1,651 results match your criteria Journal of Biopharmaceutical Statistics [Journal]


Simulation optimization for Bayesian multi-arm multi-stage clinical trial with binary endpoints.

J Biopharm Stat 2019 Feb 14:1-12. Epub 2019 Feb 14.

c Data Coordination Unit, Department of Public Health Sciences , Medical University of South Carolina , Charleston , SC , USA.

Multi-arm multi-stage designs, in which multiple active treatments are compared to a control and accumulated information from interim data are used to add or remove arms from the trial, may reduce development costs and shorten the drug development timeline. As such, this adaptive update is a natural complement to Bayesian methodology in which the prior clinical belief is sequentially updated using the observed probability of success. Simulation is often required for planning clinical trials to accommodate the complexity of the design and to optimize key design characteristics. Read More

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http://dx.doi.org/10.1080/10543406.2019.1577682DOI Listing
February 2019

Adaptation of the robust method to large distributions of reference values: program modifications and comparison of alternative computational methods.

J Biopharm Stat 2019 Feb 13:1-13. Epub 2019 Feb 13.

d Division of Neurology , Cincinnati Children's Hospital Medical Center , Cincinnati , OH , USA.

The objective of this research was to compute reference limits using reference values from patients entering pharmaceutical development clinical trials by the nonparametric method and the robust method of Horn and Pesce, with and without outlier exclusion, and compare the methods with respect to influence on the limits. Reference limits were computed for 38 analytes with over 130,000 subjects contributing reference values. Subjects were partitioned into 10 demographic strata for limit computation. Read More

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http://dx.doi.org/10.1080/10543406.2019.1579223DOI Listing
February 2019

Semi-parametric Bayesian regression for subgroup analysis in clinical trials.

J Biopharm Stat 2019 Feb 12:1-19. Epub 2019 Feb 12.

b Division of Biostatistics , Center for Devices and Radiologic Health, Food and Drug Administration , Silver Spring , Maryland , USA.

Determining whether there are differential treatment effects in subgroups of trial participants remains an important topic in clinical trials as precision medicine becomes ever more relevant. Any assessment of differential treatment effect is predicated on being able to estimate the treatment response accurately while satisfying constraints of balancing the risk of overlooking an important subgroup with the potential to make a decision based on a false discovery. While regression models, such as marginal interaction model, have been widely used to improve accuracy of subgroup parameter estimates by leveraging the relationship between treatment and covariate, there is still a possibility that it can lead to excessively conservative or anti-conservative results. Read More

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http://dx.doi.org/10.1080/10543406.2019.1572613DOI Listing
February 2019

R-TPI: rolling toxicity probability interval design to shorten the duration and maintain safety of phase I trials.

J Biopharm Stat 2019 Feb 11:1-14. Epub 2019 Feb 11.

c Biostatistics Department , Juno Therapeutics , Seattle , Washington , USA.

To shorten trial duration and improve safety of Phase I trials, we propose R-TPI, a rolling enrollment design that combines the features in model-based designs such as mTPI-2 and rule-based designs such as rolling six. R-TPI employs a novel rolling enrollment scheme, which allows concurrent patient enrollment that is faster than cohort-based enrollment. Bench-marking against rolling six, we find that the R-TPI design is as fast in completing clinical trials but with fewer toxicity events and higher chance of finding the maximum tolerated dose (MTD) in the single scenario laid out in the 2008 rolling six publication. Read More

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http://dx.doi.org/10.1080/10543406.2019.1577683DOI Listing
February 2019
0.716 Impact Factor

Approaches for testing noninferiority in two-arm trials for risk ratio and odds ratio.

J Biopharm Stat 2019 Feb 11:1-21. Epub 2019 Feb 11.

a Center of Molecular Medicine and Genetics , Wayne State University , Detroit , MI , USA.

For an existing established drug regimen, active control trials are defacto standard due to ethical reason as well as for clinical equipoise. However, when superiority claim of a new drug against the active control is unlikely to be successful, researchers often address the issue in terms of noninferiority (NI), provided the experimental drug demonstrates the evidence of other benefits beyond efficacy. Such trials aim to demonstrate that an experimental treatment is non-inferior to an existing comparator by not more than a pre-specified margin. Read More

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http://dx.doi.org/10.1080/10543406.2019.1572616DOI Listing
February 2019

A fast and reliable test for parallelism in bioassay.

