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Journal of Biopharmaceutical Statistics

Tarek Haddad, Adam Himes, Laura Thompson, Telba Irony, Rajesh Nair
Evaluation of medical devices via clinical trial is often a necessary step in the process of bringing a new product to market. In recent years, device manufacturers are increasingly using stochastic engineering models during the product development process. These models have the capability to simulate virtual patient outcomes. This article presents a novel method based on the power prior for augmenting a clinical trial using virtual patient data. To properly inform clinical evaluation, the virtual patient model must simulate the clinical outcome of interest, incorporating patient variability, as well as the uncertainty in the engineering model and in its input parameters...
March 10, 2017: Journal of Biopharmaceutical Statistics
Tao Lu
Semiparametric mixed-effects joint models are flexible for modeling complex longitudinal-competing risks data. Skew distributions are commonly observed for this type of data. Covariates in the joint models are usually measured with substantial errors. We propose a Bayesian method for semiparametric mixed-effects joint models with covariate measurement errors and skew distribution. The methods are illustrated with AIDS clinical data. Simulation results are conducted to validate the proposed methods.
March 8, 2017: Journal of Biopharmaceutical Statistics
Kijoeng Nam, Nicholas C Henderson, Patricia Rohan, Emily Jane Woo, Estelle Russek-Cohen
The Vaccine Adverse Event Reporting System (VAERS) and other product surveillance systems compile reports of product-associated adverse events (AEs), and these reports may include a wide range of information including age, gender, and concomitant vaccines. Controlling for possible confounding variables such as these is an important task when utilizing surveillance systems to monitor post-market product safety. A common method for handling possible confounders is to compare observed product-AE combinations with adjusted baseline frequencies where the adjustments are made by stratifying on observable characteristics...
March 2, 2017: Journal of Biopharmaceutical Statistics
Joseph S Koopmeiners, Andrew Wey
The primary object of a Phase I clinical trial is to determine the maximum tolerated dose (MTD). Typically, the MTD is identified using a dose-escalation study, where initial subjects are treated at the lowest dose level and subsequent subjects are treated at progressively higher dose levels until the MTD is identified. The continual reassessment method (CRM) is a popular model-based dose-escalation design, which utilizes a formal model for the relationship between dose and toxicity to guide dose finding. Recently, it was shown that the CRM has a tendency to get "stuck" on a dose level, with little escalation or de-escalation in the late stages of the trial, due to the long-memory property of the CRM...
March 1, 2017: Journal of Biopharmaceutical Statistics
Yining Du, John D Cook, J Jack Lee
We examine three variations of the regularization methods for response-adaptive randomization (RAR) and compare their operating characteristics. A power transformation (PT) is applied to refine the randomization probability. The clip method is used to bound the randomization probability within specified limits. A burn-in period of equal randomization (ER) can be added before adaptive randomization (AR). For each method, more patients are assigned to the superior arm and overall response rate increase as the scheme approximates simple AR, while statistical power increases as it approximates ER...
February 21, 2017: Journal of Biopharmaceutical Statistics
Martin Otava, Ziv Shkedy, Ludwig A Hothorn, Willem Talloen, Daniel Gerhard, Adetayo Kasim
The identification of the minimum effective dose is of high importance in the drug development process. In early stage screening experiments, establishing the minimum effective dose can be translated into a model selection based on information criteria. The presented alternative, Bayesian variable selection approach, allows for selection of the minimum effective dose, while taking into account model uncertainty. The performance of Bayesian variable selection is compared with the generalized order restricted information criterion on two dose-response experiments and through the simulations study...
February 16, 2017: Journal of Biopharmaceutical Statistics
Audrey Mauguen, Stefan Michiels, Virginie Rondeau
Clinical trial duration may be a concern in clinical research, especially in cancer trials where the endpoint is overall survival. A surrogate endpoint can be used as an auxiliary variable to analyze the treatment effect earlier. At an early time point, the high number of censored observations can be compensated by the imputation of the unobserved deaths times. We propose to use predictions of the risk of death from a joint model for a recurrent event and a terminal event, which account for disease relapse information...
