Pharmaceutical Statistics最新文献

In vitro dissolution testing is a regulatory required critical quality measure for solid dose pharmaceutical drug products. Setting the acceptance criteria to meet compendial criteria is required for a product to be filed and approved for marketing. Statistical approaches for analyzing dissolution data, setting specifications and visualizing results could vary according to product requirements, company's practices, and scientific judgements. This paper provides a general description of the steps taken in the evaluation and setting of in vitro dissolution specifications at release and on stability.

In a causal inference framework, a new metric has been proposed to quantify surrogacy for a continuous putative surrogate and a binary true endpoint, based on information theory. The proposed metric, termed the individual causal association (ICA), was quantified using a joint causal inference model for the corresponding potential outcomes. Due to the non-identifiability inherent in this type of models, a sensitivity analysis was introduced to study the behavior of the ICA as a function of the non-identifiable parameters characterizing the aforementioned model. In this scenario, to reduce uncertainty, several plausible yet untestable assumptions like monotonicity, independence, conditional independence or homogeneous variance-covariance, are often incorporated into the analysis. We assess the robustness of the methodology regarding these simplifying assumptions via simulation. The practical implications of the findings are demonstrated in the analysis of a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine.

With contemporary anesthetic drugs, the efficacy of general anesthesia is assured. Health-economic and clinical objectives are related to reductions in the variability in dosing, variability in recovery, etc. Consequently, meta-analyses for anesthesiology research would benefit from quantification of ratios of standard deviations of log-normally distributed variables (e.g., surgical duration). Generalized confidence intervals can be used, once sample means and standard deviations in the raw, time, scale, for each study and group have been used to estimate the mean and standard deviation of the logarithms of the times (i.e., "log-scale"). We examine the matching of the first two moments versus also using higher-order terms, following Higgins et al. 2008 and Friedrich et al. 2012. Monte Carlo simulations revealed that using the first two moments 95% confidence intervals had coverage 92%-95%, with small bias. Use of higher-order moments worsened confidence interval coverage for the log ratios, especially for coefficients of variation in the time scale of 50% and for larger $\left(n=50\right)$ sample sizes per group, resulting in 88% coverage. We recommend that for calculating confidence intervals for ratios of standard deviations based on generalized pivotal quantities and log-normal distributions, when relying on transformation of sample statistics from time to log scale, use the first two moments, not the higher order terms.

This tutorial describes single-step low-dimensional simultaneous inference with a focus on the availability of adjusted p values and compatible confidence intervals for more than just the usual mean value comparisons. The basic idea is, first, to use the influence of correlation on the quantile of the multivariate t-distribution: the higher the less conservative. In addition, second, the estimability of the correlation matrix using the multiple marginal models approach (mmm) using multiple models in the class of linear up to generalized linear mixed models. The underlying maxT-test using mmm is discussed by means of several real data scenarios using selected R packages. Surprisingly, different features are highlighted, among them: (i) analyzing different-scaled, correlated, multiple endpoints, (ii) analyzing multiple correlated binary endpoints, (iii) modeling dose as qualitative factor and/or quantitative covariate, (iv) joint consideration of several tuning parameters within the poly-k trend test, (v) joint testing of dose and time, (vi) considering several effect sizes, (vii) joint testing of subgroups and overall population in multiarm randomized clinical trials with correlated primary endpoints, (viii) multiple linear mixed effect models, (ix) generalized estimating equations, and (x) nonlinear regression models.

For topical, dermatological drug products, an in vitro option to determine bioequivalence (BE) between test and reference products is recommended. In particular, in vitro permeation test (IVPT) data analysis uses a reference-scaled approach for two primary endpoints, cumulative penetration amount (AMT) and maximum flux (J _max), which takes the within donor variability into consideration. In 2022, the Food and Drug Administration (FDA) published a draft IVPT guidance that includes statistical analysis methods for both balanced and unbalanced cases of IVPT study data. This work presents a comprehensive evaluation of various methodologies used to estimate critical parameters essential in assessing BE. Specifically, we investigate the performance of the FDA draft IVPT guidance approach alongside alternative empirical and model-based methods utilizing mixed-effects models. Our analyses include both simulated scenarios and real-world studies. In simulated scenarios, empirical formulas consistently demonstrate robustness in approximating the true model, particularly in effectively addressing treatment-donor interactions. Conversely, the effectiveness of model-based approaches heavily relies on precise model selection, which significantly influences their results. The research emphasizes the importance of accurate model selection in model-based BE assessment methodologies. It sheds light on the advantages of empirical formulas, highlighting their reliability compared to model-based approaches and offers valuable implications for BE assessments. Our findings underscore the significance of robust methodologies and provide essential insights to advance their understanding and application in the assessment of BE, employed in IVPT data analysis.

