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Pharmaceutical Statistics

Jason J Z Liao, Ziji Yu, Yulan Li
With the increasing globalization of drug development, the multiregional clinical trial (MRCT) has gained extensive use. The data from MRCTs could be accepted by regulatory authorities across regions and countries as the primary sources of evidence to support global marketing drug approval simultaneously. The MRCT can speed up patient enrollment and drug approval, and it makes the effective therapies available to patients all over the world simultaneously. However, there are many challenges both operationally and scientifically in conducting a drug development globally...
June 17, 2018: Pharmaceutical Statistics
Amy Cotterill, Thomas Jaki
Dose-escalation trials commonly assume a homogeneous trial population to identify a single recommended dose of the experimental treatment for use in future trials. Wrongly assuming a homogeneous population can lead to a diluted treatment effect. Equally, exclusion of a subgroup that could in fact benefit from the treatment can cause a beneficial treatment effect to be missed. Accounting for a potential subgroup effect (ie, difference in reaction to the treatment between subgroups) in dose-escalation can increase the chance of finding the treatment to be efficacious in a larger patient population...
June 13, 2018: Pharmaceutical Statistics
Beibei Guo, Daniel Li, Ying Yuan
Immunotherapy-treatments that enlist the immune system to battle tumors-has received widespread attention in cancer research. Due to its unique features and mechanisms for treating cancer, immunotherapy requires novel clinical trial designs. We propose a Bayesian seamless phase I/II randomized design for immunotherapy trials (SPIRIT) to find the optimal biological dose (OBD) defined in terms of the restricted mean survival time. We jointly model progression-free survival and the immune response. Progression-free survival is used as the primary endpoint to determine the OBD, and the immune response is used as an ancillary endpoint to quickly screen out futile doses...
June 7, 2018: Pharmaceutical Statistics
Brooke A Rabe, Simon Day, Mallorie H Fiero, Melanie L Bell
BACKGROUND: Non-inferiority (NI) and equivalence clinical trials test whether a new treatment is therapeutically no worse than, or equivalent to, an existing standard of care. Missing data in clinical trials have been shown to reduce statistical power and potentially bias estimates of effect size; however, in NI and equivalence trials, they present additional issues. For instance, they may decrease sensitivity to differences between treatment groups and bias toward the alternative hypothesis of NI (or equivalence)...
May 25, 2018: Pharmaceutical Statistics
Bruno Delafont, Kevin Carroll, Claire Vilain, Emmanuel Pham
The longitudinal data from 2 published clinical trials in adult subjects with upper limb spasticity (a randomized placebo-controlled study [NCT01313299] and its long-term open-label extension [NCT01313312]) were combined. Their study designs involved repeat intramuscular injections of abobotulinumtoxinA (Dysport®), and efficacy endpoints were collected accordingly. With the objective of characterizing the pattern of response across cycles, Mixed Model Repeated Measures analyses and Non-Linear Random Coefficient (NLRC) analyses were performed and their results compared...
May 20, 2018: Pharmaceutical Statistics
Fangrong Yan, Huihong Zhu, Junlin Liu, Liyun Jiang, Xuelin Huang
A bioequivalence test is to compare bioavailability parameters, such as the maximum observed concentration (Cmax ) or the area under the concentration-time curve, for a test drug and a reference drug. During the planning of a bioequivalence test, it requires an assumption about the variance of Cmax or area under the concentration-time curve for the estimation of sample size. Since the variance is unknown, current 2-stage designs use variance estimated from stage 1 data to determine the sample size for stage 2...
May 3, 2018: Pharmaceutical Statistics
Peter Laud
No abstract text is available yet for this article.
May 3, 2018: Pharmaceutical Statistics
Wong-Shian Huang, Yuan-Chin Ivan Chang
In pharmaceutical-related research, we usually use clinical trials methods to identify valuable treatments and compare their efficacy with that of a standard control therapy. Although clinical trials are essential for ensuring the efficacy and postmarketing safety of a drug, conducting clinical trials is usually costly and time-consuming. Moreover, to allocate patients to the little therapeutic effect treatments is inappropriate due to the ethical and cost imperative. Hence, there are several 2-stage designs in the literature where, for reducing cost and shortening duration of trials, they use the conditional power obtained from interim analysis results to appraise whether we should continue the lower efficacious treatments in the next stage...
May 2, 2018: Pharmaceutical Statistics
Kentaro Takeda, Masataka Taguri, Satoshi Morita
One of the main purposes of a phase I dose-finding trial in oncology is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent phase II and III trials. Many phase I dose-finding methods based solely on toxicity considerations have been proposed under the assumption that both toxicity and efficacy monotonically increase with the dose level. Such an assumption may not be necessarily the case, however, when evaluating the OD for molecular targeted, cytostatic, and biological agents, as well as immune-oncology therapy...
April 26, 2018: Pharmaceutical Statistics
Meinhard Kieser, Marietta Kirchner, Eva Dölger, Heiko Götte
Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis...