February 16, 2017: Journal of Biopharmaceutical Statistics
Yuhui Chen, Timothy Hanson
Although the Poisson model has been widely used to fit count data, a well-known drawback is that the Poisson mean equals its variance. Many alternative models for counts that are overdispersed relative to Poisson have been developed to solve this issue, including the negative binomial model. In this article, the negative binomial model with a four-parameter logistic mean is proposed to handle these types of counts, with variance that flexibly depends on the mean. Various parameterizations for the variance are considered, including extra-Poisson variability modeled as an exponentiated B-spline...
February 14, 2017: Journal of Biopharmaceutical Statistics
Junichi Asano, Akihiro Hirakawa
The Cox proportional hazards cure model is a survival model incorporating a cure rate with the assumption that the population contains both uncured and cured individuals. It contains a logistic regression for the cure rate, and a Cox regression to estimate the hazard for uncured patients. A single predictive model for both the cure and hazard can be developed by using a cure model that simultaneously predicts the cure rate and hazards for uncured patients; however, model selection is a challenge because of the lack of a measure for quantifying the predictive accuracy of a cure model...
February 13, 2017: Journal of Biopharmaceutical Statistics
Toshiro Tango
Tango (Biostatistics 2016) proposed a new repeated measures design called the S:T repeated measures design, combined with generalized linear mixed-effects models and sample size calculations for a test of the average treatment effect that depend not only on the number of subjects but on the number of repeated measures before and after randomization per subject used for analysis. The main advantages of the proposed design combined with the generalized linear mixed-effects models are (1) it can easily handle missing data by applying the likelihood-based ignorable analyses under the missing at random assumption and (2) it may lead to a reduction in sample size compared with the simple pre-post design...
February 13, 2017: Journal of Biopharmaceutical Statistics
Yuanyuan Tang, Chunyan Cai, Liangrui Sun, Jianghua He
In literature, there are a few unified approaches to test proof of concept and estimate a target dose, including the multiple comparison procedure using modeling approach, and the permutation approach proposed by Klingenberg. We discuss and compare the operating characteristics of these unified approaches and further develop an alternative approach in a Bayesian framework based on the posterior distribution of a penalized log-likelihood ratio test statistic. Our Bayesian approach is much more flexible to handle linear or nonlinear dose-response relationships and is more efficient than the permutation approach...
February 13, 2017: Journal of Biopharmaceutical Statistics
Guoqing Diao, Donglin Zeng, Joseph G Ibrahim, Alan Rong, Oliver Lee, Kathy Zhang, Qingxia Chen
Noninferiority multiregional clinical trials (MRCTs) have recently received increasing attention in drug development. While a major goal in an MRCT is to estimate the global treatment effect, it is also important to assess the consistency of treatment effects across multiple regions. In this paper, we propose an intuitive definition of consistency of noninferior treatment effects across regions under the random-effects modeling framework. Specifically, we quantify the consistency of treatment effects by the percentage of regions that meet a predefined treatment margin...
February 13, 2017: Journal of Biopharmaceutical Statistics
William Wang, Zhiwei Jiang, Jingjun Qiu, Jielai Xia, Xiang Guo
The primary objective of a multiregional clinical trial (MRCT) is to assess the efficacy of all participating regions and evaluate the probability of applying the overall results to a specific region. The consistency assessment of the target region with the overall results is the most common way of evaluating the efficacy in a specific region. Recently, Huang et al. (2012) proposed an additional trial in the target region to an MRCT to evaluate the efficacy in the target ethnic (TE) population under the framework of simultaneous global drug development program (SGDDP)...
February 10, 2017: Journal of Biopharmaceutical Statistics
Hengrui Sun, Bruce Binkowitz, Gary G Koch
Multiplicity is an important statistical issue that arises in clinical trials when the efficacy of the test treatment is evaluated in multiple ways. The major concern for multiplicity is that uncontrolled multiple assessments lead to inflated family-wise Type I error, and they thereby undermine the integrity of the statistical inferences. Multiplicity comes from different sources, for example, making inferences either on the overall population or some pre-specified sub-populations, while multiple endpoints need to be evaluated for each population...
