Journal of Biopharmaceutical Statistics (J BIOPHARM STAT)

Publications in this journal

• Article: Robust Joint Modeling of Longitudinal Measurements and Time to Event Data with Normal/Independent Distributions: A Bayesian Perspective

  Taban Baghfalaki, Mojtaba Ganjali, RezaHashemi
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  ABSTRACT: Distributional assumptions of most of the existing methods for joint modeling of longitudinal measurements and time to event data can not allow incorporation of outlier robustness. In this paper, we develop and implement a joint modeling of longitudinal and time to event data using some powerful distributions for robust analyzing which are known as normal/independent distributions. These distributions include univariate and multivariate versions of the Student's t, the slash and the contaminated normal distributions. The proposed model implements a linear mixed effects model under normal/independent distribution assumption for both random effects and residuals of the longitudinal process. For time to event process a parametric proportional hazard model with a Weibull baseline hazard is used. Also, a Bayesian approach using Markov chain Monte Carlo is adopted for parameter estimation. Some simulation studies are performed to investigate the performance of the proposed method under presence and absence of outliers. Also, the proposed methods are applied for analyzing a real AIDS clinical trial, with the aim of comparing the efficiency and safety of two antiretroviral drugs, where CD4 count measurements are gathered as longitudinal outcomes. In these data, time to death or dropout is considered as the interested time to event outcome variable. Different model structures are developed for analyzing these data set, where model selection is performed by DIC, EAIC and EBIC criteria.
  Journal of Biopharmaceutical Statistics 01/2013;
• Article: Guest-Editor's Note: “Statistical Issues in Adaptive Design Methods in Clinical Trials”

  Annpey Pong
  Journal of Biopharmaceutical Statistics 11/2007; 17(6):1133-1134.
• Article: Calculation of tolerance limits and sample size determination for clinical trials with dichotomous outcomes.

  Boris G Zaslavsky
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  ABSTRACT: This research provides an algorithm for calculating uniformly most accurate tolerance intervals in clinical trials with dichotomous outcomes. The link between confidence intervals for proportions and tolerance intervals for numbers of outcomes is established. Tolerance intervals and sample size estimates for clinical trials may be calculated using StatXact.
  Journal of Biopharmaceutical Statistics 02/2007; 17(3):481-91.
• Article: The subject-by-formulation interaction in multivariate bioequivalence.

  Li Cao, Thomas Mathew
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  ABSTRACT: This paper addresses hypothesis testing problems concerning the subject-by-formulation interaction matrix for the assessment of multivariate bioequivalence. Two problems are addressed: (a) the problem of testing if the subject-by-formulation interaction matrix itself is zero, and (b) the problem of testing if suitable scalar valued functions of the subject-by-formulation interaction matrix is below a threshold. Approximate tests are developed in both cases and the accuracy of the approximation is numerically investigated. The results are illustrated with an example. Even though the literature on univariate bioequivalence testing addresses average bioequivalence, variance bioequivalence and subject-by-formulation interaction, the literature on multivariate bioequivalence deals only with the problem of average bioequivalence. This work appears to be the first attempt to address tests for the subject-by-formulation interaction matrix for testing multivariate bioequivalence.
  Journal of Biopharmaceutical Statistics 02/2007; 17(3):367-79.
• Article: The bayesian bias correction method of the first-order approximation of nonlinear mixed-effects models for population pharmacokinetics.

  Takashi Funatogawa, Ikuko Funatogawa
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  ABSTRACT: Population pharmacokinetic analysis usually employs nonlinear mixed-effects models. To estimate the parameters, Beal and Sheiner (1982) proposed the first-order method that employs a first-order Taylor series expansion around the means of random individual parameters. Because of the small computational burden and the high convergence proportion of maximization of the log likelihood function, this method is often used in practice. However, it is known that the estimates are biased. This paper proposes a simple procedure to reduce the bias. The proposed method maximizes the nonapproximated log likelihood functions of each individual given estimates of the population parameters derived from the first-order method, and the derived Bayes estimates of the random individual parameters are utilized to improve the estimates of the population mean parameters. We confirmed that the proposed method reduced the bias using simulated data and actual erythropoietin concentration data.
  Journal of Biopharmaceutical Statistics 02/2007; 17(3):381-92.
• Article: Assessing equivalence of two assays using sensitivity and specificity.

  Jorge Quiroz, Richard K Burdick
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  ABSTRACT: The equivalence of two assays is determined using the sensitivity and specificity relative to a gold standard. The equivalence-testing criterion is based on a misclassification rate proposed by Burdick et al. (2005) and the intersection-union test (IUT) method proposed by Berger (1982). Using a variance components model and IUT methods, we construct bounds for the sensitivity and specificity relative to the gold standard assay based on generalized confidence intervals. We conduct a simulation study to assess whether the bounds maintain the stated test size. We present a computational example to demonstrate the method described in the paper.
  Journal of Biopharmaceutical Statistics 02/2007; 17(3):433-43.
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  Available from: biometrics.com

  Article: Adaptive designs for dose-finding studies based on sigmoid Emax model.

