Epidemiological Study Design: Architecture for Research

2014 ◽  
pp. 22-33
Keyword(s):  
Epidemiological Study ◽  
Study Design

EPIDEMIOLOGICAL STUDY OF CORONARY DISEASE RISK FACTORS: 1. STUDY DESIGN AND CHARACTERISTICS OF INDIVIDUAL STUDY SUBJECTS12

1968 ◽  
Vol 87 (1) ◽  
pp. 73-86 ◽  
Author(s):  
LEN HUGHES ANDRUS ◽  
DAVID C. MILLER ◽  
REUEL A. STALLONES ◽  
S. PAUL EHRLICH ◽  
JOHN PAUL JONES
Keyword(s):  
Risk Factors ◽  
Epidemiological Study ◽  
Coronary Disease ◽  
Study Design ◽  
Disease Risk ◽  
Individual Study

scholarly journals Using compartmental models to simulate directed acyclic graphs to explore competing causal mechanisms underlying epidemiological study data

2020 ◽  
Vol 17 (167) ◽  
pp. 20190675
Author(s):  
Joshua Havumaki ◽  
Marisa C. Eisenberg
Keyword(s):  
Epidemiological Study ◽  
Study Design ◽  
Simulated Data ◽  
Population Level ◽  
Obesity Paradox ◽  
Directed Acyclic Graphs ◽  
Compartmental Models ◽  
Causal Mechanisms ◽  
Acyclic Graphs ◽  
Study Results

Accurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG-derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g. reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.

Self-Controlled Case Series Methodology

2019 ◽  
Vol 6 (1) ◽  
pp. 241-261
Author(s):  
Heather J. Whitaker ◽  
Yonas Ghebremichael-Weldeselassie
Keyword(s):  
Epidemiological Study ◽  
Study Design ◽  
Case Series ◽  
The Self ◽  
Series Method

The self-controlled case series method is an epidemiological study design in which individuals act as their own control. The method offers advantages but has several limitations. This article outlines the self-controlled case series method and reviews methodological developments that address some of these limitations.

scholarly journals Tuberculosis as a complex trait: impact of genetic epidemiological study design

2010 ◽  
Vol 22 (1-2) ◽  
pp. 91-99 ◽  
Author(s):  
Catherine M. Stein ◽  
Allison R. Baker
Keyword(s):  
Epidemiological Study ◽  
Study Design ◽  
Complex Trait

scholarly journals Mathematical modeling of directed acyclic graphs to explore competing causal mechanisms underlying epidemiological study data

2019 ◽  
Author(s):  
Joshua Havumaki ◽  
Marisa C. Eisenberg
Keyword(s):  
Epidemiological Study ◽  
Study Design ◽  
Simulated Data ◽  
Population Level ◽  
Obesity Paradox ◽  
Epidemiological Studies ◽  
Directed Acyclic Graphs ◽  
Causal Mechanisms ◽  
Acyclic Graphs ◽  
Study Results

1AbstractAccurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG–derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g., reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.

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