The Epidemiologic toolbox: Identifying, honing, and using the right tools for the job

Catherine R. Lesko, Alexander P. Keil, Jessie K. Edwards

Research output: Contribution to journalReview articlepeer-review

Abstract

There has been much debate about the relative emphasis of the field of epidemiology on causal inference. We believe this debate does short shrift to the breadth of the field. Epidemiologists answer myriad questions that are not causal and hypothesize about and investigate causal relationships without estimating causal effects. Descriptive studies face significant and often overlooked inferential and interpretational challenges; we briefly articulate some of them and argue that a more detailed treatment of biases that affect single-sample estimation problems would benefit all types of epidemiologic studies. Lumping all questions about causality creates ambiguity about the utility of different conceptual models and causal frameworks; 2 distinct types of causal questions include 1) hypothesis generation and theorization about causal structures and 2) hypothesis-driven causal effect estimation. The potential outcomes framework and causal graph theory help efficiently and reliably guide epidemiologic studies designed to estimate a causal effect to best leverage prior data, avoid cognitive fallacies, minimize biases, and understand heterogeneity in treatment effects. Appropriate matching of theoretical frameworks to research questions can increase the rigor of epidemiologic research and increase the utility of such research to improve public health.

Original languageEnglish (US)
Pages (from-to)511-517
Number of pages7
JournalAmerican journal of epidemiology
Volume189
Issue number6
DOIs
StatePublished - Jun 1 2020

Keywords

  • Bias
  • Causality
  • Descriptive studies
  • Epidemiologic methods
  • Inference

ASJC Scopus subject areas

  • Epidemiology

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