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The use of the ‘reverse Cornfield inequality’ to assess the sensitivity of a non‐significant association to an omitted variable
Author(s) -
Yu Binbing,
Gastwirth Joseph L.
Publication year - 2003
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1639
Subject(s) - observational study , covariate , econometrics , statistics , inference , medicine , psychology , mathematics , computer science , artificial intelligence
Abstract Unlike randomized experimental studies, investigators do not have control over the treatment assignment in observational studies. Hence, the treated and control (non‐treated) groups may have widely different distributions of unobserved covariates. Thus, if observational data are analysed as if they had arisen from a controlled study, the analyses are subject to potential bias. Sensitivity analysis is a technique for assessing whether the inference drawn from a study could be altered by a moderate ‘imbalance’, between the distribution of the covariates in different groups. In this paper, we examine the sensitivity analysis of the test of proportions in 2 × 2 tables from a new perspective: ‘could a non‐significant result have occurred because the treated group has a higher prevalence of an unobserved risk factor?’. The study was motivated by an analysis of the studies concerning with the possible effect of spermicide use on birth defects that were cited in a legal decision. Copyright © 2003 John Wiley & Sons, Ltd.