J Biopharm Stat 2019 Feb 4:1-13. Epub 2019 Feb 4.

a Department of Statistical Sciences , MedImmune LLC , Gaithersburg , Maryland , USA.

Parallelism in bioassay is a synonym of similarity between two concentration-response curves. Before the determination of relative potency in bioassays, it is necessary to test for and claim parallelism between the pair of concentration-response curves of reference standard and test sample. Methods for parallelism testing include p-value-based significance tests and interval-based equivalence tests. Read More

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http://dx.doi.org/10.1080/10543406.2019.1572615DOI Listing
February 2019
1 Read

Letter to the editor.

J Biopharm Stat 2019 Jan 29:1-2. Epub 2019 Jan 29.

a Institute of Cellular Medicine , Newcastle University , Newcastle upon Tyne , UK.

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http://dx.doi.org/10.1080/10543406.2019.1572618DOI Listing
January 2019

A reflection on the possibility of finding a good surrogate.

J Biopharm Stat 2019 Jan 26:1-10. Epub 2019 Jan 26.

a I-BioStat , KU Leuven , Leuven , Belgium.

Surrogate endpoints need to be statistically evaluated before they can be used as substitutes of true endpoints in clinical studies. However, even though several evaluation methods have been introduced over the last decades, the identification of good surrogate endpoints remains practically and conceptually challenging. In the present work, the question regarding the existence of a good surrogate is addressed using information-theoretic concepts, within a causal-inference framework. Read More

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http://dx.doi.org/10.1080/10543406.2018.1559854DOI Listing
January 2019

Design of randomized controlled confirmatory trials using historical control data to augment sample size for concurrent controls.

J Biopharm Stat 2019 Jan 6:1-16. Epub 2019 Jan 6.

c Drug Development Operations , Allergan Inc , Madison , NJ , USA.

This paper deals with the methods to augment concurrent controls (CC) in a randomized controlled trial with available historical data in clinical studies. In their article, Matching with multiple control groups and adjusting for group differences, Stuart and Rubin proposed a matching method where the primary/local control and the secondary/non-local control are both included in the propensity score estimates. The authors discuss a similar approach taking the CC as the primary and the historical control as the secondary, and find that this approach does not save the sample size of the randomized trial compared to the traditional randomized design without supplementation of historical data. Read More

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http://dx.doi.org/10.1080/10543406.2018.1559853DOI Listing
January 2019
1 Read

Interim analysis of binary outcome data in clinical trials: a comparison of five estimators.

J Biopharm Stat 2019 Jan 1:1-11. Epub 2019 Jan 1.

d Department of Management Sciences , City University of Hong Kong , Kowloon , Hong Kong.

In clinical trials, where the outcome of interest is the occurrence of an event over a fixed time period, estimation of the event proportion at interim analysis can form a basis for decision-making such as early trial termination, sample size re-estimation, and/or dropping inferior treatment arms. In addition to derivation of mean squared error under an exponential time-to-event distribution, we performed a simulation study to examine the performance of five estimators of the event proportion when time to the event is assessable. The simulation results showed advantages of the Kaplan-Meier estimator over others in terms of robustness, and the bias and variability of the event proportion estimate. Read More

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http://dx.doi.org/10.1080/10543406.2018.1559852DOI Listing
January 2019
2 Reads

Comparison of balancing scores using the ANCOVA approach for estimating average treatment effect: a simulation study.

J Biopharm Stat 2018 Dec 18:1-8. Epub 2018 Dec 18.

b Department of Mathematical Sciences , University of New England , Biddeford , ME , USA.

In this article, we conducted a simulation study to evaluate the performance of five balancing scores using the Analysis of Covariance (ANCOVA) approach, for adjusting bias in estimating average treatment effects (ATE) in observational studies. The five balancing scores which we used as the covariate(s) in the ANCOVA model were (1) propensity score (P), (2) prognostic score (G), (3) propensity score estimated by prognostic score (PG), (4) prognostic score estimated by propensity score (GP), and (5) both propensity and prognostic scores (P&G). The results of the five balancing scores using the ANCOVA approach were compared to the results of the classic regression approach, which included all observed covariates as the predictors. Read More

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http://dx.doi.org/10.1080/10543406.2018.1559855DOI Listing
December 2018
1 Read

Improved adaptive randomization strategies for a seamless Phase I/II dose-finding design.

J Biopharm Stat 2018 Nov 17:1-15. Epub 2018 Nov 17.

c Division of Biostatistics,Wake Forest School of Medicine , Winston-Salem , NC.

