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Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies

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 hypo...

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Autores principales: Burstyn, Igor, Yang, Yunwen, Schnatter, A. Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211016/
https://www.ncbi.nlm.nih.gov/pubmed/25337942
http://dx.doi.org/10.3390/ijerph111010951
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author Burstyn, Igor
Yang, Yunwen
Schnatter, A. Robert
author_facet Burstyn, Igor
Yang, Yunwen
Schnatter, A. Robert
author_sort Burstyn, Igor
collection PubMed
description 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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spelling pubmed-42110162014-10-28 Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies Burstyn, Igor Yang, Yunwen Schnatter, A. Robert Int J Environ Res Public Health Concept Paper 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. MDPI 2014-10-21 2014-10 /pmc/articles/PMC4211016/ /pubmed/25337942 http://dx.doi.org/10.3390/ijerph111010951 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Concept Paper
Burstyn, Igor
Yang, Yunwen
Schnatter, A. Robert
Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies
title Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies
title_full Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies
title_fullStr Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies
title_full_unstemmed Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies
title_short Effects of Non-Differential Exposure Misclassification on False Conclusions in Hypothesis-Generating Studies
title_sort effects of non-differential exposure misclassification on false conclusions in hypothesis-generating studies
topic Concept Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4211016/
https://www.ncbi.nlm.nih.gov/pubmed/25337942
http://dx.doi.org/10.3390/ijerph111010951
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