April 26, 2018: Pharmaceutical Statistics
Masashi Shimura, Kazushi Maruo, Masahiko Gosho
Two-stage designs are widely used to determine whether a clinical trial should be terminated early. In such trials, a maximum likelihood estimate is often adopted to describe the difference in efficacy between the experimental and reference treatments; however, this method is known to display conditional bias. To reduce such bias, a conditional mean-adjusted estimator (CMAE) has been proposed, although the remaining bias may be nonnegligible when a trial is stopped for efficacy at the interim analysis. We propose a new estimator for adjusting the conditional bias of the treatment effect by extending the idea of the CMAE...
April 23, 2018: Pharmaceutical Statistics
Kristina Weber, Rob Hemmings, Armin Koch
A common challenge for the development of drugs in rare diseases and special populations, eg, paediatrics, is the small numbers of patients that can be recruited into clinical trials. Extrapolation can be used to support development and licensing in paediatrics through the structured integration of available data in adults and prospectively generated data in paediatrics to derive conclusions that support licensing decisions in the target paediatric population. In this context, Bayesian analyses have been proposed to obtain formal proof of efficacy of a new drug or therapeutic principle by using additional information (data, opinion, or expectation), expressed through a prior distribution...
April 17, 2018: Pharmaceutical Statistics
Adam Crisp, Sam Miller, Douglas Thompson, Nicky Best
All clinical trials are designed for success of their primary objectives. Hence, evaluating the probability of success (PoS) should be a key focus at the design stage both to support funding approval from sponsor governance boards and to inform trial design itself. Use of assurance-that is, expected success probability averaged over a prior probability distribution for the treatment effect-to quantify PoS of a planned study has grown across the industry in recent years, and has now become routine within the authors' company...
April 10, 2018: Pharmaceutical Statistics
Nigel Dallow, Nicky Best, Timothy H Montague
With the continued increase in the use of Bayesian methods in drug development, there is a need for statisticians to have tools to develop robust and defensible informative prior distributions. Whilst relevant empirical data should, where possible, provide the basis for such priors, it is often the case that limitations in data and/or our understanding may preclude direct construction of a data-based prior. Formal expert elicitation methods are a key technique that can be used to determine priors in these situations...
March 30, 2018: Pharmaceutical Statistics
Hans-Peter Piepho, Laurence V Madden, James Roger, Roger Payne, Emlyn R Williams
Network meta-analysis can be implemented by using arm-based or contrast-based models. Here we focus on arm-based models and fit them using generalized linear mixed model procedures. Full maximum likelihood (ML) estimation leads to biased trial-by-treatment interaction variance estimates for heterogeneity. Thus, our objective is to investigate alternative approaches to variance estimation that reduce bias compared with full ML. Specifically, we use penalized quasi-likelihood/pseudo-likelihood and hierarchical (h) likelihood approaches...
May 2018: Pharmaceutical Statistics
Ming-Dauh Wang, Jiajun Liu, Geert Molenberghs, Craig Mallinckrodt
The trimmed mean is a method of dealing with patient dropout in clinical trials that considers early discontinuation of treatment a bad outcome rather than leading to missing data. The present investigation is the first comprehensive assessment of the approach across a broad set of simulated clinical trial scenarios. In the trimmed mean approach, all patients who discontinue treatment prior to the primary endpoint are excluded from analysis by trimming an equal percentage of bad outcomes from each treatment arm...
May 2018: Pharmaceutical Statistics
Qi Gong, Douglas E Schaubel
Mean survival time is often of inherent interest in medical and epidemiologic studies. In the presence of censoring and when covariate effects are of interest, Cox regression is the strong default, but mostly due to convenience and familiarity. When survival times are uncensored, covariate effects can be estimated as differences in mean survival through linear regression. Tobit regression can validly be performed through maximum likelihood when the censoring times are fixed (ie, known for each subject, even in cases where the outcome is observed)...
March 2018: Pharmaceutical Statistics
James Dunyak, Patrick Mitchell, Bengt Hamrén, Gabriel Helmlinger, James Matcham, Donald Stanski, Nidal Al-Huniti
Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response...
March 2018: Pharmaceutical Statistics
Yu-Chuan Chen, Un Jung Lee, Chen-An Tsai, James J Chen
For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker-positive and biomarker-negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially...
March 2018: Pharmaceutical Statistics
David Dejardin, Paul Delmar, Charles Warne, Katie Patel, Joost van Rosmalen, Emmanuel Lesaffre
When recruitment into a clinical trial is limited due to rarity of the disease of interest, or when recruitment to the control arm is limited due to ethical reasons (eg, pediatric studies or important unmet medical need), exploiting historical controls to augment the prospectively collected database can be an attractive option. Statistical methods for combining historical data with randomized data, while accounting for the incompatibility between the two, have been recently proposed and remain an active field of research...
March 2018: Pharmaceutical Statistics
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