Journal of Biopharmaceutical Statistics (J Biopharm Stat )

Publisher: Taylor & Francis

Description

This rapid publication periodical discusses quality applications of statistics in biopharmaceutical research and development and expositions of statistical methodology with immediate applicability to such work in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers drug, device, and biological research and development drug screening and drug design assessment of pharmacological activity pharmaceutical formulation and scale-up preclinical safety assessment bioavailability, bioequivalence, and pharmacokinetics phase I, II, and III clinical development premarket approval assessment of clinical safety postmarketing surveillance manufacturing and quality control technical operations regulatory issues.

Impact factor 0.72

  • Hide impact factor history
     
    Impact factor
  • 5-year impact
    1.18
  • Cited half-life
    5.80
  • Immediacy index
    0.25
  • Eigenfactor
    0.00
  • Article influence
    0.65
  • Website
    Journal of Biopharmaceutical Statistics website
  • Other titles
    Journal of biopharmaceutical statistics (Online), Journal of biopharmaceutical statistics, JBS
  • ISSN
    1520-5711
  • OCLC
    39496949
  • Material type
    Document, Periodical, Internet resource
  • Document type
    Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Taylor & Francis

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Some individual journals may have policies prohibiting pre-print archiving
    • On author's personal website or departmental website immediately
    • On institutional repository or subject-based repository after either 12 months embargo for STM, Behavioural Science and Public Health Journals or 18 months embargo for SSH journals
    • Publisher's version/PDF cannot be used
    • On a non-profit server
    • Published source must be acknowledged
    • Must link to publisher version
    • Set statements to accompany deposits (see policy)
    • The publisher will deposit in on behalf of authors to a designated institutional repository including PubMed Central, where a deposit agreement exists with the repository
    • STM: Science, Technology and Medicine
    • SSH: Social Science and Humanities
    • Publisher last contacted on 25/03/2014
    • 'Taylor & Francis (Psychology Press)' is an imprint of 'Taylor & Francis'
  • Classification
    ​ green

