6.5 strengths and challenges
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A MCase-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.
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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.
5.2 cohort studies basic approach
This document provides an overview of cohort studies, including their basic approach, history, strengths and challenges. Cohort studies follow groups of individuals over time to study how exposures affect outcomes. They can follow either a single group or multiple exposure groups. Exposures and outcomes are recorded longitudinally. Cohort studies allow for analysis of how time-varying exposures relate to disease occurrence and provide a comprehensive view of exposure-disease dynamics. However, accurately characterizing exposures over long periods can be complex.
6.2 principles
This document discusses the principles of cohort studies, including defining the primary and secondary study bases. The primary study base is defined as a specific population in space and time, such as residents of Oakland, CA in 2010. The secondary study base is defined by the identified cases and represents the population that could have been cases. Another principle is deconfounding, which aims to control for measured confounders and reduce variability in unmeasured confounders, such as through matching on factors like genetics.
6.3 sources of controls
This document discusses principles of case-control studies, focusing on the importance of selecting controls from the same study base as cases. It reviews potential sources of controls, noting issues like whether the controls truly represent the study base, potential for biases, and feasibility. Controls from random digit dialing, neighborhoods, hospitals/clinics, friends, and relatives are discussed, with relatives and dead controls being problematic as they violate the study base principle. Using multiple control groups is noted to potentially yield discordant results requiring careful design.
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.
6.1 big picture
A case-control study collects data from a subset of a cohort by identifying incident cases of disease and selecting controls without the disease from the same study base. Exposure information is gathered from cases and controls through interviews, records, or tests. Case-control studies are useful when diseases are rare, making cohort studies impractical, or when exposure assessment has high costs by allowing evaluation of only a sample.
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.
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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.
5.2 cohort studies basic approach
This document provides an overview of cohort studies, including their basic approach, history, strengths and challenges. Cohort studies follow groups of individuals over time to study how exposures affect outcomes. They can follow either a single group or multiple exposure groups. Exposures and outcomes are recorded longitudinally. Cohort studies allow for analysis of how time-varying exposures relate to disease occurrence and provide a comprehensive view of exposure-disease dynamics. However, accurately characterizing exposures over long periods can be complex.
6.2 principles
This document discusses the principles of cohort studies, including defining the primary and secondary study bases. The primary study base is defined as a specific population in space and time, such as residents of Oakland, CA in 2010. The secondary study base is defined by the identified cases and represents the population that could have been cases. Another principle is deconfounding, which aims to control for measured confounders and reduce variability in unmeasured confounders, such as through matching on factors like genetics.
6.3 sources of controls
This document discusses principles of case-control studies, focusing on the importance of selecting controls from the same study base as cases. It reviews potential sources of controls, noting issues like whether the controls truly represent the study base, potential for biases, and feasibility. Controls from random digit dialing, neighborhoods, hospitals/clinics, friends, and relatives are discussed, with relatives and dead controls being problematic as they violate the study base principle. Using multiple control groups is noted to potentially yield discordant results requiring careful design.
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.
6.1 big picture
A case-control study collects data from a subset of a cohort by identifying incident cases of disease and selecting controls without the disease from the same study base. Exposure information is gathered from cases and controls through interviews, records, or tests. Case-control studies are useful when diseases are rare, making cohort studies impractical, or when exposure assessment has high costs by allowing evaluation of only a sample.
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.
Recall bais
Irrespective of study design, the first step in the process of avoiding any type of bias is the proper definition and articulation of the research question.
Consequently, this step will lead to a number of questions that need to be adequately addressed by the investigator during the planning stage of research:
what kind of information are required to answer this question in the study in terms of exposure, outcome, and possible confounders?
what is the most appropriate method to collect these information?
how to achieve comparable accuracy of data collection between the study groups?
3.1 big picture
The 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.
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.
2.1 big picture
This document discusses key concepts in epidemiology for comparing disease frequency between groups with different exposures. It explains that epidemiological studies measure associations between exposures and outcomes, but not necessarily causal effects. An association indicates how likely past events were, while a causal effect shows how probabilities change if exposures change. Studies aim to minimize bias and chance error to help infer causality from significant associations.
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.
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.
Observational Studies and their Reporting Guidelines
Observational studies observe individuals and outcomes without influencing them. There are several types including case reports, ecological studies, cross-sectional studies, case-control studies, and cohort studies. Guidelines like STROBE were created to improve reporting transparency. Upcoming developments include using clinical registries as cohort studies and propensity score matching to assemble comparable groups. Observational studies provide important descriptive data and can study long-term outcomes unlike clinical trials.
