A Bayesian phase II proof-of-concept design for clinical trials with longitudinal endpoints.

Existing phase II clinical trial designs focus on a single scalar endpoint, such as a binary, continuous, or survival endpoint. In some clinical trials, such as pain management studies, the efficacy endpoint of interest is measured longitudinally. We propose a Bayesian phase II design for such clinical trials. We model the longitudinal measurement process using Bayesian hierarchical model, where subject-specific trajectory shrinks toward the population trajectory to borrow information across subjects. The Bayesian penalized spline is used to model subject-specific and population trajectories without making strong parametric assumption on their shapes. We use the area under the curve of the trajectory as the summary of the treatment effect over time. The design takes a group sequential approach and takes into account both statistical significance and clinical relevance. Bayesian criteria is proposed to make interim and final decisions based on the evidence of statistical significance and clinical relevance. The proposed design is highly flexible and can accommodate trials with one or multiple longitudinal endpoints, as well as a longitudinal primary endpoint with a secondary endpoint. Simulation study shows that the proposed design is robust with desirable operating characteristics.

A confirmatory basket design considering non-inferiority and superiority testing.

For multiple rare diseases as defined by a common biomarker signature, or a disease with multiple disease subtypes of low frequency, it is often possible to provide confirmatory evidence for these disease or subtypes (baskets) as a combined group. A novel drug, as a second generation, may have marginal improvement in efficacy overall but superior efficacy in some baskets. In this situation, it is appealing to test hypotheses of both non-inferiority overall and superiority on certain baskets. The challenge is designing a confirmatory study efficient to address multiple questions in one trial. A two-stage adaptive design is proposed to test the non-inferiority hypothesis at the interim stage, followed by pruning and pooling before testing a superiority hypothesis at the final stage. Such a design enables an efficient and novel registration pathway, including an early claim of non-inferiority followed by a potential label extension with superiority on certain baskets and an improved benefit-risk profile demonstrated by longer term efficacy and safety data. Operating characteristics of this design are examined by simulation studies, and its appealing features make it ready for use in a confirmatory setting, especially in emerging markets, where both the need and the possibility for efficient use of resources may be the greatest.

Burden of cystic fibrosis in children <12 years of age prior to the introduction of CFTR modulator therapies.

BACKGROUND: Cystic fibrosis (CF) is a genetic, multisystemic, progressive and life-shortening disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. Different genotypes have been linked to variations in disease progression among people with CF. The burden of illness (BOI) in children with CF is incompletely characterised, particularly as it relates to CFTR genotypes prior to the availability of the first CFTR modulators. This retrospective, cross-sectional, descriptive study evaluated the BOI in US children with CF <12 years of age prior to the first approval of CFTR modulators. METHODS: Data from the US Cystic Fibrosis Foundation Patient Registry from 2011 were used to summarise key patient and disease characteristics using descriptive statistics, overall and grouped by age (0 to <2 years, 2 to <6 years and 6 to <12 years) and genotype (F508del/F508del, F508del/minimal function (MF), MF/MF, gating mutation on ≥1 allele, residual function mutation on ≥1 allele and R117H on ≥1 allele) group. RESULTS: The analysis included 9185 children. Among 6-year-olds to <12-year-olds, mean (SD) per cent predicted FEV1 in 1 s was 92.6% (17.5%). Among all children <12 years of age, the mean (SD) all-cause hospitalisation and pulmonary exacerbation rates in 2011 were 0.4 (1.0) and 0.3 (0.8), respectively. Most (93.6%) had ≥1 positive lung microbiology culture. CF-related medication and nutritional supplementation use was common across all ages and genotypes. More than half (54.7%) had ≥1 CF-related complication. Evidence of disease burden was observed across the age and genotype groups studied. CONCLUSIONS: Prior to the approval of the first CFTR modulator therapies in children <12 years of age, CF was associated with substantial BOI from an early age-including respiratory infections, hospitalisations/pulmonary exacerbations, need for supplemental nutrition and pharmacological treatments-irrespective of genotype.

Design and analysis of drop-the-losers studies using binary endpoints in the rare disease setting.

The drop-the-losers design combines a phase 2 trial of k treatments and a confirmatory phase 3 trial under a single adaptive protocol, thereby gaining efficiency over a traditional clinical development approach. Such designs may be particularly useful in the rare disease setting, where conserving sample size is paramount, and control arms may not be feasible. We propose an unconditional exact likelihood (UEL) testing and inference procedure for these designs for a binary endpoint using small sample sizes, comparing its operating characteristics to existing methods. Additional practical considerations are evaluated, including the choice of stagewise sample sizes and effect of ties.

Beyond exposure-response: A tutorial on statistical considerations in dose-ranging studies.

