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Conditional power calculations are frequently used to guide the decisionwhether or not to stop a trial for futility or to modify planned sample size.These ignore the information in short-term endpoints and baseline covariates,and thereby do not make fully efficient use of the information in the data. Wetherefore propose an interim decision procedure based on the conditional powerapproach which exploits the information contained in baseline covariates andshort-term outcomes. We will realise this by considering the estimation of thetreatment effect at the interim analysis as a missing data problem. Thisproblem is addressed by employing specific prediction models for the long-termendpoint which enable the incorporation of baseline covariates and multipleshort-term endpoints. We show that the proposed procedure leads to anefficiency gain and a reduced sample size, without compromising the Type Ierror rate of the procedure, even when the adopted prediction models aremisspecified. In particular, implementing our proposal in the conditional powerapproach allows earlier decisions relative to standard approaches, whilstcontrolling the probability of an incorrect decision. This time gain results ina lower expected number of recruited patients in case of stopping for futility,such that fewer patients receive the futile regimen. We explain how thesemethods can be used in adaptive designs with unblinded sample size reassessmentbased on the inverse normal $p$-value combination method to control type Ierror. We support the proposal by Monte Carlo simulations based on data from areal clinical trial.

Improving interim decisions in randomized trials by exploiting information on short-term outcomes and prognostic baseline covariates