6.4.2 designs case cohort
This document discusses case-cohort study designs. It explains that case-cohort designs identify incident cases of a disease as the numerator and use a sample of controls from the overall study population at risk as the denominator. The controls are sampled regardless of time in the study or disease outcome. It also describes how case-cohort designs can estimate cumulative incidence ratios by comparing pseudo-risks between exposed and unexposed groups, where the control sampling fraction must be equal between groups.
Clinical Research Issues
The document discusses several key issues in clinical trial design and analysis. It outlines the steps in decision analysis to help make explicit uncertain decisions. It also discusses key points in conducting meta-analyses, such as systematically reviewing studies, accounting for differences in precision, and examining heterogeneity. Benefits of meta-analyses include combining results from different study designs and accounting for how outcomes are measured. The document also discusses defining clinical endpoints, identifying targets for targeted therapies, and innovative trial designs such as randomized discontinuation designs.
Finding Information Framework
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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.
Evidence Based Practice
This document outlines the steps of the evidence-based medicine process which includes assessing the patient, asking a question, acquiring resources, appraising the evidence, and applying to the patient. It discusses how to formulate a well-built clinical question using PICO(TT) and describes the different types of study designs and evidence hierarchies. Guidelines are provided for evaluating the quality of individual studies, such as using the FRISBE checklist, and for critically appraising systematic reviews and meta-analyses.
Randomised controlled trials
Randomized controlled trials (RCTs) are considered the gold standard for clinical research. An RCT involves randomly assigning participants into experimental and control groups to receive different interventions. Randomization aims to make the groups comparable to limit bias. It reduces the influence of unknown factors and ensures the only difference between groups is the intervention being tested. RCTs can be single blind, double blind, or triple blind depending on who is aware of group assignments. They provide the most powerful and least biased assessments of clinical interventions.
Comparing Research Designs
This is the handout version of a lecture I give to medical residents and fellows on the basics of clinical research designs and the inherent issues that go along with each one. I give this lecture as part of a multi-module lecture series on research design and statistical analysis.
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.
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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.
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.
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6.4.3 designs cumulative
This document discusses different study designs for investigating disease outbreaks, including cumulative case-control designs. A cumulative case-control design is often used to study acute outbreaks where all cases have already occurred before the study begins. Controls are selected from those who did not have the disease after the outbreak ended. The odds ratio from a cumulative case-control study approximates the rate ratio if the disease is rare and exposure remained steady.
6.6 examples
This document describes three examples of case-control study designs:
1) A nested case-control study examining the association between antipsychotic drugs and venous thromboembolism using a large UK database.
2) A cumulative case-control study investigating a diarrhea outbreak in India to identify risk factors.
3) A case-control study comparing hospital and community controls to identify risk factors for hip fracture.
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Recall bais
Irrespective of study design, the first step in the process of avoiding any type of bias is the proper definition and articulation of the research question.
Consequently, this step will lead to a number of questions that need to be adequately addressed by the investigator during the planning stage of research:
what kind of information are required to answer this question in the study in terms of exposure, outcome, and possible confounders?
what is the most appropriate method to collect these information?
how to achieve comparable accuracy of data collection between the study groups?
3.1 big picture
The 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.
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.
2.1 big picture
This document discusses key concepts in epidemiology for comparing disease frequency between groups with different exposures. It explains that epidemiological studies measure associations between exposures and outcomes, but not necessarily causal effects. An association indicates how likely past events were, while a causal effect shows how probabilities change if exposures change. Studies aim to minimize bias and chance error to help infer causality from significant associations.
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.
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.
Observational Studies and their Reporting Guidelines
Observational studies observe individuals and outcomes without influencing them. There are several types including case reports, ecological studies, cross-sectional studies, case-control studies, and cohort studies. Guidelines like STROBE were created to improve reporting transparency. Upcoming developments include using clinical registries as cohort studies and propensity score matching to assemble comparable groups. Observational studies provide important descriptive data and can study long-term outcomes unlike clinical trials.
6.4.2 designs case cohort
This document discusses case-cohort study designs. It explains that case-cohort designs identify incident cases of a disease as the numerator and use a sample of controls from the overall study population at risk as the denominator. The controls are sampled regardless of time in the study or disease outcome. It also describes how case-cohort designs can estimate cumulative incidence ratios by comparing pseudo-risks between exposed and unexposed groups, where the control sampling fraction must be equal between groups.