Publications in this journal

  • Walthère Dewé, Christelle Durand, Sandie Marion, Lidia Oostvogels, Jeanne-Marie Devaster, Marc Fourneau
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    ABSTRACT: This paper illustrates the use of a multi-criteria decision making approach, based on desirability functions, to identify an appropriate adjuvant composition for an influenza vaccine to be used in elderly. The proposed adjuvant system contained two main elements: monophosphoryl lipid and α-tocopherol with squalene in an oil/water emulsion. The objective was to elicit a stronger immune response while maintaining an acceptable reactogenicity and safety profile. The study design, the statistical models, the choice of the desirability functions, the computation of the overall desirability index and the assessment of the robustness of the ranking are all detailed in this manuscript.
    Journal of Biopharmaceutical Statistics 01/2015;
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    ABSTRACT: The high consumption of psychotropic drugs is a public health problem. Rigorous statistical methods are needed to identify consumption characteristics in post-marketing phase. Agglomerative hierarchical clustering (AHC) and latent class analysis (LCA) can both provide clusters of subjects with similar characteristics. The objective of this study was to compare these two methods in pharmacoepidemiology, on several criteria: number of clusters, concordance, interpretation and stability over time. From a data set on bromazepam consumption, the two methods present a good concordance. AHC is a very stable method and provides homogeneous classes. LCA is an inferential approach and seems to allow identifying more accurately extreme deviant behaviour.
    Journal of Biopharmaceutical Statistics 12/2014; Under Press.
  • Journal of Biopharmaceutical Statistics 11/2014;
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    ABSTRACT: Abstract We propose a chi-square goodness-of-fit test for autoregressive logistic regression (ALR) models. General guidelines for a two-dimensional binning strategy are provided, which make use of two types of maximum likelihood parameter estimates. For smaller sample sizes, a bootstrap p-value procedure is discussed. Simulation studies indicate that the test procedure satisfactorily approximates the correct size and has good power for detecting model misspecification. In particular, the test is very good at detecting the need for an additional lag. An application to a dataset relating to screening patients for late-onset Alzheimer's disease is provided.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: ABSTRACT The role of biomarkers has increased in cancer clinical trials such that novel designs are needed to efficiently answer questions of both drug effects and biomarker performance. We advocate Bayesian hierarchical models for response-adaptive randomized phase II studies integrating single or multiple biomarkers. Prior selection allows one to control a gradual and seamless transition from randomized-blocks to marker-enrichment during the trial. Adaptive randomization is an efficient design for evaluating treatment efficacy within biomarker subgroups, with less variable final sample sizes when compared to nested staged designs. Inference based on the Bayesian hierarchical model also has improved performance in identifying the sub-population where therapeutics are effective over independent analyses done within each biomarker subgroup.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: ABSTRACT In clinical trials with counts of recurrent event data, it is often of particular interest to test if the experimental treatment reduces the event rate in comparison with a control. The sample size calculation for such a trial often assumes fixed follow-up time for each patient. In many trials, however, we follow all patients for a predetermined follow-up time after the end of the accrual period, in which case the follow-up time is variable. This paper provides methods for sample size calculation for clinical trials with ordinary Poisson count data and variable follow-up time allowing for non-uniform accrual and early dropouts. We also generalize the sample size formula to count data with over-dispersion.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: Clinical trials often use a binary "fold increase" endpoint defined according to the ratio of interval-censored measurement at end-of-study to that at baseline. We propose a simple yet principled analytic approach based on the linear mixed effects model for interval-censored data for the analysis of such paired measurements. Having estimated the model parameters, the risk ratio can be estimated by explicit composite estimand and the variance is estimated using the delta method. The estimation can be implemented using existing procedures in popular statistical software. We use antibody data from the Chloroquine for Influenza Prevention Trial for illustration.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: Abstract Clinical trials often involve comparing 2-4 doses or regimens of an experimental therapy with a control treatment. These studies might occur early in a drug development process, where the aim might be to demonstrate a basic level of proof (so called proof of concept (PoC) studies), at a later stage, to help establish a dose or doses that should be used in phase III trials (dose-finding), or even in confirmatory studies, where the registration of several doses might be considered. When a small number of doses are examined, the ability to implement parametric modeling is somewhat limited. As an alternative, in this paper, a flexible Bayesian model is suggested. In particular, we draw on the idea of using Bayesian model averaging (BMA), to exploit an assumed monotonic dose-response relationship, without using strong parametric assumptions. The approach is exemplified by assessing operating characteristics in the design of a PoC study examining a new treatment for psoriatic arthritis and a post-hoc data analysis involving three confirmatory clinical trials, which examined an adjunctive treatment for partial epilepsy. Key difficulties, such as prior specification and computation are discussed. A further extension, based on combining the flexible modeling with a classical multiple comparisons procedure, known as MCP-MOD, is examined. The benefit of this extension is a potential reduction in the number of simulations that might be needed to investigate operating characteristics of the statistical analysis.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: Abstract A new non-inferiority test for the difference between two independent proportions is presented. The test is based on a Wald-type statistic in which maximum likelihood estimators and a type of shrinkage estimator are used to estimate proportions. This new test was compared with another Wald-type test that been shown to behave well in terms of test size and power. For the comparison, the behavior of the new test, in terms of its size and power, was analyzed over several configurations. While the two tests exhibited similar behavior, the new test is easier to implement and thus constitutes a practical alternative.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: ABSTRACT When comparing two doses of a new drug with a placebo, we may consider using a crossover design subject to the condition that the high dose cannot be administered before the low dose. Under a random effects logistic regression model, we focus our attentions on dichotomous responses when the high dose cannot be used first under a three-period crossover trial. We derive asymptotic test procedures for testing equality between treatments. We further derive interval estimators to assess the magnitude of the relative treatment effects. We employ Monte Carlo simulation to evaluate the performance of these test procedures and interval estimators in a variety of situations. We use the data taken as a part of trial comparing two different doses of an analgesic with a placebo for the relief of primary dysmenorrhea to illustrate the use the proposed test procedures and estimators.