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A two-stage strategy to accommodate general patterns of confounding in the design of observational studies

Content Provider	PubMed Central
Author	Sebastien, Haneuse Jonathan, Schildcrout Gillen, Daniel
Copyright Year	2012
Abstract	Accommodating general patterns of confounding in sample size/power calculations for observational studies is extremely challenging, both technically and scientifically. While employing previously implemented sample size/power tools is appealing, they typically ignore important aspects of the design/data structure. In this paper, we show that sample size/power calculations that ignore confounding can be much more unreliable than is conventionally thought; using real data from the US state of North Carolina, naive calculations yield sample size estimates that are half those obtained when confounding is appropriately acknowledged. Unfortunately, eliciting realistic design parameters for confounding mechanisms is difficult. To overcome this, we propose a novel two-stage strategy for observational study design that can accommodate arbitrary patterns of confounding. At the first stage, researchers establish bounds for power that facilitate the decision of whether or not to initiate the study. At the second stage, internal pilot data are used to estimate key scientific inputs that can be used to obtain realistic sample size/power. Our results indicate that the strategy is effective at replicating gold standard calculations based on knowing the true confounding mechanism. Finally, we show that consideration of the nature of confounding is a crucial aspect of the elicitation process; depending on whether the confounder is positively or negatively associated with the exposure of interest and outcome, naive power calculations can either under or overestimate the required sample size. Throughout, simulation is advocated as the only general means to obtain realistic estimates of statistical power; we describe, and provide in an R package, a simple algorithm for estimating power for a case–control study.
Related Links	http://dx.doi.org/10.1093/biostatistics/kxr044
Ending Page	288
Page Count	15
Starting Page	274
File Format	PDF
ISSN	14654644
e-ISSN	14684357
Journal	Biostatistics (Oxford, England)
Issue Number	2
Volume Number	13
Language	English
Publisher	Oxford University Press
Publisher Date	2012-04-01
Access Restriction	Open
Rights Holder	Oxford University Press
Subject Keyword	Statistics, Probability and Uncertainty Statistics and Probability Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Medicine Statistics, Probability and Uncertainty

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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