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Journal of Biopharmaceutical Statistics

Yongqiang Tang
Five algorithms are described for imputing partially observed recurrent events modeled by a negative binomial process, or more generally by a mixed Poisson process when the mean function for the recurrent events is continuous over time. We also discuss how to perform the imputation when the mean function of the event process has jump discontinuities. The validity of these algorithms is assessed by simulations. These imputation algorithms are potentially very useful in the implementation of pattern mixture models, which have been popularly used as sensitivity analysis under the non-ignorability assumption in clinical trials...
May 25, 2017: Journal of Biopharmaceutical Statistics
Yi-Ting Hwang, Chun-Chao Wang
Disease prevention is important and can be accomplished by developing diagnostic tests. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) is used to assess the accuracy of diagnostic tests. The superiority assessment between evaluating two diagnostic tests is needed when comparing two diagnostic tests. Existing tests are constructed by comparing two AUCs under the paired samples. Nevertheless, it is problematic when two ROC curves are crossing. This paper proposes a test that takes into account of the possible correlation between pairs...
May 25, 2017: Journal of Biopharmaceutical Statistics
Hongmei Yang, Jeanne Holden-Wiltse, David J Topham, John Treanor
For many laboratory assays, the readouts are presence or absence of a particular function, and the binary outcomes are correlated. The research interest is often focused on the estimation of titers, at which 50% positivity occurs. The classical approach by Reed and Muench (RM) assumes of a linear dose-response relationship around the potential titer, and uses only information from two points around the potential titer, which is inefficient in both precision and accuracy. While the model-based methods such as four-parameter logistic regression (4PL) use all the data, they do not consider the correlation among binary outcomes from same identities, which may lead to estimates with overstated precision...
May 25, 2017: Journal of Biopharmaceutical Statistics
Julie Bertrand
No abstract text is available yet for this article.
May 25, 2017: Journal of Biopharmaceutical Statistics
Yongqiang Tang
We derive the sample size formulae for comparing two negative binomial rates based on both the relative and absolute rate difference metrics in noninferiority and equivalence trials with unequal follow-up times, and establish an approximate relationship between the sample sizes required for the treatment comparison based on the two treatment effect metrics. The proposed method allows the dispersion parameter to vary by treatment groups. The accuracy of these methods is assessed by simulations. It is demonstrated that ignoring the between-subject variation in the follow-up time by setting the follow-up time for all individuals to be the mean follow-up time may greatly underestimate the required size, resulting in underpowered studies...
May 25, 2017: Journal of Biopharmaceutical Statistics
(no author information available yet)
No abstract text is available yet for this article.
May 3, 2017: Journal of Biopharmaceutical Statistics
Jun Yin, Rui Qin, Daniel J Sargent, Charles Erlichman, Qian Shi
Enhanced knowledge of the biological and genetic basis of cancer is re-defining the target population for new treatments. In oncology, potential targets for a new therapeutic agent often include various solid and hematologic malignancies that share common signaling pathways. New agents are often tested in multiple tumor types across which information can be borrowed. We propose a hierarchical Bayesian design (HBD) to simultaneously test a novel agent in multiple groups for randomized Phase II clinical trials with binary endpoints...
April 27, 2017: Journal of Biopharmaceutical Statistics
Getachew A Dagne
In this article, we show how to estimate a transition period for the evolvement of drug resistance to antiretroviral (ARV) drug or other related treatments in the framework of developing a Bayesian method for jointly analyzing time-to-event and longitudinal data. For HIV/AIDS longitudinal data, developmental trajectories of viral loads tend to show a gradual change from a declining trend after initiation of treatment to an increasing trend without an abrupt change. Such characteristics of trajectories are also associated with a time-to-event process...
April 19, 2017: Journal of Biopharmaceutical Statistics
Steven Novick, Buffy Hudson-Curtis
Historically in the biopharmaceutical setting, USP<905> has been used to establish that a batch of drug product has acceptable content uniformity. More recently, alternative approaches such as the two one-sided parametric tolerance interval test (PTI-TOST) have been proposed to establish content uniformity. Traditionally, the PTI-TOST is implemented as a sequential, two-tiered test, under the generally accepted assumption that the data are independently and identically distributed. Since the material is sequenced through the manufacturing process over a period of time, there are conceptually arguable locations within each batch, for instance: beginning, middle, and end...
April 19, 2017: Journal of Biopharmaceutical Statistics
Hong Liu-Seifert, Scott Andersen, Michael Case, JonDavid Sparks, Karen C Holdridge, Alette M Wessels, Suzanne Hendrix, Paul Aisen, Eric Siemers
Little research has been conducted on the statistical properties of composite measures comprising linear combinations of continuous component scales. We assessed the quantitative relationship between the composites and their individual components regarding their abilities to detect treatment effects. In particular, we developed the mathematical derivation of the treatment effect size of a continuous composite in relation to the treatment effect sizes of its components and proved multiple properties of the composite...
April 12, 2017: Journal of Biopharmaceutical Statistics
Jianrong Wu, Shengping Yang
Pre-clinical tumor xenograft experiments usually require a small sample size that is rarely greater than 20, and data generated from such experiments very often do not have censored observations. Many statistical tests can be used for analyzing such data, but most of them were developed based on large sample approximation. We demonstrate that the type-I error rates of these tests can substantially deviate from the designated rate, especially when the data to be analyzed has a skewed distribution. Consequently, the sample size calculated based on these tests can be erroneous...
April 7, 2017: Journal of Biopharmaceutical Statistics
Tao Lu
The gene regulatory network (GRN) is critical for understanding the regulatory interaction between genes. Time-course microarray experiments provide ample information for constructing GRN. The designs for microarray experiments serve different purposes. However, the experiment design specifically for GRN identification is still sparse. In this article, we use a simulation-based approach to deal with design problems in the framework of nonparametric differential equations. We investigate a number of feasible designs...
April 4, 2017: 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
P Colin, M Delattre, P Minini, S Micallef
For about a decade, early clinical development in oncology is facing new challenges. This is due to two main reasons. The first one is linked to the developed molecular targeted agents (MTA) themselves for which the maximum tolerated dose (MTD) is no longer the only dose of interest. The second reason is related to the fact that costs and attrition rates are huge. When selecting a dose, evidence of early activity signal becomes required for future engagements. This implies the need to handle both toxicity and activity endpoints in the analysis and also in the dose escalation design of early-phase trials...
February 21, 2017: Journal of Biopharmaceutical Statistics
Jan Rekowski, Claudia Köllmann, Björn Bornkamp, Katja Ickstadt, André Scherag
Phase II trials are intended to provide information about the dose-response relationship and to support the choice of doses for a pivotal phase III trial. Recently, new analysis methods have been proposed to address these objectives, and guidance is needed to select the most appropriate analysis method in specific situations. We set up a simulation study to evaluate multiple performance measures of one traditional and three more recent dose-finding approaches under four design options and illustrate the investigated analysis methods with an example from clinical practice...
February 21, 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
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