Clinical Research Issues
The document discusses several key issues in clinical trial design and analysis. It outlines the steps in decision analysis to help make explicit uncertain decisions. It also discusses key points in conducting meta-analyses, such as systematically reviewing studies, accounting for differences in precision, and examining heterogeneity. Benefits of meta-analyses include combining results from different study designs and accounting for how outcomes are measured. The document also discusses defining clinical endpoints, identifying targets for targeted therapies, and innovative trial designs such as randomized discontinuation designs.
Finding Information Framework
Finding Information Framework adapted from Boston University School of Medicine for JMU's Physician Assistant program
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.
Evidence Based Practice
This document outlines the steps of the evidence-based medicine process which includes assessing the patient, asking a question, acquiring resources, appraising the evidence, and applying to the patient. It discusses how to formulate a well-built clinical question using PICO(TT) and describes the different types of study designs and evidence hierarchies. Guidelines are provided for evaluating the quality of individual studies, such as using the FRISBE checklist, and for critically appraising systematic reviews and meta-analyses.
Randomised controlled trials
Randomized controlled trials (RCTs) are considered the gold standard for clinical research. An RCT involves randomly assigning participants into experimental and control groups to receive different interventions. Randomization aims to make the groups comparable to limit bias. It reduces the influence of unknown factors and ensures the only difference between groups is the intervention being tested. RCTs can be single blind, double blind, or triple blind depending on who is aware of group assignments. They provide the most powerful and least biased assessments of clinical interventions.
Comparing Research Designs
This is the handout version of a lecture I give to medical residents and fellows on the basics of clinical research designs and the inherent issues that go along with each one. I give this lecture as part of a multi-module lecture series on research design and statistical analysis.
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.
1.1 research question formulation - PubMed search strategy - references manag...
Lecture 1.1 - Medical Research and Medical Biostatistics using R Course
By Dr. Mohamed Ayoub, MD and simple research - www.simpleresearch.net
How to formulate your research question using PICO or PICOTS format
How to conduct effective advanced PubMed Strategy using MESH terms combined with manual search
How to do proper reference management and citation using mendley software
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.
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.
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6.4.3 designs cumulative
This document discusses different study designs for investigating disease outbreaks, including cumulative case-control designs. A cumulative case-control design is often used to study acute outbreaks where all cases have already occurred before the study begins. Controls are selected from those who did not have the disease after the outbreak ended. The odds ratio from a cumulative case-control study approximates the rate ratio if the disease is rare and exposure remained steady.
6.6 examples
This document describes three examples of case-control study designs:
1) A nested case-control study examining the association between antipsychotic drugs and venous thromboembolism using a large UK database.
2) A cumulative case-control study investigating a diarrhea outbreak in India to identify risk factors.
3) A case-control study comparing hospital and community controls to identify risk factors for hip fracture.
6.4.1 designs density
A density sampled case-control study estimates the incidence density ratio by identifying incident cases of disease (the numerator) and taking a sample of controls (the denominator) from the study base population. The sampling probabilities for controls are based on the person-time of observation in the study base, which is called density sampling. This design estimates the odds ratio, which approximates the incidence density ratio. The data are usually analyzed using conditional logistic regression to account for matching on time. A density sampled case-control study can be thought of as nested within an enumerated source population, similar to how a nested case-control study would be conducted within an actual cohort study.
5.3 history
This document provides a brief history of cohort studies, beginning in the early 1900s when the first studies resembling cohort designs examined tuberculosis outcomes. It describes several landmark cohort studies established around 1950 including Framingham Heart Study, the atomic bomb survivors study, and a British dye workers study. It notes the current era starting in 1970s featured larger but more focused studies combining questionnaires, physical measures, and follow up. Several examples of major cohort studies are provided such as the Nurses' Health Study.
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.
5.6 examples whi
This document summarizes and compares two studies on hormone use and heart disease rates in women: an NHS observational study and the WHI randomized controlled trial. The NHS study found decreased heart disease rates associated with hormone use, as it examined a younger population of women using hormones for shorter terms. The WHI trial found increased heart disease rates associated with hormone use, as it was a randomized study of an older population using hormones long-term. The document outlines key differences between the two studies that could explain their divergent findings.
5.5 example estrogen and chd
This document summarizes a study that analyzed the association between postmenopausal hormone therapy and cardiovascular disease using data from the Nurses' Health Study. Over 70,000 postmenopausal nurses were followed through questionnaires for up to 20 years. The study found that current hormone therapy was associated with a decreased risk of major coronary events but a modest increase in stroke risk at higher doses. Previous publications from this study had endorsed hormone therapy for cardiovascular disease prevention but this study's conclusions were more cautious given other research linking hormone therapy to increased breast cancer risk.
