Transparency4
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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.
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Transparency7
Transparency7A M
油
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.Transparency1
Transparency1A M
油
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.Transparency2
Transparency2A M
油
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.Transparency3
Transparency3A M
油
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.Transparency5
Transparency5A M
油
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.Transparency6
Transparency6A M
油
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.5.3.5 causal inference in research
5.3.5 causal inference in researchA M
油
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
5.3.4 reporting emA M
油
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
5.3.2 sufficient cause emA M
油
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
5.2.3 dags for selection biasA M
油
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 inferences5.1.2 counterfactual framework
5.1.2 counterfactual frameworkA M
油
1. The document discusses approaches for identifying confounding using directed acyclic graphs (DAGs). It outlines two main approaches: (1) removing direct effects and checking for common causes, and (2) checking for open back-door paths and blocking them.
2. An example applies these approaches to assess whether adjusting for prenatal care is sufficient to control for confounding between vitamin use and birth defects. It determines that additional adjustment is needed based on remaining connections in the DAG.
3. Traditional criteria for confounding may sometimes disagree with the DAG approach, and the DAG approach is more reliable as it can identify situations like conditioning on a collider that may bias results.5.1.1 sufficient component cause model
5.1.1 sufficient component cause modelA M
油
This document outlines concepts related to causality in epidemiology. It discusses different models of causality including the sufficient-component cause model (Rothman's pies), counterfactual framework, Hill's criteria, and graphical models. The sufficient-component cause model depicts disease causation as occurring through combinations of component causes that are sufficient to result in the disease outcome. It illustrates concepts like necessary and component causes. The model contributes to understanding how the strength of associations can depend on the prevalence of other component causes in the population.5.2.1 dags
5.2.1 dagsA M
油
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
4.4. effect modificationA M
油
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
4.5. logistic regressionA M
油
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
4.3.2. controlling confounding stratificationA M
油
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.4.3.1. controlling confounding matching
4.3.1. controlling confounding matchingA M
油
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.4.2.4. confounding counterfactual
4.2.4. confounding counterfactualA M
油
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.4.2.3. confounding collapsability
4.2.3. confounding collapsabilityA M
油
This document discusses approaches for identifying confounding variables, including evaluating confounding using crude and adjusted effect measures. It notes that a variable is confounding if the effect measure is consistent across strata but the crude and adjusted measures are unequal, indicating a lack of collapsibility. Simple stratified analyses and 2x2 tables are recommended to compare the crude effect to the stratum-specific adjusted effects. Examples are given comparing crude risk ratios to adjusted risk ratios to demonstrate possible confounding.4.2.2. confounding classical approach
4.2.2. confounding classical approachA M
油
This document discusses confounding and the criteria for determining a confounding factor:
1) A confounder must be associated with the exposure in the study population.
2) A confounder must be a risk factor for the disease.
3) A confounder must not be an intermediate step between the exposure and disease.
The document provides examples of applying these criteria to determine if a factor is a confounder in various exposure-disease relationships.4.2.1. confounding mixing of effects
4.2.1. confounding mixing of effectsA M
油
This document discusses confounding, which is one of the key biases in identifying causal effects. It describes confounding as the "mixing of effects" or "confusion" where the effect of the exposure is mixed together with the effect of another variable, leading to bias. This mixing can occur when the exposure and disease share a common cause. The document presents an example of the association between birth order and Down syndrome to illustrate confounding and how controlling for the confounding variable through stratification can provide a clearer view of the exposure-disease association.4.1. introduction
4.1. introductionA M
油
This document discusses threats to validity from confounding and effect modification in epidemiology studies. It provides an overview of random versus systematic error. It also discusses confounding, effect modification, and uses logistic regression as an example. It thanks professors who provided materials for the lectures. It emphasizes that results from epidemiology studies are likely wrong unless all sources of error are eliminated. It provides an example of confounding using a study on the relationship between matches and lung cancer that fails to account for smoking. Analyzing the data separately for smokers and non-smokers shows the true relationship is with smoking, not matches. The document stresses that confounding is the most important cause of spurious associations in observational epidemiology.4 Threats to validity from confounding bias and effect modification
4 Threats to validity from confounding bias and effect modificationA M
油
This document discusses various approaches to understanding confounding in epidemiology, including:
1. The "mixing of effects" approach which views confounding as the confusion or mixing of the effect of an exposure with the effect of another variable.
2. The "classical" approach based on a priori criteria of a confounder being associated with both the exposure and outcome but not an intermediate variable.
