control study is a type of observational study in which two
existing groups differing in outcome are identified and compared on
the basis of some supposed causal attribute.
• Case-control studies are often used to identify factors that may
contribute to a medical condition by comparing subjects who have that
condition/disease (the "cases") with patients who do not have the
condition/disease but are otherwise similar (the "controls").
• They require fewer resources but provide less evidence for causal
inference than a randomized controlled trial.
• We only get odds ratio from a case control study which is an inferior
measure of strength of association as compared to relative risk.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD8
9. Research Methodology and
Evidence Based Healthcare (EBHC481)
Control group selection
• Controls need not be in good health; inclusion of sick people is sometimes
appropriate, as the control group should represent those at risk of becoming a case.
• Controls should come from the same population as the cases, and their selection
should be independent of the exposures of interest.
• Controls can carry the same disease as the experimental group, but of another
grade/severity, therefore being different from the outcome of interest. However,
because the difference between the cases and the controls will be smaller, this
results in a lower power to detect an exposure effect.
• As with any epidemiological study, greater numbers in the study will increase the
power of the study. Numbers of cases and controls do not have to be equal. In many
situations, it is much easier to recruit controls than to find cases. Increasing the
number of controls above the number of cases, up to a ratio of about 4 to 1, may be
a cost-effective way to improve the study.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD9
10. Research Methodology and
Evidence Based Healthcare (EBHC481)
Selection of cases
• This requires a suitable case definition. In addition, care is needed
that bias does not arise from the way in which cases are selected.
• A study of benign prostatic hypertrophy might be misleading if
cases were identified from hospital admissions and admission to
hospital was influenced not only by the presence and severity of
disease but also by other variables, such as social class.
• In general it is better to use incident rather than prevalent cases.
Prevalence is influenced not only by the risk of developing disease
but also by factors that determine the duration of illness.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD10
11. Research Methodology and
Evidence Based Healthcare (EBHC481)
Advantages of case-control study
• Case-control studies are a relatively inexpensive and frequently
used type of epidemiological study that can be carried out by
small teams or individual researchers in single facilities in a way
that more structured experimental studies often cannot be.
• The case-control study design is often used in the study of rare
diseases or as a preliminary study where little is known about the
association between the risk factor and disease of interest.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD11
12. Research Methodology and
Evidence Based Healthcare (EBHC481)
Advantages of case-control study cont.
• Compared to prospective cohort studies they tend to be less costly
and shorter in duration.
• In several situations they have greater statistical power than
cohort studies, which must often wait for a 'sufficient' number of
disease events to accrue.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD12
13. Research Methodology and
Evidence Based Healthcare (EBHC481)
Examples
• One of the most significant triumphs of the case-control study was
the demonstration of the link between tobacco smoking and lung
cancer,
• Richard Doll and Bradford Hill. They showed a statistically
significant association in a large case-control study. Opponents
argued for many years that this type of study cannot prove
causation, but the eventual results of cohort studies confirmed
the causal link which the case-control studies suggested, and it is
now accepted that tobacco smoking is the cause of about 87% of
all lung cancer mortality in the US.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD13
14. Research Methodology and
Evidence Based Healthcare (EBHC481)
How does a retrospective cohort study differ from
a case-control study?
• Both types of studies identify present cases and non-cases.
• The case-control study identifies the cases and then selects
appropriate controls. An entire cohort is not used.
• If you were investigating an environmentally-related cancer among
university students with a case-control study, you would identify
students within certain years who met the case definition for the
cancer. You would select controls among students who were not a case
of cancer, but matched on characteristics such as age, gender and
graduation year, then determine their exposure status (perhaps
proximity of their campus address to the identified toxin) and compare
exposures between cases and non-cases.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD14
15. Research Methodology and
Evidence Based Healthcare (EBHC481)
Continue …
• A retrospective cohort study uses the entire cohort; all cases and
non-cases within the identified group.
• A retrospective cohort design might designate the cohort to be
students enrolled at the university over a 5 year time span. The
present case status of all these students is determined and
historical data about their exposure status accessed, in order to
assess the relationship between being a case of the cancer with
the exposure.
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD15
16. Research Methodology and
Evidence Based Healthcare (EBHC481)
References
Research methodology manual 2015
Dr. Prabhat Pandey
Dr. Meenu Mishra Pandey
First published, 2015
editor@euacademic.org
ISBN 978-606-93502-7-0
WRITTEN AND COMPILED BY DR. EMAN ABD ALHALIM MD, PHD16