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Effects of non-differential exposure misclassification on false conclusions in hypothesis-generating studies
Journal article   Open access   Peer reviewed

Effects of non-differential exposure misclassification on false conclusions in hypothesis-generating studies

Igor Burstyn, Yunwen Yang and A Robert Schnatter
International journal of environmental research and public health, v 11(10), pp 10951-10966
21 Oct 2014
DOI: https://doi.org/10.3390/ijerph111010951
PMID: 25337942
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.3390/ijerph111010951View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Bayes Theorem Bias Case-Control Studies Data Interpretation, Statistical Humans Models, Theoretical Monte Carlo Method Probability Research Design
Despite the theoretical success of obviating the need for hypothesis-generating studies, they live on in epidemiological practice. Cole asserted that "… there is boundless number of hypotheses that could be generated, nearly all of them wrong" and urged us to focus on evaluating "credibility of hypothesis". Adopting a Bayesian approach, we put this elegant logic into quantitative terms at the study planning stage for studies where the prior belief in the null hypothesis is high (i.e., "hypothesis-generating" studies). We consider not only type I and II errors (as is customary) but also the probabilities of false positive and negative results, taking into account typical imperfections in the data. We concentrate on a common source of imperfection in the data: non-differential misclassification of binary exposure classifier. In context of an unmatched case-control study, we demonstrate-both theoretically and via simulations-that although non-differential exposure misclassification is expected to attenuate real effect estimates, leading to the loss of ability to detect true effects, there is also a concurrent increase in false positives. Unfortunately, most investigators interpret their findings from such work as being biased towards the null rather than considering that they are no less likely to be false signals. The likelihood of false positives dwarfed the false negative rate under a wide range of studied settings. We suggest that instead of investing energy into understanding credibility of dubious hypotheses, applied disciplines such as epidemiology, should instead focus attention on understanding consequences of pursuing specific hypotheses, while accounting for the probability that the observed "statistically significant" association may be qualitatively spurious.

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17 citations in Web of Science
21 citations in Scopus

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Environmental Sciences
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