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The primary analysis in two-arm clinical trials usually involves inference ona scalar treatment effect parameter; e.g., depending on the outcome, thedifference of treatment-specific means, risk difference, risk ratio, or oddsratio. Most clinical trials are monitored for the possibility of earlystopping. Because ordinarily the outcome on any given subject can beascertained only after some time lag, at the time of an interim analysis, amongthe subjects already enrolled, the outcome is known for only a subset and iseffectively censored for those who have not been enrolled sufficiently long forit to be observed. Typically, the interim analysis is based only on the datafrom subjects for whom the outcome has been ascertained. A goal of an interimanalysis is to stop the trial as soon as the evidence is strong enough to doso, suggesting that the analysis ideally should make the most efficient use ofall available data, thus including information on censoring as well as otherbaseline and time-dependent covariates in a principled way. A general groupsequential framework is proposed for clinical trials with a time-laggedoutcome. Treatment effect estimators that take account of censoring andincorporate covariate information at an interim analysis are derived usingsemiparametric theory and are demonstrated to lead to stronger evidence forearly stopping than standard approaches. The associated test statistics areshown to have the independent increments structure, so that standard softwarecan be used to obtain stopping boundaries.

Group sequential methods for interim monitoring of randomized clinical trials with time-lagged outcome