  Vladimir Dragalin, Francis Hsuan, S Krishna Padmanabhan
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  ABSTRACT: We propose an adaptive procedure for dose-finding in clinical trials when the primary efficacy endpoint is continuous. We model the mean of the efficacy endpoint, given the dose, as a four-parameter logistic function. The efficacy endpoint at each dose is distributed according to either a normal or a gamma distribution. We consider the cases of fixed variance and fixed coefficient of variation assuming them to be both known and unknown. The analytic formulae for the Fisher information matrix are obtained, which are used to build the locally and adaptive D-optimal designs.
  Journal of Biopharmaceutical Statistics 02/2007; 17(6):1051-70.
• Article: Bayesian estimation of intervention effect with pre- and post-misclassified binomial data.

  James D Stamey, John W Seaman, Dean M Young
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  ABSTRACT: We consider studies in which an enrolled subject tests positive on a fallible test. After an intervention, disease status is re-diagnosed with the same fallible instrument. Potential misclassification in the diagnostic test causes regression to the mean that biases inferences about the true intervention effect. The existing likelihood approach suffers in situations where either sensitivity or specificity is near 1. In such cases, common in many diagnostic tests, confidence interval coverage can often be below nominal for the likelihood approach. Another potential drawback of the maximum likelihood estimator (MLE) method is that it requires validation data to eliminate identification problems. We propose a Bayesian approach that offers improved performance in general, but substantially better performance than the MLE method in the realistic case of a highly accurate diagnostic test. We obtain this superior performance using no more information than that employed in the likelihood method. Our approach is also more flexible, doing without validation data if necessary, but accommodating multiple sources of information, if available, thereby systematically eliminating identification problems. We show via a simulation study that our Bayesian approach outperforms the MLE method, especially when the diagnostic test has high sensitivity, specificity, or both. We also consider a real data example for which the diagnostic test specificity is close to 1 (false positive probability close to 0).
  Journal of Biopharmaceutical Statistics 02/2007; 17(1):93-108.
• Article: Adaptive designs in clinical drug development: opportunities, challenges, and scope reflections following PhRMA's November 2006 workshop.

  Michael Krams, Carl-Fredrik Burman, Vladimir Dragalin, Brenda Gaydos, Andrew P Grieve, Jose Pinheiro, Willi Maurer, Paul Gallo
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  ABSTRACT: This paper provides reflections on the opportunities, scope and challenges of adaptive design as discussed at PhRMA's workshop held in November 2006. We also provide a status report of workstreams within PhRMA's working group on adaptive designs, which were triggered by the November workshop. Rather than providing a comprehensive review of the presentations given, we limit ourselves to a selection of key statements. The authors reflect the position of PhRMA's working group on adaptive designs.
  Journal of Biopharmaceutical Statistics 02/2007; 17(6):957-64.
• Article: Innovative approaches in drug development.

  Mark Chang, Susan Kenley, Jonca Bull, Yuan-Yuan Chiu, Wenjin Wang, Charles Wakeford, Katherine McCarthy
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  ABSTRACT: This is the white paper on Innovative Approaches in Drug Development developed by the Biotechnology Industry Organization's (BIO) Clinical Trial Designs Workgroup. As recognized by the Food and Drug Administration's Critical Path Opportunities Report, the need to develop and apply innovative approaches to create new trial designs and clinical development programs is rapidly on the rise. Such novel approaches hold tremendous potential in the ability to refine mechanisms lying at the fundamental core of product safety and efficacy. The paper addresses the opportunities, challenges in pre-clinical and clinical trial design innovations; emphasizes the importance of safety database; and makes recommendations for carrying out the innovative approaches. The views expressed here in are solely those of the authors and do not reflect the official views of the Biotechnology Industry Organization.
  Journal of Biopharmaceutical Statistics 02/2007; 17(5):775-89.
• Article: Sample size calculation for weighted rank tests comparing survival distributions under cluster randomization: a simulation method.

  Sin-Ho Jung
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  ABSTRACT: We propose a sample size calculation method for rank tests comparing two survival distributions under cluster randomization with possibly variable cluster sizes. Here, sample size refers to number of clusters. Our method is based on simulation procedure generating clustered exponential survival variables whose distribution is specified by the marginal hazard rate and the intracluster correlation coefficient. Sample size is calculated given significance level, power, marginal hazard rates (or median survival times) under the alternative hypothesis, intracluster correlation coefficient, accrual rate, follow-up period, and cluster size distribution.
  Journal of Biopharmaceutical Statistics 02/2007; 17(5):839-49.
• Article: Estimating a positive false discovery rate for variable selection in pharmacogenetic studies.

