Pharmaceutical Statistics最新文献

The ICH E9(R1) guideline outlines the estimand framework, which aligns planning, design, conduct, analysis, and interpretation of a clinical trial. The benefits and value of using this framework in clinical trials have been outlined in the literature, and guidance has been provided on how to choose the estimand and define the estimand attributes. Although progress has been made in the implementation of estimands in clinical trials, to the best of our knowledge, there is no published discussion on the basic principles that estimands in clinical trials should fulfill to be well defined and consistent with the ideas presented in the ICH E9(R1) guideline. Therefore, in this Viewpoint article, we propose four key principles for defining an estimand. These principles form a basis for well-defined treatment effects that reflect the estimand thinking process. We hope that this Viewpoint will complement ICH E9(R1) and stimulate a discussion on which fundamental properties an estimand in a clinical trial should have and that such discussions will eventually lead to an improved clarity and precision for defining estimands in clinical trials.

The innovative use of real-world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups; another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta-analysis and a regular meta-analysis.

Immunoassays play an important role in drug development of products targeting the immune system. Consistent quality of the results from an immunoassay is essential to make unbiased and accurate claims about the drug product during preclinical and clinical development stages. Assay qualification and validation shed light on the performance of the assay. It is the first evaluation and the verification, respectively, of the assay's performance. This tutorial explains and illustrates the calculation methodology for important assay qualification parameters including precision, relative accuracy, linearity, the lower limit of quantification (LLOQ), the upper limit of quantification (ULOQ), the assay range and dilutability. This tutorial focuses on assays used for (pre-) clinical purposes, characterized by a lognormal distribution of the measurements on its original untransformed scale and by the lack of well characterized reference material. Statistical calculations are illustrated with qualification data from an enzyme-linked immunosorbent assay (ELISA) vaccine immunoassay.

A three-arm comparative clinical endpoint bioequivalence (BE) study is often used to establish bioequivalence (BE) between a locally acting generic drug (T) and reference drug (R), where superiority needs to be established for T and R over Placebo (P) and equivalence needs to be established for T vs. R. Sometimes, when study design parameters are uncertain, a fixed design study may be under- or over-powered and result in study failure or unnecessary cost. In this paper, we propose a two-stage adaptive clinical endpoint BE study with unblinded sample size re-estimation, standard or maximum combination method, optimized allocation ratio, optional re-estimation of the effect size based on likelihood estimation, and optional re-estimation of the R and P treatment means at interim analysis, which have not been done previously. Our proposed method guarantees control of Type 1 error rate analytically. It helps to reduce the average sample size when the original fixed design is overpowered and increases the sample size and power when the original study and group sequential design are under-powered. Our proposed adaptive design can help generic drug sponsors cut cost and improve success rate, making clinical study endpoint BE studies more affordable and more generic drugs accessible to the public.

In this tutorial we explore the valuable partnership between statisticians and Institutional Animal Care and Use Committees (IACUCs) in the context of animal research, shedding light on the critical role statisticians play in ensuring the ethical and scientifically rigorous use of animals in research. Pharmaceutical statisticians have increasingly become vital members of these committees, contributing expertise in study design, data analysis, and interpretation, and working more generally to facilitate the integration of good statistical practices into experimental procedures. We review the "3Rs" principles (Replacement, Reduction, and Refinement) which are the foundation for the humane use of animals in scientific research, and how statisticians can partner with IACUC to help ensure robust and reproducible research while adhering to the 3Rs principles. We also highlight emerging areas of interest, such as the use of virtual control groups.

Clinical trials (CTs) often suffer from small sample sizes due to limited budgets and patient enrollment challenges. Using historical data for the CT data analysis may boost statistical power and reduce the required sample size. Existing methods on borrowing information from historical data with right-censored outcomes did not consider matching between historical data and CT data to reduce the heterogeneity. In addition, they studied the survival outcome only, not competing risk outcomes. Therefore, we propose a clustering-based commensurate prior model with random effects for both survival and competing risk outcomes that effectively borrows information based on the degree of comparability between historical and CT data. Simulation results show that the proposed method controls type I errors better and has a lower bias than some competing methods. We apply our method to a phase III CT which compares the effectiveness of bone marrow donated from family members with only partially matched bone marrow versus two partially matched cord blood units to treat leukemia and lymphoma.

A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1. In stage 2, participants are re-randomized to either dose level depending on their initial treatment and a dichotomous response. A Bayesian analytic approach borrowing information from both stages was proposed and shown to improve the efficiency of estimation. In this paper, we propose two sample size determination (SSD) methods for the proposed snSMART comparing two dose levels with placebo. Both methods adopt the average coverage criterion (ACC) approach. In the first approach, the sample size is calculated in one step, taking advantage of the explicit posterior variance of the treatment effect. In the other two step approach, we update the sample size needed for a single-stage parallel design with a proposed adjustment factor (AF). Through simulations, we demonstrate that the required sample sizes calculated using the two SSD approaches both provide the desired power. We also provide an applet to allow for convenient and fast sample size calculation in this snSMART setting.

Quality by Design (QbD) is an approach to assay development to determine the design space, which is the range of assay variable settings that should result in satisfactory assay quality. Typically, QbD is applied in manufacturing, but it works just as well in the preclinical space. Through three examples, we illustrate the QbD approach with experimental design and associated data analysis to determine the design space for preclinical assays.

Preclinical studies are broad and can encompass cellular research, animal trials, and small human trials. Preclinical studies tend to be exploratory and have smaller datasets that often consist of biomarker data. Logistic regression is typically the model of choice for modeling a binary outcome with explanatory variables such as genetic, imaging, and clinical data. Small preclinical studies can have challenging data that may include a complete separation or quasi-complete separation issue that will result in logistic regression inflated coefficient estimates and standard errors. Penalized regression approaches such as Firth's logistic regression are a solution to reduce the bias in the estimates. In this tutorial, a number of examples with separation (complete or quasi-complete) are illustrated and the results from both logistic regression and Firth's logistic regression are compared to demonstrate the inflated estimates from the standard logistic regression model and bias-reduction of the estimates from the penalized Firth's approach. R code and datasets are provided in the supplement.

Clinical endpoints based on repeated measurements arise in many clinical research studies, and require specialized methods for sample size and power calculations. In clinical trials that measure counts over time, such as bleeding events in hemophilia, the dispersion of their distributions might change upon treatment and the measurements might be correlated. The generalized estimating equations (GEE) approach has been widely used for modeling correlated data and comparing rates. In this paper, we investigate the properties of GEE when applied to count outcomes with changes in dispersion. We derive general closed-form formulas to estimate sample size when the dispersion parameters and distributions of count data vary across two correlated measurements based on the GEE approach. These formulas allow for power and sample size estimation for intra-participant comparison of rates before and after an intervention, randomized controlled trials with equal allocation, or matched pairs designs. These formulas are derived for the following distributions: Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial distributions, and do not assume that measurements before and after an intervention come from the same distribution. Furthermore, we propose modified methods for estimating sample size and confidence intervals for the negative binomial distributions to overcome Type I error inflation, which is especially useful for large changes in the negative binomial dispersion parameter. We perform simulations, and evaluate the performance of the empirical power and Type I error over a range of parameters. Applications and R functions implementing the methods are also provided.