BACKGROUNDApalutamide, a competitive inhibitor of the androgen receptor, is under development for the treatment of prostate cancer. We evaluated the efficacy of apalutamide in men with nonmetastatic castration-resistant prostate cancer who were at high risk for the development of metastasis. METHODSWe conducted a double-blind, placebo-controlled, phase 3 trial involving men with nonmetastatic castration-resistant prostate cancer and a prostate-specific antigen doubling time of 10 months or less. Patients were randomly assigned, in a 2:1 ratio, to receive apalutamide (240 mg per day) or placebo. All the patients continued to receive androgen-deprivation therapy. The primary end point was metastasis-free survival, which was defined as the time from randomization to the first detection of distant metastasis on imaging or death. RESULTSA total of 1207 men underwent randomization (806 to the apalutamide group and 401 to the placebo group). In the planned primary analysis, which was performed after 378 events had occurred, median metastasis-free survival was 40.5 months in the apalutamide group as compared with 16.2 months in the placebo group (hazard ratio for metastasis or death, 0.28; 95% confidence interval [CI], 0.23 to 0.35; P<0.001). Time to symptomatic progression was significantly longer with apalutamide than with placebo (hazard ratio, 0.45; 95% CI, 0.32 to 0.63; P<0.001). The rate of adverse events leading to discontinuation of the trial regimen was 10.6% in the apalutamide group and 7.0% in the placebo group. The following adverse events occurred at a higher rate with apalutamide than with placebo: rash (23.8% vs. 5.5%), hypothyroidism (8.1% vs. 2.0%), and fracture (11.7% vs. 6.5%). CONCLUSIONSAmong men with nonmetastatic castration-resistant prostate cancer, metastasisfree survival and time to symptomatic progression were significantly longer with apalutamide than with placebo. (Funded by Janssen Research and Development; SPARTAN ClinicalTrials.gov number, NCT01946204.)

High-dose chemotherapy followed by stem cell recovery, more commonly called a bone marrow transplant, is a common treatment for a number of diseases. This article examines four problems commonly encountered when dealing with bone marrow transplant studies. First, we look at the problem of competing causes of failure and at methods based on a multi-state model to estimate meaningful probabilities for these risks. Second, we examine methods for estimating the prevalence of an intermediate condition, here the prevalence of chronic GVHD. Third, we look at the problem of modeling the post transplant recovery process and we provide two examples of how these estimates can be used to assess dynamically a patient's prognosis or how these probabilities can be used to design trials of new therapy. Finally, we present an estimate of a new measure of treatment efficiency, the current leukemia free survival function, which is derived from a multi-state model approach.

Summary. A significant number of patients who relapse after allogeneic stem cell transplantation (SCT) for chronic myeloid leukaemia (CML) will achieve sustained molecular remissions after treatment with donor lymphocyte infusions (DLI) from the original stem cell donor. Leukaemia-free survival, defined as survival without evidence of relapse at any time after transplant does not account for patients who are successfully treated with DLI. To summarize adequately the response to treatment, a new summary probability, called the current leukaemia-free survival (CLFS), is proposed. This quantity is defined as the probability that a patient is alive and in remission at a given time after transplant. We discuss two statistical methods for estimating CLFS. The first is based on a multistate modelling approach. The second is based on an estimate constructed by looking at appropriate differences between Kaplan±Meier estimates. We compare these estimates using data on 189 consecutive patients who underwent SCT over a 7-year period.

The copula of a bivariate distribution, constructed by making marginal transformations of each component, captures all the information in the bivariate distribution about the dependence between two variables. For frailty models for bivariate data the choice of a family of distributions for the random frailty corresponds to the choice of a parametric family for the copula. A class of tests of the hypothesis that the copula is in a given parametric family, with unspecified association parameter, based on bivariate right censored data is proposed. These tests are based on first making marginal Kaplan-Meier transformations of the data and then comparing a non-parametric estimate of the copula to an estimate based on the assumed family of models. A number of options are available for choosing the scale and the distance measure for this comparison. Significance levels of the test are found by a modified bootstrap procedure. The procedure is used to check the appropriateness of a gamma or a positive stable frailty model in a set of survival data on Danish twins.

Irreversible illness-death models are used to model disease processes and in cancer studies to model disease recovery. In most applications, a Markov model is assumed for the multistate model. When there are covariates, a Cox (1972, J Roy Stat Soc Ser B 34:187-220) model is used to model the effect of covariates on each transition intensity. Andersen et al. (2000, Stat Med 19:587-599) proposed a Cox semi-Markov model for this problem. In this paper, we study the large sample theory for that model and provide the asymptotic variances of various probabilities of interest. A Monte Carlo study is conducted to investigate the robustness and efficiency of Markov/Semi-Markov estimators. A real data example from the PROVA (1991, Hepatology 14:1016-1024) trial is used to illustrate the theory.