A simple method for analyzing data from a randomized trial with a missing binary outcome.

ABSTRACT:

Background

Many randomized trials involve missing binary outcomes. Although many previous adjustments for missing binary outcomes have been proposed, none of these makes explicit use of randomization to bound the bias when the data are not missing at random.

Methods

We propose a novel approach that uses the randomization distribution to compute the anticipated maximum bias when missing at random does not hold due to an unobserved binary covariate (implying that missingness depends on outcome and treatment group). The anticipated maximum bias equals the product of two factors: (a) the anticipated maximum bias if there were complete confounding of the unobserved covariate with treatment group among subjects with an observed outcome and (b) an upper bound factor that depends on

SUBMITTER: Baker SG 

PROVIDER: S-EPMC194902 | biostudies-literature | 2003 May

REPOSITORIES: biostudies-literature