5.7 poisson regression in the analysis of cohort data
This document provides an overview of Poisson regression, which is a statistical analysis technique used to model count data and rates. It can be used to analyze cohort data with dichotomous outcomes and categorical or continuous predictor variables. Poisson regression models the outcome rate as an exponential function of the predictor variables. It permits the estimation of rate ratios by exponentiating the regression coefficients. The document includes examples of how Poisson regression can be used to estimate the relative risk of outcomes like coronary heart disease and death associated with risk factors like smoking.
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3. Phase III trials which are comparative effectiveness trials that use clinical outcomes like survival to compare new treatments to standard of care through randomized controlled designs.
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this presentation has a simple explanation about the case and control studies for the researchers who are interested to know more information
Case_Control_Study.pptx
This document provides an overview of case-control studies in epidemiology. It discusses the key steps in conducting a case-control study, including selecting cases and controls, measuring exposure, and analyzing the data. Some advantages are that case-control studies are relatively inexpensive and allow investigation of rare diseases. However, they are subject to biases related to recall and selection of appropriate controls. In conclusion, the case-control design is a useful method for investigating hypotheses about disease causation in epidemiology.
Research methodology by hw
The document discusses research methodology and methods and materials sections of research proposals. It provides details on what should be included in the methods and materials section such as the study design, population, variables, data collection plan, and limitations. The methods section is important as it shows how the research will achieve its objectives and answer the research questions.
Epidemiological methods
Epidemiology stringently focuses on the application of appropriate study designs and analysis to draw an inference.ย
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.
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Epidemiological Study Designs overview explanation
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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.
5.2.1 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.
4.2.4. confounding counterfactual
This document discusses counterfactual modeling for determining causality in epidemiology. It explains that ideally, a causal contrast compares disease frequency between an exposed group and an unexposed group that are from the same target population during the same time period, ensuring identical initial conditions. However, in reality the unexposed counterfactual state is not observed, so a substitute population is used that may differ in initial conditions, allowing for confounding. Confounding arises when the substitute group imperfectly represents what the target population would have been like without exposure. Randomized controlled trials can help simulate the counterfactual comparison by randomizing subjects to treatment or control, making the groups comparable at baseline and reducing confounding.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง ๐)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
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๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐๐๐จ๐ฉ๐ ๐จ๐ ๐๐ง ๐๐ง๐ญ๐ซ๐๐ฉ๐ซ๐๐ง๐๐ฎ๐ซ:
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6.5 strengths and challenges
- 1. Case-control studies outline โข Strengths and challenges โข Example: anti-psychotic drugs and venous thromboembolism โ nested case-control โข Example: diarrhea outbreak in India โ cumulative case-control โข Example: hip fracture โ comparison of hospital and community controls โข Summary
- 2. Strengths โ Can assess multiple exposures โ Possible to examine multiple outcomes (with case-cohort) โ Efficient design โ less expensive and time consuming vs. cohort โ Especially good choice for rare diseases โ Good choice when exposure assessment is expensive
- 3. Challenges โ BIG ISSUE: Finding source of controls that reflect exposure experience in the study base โ SECOND BIG ISSUE: Differential recall of exposure depending on disease status is a concern for self report exposures (recall bias) โ Concern about availability and quality when relying on record-based or stored sample- based exposure assessment
- 4. Challenges โ Inefficient for rare exposure (rare in the study population) โ Outcome has to be dichotomized (yes/no) โ Often a less intuitive study design for non- specialists; results may be more difficult to communicate to different audiences
- 5. Challenges โข Finding source of controls that satisfies the study base principle is a major concern โ Even in a study with a primary study base it is challenging to identify a general population representative sample of controls โ In a study with a secondary study base it is even more difficult to identify a source of controls representing the exposure experience in the study base โ Whenever the exposure experience in the controls does not represent the exposure experience in the study base it will introduce bias
- 6. Challenges โข Bias in exposure information is major concern โ If exposure information is gathered from interviews, cases will report it after developing disease while controls will not have the disease โ Occurrence of disease may improve memory of exposure among cases โข Increased sensitivity โ Occurrence of disease may provoke more false memory of exposure among cases โข Reduced specificity โ Whenever accuracy of information on exposure is different between cases and controls this is recall bias