Pharmaceutical Statistics最新文献

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.

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.

The duration of response (DoR) is defined as the time from the onset of response to treatment up to progression of disease or death due to any reason, whichever occurs earlier. The expected DoR could be a suitable estimand to measure the efficacy of a treatment but is in practice difficult to estimate, since patients' follow-up times are often right-censored. Instead, the restricted mean duration of response (RMDoR) is often used. The RMDoR in a time $\tau$ is equal to the expected DoR restricted to the interval $\left(0,\tau \right)$ . In this paper, we consider the behaviour of the RMDoR as a function of $\tau$ and its suitability as a measure to quantify the efficacy of a treatment. Besides, we focus on the estimation of the RMDoR. In oncology, the events response to treatment and progression of disease are typically detected through time-scheduled scans and are therefore interval-censored. We describe multiple estimators for the RMDoR that deal with the interval censoring in different ways and study the performance of these estimators in single arm trials and randomised controlled trials.

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.

Stochastic curtailment tests for Phase II two-arm trials with time-to-event end points are traditionally performed using the log-rank test. Recent advances in designing time-to-event trials have utilized the Weibull distribution with a known shape parameter estimated from historical studies. As sample size calculations depend on the value of this shape parameter, these methods either cannot be used or likely underperform/overperform when the natural variation around the point estimate is ignored. We demonstrate that when the magnitude of the Weibull shape parameters changes, unblinded interim information on the shape of the survival curves can be useful to enrich the final analysis for reestimation of the sample size. For such scenarios, we propose two Bayesian solutions to estimate the natural variations of the Weibull shape parameter. We implement these approaches under the framework of the newly proposed relative time method that allows nonproportional hazards and nonproportional time. We also demonstrate the sample size reestimation for the relative time method using three different approaches (internal pilot study approach, conditional power, and predictive power approach) at the interim stage of the trial. We demonstrate our methods using a hypothetical example and provide insights regarding the practical constraints for the proposed methods.

In compound hit screening, an important chemical property is target binding affinity, represented by a parameter ΔΔG. You can measure ΔΔG experimentally (ΔΔG_exp) or by calculations via simulations (ΔΔG_calc). Because it is expensive to measure ΔΔG experimentally, only a few experimental runs are performed. The relationship between the experimental data and the calculated results is a straight line with a slope that is not necessarily one. The goal is to estimate the linear relationship between ΔΔG_exp and ΔΔG_calc by fitting a Deming regression model that will be used to predict future values of ΔΔG_true based on the obtained ΔΔG_calc.

In oncology, Phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent Phase III trials. Traditionally, Phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important consideration. In the context of such multiple-outcome designs, predictive probability-based Bayesian monitoring strategies have been developed to assess whether a clinical trial will provide enough evidence to continue with a Phase III study at the scheduled end of the trial. Therefore, we propose a new simple index vector to summarize the results that cannot be captured by existing strategies. Specifically, we define the worst and most promising situations for the potential effect of a treatment, then use the proposed index vector to measure the deviation between the two situations. Finally, simulation studies are performed to evaluate the operating characteristics of the design. The obtained results demonstrate that the proposed method makes appropriate interim go/no-go decisions.

Chemistry, manufacturing, and control (CMC) statisticians play a key role in the development and lifecycle management of pharmaceutical and biological products, working with their non-statistician partners to manage product quality. Information used to make quality decisions comes from studies, where success is facilitated through adherence to the scientific method. This is carried out in four steps: (1) an objective, (2) design, (3) conduct, and (4) analysis. Careful consideration of each step helps to ensure that a study conclusion and associated decision is correct. This can be a development decision related to the validity of an assay or a quality decision like conformance to specifications. Importantly, all decisions are made with risk. Conventional statistical risks such as Type 1 and Type 2 errors can be coupled with associated impacts to manage patient value as well as development and commercial costs. The CMC statistician brings focus on managing risk across the steps of the scientific method, leading to optimal product development and robust supply of life saving drugs and biologicals.

Combination treatments have been of increasing importance in drug development across therapeutic areas to improve treatment response, minimize the development of resistance, and/or minimize adverse events. Pre-clinical in-vitro combination experiments aim to explore the potential of such drug combinations during drug discovery by comparing the observed effect of the combination with the expected treatment effect under the assumption of no interaction (i.e., null model). This tutorial will address important design aspects of such experiments to allow proper statistical evaluation. Additionally, it will highlight the Biochemically Intuitive Generalized Loewe methodology (BIGL R package available on CRAN) to statistically detect deviations from the expectation under different null models. A clear advantage of the methodology is the quantification of the effect sizes, together with confidence interval while controlling the directional false coverage rate. Finally, a case study will showcase the workflow in analyzing combination experiments.

In covariate-adaptive or response-adaptive randomization, the treatment assignment and outcome can be correlated. Under this situation, the re-randomization test is a straightforward and attractive method to provide valid statistical inferences. In this paper, we investigate the number of repetitions in tests. This is motivated by a group sequential design in clinical trials, where the nominal significance bound can be very small at an interim analysis. Accordingly, re-randomization tests lead to a very large number of required repetitions, which may be computationally intractable. To reduce the number of repetitions, we propose an adaptive procedure and compare it with multiple approaches under predefined criteria. Monte Carlo simulations are conducted to show the performance of different approaches in a limited sample size. We also suggest strategies to reduce total computation time and provide practical guidance in preparing, executing, and reporting before and after data are unblinded at an interim analysis, so one can complete the computation within a reasonable time frame.