  Lang Li, Siu Hui, Gene Pennello, Zeruesenay Desta, Skaar Todd, Anne Nguyen, David Flockhart
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  ABSTRACT: Selecting predictors to optimize the outcome prediction is an important statistical method. However, it usually ignores the false positives in the selected predictors. In this paper, we develop a positive false discovery rate (pFDR) estimate for a conventional step-wise forward variable selection procedure. We propose two views of a variable selection process, an overall and an individual test. An interesting feature of the overall test is that its power of selecting non-null predictors increases with the proportion of non-null predictors among all candidate predictors. Data analysis is illustrated with a pharmacogenetics example.
  Journal of Biopharmaceutical Statistics 02/2007; 17(5):883-902.
• Article: A two-stage binomial test approach of gene identification in oligonucleotide arrays.

  Dung-Tsa Chen, James J Chen, Gary Cheng, Sue-Hwa Lin, Seng-Jaw Soong
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  ABSTRACT: Most statistical approaches summarize the probe-level expression data into gene-level measures, which then are used for downstream statistical analyses. However, there are some limitations in using the gene level data for analysis, such as nonhomogeneous probe effects and the interaction effect (e.g., alternative splicing). In this paper, we consider a two-stage binomial test with a weighted probe rank approach to determine differentially expressed genes. Using a series of benchmark gene array datasets, we show the two-stage binomial test approach yielded a higher positive predictivity and a higher sensitivity than the conventional RMA, GCRMA, Dchip, and ANOVA approaches. In data application, the two-stage binomial test identified a subset of genes strongly related to cell proliferation in the prolactin study, and a subset of genes associated with lymph node metastasis in the breast cancer dataset. In addition, by exploring the proportion of probes with expression changes and the probe expression plot, the two-stage binomial test helped detect an alternative splicing form of the prolactin gene in the prolactin study. In the breast cancer dataset, the approach also identified one potential alternative splicing gene.
  Journal of Biopharmaceutical Statistics 02/2007; 17(5):903-18.
• Article: Population pharmacokinetic measures, their estimation and selection of sampling times.

  Valerii Fedorov, Sergei Leonov
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  ABSTRACT: In pharmacokinetic (PK) studies, including bioavailability assessment, various population PK measures, such as area under the curve (AUC), maximal concentration (C(max)) and time to maximal concentration (T(max)) are estimated. In this paper we compare a model-based approach, where parameters of a compartmental model are estimated and the explicit formulae for PK measures are used, and a model-independent approach, where numerical integration algorithms are used for AUC and sample estimates for C(max) and T(max). Since regulatory agencies usually require the model-independent estimation of PK measures, we focus on the empirical approach while using the model-based approach and corresponding measures as a benchmark. We show how to "split" a single sampling grid into two or more subsets, which substantially reduces the number of samples taken for each patient, but often has little effect on the precision of estimation of PK measures in terms of mean squared error (MSE). We give explicit formulae for the MSE of the empirical estimator of AUC for a simple example and discuss how costs may be taken into account.
  Journal of Biopharmaceutical Statistics 02/2007; 17(5):919-41.
• Article: Choice of delta noninferiority margin and dependency of the noninferiority trials.

  Yi Tsong, Joanne Zhang, Mark Levenson
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  ABSTRACT: For a two-arm active control clinical trial designed to test for noninferiority of the test treatment to the active control standard treatment, data of historical studies were often used. For example, with a cross-trial comparison approach (also called synthetic approach or lambda-margin approach), the trial is conducted to test the hypothesis that the mean difference or the ratio between the current test product and the active control is no larger than a certain portion of the mean difference or no smaller that a certain portion of the ratio of the active control and placebo obtained in the historical data when the positive response indicates treatment effective. For a generalized historical control approach (also known as confidence interval approach or delta -margin approach), the historical data is often used to determine a fixed value noninferiority margin delta for all trials involving the active control treatment. The regulatory agency usually requires that the clinical trials of two different test treatments need to be independent and in most regular cases, it also requires to have two independent positive trials of the same test treatment in order to provide confirmatory evidence of the efficacy of the test product. Because of the nature of information (historical data) shared in active-controlled trials, the independency assumption of the trials is not satisfied in general. The correlation between two noninferiority tests has been examined which showed that it is an increasing function of (1 - lambda ) when the response variable is normally distributed. In this article, we examine the relationship between the correlation of the two test statistics and the choice of the noninferiority margin, delta as well as the sample sizes and variances under the normality assumption. We showed that when delta is determined by the lower limit of the confidence interval of the adjusted effect size of the active control treatment (muC - muP) using data from historical studies, dependency of the two noninferiority tests can be very high. In order to control the correlation under 15%, the overall sample size of the historical studies needs to be at least five times of the current active control trial.
  Journal of Biopharmaceutical Statistics 02/2007; 17(2):279-88.
• Article: Noninferiority testing beyond simple two-sample comparison.

