Pharmaceutical Statistics最新文献

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.

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.

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.

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.

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 preclinical drug discovery, at the step of lead optimization of a compound, in vivo experimentation can differentiate several compounds in terms of efficacy and potency in a biological system of whole living organisms. For the lead optimization study, it may be desirable to implement a dose-response design so that compound comparisons can be made from nonlinear curves fitted to the data. A dose-response design requires more thought relative to a simpler study design, needing parameters for the number of doses, the dose values, and the sample size per dose. This tutorial illustrates how to calculate statistical power, choose doses, and determine sample size per dose for a comparison of two or more dose-response curves for a future in vivo study.

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.

Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting characteristics of new samples or patients. While there are many resources that describe such models, there are few that explain how to develop a robust model that extracts the highest possible performance from the available data, especially in support of pharmaceutical applications. This tutorial will describe pitfalls and best practices for developing and validating predictive models with a specific application to a monitoring a pharmaceutical manufacturing process. The pitfalls and best practices will be highlighted to call attention to specific points that are not generally discussed in other resources.

Animal models are used in cancer pre-clinical research to identify drug targets, select compound candidates for clinical trials, determine optimal drug dosages, identify biomarkers, and ensure compound safety. This tutorial aims to provide an overview of study design and data analysis from animal studies, focusing on tumor growth inhibition (TGI) studies used for prioritization of anticancer compounds. Some of the experimental design aspects discussed here include the selection of the appropriate biological models, the choice of endpoints to be used for the assessment of anticancer activity (tumor volumes, tumor growth rates, events, or categorical endpoints), considerations on measurement errors and potential biases related to this type of study, sample size estimation, and discussions on missing data handling. The tutorial also reviews the statistical analyses employed in TGI studies, considering both continuous endpoints collected at single time-point and continuous endpoints collected longitudinally over multiple time-points. Additionally, time-to-event analysis is discussed for studies focusing on event occurrences such as animal deaths or tumor size reaching a certain threshold. Furthermore, for TGI studies involving categorical endpoints, statistical methodology is outlined to compare outcomes among treatment groups effectively. Lastly, this tutorial also discusses analysis for assessing drug combination synergy in TGI studies, which involves combining treatments to enhance overall treatment efficacy. The tutorial also includes R sample scripts to help users to perform relevant data analysis of this topic.