Dose-ranging studies are a crucial part of the phase II drug development process. They complement the understanding gained from exposure-response analyses. However, the statistical design issues related to dose-ranging studies are not always keenly understood and a poorly designed study can be costly for later development. In this tutorial, we review five key statistical principles in designing such a study. We also describe some popular statistical approaches, including pairwise comparison, modeling, and Multiple Comparison Procedure modeling in the context of principles.

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

Goodness-of-fit test for proportional subdistribution hazards model.

This paper concerns using modified weighted Schoenfeld residuals to test the proportionality of subdistribution hazards for the Fine-Gray model, similar to the tests proposed by Grambsch and Therneau for independently censored data. We develop a score test for the time-varying coefficients based on the modified Schoenfeld residuals derived assuming a certain form of non-proportionality. The methods perform well in simulations and a real data analysis of breast cancer data, where the treatment effect exhibits non-proportional hazards.

A Bayesian case study in oncology Phase I combination dose-finding using logistic regression with covariates.

A Bayesian approach to finding the maximum tolerated dose (MTD) is presented. The approach is flexible, allowing inclusion of covariates, and enables transparent dose recommendations based on comprehensive inferential summaries on the probability of dose-limiting toxicities (DLT). A case study is presented for a Phase I combination of two oncology drugs, nilotinib and imatinib. Data obtained and decisions made during the study are described. Final determination of the MTD pair is outlined, along with discussion regarding the use and interpretability of within- and end-of-study data.

A phase I study of intravenous LBH589, a novel cinnamic hydroxamic acid analogue histone deacetylase inhibitor, in patients with refractory hematologic malignancies.

PURPOSE: LBH589 is a novel histone deacetylase inhibitor that inhibits proliferation and induces apoptosis in tumor cell lines. In this phase I study, LBH589 was administered i.v. as a 30-minute infusion on days 1 to 7 of a 21-day cycle. EXPERIMENTAL DESIGN: Fifteen patients (median age, 63 years; range, 42-87 years) with acute myeloid leukemia (13 patients), acute lymphocytic leukemia (1 patient), or myelodysplastic syndrome (1 patient) were treated with LBH589 at the following dose levels (mg/m(2)): 4.8 (3 patients), 7.2 (3 patients), 9.0 (1 patient), 11.5 (3 patient), and 14.0 (5 patients). The levels of histone acetylation were measured using quantitative flow cytometry and plasma LBH589 concentrations were assayed. RESULTS: Four dose-limiting toxicities (grade 3 QTcF prolongation) were observed, four at 14.0 mg/m(2) and one at 11.5 mg/m(2). QTcF prolongation was asymptomatic and reversed on LBH589 discontinuation. Other potentially LBH589-related toxicities included nausea (40%), diarrhea (33%), vomiting (33%), hypokalemia (27%), loss of appetite (13%), and thrombocytopenia (13%). In 8 of 11 patients with peripheral blasts, transient reductions occurred with a rebound following the 7-day treatment period. H3 acetylation increase was significant in B-cells (CD19(+); P = 0.02) and blasts (CD34(+); P = 0.04). The increase in H2B acetylation was highest in CD19(+) and CD34(+) cells [3.8-fold (P = 0.01) and 4.4-fold (P = 0.03), respectively]. The median acetylation of histones H2B and H3 in CD34(+) and CD19(+) cells significantly increased on therapy as did apoptosis in CD14(+) cells. Area under the curve increased proportionally with dose with a terminal half-life of approximately 11 hours. CONCLUSION: Intravenous administration of LBH589 was well tolerated at doses <11.5 mg/m(2) with consistent transient antileukemic and biological effects.

Psychiatric comorbidity and not completing jail-based substance abuse treatment.

Many jail inmates have a history of mental illness, substance use, and drug-related crime. This article assesses the effect of psychiatric comorbidity on retention in jail-based substance abuse treatment. Secondary data from five jail-based substance abuse treatment programs were studied using descriptive and multivariate analyses. Controlling for age, sex, race, education, and program, the odds of an offender with a history of mental illness being terminated from treatment were nearly three times that of those with no such history. The data suggest that psychiatric comorbidity may be an important correlate of retention in jail-based substance abuse treatment.

Jail-based substance user treatment: an analysis of retention.

Many jail inmates have a history of substance use and "abuse"; few, however, receive comprehensive treatment for substance use disorders while in jail. The authors offer a longitudinal reanalysis of data from five jail-based substance user treatment programs. Survival analysis was used to identify client characteristics associated with length of time in treatment. Survival curves for the five programs were compared, indicating which ones retained inmates the longest. Results from a model stratified by jail site revealed that inmates over 25 years of age and those already sentenced had significantly longer treatment stays. The Substance Abuse Intervention Division (SAID) program, a modified therapeutic community in a New York jail, and the Deciding, Educating, Understanding, Counseling, and Evaluation (DEUCE) program, a curriculum-based intervention, had the longest survival curves and were, therefore, most effective at retaining inmates in treatment.