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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
Gerd Rosenkranz
No abstract text is available yet for this article.
December 14, 2016: 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
Laura Flight, Steven A Julious
No abstract text is available yet for this article.
December 2, 2016: 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)...
December 2, 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
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...
November 8, 2016: Pharmaceutical Statistics
O'Kelly M, Anisimov V, Campbell C, Hamilton S
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...
November 3, 2016: Pharmaceutical Statistics
Francesca Matano, Valeria Sambucini
In phase II single-arm studies, the response rate of the experimental treatment is typically compared with a fixed target value that should ideally represent the true response rate for the standard of care therapy. Generally, this target value is estimated through previous data, but the inherent variability in the historical response rate is not taken into account. In this paper, we present a Bayesian procedure to construct single-arm two-stage designs that allows to incorporate uncertainty in the response rate of the standard treatment...
November 2016: Pharmaceutical Statistics
Wim Van der Elst, Geert Molenberghs, Ralf-Dieter Hilgers, Geert Verbeke, Nicole Heussen
There are various settings in which researchers are interested in the assessment of the correlation between repeated measurements that are taken within the same subject (i.e., reliability). For example, the same rating scale may be used to assess the symptom severity of the same patients by multiple physicians, or the same outcome may be measured repeatedly over time in the same patients. Reliability can be estimated in various ways, for example, using the classical Pearson correlation or the intra-class correlation in clustered data...
November 2016: Pharmaceutical Statistics
Ming Zhou, Sudeep Kundu
Non-inferiority trials aim to demonstrate whether an experimental therapy is not unacceptably worse than an active reference therapy already in use. When applicable, a three-arm non-inferiority trial, including an experiment therapy, an active reference therapy, and a placebo, is often recommended to assess assay sensitivity and internal validity of a trial. In this paper, we share some practical considerations based on our experience from a phase III three-arm non-inferiority trial. First, we discuss the determination of the total sample size and its optimal allocation based on the overall power of the non-inferiority testing procedure and provide ready-to-use R code for implementation...
November 2016: Pharmaceutical Statistics
P Bunouf, G Molenberghs
Modern analysis of incomplete longitudinal outcomes involves formulating assumptions about the missingness mechanisms and then using a statistical method that produces valid inferences under this assumption. In this manuscript, we define missingness strategies for analyzing randomized clinical trials (RCTs) based on plausible clinical scenarios. Penalties for dropout are also introduced in an attempt to balance benefits against risks. Some missingness mechanisms are assumed to be non-future dependent, which is a subclass of missing not at random...
November 2016: Pharmaceutical Statistics
Ronald W Helms
Biostatisticians recognize the importance of precise definitions of technical terms in randomized controlled clinical trial (RCCT) protocols, statistical analysis plans, and so on, in part because definitions are a foundation for subsequent actions. Imprecise definitions can be a source of controversies about appropriate statistical methods, interpretation of results, and extrapolations to larger populations. This paper presents precise definitions of some familiar terms and definitions of some new terms, some perhaps controversial...
November 2016: Pharmaceutical Statistics
Andrew P Grieve, Shah-Jalal Sarker
There have been many approximations developed for sample sizing of a logistic regression model with a single normally-distributed stimulus. Despite this, it has been recognised that there is no consensus as to the best method. In pharmaceutical drug development, simulation provides a powerful tool to characterise the operating characteristics of complex adaptive designs and is an ideal method for determining the sample size for such a problem. In this paper, we address some issues associated with applying simulation to determine the sample size for a given power in the context of logistic regression...
November 2016: Pharmaceutical Statistics
Tarylee Reddy, Geert Molenberghs, Edmund Njeru Njagi, Marc Aerts
In longitudinal studies of biomarkers, an outcome of interest is the time at which a biomarker reaches a particular threshold. The CD4 count is a widely used marker of human immunodeficiency virus progression. Because of the inherent variability of this marker, a single CD4 count below a relevant threshold should be interpreted with caution. Several studies have applied persistence criteria, designating the outcome as the time to the occurrence of two consecutive measurements less than the threshold. In this paper, we propose a method to estimate the time to attainment of two consecutive CD4 counts less than a meaningful threshold, which takes into account the patient-specific trajectory and measurement error...
November 2016: Pharmaceutical Statistics
Akihiro Hirakawa, Hiroyuki Sato, Masahiko Gosho
Model-based dose-finding methods for a combination therapy involving two agents in phase I oncology trials typically include four design aspects namely, size of the patient cohort, three-parameter dose-toxicity model, choice of start-up rule, and whether or not to include a restriction on dose-level skipping. The effect of each design aspect on the operating characteristics of the dose-finding method has not been adequately studied. However, some studies compared the performance of rival dose-finding methods using design aspects outlined by the original studies...
November 2016: Pharmaceutical Statistics
Wei Jiang, Jonathan D Mahnken, Jianghua He, Matthew S Mayo
For two-arm randomized phase II clinical trials, previous literature proposed an optimal design that minimizes the total sample sizes subject to multiple constraints on the standard errors of the estimated event rates and their difference. The original design is limited to trials with dichotomous endpoints. This paper extends the original approach to be applicable to phase II clinical trials with endpoints from the exponential dispersion family distributions. The proposed optimal design minimizes the total sample sizes needed to provide estimates of population means of both arms and their difference with pre-specified precision...
November 2016: Pharmaceutical Statistics
Bernard Sébastien, David Hoffman, Clémence Rigaux, Franck Pellissier, Jérôme Msihid
This article describes how a frequentist model averaging approach can be used for concentration-QT analyses in the context of thorough QTc studies. Based on simulations, we have concluded that starting from three candidate model families (linear, exponential, and Emax) the model averaging approach leads to treatment effect estimates that are quite robust with respect to the control of the type I error in nearly all simulated scenarios; in particular, with the model averaging approach, the type I error appears less sensitive to model misspecification than the widely used linear model...
November 2016: Pharmaceutical Statistics
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