title: A shrinkage approach for estimating a treatment effect using intermediate biomarker data in clinical trials.
abstract: In clinical trials, a biomarker (S ) that is measured after randomization and is strongly associated with the true endpoint (T) can often provide information about T and hence the effect of a treatment (Z ) on T. A useful biomarker can be measured earlier than T and cost less than T. In this article, we consider the use of S as an auxiliary variable and examine the information recovery from using S for estimating the treatment effect on T, when S is completely observed and T is partially observed. In an ideal but often unrealistic setting, when S satisfies Prentice's definition for perfect surrogacy, there is the potential for substantial gain in precision by using data from S to estimate the treatment effect on T. When S is not close to a perfect surrogate, it can provide substantial information only under particular circumstances. We propose to use a targeted shrinkage regression approach that data-adaptively takes advantage of the potential efficiency gain yet avoids the need to make a strong surrogacy assumption. Simulations show that this approach strikes a balance between bias and efficiency gain. Compared with competing methods, it has better mean squared error properties and can achieve substantial efficiency gain, particularly in a common practical setting when S captures much but not all of the treatment effect and the sample size is relatively small. We apply the proposed method to a glaucoma data example.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA. yunlisph@umich.edu
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
authors.forename: Roderick J A
authors.initials: RJ
authors.lastname: Little
mesh_terms: D015415:Biomarkers / Q000032:analysis; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000379:methods*; D005901:Glaucoma / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy*; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers