[Second symposium on transfusion security].

Surrogate endpoints in clinical trials are biological markers or events observable earlier than the clinical endpoints (such as death) that are actually of primary interest. The "proportion of treatment effect captured" by a surrogate endpoint (PTE) is a frequentist measure intended to address the question of whether trials based on a surrogate endpoint reach the same conclusions as would have been reached using the true endpoint. The question of inferential interest is whether PTE for a given marker exceeds some threshold value, say 0.5. Calculating PTE requires fitting two different models to the same data. We develop a Markov chain Monte Carlo based method for estimating the Bayesian posterior distribution of PTE. The new method conditions on the truth of a single model. Obtaining the full posterior distribution enables direct statements such as "the posterior probability that PTE >0.5 is 0.085". Furthermore, credible sets do not depend on asymptotic approximations and can be computed using data sets for which the frequentist methods may be inaccurate or even impossible to apply. We illustrate with Bayesian proportional hazards models for clinical trial data. As a by-product of developing the Bayesian method, we show that the frequentist estimate of PTE also may be computed from quantities in a single model and calculate frequentist confidence intervals for PTE that tend to be narrower than those produced by standard methods but that provide equally good coverage.

Acquired Immunodeficiency Syndrome↗

PubMed2002

Identification of significant host factors for HIV dynamics modelled by non-linear mixed-effects models.

Non-linear mixed-effects models are powerful tools for modelling HIV viral dynamics. In AIDS clinical trials, the viral load measurements for each subject are often sparse. In such cases, linearization procedures are usually used for inferences. Under such linearization procedures, however, standard covariate selection methods based on the approximate likelihood, such as the likelihood ratio test, may not be reliable. In order to identify significant host factors for HIV dynamics, in this paper we consider two alternative approaches for covariate selection: one is based on individual non-linear least square estimates and the other is based on individual empirical Bayes estimates. Our simulation study shows that, if the within-individual data are sparse and the between-individual variation is large, the two alternative covariate selection methods are more reliable than the likelihood ratio test, and the more powerful method based on individual empirical Bayes estimates is especially preferable. We also consider the missing data in covariates. The commonly used missing data methods may lead to misleading results. We recommend a multiple imputation method to handle missing covariates. A real data set from an AIDS clinical trial is analysed based on various covariate selection methods and missing data methods.

Acquired Immunodeficiency Syndrome↗