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Pharmaceutical Statistics

Samer A Kharroubi, Richard Edlin, David Meads, Christopher McCabe
It is well documented that the modelling of health-related quality of life data is difficult as the distribution of such data is often strongly right/left skewed and it includes a significant percentage of observations at one. The objective of this study is to develop a series of two-part models (TPMs) that deal with these issues. Data from the UK Medical Research Council Myeloma IX trial were used to examine the relationship between the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30/QLQ-MY20 scores and the European QoL-5 Dimensions (EQ-5D) utility score...
February 20, 2018: Pharmaceutical Statistics
Johanna Mielke, Heike Woehling, Byron Jones
Patients, physicians, and health care providers in Europe have more than 10 years of experience with biosimilars. However, there are still debates if switching between a biosimilar and its reference product influences the efficacy of the treatment. In this paper, we address this uncertainty by developing a formal statistical test that can be used for showing that switching has no negative impact on the efficacy of biosimilars. For that, we first introduce a linear mixed-effects model that is used for defining the null hypothesis (switching influences the efficacy) and the alternative hypothesis (switching has no influence on the efficacy)...
February 8, 2018: Pharmaceutical Statistics
Kentaro Takeda, Satoshi Morita
We consider the problem of incorporating historical data from a preceding trial to design and conduct a subsequent dose-finding trial in a possibly different population of patients. In oncology, for example, after a phase I dose-finding trial is completed in Caucasian patients, investigators often conduct a further phase I trial to determine the maximum tolerated dose in Asian patients. This may be due to concerns about possible differences in treatment tolerability between populations. In this study, we propose to adaptively incorporate historical data into prior distributions assumed in a new dose-finding trial...
January 25, 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)...
January 22, 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...
January 10, 2018: Pharmaceutical Statistics
Frank Miller, Sarah Zohar, Nigel Stallard, Jason Madan, Martin Posch, Siew Wan Hee, Michael Pearce, Mårten Vågerö, Simon Day
We discuss 3 alternative approaches to sample size calculation: traditional sample size calculation based on power to show a statistically significant effect, sample size calculation based on assurance, and sample size based on a decision-theoretic approach. These approaches are compared head-to-head for clinical trial situations in rare diseases. Specifically, we consider 3 case studies of rare diseases (Lyell disease, adult-onset Still disease, and cystic fibrosis) with the aim to plan the sample size for an upcoming clinical trial...
January 10, 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...
January 3, 2018: Pharmaceutical Statistics
Bo Huang, Pei-Fen Kuan
With the emergence of novel therapies exhibiting distinct mechanisms of action compared to traditional treatments, departure from the proportional hazard (PH) assumption in clinical trials with a time-to-event end point is increasingly common. In these situations, the hazard ratio may not be a valid statistical measurement of treatment effect, and the log-rank test may no longer be the most powerful statistical test. The restricted mean survival time (RMST) is an alternative robust and clinically interpretable summary measure that does not rely on the PH assumption...
December 28, 2017: 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...
December 28, 2017: Pharmaceutical Statistics
Sofía S Villar, Jack Bowden, James Wason
Response-adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial...
December 19, 2017: Pharmaceutical Statistics
Tobias Mütze, Heinz Schmidli, Tim Friede
Prior information is often incorporated informally when planning a clinical trial. Here, we present an approach on how to incorporate prior information, such as data from historical clinical trials, into the nuisance parameter-based sample size re-estimation in a design with an internal pilot study. We focus on trials with continuous endpoints in which the outcome variance is the nuisance parameter. For planning and analyzing the trial, frequentist methods are considered. Moreover, the external information on the variance is summarized by the Bayesian meta-analytic-predictive approach...
November 27, 2017: Pharmaceutical Statistics
Abidemi K Adeniji, Jesse Y Hsu, Abdus S Wahed
The product limit or Kaplan-Meier (KM) estimator is commonly used to estimate the survival function in the presence of incomplete time to event. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method...
November 26, 2017: Pharmaceutical Statistics
Xiangmin Zhang, Yeh-Fong Chen, Roy Tamura
To deal with high placebo response in clinical trials for psychiatric and other diseases, different enrichment designs, such as the sequential parallel design, two-way enriched design, and sequential enriched design, have been proposed and implemented recently. Depending on the historical trial information and the trial sponsors' resources, detailed design elements are needed for determining which design to adopt. To assist in making more suitable decisions, we perform evaluations for selecting required design elements in terms of power optimization and sample size planning...