3. Collapsibility and data-based approaches which evaluate if crude and adjusted effect measures are equal, indicating no confounding.
4. Counterfactual and non-comparability approaches which recognize the ideal counterfactual comparison is rarely possible due to differences in initial conditions between exposed and unexposed groups.6.5 strengths and challenges
6.5 strengths and challengesA M
油
Case-control studies have strengths such as the ability to assess multiple exposures and outcomes efficiently with lower costs than cohort studies. However, they also face challenges such as finding a control group that accurately represents the exposure experience of the study base and avoiding differential recall bias between cases and controls when using self-reported exposure data. Additionally, rare exposures may be inefficient to study and outcomes must be dichotomized as present or absent. Overall, ensuring an unbiased control group and accurate exposure data are two major issues that can introduce bias if not properly addressed in case-control study designs.Lesson-3_Program-Outcomes-and-Student-Learning-Outcomes_For-Students.pdf
Lesson-3_Program-Outcomes-and-Student-Learning-Outcomes_For-Students.pdfSarahMaeDuallo
油
tendency and variability. Data visualization techniques.All the DataOps, all the paradigms .
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Data warehouses, lakes, lakehouses, streams, fabrics, hubs, vaults, and meshes. We sometimes choose deliberately, sometimes influenced by trends, yet often get an organic blend. But the choices have orders of magnitude in impact on operations cost and iteration speed. Let's dissect the paradigms and their operational aspects once and for all.More Related Content
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5.3.2 sufficient cause em
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油
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
5.2.3 dags for selection biasA M
油
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 inferences5.1.2 counterfactual framework
5.1.2 counterfactual frameworkA M
油
1. The document discusses approaches for identifying confounding using directed acyclic graphs (DAGs). It outlines two main approaches: (1) removing direct effects and checking for common causes, and (2) checking for open back-door paths and blocking them.
2. An example applies these approaches to assess whether adjusting for prenatal care is sufficient to control for confounding between vitamin use and birth defects. It determines that additional adjustment is needed based on remaining connections in the DAG.
3. Traditional criteria for confounding may sometimes disagree with the DAG approach, and the DAG approach is more reliable as it can identify situations like conditioning on a collider that may bias results.5.1.1 sufficient component cause model
5.1.1 sufficient component cause modelA M
油
This document outlines concepts related to causality in epidemiology. It discusses different models of causality including the sufficient-component cause model (Rothman's pies), counterfactual framework, Hill's criteria, and graphical models. The sufficient-component cause model depicts disease causation as occurring through combinations of component causes that are sufficient to result in the disease outcome. It illustrates concepts like necessary and component causes. The model contributes to understanding how the strength of associations can depend on the prevalence of other component causes in the population.5.2.1 dags
5.2.1 dagsA M
油
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
4.4. effect modificationA M
油
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
4.5. logistic regressionA M
油
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
4.3.2. controlling confounding stratificationA M
油
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.4.3.1. controlling confounding matching
4.3.1. controlling confounding matchingA M
油
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.4.2.4. confounding counterfactual
4.2.4. confounding counterfactualA M
油
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.4.2.3. confounding collapsability
4.2.3. confounding collapsabilityA M
油
This document discusses approaches for identifying confounding variables, including evaluating confounding using crude and adjusted effect measures. It notes that a variable is confounding if the effect measure is consistent across strata but the crude and adjusted measures are unequal, indicating a lack of collapsibility. Simple stratified analyses and 2x2 tables are recommended to compare the crude effect to the stratum-specific adjusted effects. Examples are given comparing crude risk ratios to adjusted risk ratios to demonstrate possible confounding.4.2.2. confounding classical approach
4.2.2. confounding classical approachA M
油
This document discusses confounding and the criteria for determining a confounding factor:
1) A confounder must be associated with the exposure in the study population.
2) A confounder must be a risk factor for the disease.
3) A confounder must not be an intermediate step between the exposure and disease.
