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

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.

SUBMITTER: Haneuse S

PROVIDER: S-EPMC3297823 | biostudies-other | 2012 Apr

REPOSITORIES: biostudies-other

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

Haneuse Sebastien S Schildcrout Jonathan J Gillen Daniel D

Biostatistics (Oxford, England) 20111130 2

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 Nort ...[more]

PMID: 22130627

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Project description:Numerous observational studies suggest that bisphosphonates reduce mortality. This study showed that bisphosphonate use is associated with lower mortality within days of treatment, although the association was not significant until the second week. Such an early association is consistent with confounding, although an early treatment effect cannot be ruled out.IntroductionThe purpose of this study was to examine whether confounding explains why numerous observational studies show that bisphosphonate use is associated with lower mortality. To this end, we examined how soon after treatment initiation a lower mortality rate can be observed. We hypothesized that, due to confounding, the association would be observed immediately.MethodsThis was a retrospective cohort study of hip fracture patients discharged from Swedish hospitals between 1 July 2006 and 31 December 2015. The data covered 260,574 hip fracture patients and were obtained from the Swedish Hip Fracture Register and national registers. Of the 260,574 patients, 49,765 met all eligibility criteria and 10,178 were pair matched (bisphosphonate users to controls) using time-dependent propensity scores. The matching variables were age, sex, diagnoses, prescription medications, type of hip fracture, type of surgical procedure, known or suspected dementia, and physical functioning status.ResultsOver a median follow-up of 2.8 years, 2922 of the 10,178 matched patients died. The mortality rate was 7.9 deaths per 100 person-years in bisphosphonate users and 9.4 deaths in controls, which corresponded to a 15% lower mortality rate in bisphosphonate users (hazard ratio 0.85, 95% confidence interval 0.79-0.91). The risk of death was lower in bisphosphonate users from day 6 of treatment, although the association was not significant until the second week.ConclusionBisphosphonate use was associated with lower mortality within days of treatment initiation. This finding is consistent with confounding, although an early treatment effect cannot be ruled out.

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

Publications

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

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