Methods for estimating relative risk in studies of common binary outcomes

Alok Kumar Dwivedi, Indika Mallawaarachchi, Soyoung Lee, Patrick Tarwater

Research output: Contribution to journalArticlepeer-review

Abstract

Studying the effect of exposure or intervention on a dichotomous outcome is very common in medical research. Logistic regression (LR) is often used to determine such association which provides odds ratio (OR). OR often overestimates the effect size for prevalent outcome data. In such situations, use of relative risk (RR) has been suggested. We propose modifications in Zhang and Yu and Diaz-Quijano methods. These methods were compared with stratified Mantel Haenszel method, LR, log binomial regression (LBR), Zhang and Yu method, Poisson/Cox regression, modified Poisson/Cox regression, marginal probability method, COPY method, inverse probability of treatment weighted LBR, and Diaz-Quijano method. Our proposed modified Diaz-Quijano (MDQ) method provides RR and its confidence interval similar to those estimated by modified Poisson/Cox and LBRs. The proposed modifications in Zhang and Yu method provides better estimate of RR and its standard error as compared to Zhang and Yu method in a variety of situations with prevalent outcome. The MDQ method can be used easily to estimate the RR and its confidence interval in the studies which require reporting of RRs. Regression models which directly provide the estimate of RR without convergence problems such as the MDQ method and modified Poisson/Cox regression should be preferred.

Original languageEnglish (US)
Pages (from-to)484-500
Number of pages17
JournalJournal of Applied Statistics
Volume41
Issue number3
DOIs
StatePublished - Mar 1 2014
Externally publishedYes

Keywords

  • logistic regression
  • modified Diaz-Quijano method
  • modified Zhang and Yu method
  • odds ratio
  • relative risk

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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