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 observatio

SUBMITTER: Haneuse S 

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

REPOSITORIES: biostudies-literature