Apalutamide Treatment and Metastasis-free Survival in Prostate Cancer.

BACKGROUND: Apalutamide, 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. METHODS: We 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. RESULTS: A 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%). CONCLUSIONS: Among men with nonmetastatic castration-resistant prostate cancer, metastasis-free 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 .).

Bioequivalence of budesonide plus formoterol (BF) Spiromax® and BF Turbohaler® (with and without charcoal block) in healthy volunteers.

OBJECTIVE: Budesonide formoterol (BF) Spiromax® is a breath-actuated dry-powder inhaler designed to deliver similar combinations of budesonide and formoterol as Symbicort® Turbohaler®. We performed two studies to demonstrate pharmacokinetic (PK) equivalence of BF Spiromax with BF Turbohaler. MATERIALS AND METHODS: Two single-center, open-label, randomized, 5-period crossover studies were performed. The first study compared BF Spiromax 160/4.5 µg with BF Turbohaler 200/6 µg, while the second study compared BF Spiromax 320/9 µg with BF Turbohaler 400/12 µg. All treatments were administered with and without charcoal. PK parameters were calculated by measuring plasma drug concentrations from blood samples taken pre-dose and up to 24 hours post-dose. RESULTS: In each study, 90 healthy volunteers were randomized. Bioequivalence of BF Spiromax with BF Turbohaler was demonstrated for budesonide and formoterol (AUC0-t and Cmax (90% confidence intervals of the geometric mean between-device ratios for both parameters were within the predefined range of 0.80-1.25 in both studies)). Equivalence was observed without use of charcoal (overall absorption post-inhalation) and with charcoal (pulmonary absorption). There were no major differences between treatments in tmax for either budesonide or formoterol. All study treatments were well tolerated (one treatment-emergent adverse event (TEAE) in the medium-dose study and four TEAEs in the high-dose study). CONCLUSIONS: These studies indicate that BF Spiromax (±charcoal block) is bioequivalent to BF Turbohaler with respect to the PK parameters assessed. Single doses of BF Spiromax were well tolerated; the overall safety profile of BF Spiromax and BF Turbohaler was similar.

Asymptotic theory for the Cox semi-Markov illness-death model.

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.

A class of goodness of fit tests for a copula based on bivariate right-censored data.

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.

Multi-state models for bone marrow transplantation studies.

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.