title: Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model.
abstract: In clinical trials, an intermediate marker measured after randomization can often provide early information about the treatment effect on the final outcome of interest. We explore the use of recurrence time as an auxiliary variable for estimating the treatment effect on overall survival in phase three randomized trials of colon cancer. A multi-state model with an incorporated cured fraction for recurrence is used to jointly model time to recurrence and time to death. We explore different ways in which the information about recurrence time and the assumptions in the model can lead to improved efficiency. Estimates of overall survival and disease-free survival can be derived directly from the model with efficiency gains obtained as compared to Kaplan-Meier estimates. Alternatively, efficiency gains can be achieved by using the model in a weaker way in a multiple imputation procedure, which imputes death times for censored subjects. By using the joint model, recurrence is used as an auxiliary variable in predicting survival times. We demonstrate the potential use of the proposed methods in shortening the length of a trial and reducing sample sizes.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors.forename: A S C
authors.initials: AS
authors.lastname: Conlon
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors.forename: J M G
authors.initials: JM
authors.lastname: Taylor
authors.affiliation: Mayo Clinic, Rochester, Minnesota, U.S.A.
authors.forename: D J
authors.initials: DJ
authors.lastname: Sargent
mesh_terms: D001699:Biometry; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D003110:Colonic Neoplasms / Q000401:mortality / Q000628:therapy; D003198:Computer Simulation; D018572:Disease-Free Survival; D006801:Humans; D053208:Kaplan-Meier Estimate; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D016019:Survival Analysis
keywords: Auxiliary variable; Colon cancer; Cure models; Multi-state model; Multiple imputation