February 2018: Pharmaceutical Statistics
Steven Sun, Grace Liu, Tianmeng Lyu, Fubo Xue, Tzu-Min Yeh, Sudhakar Rao
For clinical trials with time-to-event as the primary endpoint, the clinical cutoff is often event-driven and the log-rank test is the most commonly used statistical method for evaluating treatment effect. However, this method relies on the proportional hazards assumption in that it has the maximal power in this circumstance. In certain disease areas or populations, some patients can be curable and never experience the events despite a long follow-up. The event accumulation may dry out after a certain period of follow-up and the treatment effect could be reflected as the combination of improvement of cure rate and the delay of events for those uncurable patients...
November 20, 2017: Pharmaceutical Statistics
Peter Zhang, Kevin Carroll, Mary Hobart, Carole Augustine, Gary Koch
Despite advances in clinical trial design, failure rates near 80% in phase 2 and 50% in phase 3 have recently been reported. The challenges to successful drug development are particularly acute in central nervous system trials such as for pain, schizophrenia, mania, and depression because high-placebo response rates lessen assay sensitivity, diminish estimated treatment effect sizes, and thereby decrease statistical power. This paper addresses the importance of rigorous patient selection in major depressive disorder trials through an enhanced enrichment paradigm...
November 19, 2017: Pharmaceutical Statistics
G Frank Liu
Traditionally, noninferiority hypotheses have been tested using a frequentist method with a fixed margin. Given that information for the control group is often available from previous studies, it is interesting to consider a Bayesian approach in which information is "borrowed" for the control group to improve efficiency. However, construction of an appropriate informative prior can be challenging. In this paper, we consider a hybrid Bayesian approach for testing noninferiority hypotheses in studies with a binary endpoint...
November 10, 2017: Pharmaceutical Statistics
Corine Baljé-Volkers, Thembile Mzolo, Erik Talens, Pieta IJzerman-Boon, Edwin Van den Heuvel
Similarity in bioassays means that the test preparation behaves as a dilution of the standard preparation with respect to their biological effect. Thus, similarity must be investigated to confirm this biological property. Historically, this was typically conducted with traditional hypothesis testing, but this has received substantial criticism. Failing to reject similarity does not imply that the 2 preparations are similar. Also, rejecting similarity when bioassay variability is small might simply demonstrate a nonrelevant deviation in similarity...
November 6, 2017: Pharmaceutical Statistics
Bo Huang
Immuno-oncology has emerged as an exciting new approach to cancer treatment. Common immunotherapy approaches include cancer vaccine, effector cell therapy, and T-cell-stimulating antibody. Checkpoint inhibitors such as cytotoxic T lymphocyte-associated antigen 4 and programmed death-1/L1 antagonists have shown promising results in multiple indications in solid tumors and hematology. However, the mechanisms of action of these novel drugs pose unique statistical challenges in the accurate evaluation of clinical safety and efficacy, including late-onset toxicity, dose optimization, evaluation of combination agents, pseudoprogression, and delayed and lasting clinical activity...
November 2, 2017: Pharmaceutical Statistics
Jixian Wang
Survival functions are often estimated by nonparametric estimators such as the Kaplan-Meier estimator. For valid estimation, proper adjustment for confounding factors is needed when treatment assignment may depend on confounding factors. Inverse probability weighting is a commonly used approach, especially when there is a large number of potential confounders to adjust for. Direct adjustment may also be used if the relationship between the time-to-event and all confounders can be modeled. However, either approach requires a correctly specified model for the relationship between confounders and treatment allocation or between confounders and the time-to-event...
November 1, 2017: Pharmaceutical Statistics
Peter F Thall, Peter Mueller, Yanxun Xu, Michele Guindani
Many commonly used statistical methods for data analysis or clinical trial design rely on incorrect assumptions or assume an over-simplified framework that ignores important information. Such statistical practices may lead to incorrect conclusions about treatment effects or clinical trial designs that are impractical or that do not accurately reflect the investigator's goals. Bayesian nonparametric (BNP) models and methods are a very flexible new class of statistical tools that can overcome such limitations...
November 2017: Pharmaceutical Statistics
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