In this article, we propose and evaluate three alternative randomization strategies to the adaptive randomization (AR) stage used in a seamless Phase I/II dose-finding design. The original design was proposed by Wages and Tait in 2015 for trials of molecularly targeted agents in cancer treatments, where dose-efficacy assumptions are not always monotonically increasing. Our goal is to improve the design's overall performance regarding the estimation of optimal dose as well as patient allocation to effective treatments. Read More

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http://dx.doi.org/10.1080/10543406.2018.1535496DOI Listing
November 2018
7 Reads

An adaptive multi-stage phase I dose-finding design incorporating continuous efficacy and toxicity data from multiple treatment cycles.

J Biopharm Stat 2018 Nov 7:1-16. Epub 2018 Nov 7.

b Cancer Center Statistics , Mayo Clinic , Rochester , MN , USA.

Phase I designs traditionally use the dose-limiting toxicity (DLT), a binary endpoint from the first treatment cycle, to identify the maximum-tolerated dose (MTD) assuming a monotonically increasing relationship between dose and efficacy. In this article, we establish a general framework for a multi-stage adaptive design where we jointly model a continuous efficacy outcome and continuous/quasi-continuous toxicity endpoints from multiple treatment cycles. The normalized Total Toxicity Profile (nTTP) is used as an illustration for quasi-continuous toxicity endpoints, and we replace DLT with nTTP to take into account multiple grades and types of toxicities. Read More

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https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1
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http://dx.doi.org/10.1080/10543406.2018.1535497DOI Listing
November 2018
10 Reads

Medical biostatistics, the fourth edition.

Authors:
Hua Guo

J Biopharm Stat 2018 ;28(6):1231-1232

a Biostatistics , Allergan PLC , Madison NJ , USA.

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http://dx.doi.org/10.1080/10543406.2018.1530922DOI Listing
January 2018

A closed-form estimator for meta-analysis and surrogate markers evaluation.

J Biopharm Stat 2018 Oct 26:1-15. Epub 2018 Oct 26.

b I-BioStat, KU Leuven , Leuven , Belgium.

Estimating complex linear mixed models using an iterative full maximum likelihood estimator can be cumbersome in some cases. With small and unbalanced datasets, convergence problems are common. Also, for large datasets, iterative procedures can be computationally prohibitive. Read More

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http://dx.doi.org/10.1080/10543406.2018.1535504DOI Listing
October 2018

Estimation of delay time in survival data with delayed treatment effect.

J Biopharm Stat 2018 Oct 25:1-15. Epub 2018 Oct 25.

a Data Science , Astellas Pharma Global Development, Inc ., Northbrook , Illinois , USA.

In randomized controlled trials with delayed treatment effect, there is a delay period before the experimental therapy starts to exhibit a beneficial effect. The phenomenon of delayed treatment effect is often observed in the emerging and important field of immuno-oncology. It is important to estimate the duration of delay as this information helps in characterizing the pattern of comparative treatment effect, understanding the mechanism of action of the experimental therapy, and forming optimal treatment strategies. Read More

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https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1
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http://dx.doi.org/10.1080/10543406.2018.1534857DOI Listing
October 2018
10 Reads
0.720 Impact Factor

Modeling the impact of preplanned dose titration on delayed response.

J Biopharm Stat 2018 Oct 25:1-19. Epub 2018 Oct 25.

a Global Statistical Sciences , Eli Lilly and Company , Indiana , USA.

Dose titration becomes more and more common in improving drug tolerability as well as determining individualized treatment doses, thereby maximizing the benefit to patients. Dose titration starting from a lower dose and gradually increasing to a higher dose enables improved tolerability in patients as the human body may gradually adapt to adverse gastrointestinal effects. Current statistical analyses mostly focus on the outcome at the end-of-study follow-up without considering the longitudinal impact of dose titration on the outcome. Read More

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https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1
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http://dx.doi.org/10.1080/10543406.2018.1535499DOI Listing
October 2018
10 Reads

Bayesian sample size determination for longitudinal studies with continuous response based on different scientific questions of interest.

Authors:
Taban Baghfalaki

J Biopharm Stat 2018 Oct 25:1-27. Epub 2018 Oct 25.

a Department of Statistics, Faculty of Mathematical Sciences , Tarbiat Modares University , Tehran , Iran.