Mixture experimentation is commonly seen in pharmaceutical formulation studies, where the relative proportions of the individual components are modeled for effects on product attributes. The requirement that the sum of the component proportions equals 1 has given rise to the class of designs, known as mixture designs. The first mixture designs were published by Quenouille in 1953 but it took nearly 40 years for the earliest mixture design applications to be published in the pharmaceutical sciences literature by Kettaneh-Wold in 1991 and Waaler in 1992. Since then, the advent of efficient computer algorithms to generate designs has made this class of designs easily accessible to pharmaceutical statisticians, although the use of these designs appears to be an underutilized experimental strategy even today. One goal of this tutorial is to draw the attention of experimental statisticians to this class of designs and their advantages in pursuing formulation studies such as excipient compatibility studies. We present sufficient materials to introduce the novice practitioner to this class of design, associated models, and analysis strategies. An example of a mixture-process variable design is given as a case study.

Several indices were suggested to determine the follow up duration in oncology trials from either maturity or stability perspective, by maximizing time $t$ such that the index was either greater or less than a pre-defined cutoff value. However, the selection of cutoff value was subjective and usually no commonly agreed cutoff value existed; sometimes one had to resort to simulations. To solve this problem, a new balance index was proposed, which integrated both data stability and data maturity. Its theoretical properties and relationships with other indices were investigated; then its performance was demonstrated through a case study. The highlights of the index are: (1) easy to calculate; (2) free of cutoff value selection; (3) generally consistent with the other indices while sometimes able to shorten the follow-up duration thus more flexible. For the cases where the new balance index cannot be calculated, a modified balance index was also proposed and discussed. For either single arm trial or randomized clinical trial, the two new balance indices can be implemented to widespread situations such as designing a new trial from scratch, or using aggregated trial information to inform the decision-making in the middle of trial conduct.

During the drug development process, testing potency plays an important role in the quality assessment required for the manufacturing and marketing of biologics. Due to multiple operational and biological factors, higher variability is usually observed in bioassays compared with physicochemical methods. In this paper, we discuss different sources of bioassay variability and how this variability can be statistically estimated. In addition, we propose an algorithm to estimate the variability of reportable results associated with different numbers of runs and their corresponding OOS rates under a given specification. Numerical experiments are conducted on multiple assay formats to elucidate the empirical distribution of bioassay variability.

A common feature in cohort studies is when there is a baseline measurement of the continuous follow-up or outcome variable. Common examples include baseline measurements of physiological characteristics such as blood pressure or heart rate in studies where the outcome is post-baseline measurement of the same variable. Methods incorporating the propensity score are increasingly being used to estimate the effects of treatments using observational studies. We examined six methods for incorporating the baseline value of the follow-up variable when using propensity score matching or weighting. These methods differed according to whether the baseline value of the follow-up variable was included or excluded from the propensity score model, whether subsequent regression adjustment was conducted in the matched or weighted sample to adjust for the baseline value of the follow-up variable, and whether the analysis estimated the effect of treatment on the follow-up variable or on the change from baseline. We used Monte Carlo simulations with 750 scenarios. While no analytic method had uniformly superior performance, we provide the following recommendations: first, when using weighting and the ATE is the target estimand, use an augmented inverse probability weighted estimator or include the baseline value of the follow-up variable in the propensity score model and subsequently adjust for the baseline value of the follow-up variable in a regression model. Second, when the ATT is the target estimand, regardless of whether using weighting or matching, analyze change from baseline using a propensity score that excludes the baseline value of the follow-up variable.

Treatment switching is common in randomized trials of oncology treatments. For example, control group patients may receive the experimental treatment as a subsequent therapy. One possible estimand is the effect of trial treatment if this type of switching had instead not occurred. Two-stage estimation is an established approach for estimating this estimand. We argue that other estimands of interest instead describe the effect of trial treatments if the proportion of patients who switched was different. We give precise definitions of such estimands. By motivating estimands using real-world data, decision-making in universal health care systems is facilitated. Focusing on estimation, we show that an alternative choice of secondary baseline, the time of first subsequent treatment, is easily defined, and widely applicable, and makes alternative estimands amenable to two-stage estimation. We develop methodology using propensity scores, to adjust for confounding at a secondary baseline, and a new quantile matching technique that can be used to implement any parametric form of the post-secondary baseline survival model. Our methodology was motivated by a recent immuno-oncology trial where a substantial proportion of control group patients subsequently received a form of immunotherapy.