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Pharmaceutical Statistics

Devan V Mehrotra, Li Fan, Fang Liu, Kuenhi Tsai
Since the implementation of the International Conference on Harmonization (ICH) E14 guideline in 2005, regulators have required a "thorough QTc" (TQT) study for evaluating the effects of investigational drugs on delayed cardiac repolarization as manifested by a prolonged QTc interval. However, TQT studies have increasingly been viewed unfavorably because of their low cost effectiveness. Several researchers have noted that a robust drug concentration-QTc (conc-QTc) modeling assessment in early phase development should, in most cases, obviate the need for a subsequent TQT study...
April 3, 2017: Pharmaceutical Statistics
Benjamin R Saville, Scott M Berry
Response adaptive randomization (RAR) methods for clinical trials are susceptible to imbalance in the distribution of influential covariates across treatment arms. This can make the interpretation of trial results difficult, because observed differences between treatment groups may be a function of the covariates and not necessarily because of the treatments themselves. We propose a method for balancing the distribution of covariate strata across treatment arms within RAR. The method uses odds ratios to modify global RAR probabilities to obtain stratum-specific modified RAR probabilities...
March 6, 2017: Pharmaceutical Statistics
Han Zhu, Qingzhao Yu, Donald E Mercante
Several researchers have proposed solutions to control type I error rate in sequential designs. The use of Bayesian sequential design becomes more common; however, these designs are subject to inflation of the type I error rate. We propose a Bayesian sequential design for binary outcome using an alpha-spending function to control the overall type I error rate. Algorithms are presented for calculating critical values and power for the proposed designs. We also propose a new stopping rule for futility. Sensitivity analysis is implemented for assessing the effects of varying the parameters of the prior distribution and maximum total sample size on critical values...
March 2, 2017: Pharmaceutical Statistics
Heiko Götte, Marietta Kirchner, Martin Oliver Sailer
The probability of success or average power describes the potential of a future trial by weighting the power with a probability distribution of the treatment effect. The treatment effect estimate from a previous trial can be used to define such a distribution. During the development of targeted therapies, it is common practice to look for predictive biomarkers. The consequence is that the trial population for phase III is often selected on the basis of the most extreme result from phase II biomarker subgroup analyses...
February 23, 2017: Pharmaceutical Statistics
Timothy Mutsvari, Dominique Tytgat, Rosalind Walley
No abstract text is available yet for this article.
March 2017: Pharmaceutical Statistics
Sheila M Bird, Rosemary A Bailey, Andrew P Grieve, Stephen Senn
By setting the regulatory-approved protocol for a suite of first-in-human studies on BIA 10-2474 against the subsequent French investigations, we highlight 6 key design and statistical issues, which reinforce recommendations by a Royal Statistical Society Working Party, which were made in the aftermath of cytokine release storm in 6 healthy volunteers in the United Kingdom in 2006. The 6 issues are dose determination, availability of pharmacokinetic results, dosing interval, stopping rules, appraisal by safety committee, and clear algorithm required if combining approvals for single and multiple ascending dose studies...
March 2017: Pharmaceutical Statistics
Jitendra Ganju, Yunzhi Lin, Kefei Zhou
For ethical reasons, group sequential trials were introduced to allow trials to stop early in the event of extreme results. Endpoints in such trials are usually mortality or irreversible morbidity. For a given endpoint, the norm is to use a single test statistic and to use that same statistic for each analysis. This approach is risky because the test statistic has to be specified before the study is unblinded, and there is loss in power if the assumptions that ensure optimality for each analysis are not met...
March 2017: Pharmaceutical Statistics
Satoshi Morita, Peter F Thall, Kentaro Takeda
Patient heterogeneity may complicate dose-finding in phase 1 clinical trials if the dose-toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively,it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem,we consider a generalization of the continual reassessment method on the basis of a hierarchical Bayesian dose-toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable...
March 2017: Pharmaceutical Statistics
Hengrui Sun, Atsushi Kawaguchi, Gary Koch
Confirmatory randomized clinical trials with a stratified design may have ordinal response outcomes, ie, either ordered categories or continuous determinations that are not compatible with an interval scale. Also, multiple endpoints are often collected when 1 single endpoint does not represent the overall efficacy of the treatment. In addition, random baseline imbalances and missing values can add another layer of difficulty in the analysis plan. Therefore, the development of an approach that provides a consolidated strategy to all issues collectively is essential...