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: SUMMARY In studies of screening accuracy, we may commonly encounter the data in which a confirmatory procedure is administered to only those subjects with screen positives for ethical concerns. We focus our discussion on simultaneously testing equality of sensitivity and specificity between two binary screening tests when only subjects with screen positives receive the confirmatory procedure. We develop four asymptotic test procedures and one exact test procedure. We derive sample size calculation formula for a desired power of detecting a difference at a given nominal[Formula: see text]-level. We employ Monte Carlo simulation to evaluate the performance of these test procedures and the accuracy of the sample size calculation formula developed here in a variety of situations. Finally, we use the data taken from a study of the prostate-specific-antigen (PSA) test and digital rectal examination (DRE) test on 949 black men to illustrate the practical use of these test procedures and the sample size calculation formula.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: Abstract The problem of testing treatment difference in the occurrence of a study endpoint in a randomized parallel-group comparative clinical trial with repeated responses under the assumption that the responses follow a bivariate zero-inflated Poisson (ZIP) distribution is considered. Likelihood ratio test for homogeneity of two bivariate ZIP populations is derived. Approximate formula for sample size calculation is also obtained, which achieves a desired power for detecting a clinically meaningful difference under an alternative hypothesis. An example concerning the comparison of treatment effect in an addictive clinical trial in terms of the number of days of illicit drug use during a month is given for illustration purpose.
    Journal of Biopharmaceutical Statistics 05/2014;
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    ABSTRACT: Summary In practice, there exist many disease processes with three ordinal disease classes; for example in the detection of Alzheimer's disease (AD) a patient can be classified as healthy (disease free stage), mild cognitive impairment (early disease stage) or AD (full disease stage). The treatment interventions and effectiveness of such disease processes will depend on the disease stage. Therefore it is important to develop diagnostic tests with the ability to discriminate between the three disease stages. Measuring the overall ability of diagnostic tests to discriminate between the three classes has been discussed extensively in the literature. However there has been little proposed on how to select clinically meaningful thresholds for such diagnostic tests, except for a method based on the generalized Youden index by Nakas et al. (2010). In this paper, we propose two new criterion for selecting diagnostic thresholds in the three class setting. The numerical study demonstrated that the proposed methods may provide thresholds with less variability and more balance among the correct classification rates for the three stages. The proposed methods are applied to two real examples: the clinical diagnosis of AD from the Washington University Alzheimer's Disease Research Center and on the detection of liver cancer (LC) using protein segments.
    Journal of Biopharmaceutical Statistics 04/2014;
  • Journal of Biopharmaceutical Statistics 04/2014;
  • Journal of Biopharmaceutical Statistics 04/2014;
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    ABSTRACT: Abstract A nearest neighbor-based multiple imputation approach is proposed to recover missing covariate information using the predictive covariates while estimating the association between the outcome and the covariates. To conduct the imputation, two working models are fitted to define an imputing set. This approach is expected to be robust to the underlying distribution of the data. We show in simulation and demonstrate on a colorectal dataset that the proposed approach can improve efficiency and reduce bias in a situation with missing at random compared to the complete case analysis and the modified inverse probability weighted method.
    Journal of Biopharmaceutical Statistics 04/2014;
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    ABSTRACT: Abstract Subject attrition is a ubiquitous problem in any type of clinical trials and thus needs to be taken into consideration at the design stage particularly to secure adequate statistical power. Here, we focus on longitudinal cluster randomized clinical trials (cluster-RCT) that aim to test the hypothesis that an intervention has an effect on the rate of change in the outcome over time. In this setting, the cluster-RCT assumes a three level hierarchical data structure in which subjects are nested within a higher level unit such as clinics and are evaluated for outcome repeatedly over the study period. Furthermore, the subject-specific slopes can be modeled in terms of fixed or random coefficients in a mixed-effects linear model. Closed form sample size formulas for testing the hypothesis above have been developed under assumption of no attrition. In this paper, we propose closed form approximate samples size determinations with anticipated attrition rates by modifying those existing sample size formulas. With extensive simulations, we examine performances of the modified formulas under three attrition mechanisms: attrition completely at random, attrition at random and attrition not at random. In conclusion, the proposed modification is very effective under fixed slope models but yields biased, if not substantially, statistical power under random slope models.
    Journal of Biopharmaceutical Statistics 04/2014;
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    ABSTRACT: Abstract Clinical trials in the context of comparative effectiveness research (CER) are often conducted to evaluate health outcomes under real-world conditions and standard health care settings. In such settings, three-level hierarchical study designs are increasingly common. For example, patients may be nested within treating physicians, who in turn are nested within an urgent care center or hospital. While many trials randomize the third-level units (e.g.: centers) to intervention, in some cases randomization may occur at lower levels of the hierarchy, e.g. patients or physicians. In this paper, we present and verify explicit closed-form sample size and power formulae for three-level designs assuming randomization is at the first or second level. The formulae are based on maximum likelihood estimates from mixed-effect linear models and verified by simulation studies. Results indicate that even with smaller sample sizes, theoretical power derived with known variances is nearly identical to empirically estimated power for the more realistic setting when variances are unknown. In addition, we show that randomization at the second or first level of the hierarchy provides an increasingly statistically efficient alternative to third-level randomization. Power to detect a treatment effect under second level randomization approaches that of patient-level randomization when there are few patients within each randomized second level cluster, and, most importantly, when the correlation attributable to second-level variation is a small proportion of the overall correlation between patient outcomes.
    Journal of Biopharmaceutical Statistics 04/2014;