Longitudinal study designs are commonly applied in much scientific research, especially in the medical, social, and economic sciences. Longitudinal studies allow researchers to measure changes in each individual's responses over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. Read More

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https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1
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http://dx.doi.org/10.1080/10543406.2018.1535501DOI Listing
October 2018
8 Reads

Partial Youden index and its inferences.

J Biopharm Stat 2018 Oct 25:1-15. Epub 2018 Oct 25.

a Department of Mathematics and Statistics , Georgia State University , Atlanta , GA , USA.

In medical diagnostic research, medical tests with continuous values are widely employed to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a medical test can be assessed by using the receiver operating characteristic (ROC) curve of the test. To summarize the ROC curve and determine an optimal cut-off point for test results, the Youden index is commonly used. Read More

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https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1
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http://dx.doi.org/10.1080/10543406.2018.1535502DOI Listing
October 2018
9 Reads

Interval estimators of relative potency in toxicology and radiation countermeasure studies: comparing methods and experimental designs.

J Biopharm Stat 2018 Oct 23:1-11. Epub 2018 Oct 23.

b Division of Radiation Health , University of Arkansas for Medical Sciences, and Central Arkansas Veterans Healthcare System , Little Rock , Arkansas , USA.

The relative potency of one agent to another is commonly represented by the ratio of two quantal response parameters; for example, the LD of animals receiving a treatment to the LD of control animals, where LD is the dose of toxin that is lethal to 50% of animals. Though others have considered interval estimators of LD, here, we extend Bayesian, bootstrap, likelihood ratio, Fieller's and Wald's methods to estimate intervals for relative potency in a parallel-line assay context. In addition to comparing their coverage probabilities, we also consider their power in two types of dose designs: one assigning treatment and control the same doses vs. Read More

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https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1
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http://dx.doi.org/10.1080/10543406.2018.1535500DOI Listing
October 2018
10 Reads

Randomized dose-escalation designs for drug combination cancer trials with immunotherapy.

J Biopharm Stat 2018 Oct 23:1-19. Epub 2018 Oct 23.

b Service de Biostatistique et d'Epidémiologie & CESP OncoStat, INSERM , Institut Gustave Roussy, Université Paris-11 , Villejuif , France.

This work considers Phase I cancer dual-agent dose-escalation clinical trials in which one of the compounds is an immunotherapy. The distinguishing feature of trials considered is that the dose of one agent, referred to as a standard of care, is fixed and another agent is dose-escalated. Conventionally, the goal of a Phase I trial is to find the maximum tolerated combination (MTC). Read More

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http://dx.doi.org/10.1080/10543406.2018.1535503DOI Listing
October 2018
16 Reads

Assay sensitivity in "Hybrid thorough QT/QTc (TQT)" study.

J Biopharm Stat 2018 Oct 22:1-7. Epub 2018 Oct 22.

a Office of Biostatistics, Office of Translational Sciences, CDER, FDA.

A concurrent positive control should be included in a thorough QTc clinical trial to validate the study according to ICH E14 guidance. Some pharmaceutical companies have started to use "hybrid TQT" study to meet ICH E14 regulatory requirements since the release of ICH E14 Q&A (R3). The "hybrid TQT" study includes the same treatment arms (therapeutic and/or supratherapeutic dose of investigational drug, placebo, and positive control) with sample size less than traditional TQT studies, but use concentration-QTc (C-QTc) analysis as primary analysis and assay sensitivity analysis. Read More

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http://dx.doi.org/10.1080/10543406.2018.1535498DOI Listing
October 2018
21 Reads

Stochastic optimization of adaptive enrichment designs for two subpopulations.

J Biopharm Stat 2018 10;28(5):966-982. Epub 2018 Aug 10.

b Department of Biostatistics , Johns Hopkins Bloomberg School of Public Health , Baltimore , USA.

An adaptive enrichment design is a randomized trial that allows enrollment criteria to be modified at interim analyses, based on a preset decision rule. When there is prior uncertainty regarding treatment effect heterogeneity, these trial designs can provide improved power for detecting treatment effects in subpopulations. We present a simulated annealing approach to search over the space of decision rules and other parameters for an adaptive enrichment design. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489401DOI Listing
August 2018
4 Reads

Sample size calculations for comparing two groups of count data.

J Biopharm Stat 2018 Jul 19:1-13. Epub 2018 Jul 19.

a Department of Biostatistics and Programming , Sanofi US Inc , Bridgewater , NJ , USA .