February 10, 2017: Journal of Biopharmaceutical Statistics
Siying Li, Gary G Koch, John S Preisser, Diana Lam, Matilde Sanchez-Kam
Dichotomous endpoints in clinical trials have only two possible outcomes, either directly or via categorization of an ordinal or continuous observation. It is common to have missing data for one or more visits during a multi-visit study. This paper presents a closed form method for sensitivity analysis of a randomized multi-visit clinical trial that possibly has missing not at random (MNAR) dichotomous data. Counts of missing data are redistributed to the favorable and unfavorable outcomes mathematically to address possibly informative missing data...
February 8, 2017: Journal of Biopharmaceutical Statistics
Diana Lam, Gary G Koch, John S Preisser, Benjamin R Saville, Michael A Hussey
Clinical trials are designed to compare treatment effects when applied to samples from the same population. Randomization is used so that the samples are not biased with respect to baseline covariates that may influence the efficacy of the treatment. We develop randomization-based covariance adjustment methodology to estimate the log hazard ratios and their confidence intervals of multiple treatments in a randomized clinical trial with time-to-event outcomes and missingness among the baseline covariates. The randomization-based covariance adjustment method is a computationally straight-forward method for handling missing baseline covariate values...
February 8, 2017: Journal of Biopharmaceutical Statistics
Holly Kimko, Seth Berry, Michael O'Kelly, Nitin Mehrotra, Matthew Hutmacher, Venkat Sethuraman
The application of modeling and simulation (M&S) methods to improve decision-making was discussed during the Trends & Innovations in Clinical Trial Statistics Conference held in Durham, North Carolina, USA on May 1-4, 2016. Uses of both pharmacometric and statistical M&S were presented during the conference, highlighting the diversity of the methods employed by pharmacometricians and statisticians to address a broad range of quantitative issues in drug development. Five presentations are summarized herein, which cover the development strategy of employing M&S to drive decision-making; European initiatives on best practice in M&S; case studies of pharmacokinetic/pharmacodynamics modeling in regulatory decisions; estimation of exposure-response relationships in the presence of confounding; and the utility of estimating the probability of a correct decision for dose selection when prior information is limited...
February 7, 2017: Journal of Biopharmaceutical Statistics
Xiaoyu Jia, Anastasia Ivanova, Shing M Lee
In the two-stage continual reassessment method (CRM), model-based dose escalation is preceded by a pre-specified escalating sequence starting from the lowest dose level. This is appealing to clinicians because it allows a sufficient number of patients to be assigned to each of the lower dose levels before escalating to higher dose levels. While a theoretical framework to build the two-stage CRM has been proposed, the selection of the initial dose-escalating sequence, generally referred to as the initial design, remains arbitrary, either by specifying cohorts of three patients or by trial and error through extensive simulations...
February 7, 2017: Journal of Biopharmaceutical Statistics
Fei Gao, Guanghan Liu, Donglin Zeng, Guoqing Diao, Joseph F Heyse, Joseph G Ibrahim
Missing data are common in longitudinal clinical trials. How to handle missing data is critical for both sponsors and regulatory agencies to assess treatment effect from the trials. Recently, a control-based imputation has been proposed, where the missing data are imputed based on the assumption that patients who discontinued the test drug will have a similar response profile to the patients in the control group. Under control-based imputation, the variance estimation may be biased using Rubin's formula which could produce biased statistical inferences...
February 7, 2017: Journal of Biopharmaceutical Statistics
Teng Zhang, Ilya Lipkovich, Olga Marchenko
In drug development programs, an experimental treatment is evaluated across different populations and/or disease types using multiple studies conducted in countries around the world. In order to show the efficacy and safety in a specific population, a bridging study may be required. There are therapeutic areas for which enrolling patients to a trial is very challenging. Therefore, it is of interest to utilize the available historical information from previous studies. However, treatment effect may vary across different subpopulations/disease types; therefore, directly utilizing outcomes from historical studies may result in a biased estimation of treatment effect under investigation in the target trial...
February 7, 2017: Journal of Biopharmaceutical Statistics
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