Who really gets strep sore throat? Confounding and effect modification of a time-varying exposure on recurrent events

Dean Follmann, Chiung Yu Huang, Erin Gabriel

Research output: Contribution to journalArticlepeer-review

Abstract

Unmeasured confounding is the fundamental obstacle to drawing causal conclusions about the impact of an intervention from observational data. Typically, covariates are measured to eliminate or ameliorate confounding, but they may be insufficient or unavailable. In the special setting where a transient intervention or exposure varies over time within each individual and confounding is time constant, a different tack is possible. The key idea is to condition on either the overall outcome or the proportion of time in the intervention. These measures can eliminate the unmeasured confounding either by conditioning or by use of a proxy covariate. We evaluate existing methods and develop new models from which causal conclusions can be drawn from such observational data even if no baseline covariates are measured. Our motivation for this work was to determine the causal effect of Streptococcus bacteria in the throat on pharyngitis (sore throat) in Indian schoolchildren. Using our models, we show that existing methods can be badly biased and that sick children who are rarely colonized have a high probability that the Streptococcus bacteria are causing their disease. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Original languageEnglish (US)
Pages (from-to)4398-4412
Number of pages15
JournalStatistics in Medicine
Volume35
Issue number24
DOIs
StatePublished - Oct 30 2016

Keywords

  • attributable fraction
  • case-crossover design
  • pattern mixture models
  • probability of causation
  • self-controlled case series

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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