A sample size formula for comparing two groups of count data is derived using the method of moments by matching the first and second moments of the distribution of the count data, and it does not need any further distributional assumption. Compared to sample size formulas derived using a likelihood-based approach or using simulations, the proposed sample size formula applies to count data following any distribution in addition to the negative binomial distribution. The proposed sample size formula can be used even when the study is analyzed with a likelihood-based approach. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489409DOI Listing
July 2018
8 Reads

Confidence intervals for proportion ratios of stratified correlated bilateral data.

J Biopharm Stat 2019 16;29(1):203-225. Epub 2018 Jul 16.

a Department of Biostatistics , University at Buffalo , Buffalo , NY , USA.

In stratified bilateral studies, responses from two paired body parts are correlated. Confidence intervals (CIs), which reveal various features of the data, should take the correlations into account. In this article, five CI methods (sample-size weighted naïve Maximum likelihood estimation (MLE)-based Wald-type CI, complete MLE-based Wald-type CI, profile likelihood CI, MLE-based score CI and pooled MLE-based Wald-type CI) are derived for proportion ratios under the assumption of equal correlation coefficient within each stratum. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489405DOI Listing
July 2018
1 Read

Estimation of causal effects in clinical endpoint bioequivalence studies in the presence of intercurrent events: noncompliance and missing data.

J Biopharm Stat 2018 Jul 11:1-23. Epub 2018 Jul 11.

b Office of Biostatistics , Center for Drug Evaluation and Research, Food and Drug Administration (CDER/FDA) , Silver Spring , MD , USA.

In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is based on the observed per-protocol (PP) population (usually completers and compliers). However, missing data and noncompliance are post-randomization intercurrent events and may introduce selection bias. Therefore, PP analysis is generally not causal. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489408DOI Listing
July 2018
7 Reads

Rejoinder to Mr. Peter J. Laud.

J Biopharm Stat 2018 9;28(5):1021-1023. Epub 2018 Jul 9.

c Department of Biostatistics, Faculty of Medicine , Madrid Complutense University , Madrid , Spain.

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http://dx.doi.org/10.1080/10543406.2018.1489412DOI Listing

Methods for the analysis of multiple endpoints in small populations: A review.

J Biopharm Stat 2018 Jul 9:1-29. Epub 2018 Jul 9.

a Center for Medical Statistics, Informatics, and Intelligent Systems , Medical University of Vienna , Vienna , Austria.

While current guidelines generally recommend single endpoints for primary analyses of confirmatory clinical trials, it is recognized that certain settings require inference on multiple endpoints for comprehensive conclusions on treatment effects. Furthermore, combining treatment effect estimates from several outcome measures can increase the statistical power of tests. Such an efficient use of resources is of special relevance for trials in small populations. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489402DOI Listing
July 2018
19 Reads

Incorporating a companion test into the noninferiority design of medical device trials.

J Biopharm Stat 2019 9;29(1):143-150. Epub 2018 Jul 9.

a Division of Biostatistics , Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration , Silver Spring , MD , USA.

Noninferiority trials are commonly utilized to evaluate the safety and effectiveness of medical devices. It could happen that the noninferiority hypothesis is rejected while the performance of the active control is clinically not satisfactory. This may pose a great challenge when making a regulatory decision. Read More

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https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1
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http://dx.doi.org/10.1080/10543406.2018.1489403DOI Listing
July 2018
4 Reads
0.720 Impact Factor

Treatment effect on ordinal functional outcome using piecewise multistate Markov model with unobservable baseline: an application to the modified Rankin scale.

J Biopharm Stat 2018 Jul 9:1-16. Epub 2018 Jul 9.

a Department of Public Health Sciences , Medical University of South Carolina , Charleston , SC , USA.

In clinical trials, longitudinally assessed ordinal outcomes are commonly dichotomized and only the final measure is used for primary analysis, partly for ease of clinical interpretation. Dichotomization of the ordinal scale and failure to utilize the repeated measures can reduce statistical power. Additionally, in certain emergent settings, the same measure cannot be assessed at baseline prior to treatment. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489404DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326882PMC
July 2018
21 Reads
0.720 Impact Factor

Quantitative decision-making in randomized Phase II studies with a time-to-event endpoint.

J Biopharm Stat 2018 Jul 3:1-14. Epub 2018 Jul 3.

c Department of Applied Math and Statistics , Stony Brook University , Stony Brook , NY , USA.