March 2017: Pharmaceutical Statistics
Björn Bornkamp, David Ohlssen, Baldur P Magnusson, Heinz Schmidli
In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting "random high" / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem...
March 2017: Pharmaceutical Statistics
Orestis Efthimiou, Nicky Welton, Myrto Samara, Stefan Leucht, Georgia Salanti
Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. The time of each observation is accounted for, and the model allows the estimation of time-dependent relative treatment effects...
March 2017: Pharmaceutical Statistics
Shinjo Yada, Chikuma Hamada
Treatment during cancer clinical trials sometimes involves the combination of multiple drugs. In addition, in recent years there has been a trend toward phase I/II trials in which a phase I and a phase II trial are combined into a single trial to accelerate drug development. Methods for the seamless combination of phases I and II parts are currently under investigation. In the phase II part, adaptive randomization on the basis of patient efficacy outcomes allocates more patients to the dose combinations considered to have higher efficacy...
March 2017: Pharmaceutical Statistics
Michael O'Kelly, Vladimir Anisimov, Chris Campbell, Sinéad Hamilton
Modelling and simulation has been used in many ways when developing new treatments. To be useful and credible, it is generally agreed that modelling and simulation should be undertaken according to some kind of best practice. A number of authors have suggested elements required for best practice in modelling and simulation. Elements that have been suggested include the pre-specification of goals, assumptions, methods, and outputs. However, a project that involves modelling and simulation could be simple or complex and could be of relatively low or high importance to the project...
March 2017: Pharmaceutical Statistics
Gerd Rosenkranz
No abstract text is available yet for this article.
January 2017: Pharmaceutical Statistics
Laura Flight, Steven A Julious
No abstract text is available yet for this article.
January 2017: Pharmaceutical Statistics
Ann-Kristin Leuchs, Andreas Brandt, Jörg Zinserling, Norbert Benda
Randomized controlled trials (RCTs) aim at providing reliable estimates of treatment benefit. Missing data and nonadherence to treatment are distinct problems that can substantially impede this task. In practice, the fact that the handling of missing data due to nonadherence affects the question that is addressed is often ignored. Estimands allow precisely predefining the question of interest. Estimands are definitions of that which is being estimated with regard to population, endpoint, and handling of postrandomization events (eg, nonadherence)...
January 2017: Pharmaceutical Statistics
Xiaoping Xiong, Jianrong Wu
The treatment of cancer has progressed dramatically in recent decades, such that it is no longer uncommon to see a cure or log-term survival in a significant proportion of patients with various types of cancer. To adequately account for the cure fraction when designing clinical trials, the cure models should be used. In this article, a sample size formula for the weighted log-rank test is derived under the fixed alternative hypothesis for the proportional hazards cure models. Simulation showed that the proposed sample size formula provides an accurate estimation of sample size for designing clinical trials under the proportional hazards cure models...
January 2017: Pharmaceutical Statistics
Andrew P Grieve
The past 15 years has seen many pharmaceutical sponsors consider and implement adaptive designs (AD) across all phases of drug development. Given their arrival at the turn of the millennium, we might think that they are a recent invention. That is not the case. The earliest idea of an AD predates Bradford Hill's MRC tuberculosis study, appearing in Biometrika in 1933. In this paper, we trace the development of response-ADs, designs in which the allocation to intervention arms depends on the responses of subjects already treated...
January 2017: Pharmaceutical Statistics
Simon Kirby, Christy Chuang-Stein
The first trial of clinical efficacy is an important step in the development of a compound. Such a trial gives the first indication of whether a compound is likely to have the efficacy needed to be successful. Good decisions dictate that good compounds have a large probability of being progressed and poor compounds have a large probability of being stopped. In this paper, we consider and contrast five approaches to decision-making that have been used. To illustrate the use of the five approaches, we conduct a comparison for two plausible scenarios with associated assumptions for sample sizing...
January 2017: Pharmaceutical Statistics
Kaifeng Lu
We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect...
January 2017: Pharmaceutical Statistics
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