  Yi Tsong, Wen-Jen Chen
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  ABSTRACT: In order to fulfill the requirement of a new drug application, a sponsor often need to conduct multiple clinical trials. Often these trials are of designs more complicated than a randomized two-sample single-factor study. For example, these trials could be designed with multiple centers, multiple factors, covariates, group sequential and/or adaptive scheme, etc. When an active standard treatment used as the control treatment in a two-arm clinical trial, the efficacy of the test treatment is often established by performing a noninferiority test through comparison of the test treatment and the active standard treatment. Typically, the noninferiority trials are designed with either a generalized historical control approach (i.e., noninferiority margin approach or delta-margin approach) or a cross-trial comparison approach (i.e., synthesis approach or lambda-margin approach). Many of the statistical properties of the approaches discussed in the literature were focused on testing in a simple two sample comparison form. We studied the limitations of the two approaches for the consideration of switching between superiority and noninferiority testing, feasibility to be applied with group sequential design, constancy assumption requirements, test dependency in multiple trials, analysis of homogeneity of efficacy among centers in a multi-center trial, data transformation and changing analysis method from the historical studies. Our evaluation shows that the cross-trial comparison approach is more restricted to simple two sample comparison with normal approximation test because of its poor properties with more complicated design and analysis. On the other hand, the generalized historical control comparison approach may have more flexible properties when the variability of the margin delta is indeed negligibly small.
  Journal of Biopharmaceutical Statistics 02/2007; 17(2):289-308.
• Article: Simultaneous test for superiority and noninferiority hypotheses in active-controlled clinical trials.

  Yi Tsong, Joanne Zhang
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  ABSTRACT: Two stage switching between testing for superiority (SUP) and noninferiority (NI) has been an important statistical issue in the design and analysis of the active-controlled clinical trials. Tsong and Zhang (2005) has shown that the Type I error rates do not change when switching between SUP and NI with the traditional generalized historical control (GHC) approach, however, they may change when switching with the cross-trial comparison (X-trial) approach. Tsong and Zhang (2005) further proposed a simultaneous test for both hypotheses to avoid the problem. The procedure was based on Fieller's confidence interval proposed by Hauschke et al. (1999). Since with the X-trial approach, using the simultaneous test, superiority is tested using all four treatment arms (current test and active control arms, active control and placebo arms in historical trials), the Type I error rate and power are expected to be somewhat different from the conventional superiority test (using the current test and active control arms only). Through a simulation study, we demonstrate that the Type I error rate and power between simultaneous test and the conventional superiority test are compatible. We also examine the impact of the assumption of equal variances of the current trial and the historical trial.
  Journal of Biopharmaceutical Statistics 02/2007; 17(2):247-57.
• Article: Regression models for method comparison data.

  Graham Dunn
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  ABSTRACT: Regression methods for the analysis of paired measurements produced by two fallible assay methods are described and their advantages and pitfalls discussed. The difficulties for the analysis, as in any errors-in-variables problem lies in the lack of identifiability of the model and the need to introduce questionable and often naïve assumptions in order to gain identifiability. Although not a panacea, the use of instrumental variables and associated instrumental variable (IV) regression methods in this area of application has great potential to improve the situation. Large samples are frequently needed and two-phase sampling methods are introduced to improve the efficiency of the IV estimators.
  Journal of Biopharmaceutical Statistics 02/2007; 17(4):739-56.
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  Available from: econstor.eu

  Article: Optimal designs for estimating the interesting part of a dose-effect curve.

  Frank Miller, Olivier Guilbaud, Holger Dette
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  ABSTRACT: We consider a dose-finding trial in phase IIB of drug development. For choosing an appropriate design for this trial the specification of two points is critical: an appropriate model for describing the dose-effect relationship, and the specification of the aims of the trial (objectives), which will be the focus in the present paper. For many situations it is essential to have a robust trial objective that has little risk of changing during the complete trial due to external information. An important and realistic objective of a dose-finding trial is to obtain precise information about key parts of the dose-effect curve. We reflect this goal in a statistical optimality criterion and derive efficient designs using optimal design theory. In particular, we determine nonadaptive Bayesian optimal designs, i.e., designs which are not changed by information obtained from an interim analysis. Compared with a traditional balanced design for this trial, it is shown that the optimal design is substantially more efficient. This implies either a gain in information, or essential savings in sample size. Further, we investigate an adaptive Bayesian optimal design that uses different optimal designs before and after an interim analysis, and we compare the adaptive with the nonadaptive Bayesian optimal design. The basic concept is illustrated using a modification of a recent AstraZeneca trial.
  Journal of Biopharmaceutical Statistics 02/2007; 17(6):1097-115.