One of the most critical decision points in clinical development is Go/No-Go decision-making after a proof-of-concept study. Traditional decision-making relies on a formal hypothesis testing with control of type I and type II error rates, which is limited by assessing the strength of efficacy evidence in a small isolated trial. In this article, we propose a quantitative Bayesian/frequentist decision framework for Go/No-Go criteria and sample size evaluation in Phase II randomized studies with a time-to-event endpoint. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489400DOI Listing
July 2018
5 Reads

L-statistics of absolute differences for quantifying the agreement between two variables.

J Biopharm Stat 2018 Jun 28:1-15. Epub 2018 Jun 28.

a Department of Public Health Sciences , Penn State College of Medicine , Hershey , PA , USA.

In many clinical studies, Lin's (1989) concordance correlation coefficient (CCC) is a popular measure of agreement for continuous outcomes. Most commonly, it is used under the assumption that data are normally distributed. However, in many practical applications, data are often skewed and/or thick-tailed. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489406DOI Listing
June 2018
1 Read

Heterogeneous growth bent-cable models for time-to-event and longitudinal data: application to AIDS studies.

Authors:
Getachew A Dagne

J Biopharm Stat 2018 28;28(6):1216-1230. Epub 2018 Jun 28.

a Department of Epidemiology & Biostatistics, College of Public Health, MDC 56 , University of South Florida , Tampa , FL , USA.

The major limitations of growth curve mixture models for HIV/AIDS data are the usual assumptions of normality and monophasic curves within latent classes. This article addresses these limitations by using non-normal skewed distributions and multiphasic patterns for outcomes of prospective studies. For such outcomes, new skew-t (ST) distributions are proposed for modeling heterogeneous growth trajectories, which exhibit not abrupt but gradual multiphasic changes from a declining trend to an increasing trend over time. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489407DOI Listing

Optimal threshold selection methods under tree or umbrella ordering.

J Biopharm Stat 2018 Jun 25:1-17. Epub 2018 Jun 25.

b Department of Biostatistics , University at Buffalo , Buffalo , NY , USA.

Receiver operating characteristic (ROC) curve is a popular tool for evaluating diagnostic accuracy of biomarkers. In ROC framework, there exist several optimal threshold selection methods for binary classification. For diseases with multi-classes, an important category of scenarios is tree or umbrella ordering in which the marker measurement for one particular class is lower or higher than those for the rest classes. Read More

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http://dx.doi.org/10.1080/10543406.2018.1489410DOI Listing
June 2018
22 Reads

Comments on "One-tailed asymptotic inferences for the difference of proportions: analysis of 97 methods of inference" by Álvarez Hernández M, Martín Andrés A and Herranz Tejedor I. (2018).

Authors:
Peter J Laud

J Biopharm Stat 2018 22;28(5):1018-1020. Epub 2018 Jun 22.

a Statistical Services Unit , University of Sheffield , Sheffield , South Yorkshire , UK.

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http://dx.doi.org/10.1080/10543406.2018.1489411DOI Listing

A class of Covariate-Adjusted Response-Adaptive Allocation Designs for Multitreatment Binary Response Trials.

J Biopharm Stat 2018 18;28(5):809-823. Epub 2018 Jun 18.

b Department of Statistics , University of Calcutta , Kolkata , India.

A class of covariate-adjusted response-adaptive randomization procedures is developed for binary treatment outcomes in a phase III clinical trial set up involving multiple treatments. The target allocation is developed by combining the ethical aspects with statistical precision under the existence of treatment covariate interaction. Relevant measures of the performance for the proposed allocation designs are studied and compared. Read More

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http://dx.doi.org/10.1080/10543406.2018.1485683DOI Listing
June 2018
0.720 Impact Factor

Comments on "A Bayesian meta-analysis method for estimating risk difference of rare events".

J Biopharm Stat 2018 14;28(5):1015-1017. Epub 2018 Jun 14.

a Biostatistical Sciences and Pharmacometrics , Novartis Pharma AG , Basel , Switzerland.

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http://dx.doi.org/10.1080/10543406.2018.1485684DOI Listing

Comparisons of false negative rates from a trend test alone and from a trend test jointly with a control-high groups pairwise test in the determination of the carcinogenicity of new drugs.

J Biopharm Stat 2018 May 21:1-15. Epub 2018 May 21.

a Division of Biometrics 6, Office of Biostatistics, Center for Drug Evaluation and Research , U.S. Food and Drug Administration , Silver Spring , MD , USA.

