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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
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...
February 16, 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...
January 30, 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...
January 23, 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...
January 11, 2017: Pharmaceutical Statistics
M Law, M J Sweeting, G C Donaldson, J A Wedzicha
In trials comparing the rate of chronic obstructive pulmonary disease exacerbation between treatment arms, the rate is typically calculated on the basis of the whole of each patient's follow-up period. However, the true time a patient is at risk should exclude periods in which an exacerbation episode is occurring, because a patient cannot be at risk of another exacerbation episode until recovered. We used data from two chronic obstructive pulmonary disease randomized controlled trials and compared treatment effect estimates and confidence intervals when using two different definitions of the at-risk period...
December 14, 2016: 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...
December 9, 2016: 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...
December 5, 2016: 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...
November 28, 2016: 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
Peter L Bonate
The effect of correlation among covariates on covariate selection was examined with linear and nonlinear mixed effect models. Demographic covariates were extracted from the National Health and Nutrition Examination Survey III database. Concentration-time profiles were Monte Carlo simulated where only one covariate affected apparent oral clearance (CL/F). A series of univariate covariate population pharmacokinetic models was fit to the data and compared with the reduced model without covariate. The "best" covariate was identified using either the likelihood ratio test statistic or AIC...
January 2017: Pharmaceutical Statistics
Thomas Permutt, Feng Li
Dropouts from randomized trials, often for lack of efficacy or toxicity, have usually been handled as 'missing data'. We suggest that they are instead complete observations, just not numeric ones. We propose an exact test of the hypothesis of no drug effect, taking all randomized patients into account, based on a readily interpretable statistic. The method also copes with a drug that is toxic in some patients but beneficial to others, a difficult problem for standard methods. A robust conclusion of efficacy can be drawn with no assumptions other than randomization...
January 2017: Pharmaceutical Statistics
Craig Mallinckrodt, Geert Molenberghs, Suchitrita Rathmann
Recent research has fostered new guidance on preventing and treating missing data. Consensus exists that clear objectives should be defined along with the causal estimands; trial design and conduct should maximize adherence to the protocol specified interventions; and a sensible primary analysis should be used along with plausible sensitivity analyses. Two general categories of estimands are effects of the drug as actually taken (de facto, effectiveness) and effects of the drug if taken as directed (de jure, efficacy)...
January 2017: Pharmaceutical Statistics
Alan Phillips, Juan Abellan-Andres, Andersen Soren, Frank Bretz, Chrissie Fletcher, Lesley France, Andrew Garrett, Raymond Harris, Magnus Kjaer, Oliver Keene, David Morgan, Michael O'Kelly, James Roger
ICH E9 Statistical Principles for Clinical Trials was issued in 1998. In October 2014, an addendum to ICH E9 was proposed relating to estimands and sensitivity analyses. In preparation for the release of the addendum, Statisticians in the Pharmaceutical Industry held a 1-day expert group meeting in February 2015. Topics debated included definition, development, implementation, education and communication challenges associated with estimands and sensitivity analyses. The topic of estimands is an important and relatively new one in clinical development...
January 2017: Pharmaceutical Statistics
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