Journal of Biopharmaceutical Statistics最新文献

There are good reasons to perform a randomized controlled trial (RCT) even in early phases of clinical development. However, the low sample sizes in those settings lead to high variability of the treatment effect estimate. The variability could be reduced by adding external control data if available. For the common setting of suitable subject-level control group data only available from one external (clinical trial or real-world) data source, we evaluate different analysis options for estimating the treatment effect via hazard ratios. The impact of the external control data is usually guided by the level of similarity with the current RCT data. Such level of similarity can be determined via outcome and/or baseline covariate data comparisons. We provide an overview over existing methods, propose a novel option for a combined assessment of outcome and baseline data, and compare a selected set of approaches in a simulation study under varying assumptions regarding observable and unobservable confounder distributions using a time-to-event model. Our various simulation scenarios also reflect the differences between external clinical trial and real-world data. Data combinations via simple outcome-based borrowing or simple propensity score weighting with baseline covariate data are not recommended. Analysis options which conflate outcome and baseline covariate data perform best in our simulation study.

This paper discusses the problem of disease prevalence in clinical studies, focusing on multiple comparisons based on stratified partially validated series in the presence of a gold standard. Five test statistics, including two Wald-type test statistics, the inverse hyperbolic tangent transformation test statistic, likelihood ratio test statistic, and score test statistic, are proposed to conduct multiple comparisons. To control the overall type I error rate, several adjustment procedures are developed, namely the Bonferroni, Single-step adjusted MaxT, Single-step adjusted MinP, Holm's Step-down, and Hochberg's step-up procedures, based on these test statistics. The performance of the proposed methods is evaluated through simulation studies in terms of the empirical type I error rate and empirical power. Simulation results show that the Single-step adjusted MaxT procedure and Single-step adjusted MinP procedure generally outperform the other three procedures, and these two test procedures based on all test statistics have satisfactory performance. Notably, the Single-step adjusted MinP procedure tends to exhibit higher empirical power than the Single-step adjusted MaxT procedure. Furthermore, the Step-down and Step-up procedures show greater power compared to the Bonferroni method. The study also observes that as the validated ratio increases, the empirical type I errors of all test procedures approach the nominal level while maintaining higher power. Two real examples are presented to illustrate the proposed methods.

The failure rates of phase 3 trials are high. Incorrect sample size due to uncertainty of effect size could be a critical contributing factor. Adaptive sequential design (ASD), which may include one or more sample size re-estimations (SSR), has been a popular approach for dealing with such uncertainties. The operating characteristics (OCs) of ASD, including the unconditional power and mean sample size, can be substantially affected by many factors, including the planned sample size, the interim analysis schedule and choice of critical boundaries and rules for interim analysis. We propose a systematic, comprehensive strategy which uses iterative simulations to investigate the operating characteristics of adaptive designs and help achieve adequate unconditional power and cost-effective mean sample size if the effect size is in a pre-identified range.

In a prospective observational study (POS) designed to assess the average causal effect of a treatment (e.g. Drug A) compared to a comparator (e.g. Drug B) in the treatment population, enrolling all patients who are assigned to the treatments of interest for follow-up has a potentially large negative impact on the statistical efficiency and bias of the analysis of the outcomes and on the cost of the study. "Up-front matching" is an innovative enrollment method for selecting patients for long-term follow-up among those who have already been assigned to treatment or comparator which uses frequency matching and hence avoids the restrictions of individual matching that other methods have used. To achieve potential statistical and logistical efficiencies in the POS, in up-front matching, a target population is defined based on a retrospective database which then enables selecting populations of patients for follow-up that have desirable statistical properties. In particular, the resulting populations of patients who are enrolled look like the population of treatment patients were randomized to treatment or comparator for the baseline covariates that are used to select patients for follow-up. The method is illustrated in detail for a study designed to assess the effect of injectable antipsychotics versus oral antipsychotics.

Dose selection and optimization in early phase of oncology drug development serves as the foundation for the success of late phases drug development. Bivariate Bayesian logistic regression model (BLRM) is a widely utilized model-based algorithm that has been shown to improve the accuracy for identifying recommended phase 2 dose (RP2D) based on dose-limiting-toxicity (DLT) over traditional method such as 3 + 3. However, it remains a challenge to optimize dose selection that strikes a proper balance between safety and efficacy in escalation and expansion phase of phase I trials. In this paper, we first use a phase I clinical trial to demonstrate how the variability of drug exposure related to pharmacokinetic (PK) parameters among trial participants may add to the difficulties of identifying optimal dose. We use simulation to show that concurrently or retrospectively fitting BLRM model for dose/toxicity data from escalation phase with dose-independent PK parameters as covariate lead to improved accuracy of identifying dose level at which DLT rate is within a prespecified toxicity interval. Furthermore, we proposed both model- and rule-based methods to modify dose at patient level in expansion cohorts based on their PK/exposure parameters. Simulation studies show this approach leads to higher likelihood for a dose level with a manageable toxicity and desirable efficacy margin to be advanced to late phase pipeline after being screened at expansion phase of phase I trial.

In this paper, we propose a new Bayesian adaptive design, score-goldilocks design, which has the same algorithmic idea as goldilocks design. The score-goldilocks design leads to a uniform formula for calculating the probability of trial success for different endpoint trials by using the normal approximation. The simulation results show that the score-goldilocks design is not only very similar to the goldilocks design in terms of operating characteristics such as type 1 error, power, average sample size, probability of stop for futility, and probability of early stop for success, but also greatly saves the calculation time and improves the operation efficiency.

Recently, interest has grown in the development of dose-finding methods that consider both toxicity and efficacy as endpoints. Along with responses on these, the incorporation of pharmacokinetic (PK) data can be beneficial in terms of patients' safety and can also increase the efficiency of the design for finding the best dose for the next phase. In this paper, the maximum concentration ($C_{max}$) is used as the PK measure guiding the dose selection. The ethically attractive approach, which is based on the probability of efficacy, is used as a dose optimisation criterion. At each stage of an adaptive trial, that dose is selected for which the criterion is maximised, subject to the constraints imposed on the $C_{max}$ and the probability of toxicity. The inter-patient variability of the PK model parameters is considered, and population $D$-optimal sampling time points for measuring the concentration of a drug in the blood are calculated. The method is illustrated with a one-compartment PK model with first-order absorption, with the parameters being assumed to be random. The Cox model for bivariate binary responses is employed to model the dose-response outcomes. The results of a simulation study for several plausible dose-response scenarios show a significant gain in the efficiency of the design, as well as a reduction in the proportion of toxic responses.

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.

In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA$/$PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.