Interest has been expressed in using a joint test procedure that requires that the results of both a trend test and a pairwise comparison test between the control and the high groups be statistically significant simultaneously at the levels of significance recommended in the FDA 2001 draft guidance for industry document for the separate tests in order for the drug effect on the development of an individual tumor type to be considered as statistically significant. Results of our simulation studies show that there is a serious consequence of large inflations of the false negative rate through large decreases of false positive rate in the use of the above joint test procedure in the final interpretation of the carcinogenicity potential of a new drug if the levels of significance recommended for separate tests are used. The inflation can be as high as 204. Read More

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http://dx.doi.org/10.1080/10543406.2018.1473874DOI Listing
May 2018
7 Reads
0.720 Impact Factor

Utility analysis and calibration of QOL assessment in disease management.

Authors:
Mo Liu

J Biopharm Stat 2018 2;28(5):1005-1014. Epub 2018 May 2.

a National Clinical Research Center for Digestive Diseases, Beijing Friendship Hospital , Capital Medical University , Beijing , China.

In clinical trials, the assessment of health-related quality of life (QOL) (or patient-reported outcome [PRO] measure) has become very popular especially for clinical studies conducted for evaluating clinical benefits of patients with chronic, severe, and/or life threatening diseases. Health-related QOL information and PRO measures are useful for disease management for achieving best clinical practice. In this article, we will focus on health-related QOL assessment. Read More

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http://dx.doi.org/10.1080/10543406.2018.1467922DOI Listing

One-tailed asymptotic inferences for the difference of proportions: Analysis of 97 methods of inference.

J Biopharm Stat 2018 2;28(6):1090-1104. Epub 2018 Apr 2.

c Department of Biostatistics, Faculty of Medicine , Madrid Complutense University , Madrid , Spain.

Two-tailed asymptotic inferences for the difference d = p - p with independent proportions have been widely studied in the literature. Nevertheless, the case of one tail has received less attention, despite its great practical importance (superiority studies and noninferiority studies). This paper assesses 97 methods to make these inferences (test and confidence intervals [CIs]), although it also alludes to many others. Read More

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http://dx.doi.org/10.1080/10543406.2018.1452028DOI Listing
April 2018
11 Reads

Approximate confidence intervals for the likelihood ratios of a binary diagnostic test in the presence of partial disease verification.

J Biopharm Stat 2018 Mar 27:1-26. Epub 2018 Mar 27.

a Department of Statistics (Biostatistics), School of Medicine , University of Granada , Granada, Spain.

The classic parameters used to assess the accuracy of a binary diagnostic test (BDT) are sensitivity and specificity. Other parameters used to describe the performance of a BDT are likelihood ratios (LRs). The LRs depend on the sensitivity and the specificity of the diagnostic test, and they reflect how much greater the probability of a positive or negative diagnostic test result for individuals with the disease than that for the individuals without the disease. Read More

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http://dx.doi.org/10.1080/10543406.2018.1452025DOI Listing
March 2018
1 Read

Urn models for response-adaptive randomized designs: a simulation study based on a non-adaptive randomized trial.

J Biopharm Stat 2018 22;28(6):1203-1215. Epub 2018 Mar 22.

b Laboratorio di Statistica Medica, Biometria, ed Epidemiologia "G. A. Maccacaro", Dipartimento di Scienze Cliniche e di Comunità , Università degli Studi di Milano , Milano , Italy.

Recently, response-adaptive designs have been proposed in randomized clinical trials to achieve ethical and/or cost advantages by using sequential accrual information collected during the trial to dynamically update the probabilities of treatment assignments. In this context, urn models-where the probability to assign patients to treatments is interpreted as the proportion of balls of different colors available in a virtual urn-have been used as response-adaptive randomization rules. We propose the use of Randomly Reinforced Urn (RRU) models in a simulation study based on a published randomized clinical trial on the efficacy of home enteral nutrition in cancer patients after major gastrointestinal surgery. Read More

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http://dx.doi.org/10.1080/10543406.2018.1452024DOI Listing
March 2018
3 Reads

A comparison of spiking experiments to estimate the detection proportion of qualitative microbiological methods.

J Biopharm Stat 2018 Mar 19:1-26. Epub 2018 Mar 19.

a Center for Mathematical Sciences , Merck Sharp & Dohme , Oss , The Netherlands.

The detection proportion of a qualitative microbiological test method is the probability to detect a single micro-organism. A general expression for the moment estimator of the detection proportion is provided. It depends on the distribution of the spikes used in a validation study through its moment-generating function. Read More

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http://dx.doi.org/10.1080/10543406.2018.1452027DOI Listing
March 2018
9 Reads

Determining sample size for a binary diagnostic test in the presence of verification bias.