The document provides examples of applying these criteria to determine if a factor is a confounder in various exposure-disease relationships.4.2.1. confounding mixing of effects
4.2.1. confounding mixing of effectsA M
油
This document discusses confounding, which is one of the key biases in identifying causal effects. It describes confounding as the "mixing of effects" or "confusion" where the effect of the exposure is mixed together with the effect of another variable, leading to bias. This mixing can occur when the exposure and disease share a common cause. The document presents an example of the association between birth order and Down syndrome to illustrate confounding and how controlling for the confounding variable through stratification can provide a clearer view of the exposure-disease association.4.1. introduction
4.1. introductionA M
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This document discusses threats to validity from confounding and effect modification in epidemiology studies. It provides an overview of random versus systematic error. It also discusses confounding, effect modification, and uses logistic regression as an example. It thanks professors who provided materials for the lectures. It emphasizes that results from epidemiology studies are likely wrong unless all sources of error are eliminated. It provides an example of confounding using a study on the relationship between matches and lung cancer that fails to account for smoking. Analyzing the data separately for smokers and non-smokers shows the true relationship is with smoking, not matches. The document stresses that confounding is the most important cause of spurious associations in observational epidemiology.4 Threats to validity from confounding bias and effect modification
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This document discusses various approaches to understanding confounding in epidemiology, including:
1. The "mixing of effects" approach which views confounding as the confusion or mixing of the effect of an exposure with the effect of another variable.
2. The "classical" approach based on a priori criteria of a confounder being associated with both the exposure and outcome but not an intermediate variable.
3. Collapsibility and data-based approaches which evaluate if crude and adjusted effect measures are equal, indicating no confounding.
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Case-control studies have strengths such as the ability to assess multiple exposures and outcomes efficiently with lower costs than cohort studies. However, they also face challenges such as finding a control group that accurately represents the exposure experience of the study base and avoiding differential recall bias between cases and controls when using self-reported exposure data. Additionally, rare exposures may be inefficient to study and outcomes must be dichotomized as present or absent. Overall, ensuring an unbiased control group and accurate exposure data are two major issues that can introduce bias if not properly addressed in case-control study designs.Recently uploaded (20)
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SQL-Demystified-A-Beginners-Guide-to-Database-Mastery.pptx
SQL-Demystified-A-Beginners-Guide-to-Database-Mastery.pptxbhavaniteacher99
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Unlock the power of data with SQL Demystified your perfect beginner-friendly guide to mastering Structured Query Language (SQL)! Whether you're a student, aspiring analyst, or a professional stepping into the world of data, this detailed and practical PowerPoint presentation simplifies the fundamentals and builds your SQL skills step-by-step.
Whats Inside?
Introduction to SQL and its real-world importance
Understanding database structures: tables, columns & relationships
Core SQL queries: SELECT, FROM, WHERE, ORDER BY
Hands-on examples and practice exercises
Advanced SQL: JOIN, subqueries, and aggregate functions
Data manipulation: INSERT, UPDATE, DELETE
Performance tips and indexing basics
SQL security best practices and learning paths for future growth
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Beginners eager to learn SQL
Data science and computer students
Business professionals and analysts
Anyone curious about how databases work
Features:
Clear visuals and examples
Easy-to-follow explanations
Real-life applications in banking, healthcare, e-commerce, and more
Reusable and editable presentation file (PPTX format)
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based on assumption that failure of such a weld is by shear on the
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Simplify SQL migration to Azure with Microsofts DMA toolassess, plan, and move your data seamlessly with expert support from IFI Tech.
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- 1. 2016-10-17 | UC Berkeley Alasdair Cohen | Lecture for Publich Health 250B 46 Transparent Repor(ng
- 6. CONSORT: A brief history ≒ 30 experts (epidemiologist, editors, etc.) meet to discuss standardized assessment for trials ≒ But they were limited by a lack of reported data ≒ So they created the Standardized Repor)ng of Trials (SORT) statement ≒ ~Same )me: Asilomar Working Group on Recommenda)ons for Repor)ng of Clinical Trials in the Biomedical Literature statement ≒ JAMA editor facilitates mee)ng: Two groups merge proposals ≒ Result: Consolidated Standards of Repor)ng Trials (CONSORT) ≒ Follow-up mee)ngs yield the Revised CONSORT statement (2001) ≒ Further revisions, resul)ng in the CONSORT 2010 statement 2016-10-17 | UC Berkeley Alasdair Cohen | Lecture for Publich Health 250B 51 1993 1996 1999 2007
- 11. 100s of Other Repor(ng Guidelines ≒ The EQUATOR Network lists hundreds of repor)ng guidelines 2016-10-17 | UC Berkeley Alasdair Cohen | Lecture for Publich Health 250B 56 hkp://www.equator-network.org
- 12. Cochrane Systema(c Review on Repor(ng 2016-10-17 | UC Berkeley Alasdair Cohen | Lecture for Publich Health 250B 57
- 13. Repor(ng Results of Registered Trials 2016-10-17 | UC Berkeley Alasdair Cohen | Lecture for Publich Health 250B 58