J Biopharm Stat 2018 19;28(6):1193-1202. Epub 2018 Mar 19.

c Department of Statistics , Zhejiang Gongshang University , Hangzhou , Zhejiang , China.

To compare a new binary diagnostic test with the gold standard, sensitivity and specificity are the two common measurements used to evaluate the new test. When not all the patients are verified by the gold standard due to time, budget, or cost considerations, several approaches have been proposed to compute sample size for such studies under the assumption of missing completely at random. However, the majority of them are based on asymptotic approaches that generally do not guarantee the type I and II error rates, and the remaining approaches use exact binomial distributions in sample size calculation but only the verified samples are used. Read More

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http://dx.doi.org/10.1080/10543406.2018.1452029DOI Listing
March 2018
3 Reads
0.720 Impact Factor

Multiplicity-Adjusted Confidence Limits in Risk Assessment with Quantal Response Data.

Authors:
Lucy Kerns

J Biopharm Stat 2018 15;28(6):1182-1192. Epub 2018 Mar 15.

a Department of Mathematics and Statistics , Youngstown State University , Youngstown , OH , USA.

In risk assessment, it is often desired to make inferences on the risk at certain low doses or on the dose(s) at which a specific benchmark risk (BMR) is attained. At times, [Formula: see text] dose levels or BMRs are of interest, and some form of multiplicity adjustment is necessary to ensure a valid [Formula: see text] simultaneous inference. Bonferroni correction is often employed in practice for such purposes. Read More

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http://dx.doi.org/10.1080/10543406.2018.1452026DOI Listing
March 2018
1 Read

Statistical issues and advances in cancer precision medicine research.

J Biopharm Stat 2018 ;28(2):215-216

b Duke University School of Medicine , Biostatistics & Bioinformatics , Durham , North Carolina , USA.

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http://dx.doi.org/10.1080/10543406.2017.1405013DOI Listing
January 2018
6 Reads

Cancer clinical trials - current and controversial issues in design and analysis.

Authors:
Steven Sun

J Biopharm Stat 2018 ;28(2):382-383

a Janssen Research Development , New Jersey , USA.

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http://dx.doi.org/10.1080/10543406.2018.1439218DOI Listing
January 2018
1 Read

On sample size requirement for analytical similarity assessment.

J Biopharm Stat 2018 7;28(6):1143-1159. Epub 2018 Mar 7.

a Department of Biostatistics and Bioinformatics , Duke University School of Medicine , Durham , North Carolina , USA.

For approval of biosimilar products, the U.S. Food and Drug Administration (FDA) recommends a stepwise approach for obtaining the totality-of-the-evidence for assessing biosimilarirty between a proposed biosimilar product and its corresponding innovative (reference) biologic product. Read More

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https://www.tandfonline.com/doi/full/10.1080/10543406.2018.1
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http://dx.doi.org/10.1080/10543406.2018.1437171DOI Listing
March 2018
12 Reads

Bayesian sample size determination for a Phase III clinical trial with diluted treatment effect.

J Biopharm Stat 2018 7;28(6):1119-1142. Epub 2018 Mar 7.

b Department of Biostatistics and Data Sciences , Boehringer Ingelheim Pharmaceuticals, Inc ., Ridgefield , CT , USA.

When Phase III treatment effect is diluted from what was observed from Phase II results, we propose to determine the Bayesian sample size for a Phase III clinical trial based on the normal, uniform, and truncated normal prior distributions of the treatment effects on an interval, which starts from an acceptable treatment effect to the observed treatment effect from Phase II. After incorporating the prior information of the treatment effects, the Bayesian sample size is the number of patients of the Phase III trial for a given Bayesian Predictive Power (BPP) or Bayesian Historical Predictive Power (BHPP). After that, the numerical simulations are carried out to determine the Bayesian sample size for the Phase III clinical trial. Read More

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http://dx.doi.org/10.1080/10543406.2018.1436556DOI Listing
March 2018
2 Reads

A response adaptive design for ordinal categorical responses.

J Biopharm Stat 2018 5;28(6):1169-1181. Epub 2018 Mar 5.

b Department of Statistics , University of Calcutta , Kolkata , India.

A two treatment response adaptive design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis. Read More

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http://dx.doi.org/10.1080/10543406.2018.1439053DOI Listing
March 2018
2 Reads
0.720 Impact Factor