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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 (${C_{\max }}$Cmax) 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 ${C_{\max }}$Cmax and the probability of toxicity. The inter-patient variability of the PK model parameters is considered, and population $D$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. [ABSTRACT FROM AUTHOR]

In phase 2 clinical trials, we expect to make a right Go or No-Go decision during the interim analysis (IA) and make this decision at the right time. The optimal time for IA is usually determined based on a utility function. In most previous research, utility functions aim to minimize the expected sample size or total cost in confirmatory trials. However, the selected time can vary depending on different alternative hypotheses. This paper proposes a new utility function for Bayesian phase 2 exploratory clinical trials. It evaluates the predictability and robustness of the Go and No-Go decision made during the IA. We can make a robust time selection for the IA based on the function regardless of the treatment effect assumptions. [ABSTRACT FROM AUTHOR]

Selecting a safe and clinically beneficial dose can be difficult in drug development. Dose justification often relies on dose-response modeling where parametric assumptions are made in advance which may not adequately fit the data. This is especially problematic in longitudinal dose-response models, where additional parametric assumptions must be made. This paper proposes a class of longitudinal dose-response models to be used in the Bayesian model averaging paradigm which improve trial operating characteristics while maintaining flexibility a priori. A new longitudinal model for non-monotonic longitudinal profiles is proposed. The benefits and trade-offs of the proposed approach are demonstrated through a case study and simulation. [ABSTRACT FROM AUTHOR]

The author reports on the discussion of the white paper on adaptive dose-ranging designs by the Phrma working Group in Great Britain. He states that the discussion is focusing on methodological issues and drug development. He stresses out that the white paper conducts simulations to compare a traditional approach with alternative design-focused and analysis-focused approaches. He also discusses the different level of phases on the pre-clinical trials.

The article discusses the White Paper of adaptive dose-ranging study to address the early phase drug development issues through simulation methods. The author provides the evaluation metrics including the detection of dose-response (DR), identification of clinical outputs, selection of target dose and estimation of DR profile within the observed dose range.

The article discusses the adaptive dose-ranging designs. The author focuses on several aspects in pharmacology including the objectives of such trials, the decision criteria of each objective, the simulation method and accrual and long-term endpoints. Some recommendations regarding the clinical study of drug development is also presented.

In contemporary exploratory phase of oncology drug development, there has been an increasing interest in evaluating investigational drug or drug combination in multiple tumor indications in a single basket trial to expedite drug development. There has been extensive research on more efficiently borrowing information across tumor indications in early phase drug development including Bayesian hierarchical modeling and the pruning-and-pooling methods. Despite the fact that the Go/No-Go decision for subsequent Phase 2 or Phase 3 trial initiation is almost always a multi-facet consideration, the statistical literature of basket trial design and analysis has largely been limited to a single binary endpoint. In this paper we explore the application of considering clinical priorities of multiple endpoints based on matched win ratio to the basket trial design and analysis. The control arm data will be simulated for each tumor indication based on the corresponding null assumptions that could be heterogeneous across tumor indications. The matched win ratio matching on the tumor indication can be performed for individual tumor indication, pooled data, or the pooled data after pruning depending on whether an individual evaluation or a simple pooling or a pruning-and-pooling method is used. We conduct the simulation studies to evaluate the performance of proposed win ratio-based framework and the results suggest the proposed framework could provide desirable operating characteristics. [ABSTRACT FROM AUTHOR]

The graphical approach has been proposed as a general framework for clinical trial designs involving multiple hypotheses, where decisions are made only based on the observed marginal $$p$$ p -values. The graphical approach starts from a graph that includes all hypotheses as vertices and gradually removes some vertices when their corresponding hypotheses are rejected. In this paper, we propose a reverse graphical approach, which starts from a set of singleton graphs and gradually adds vertices into graphs until rejection of a set of hypotheses is made. Proofs of familywise error rate control are provided. A simulation study is conducted for statistical power analysis, and a case study is included to illustrate how the proposed approach can be applied to clinical studies. [ABSTRACT FROM AUTHOR]

In multi-regional clinical trials, hierarchical linear models have been actively studied because they can reflect that patients in the same region share common intrinsic and extrinsic factors. In this paper, we investigate the statistical properties of the hierarchical linear model including a random effect in the intercept. The big advantage of the random intercept hierarchical linear model is that it can control the type I error rates of testing the overall treatment effect when there are no or clinically negligible regional differences in the treatment effect. Moreover, we compare the pros and cons with the hierarchical linear model in which the random effect is included in the slope. For the two hierarchical linear models, the model selection criteria are determined according to the magnitude of the difference in treatment effect across the regions, and we provide the criteria through simulation studies. [ABSTRACT FROM AUTHOR]

Bayesian methods have been proposed as a natural fit for pediatric extrapolation, as they allow the incorporation of relevant external data to reduce the required sample size and hence trial burden for the pediatric patient population. In this paper we will discuss our experience and perspectives with these methods in pediatric trials. We will present some of the background and thinking underlying pediatric extrapolation and discuss the use of Bayesian methods within this context. We will present two recent case examples illustrating the value of a Bayesian approach in this setting and present perspectives on some of the issues that we have encountered in these and other cases. [ABSTRACT FROM AUTHOR]

Pediatric drug development has many unique challenges, one of which is the evaluation of growth and development changes in children that are expected and are not due to the study intervention. Children grow and mature at different pace. The potential impact of the drug could vary with the developmental age of the participants receiving the treatment. For example, sexual maturation is a critical consideration in children of age 10 and above, but not in younger age groups. How the investigational drug impacts children is ultimately a risk-benefit consideration. In this paper, practical considerations and recommendations are provided on how to assess growth and development based on data collected from clinical trials in pediatric patients. The endpoints and measures related to growth, sexual maturation and neurocognitive development are discussed. Basic analysis approaches are recommended. [ABSTRACT FROM AUTHOR]

This paper investigates the use of a general multi-arm multi-stage (MAMS) approach for time-to-event outcomes that would streamline simultaneous comparison of a large number of promising therapies in clinical trials, thus significantly reducing the time and the number of patients needed to evaluate the treatment. Controlling type I error in this setting is different than regular clinical trials as this approach incorporates both multiple comparison between arms and multiple stages. Historically, pairwise (PWER) and familywise (FWER) type I error rates have been primarily used to regulate the type I error in such designs. This paper will focus on constructing the efficacy and futility boundaries for a MAMS clinical trial in two different scenarios. In the first, it is assumed that the same outcome is used throughout the clinical trial for both intermediate and final assessments. In this scenario, we propose using the generalized Dunnett procedure that controls FWER. In the latter scenario, where intermediate and final outcomes are different in nature, we propose modifications to the existing method that originally concentrated on controlling PWER and extend the method to include FWER in the design. We also explore the performance of the proposed MAMS design in a setting where the proportional hazard assumption is violated in the presence of a delayed treatment effect and demonstrate the loss of power because of that. An alternative test statistic that can help circumvent this problem to maintain the desired power is also suggested. [ABSTRACT FROM AUTHOR]

Methods to extend the strong internal validity of randomized controlled trials to reliably estimate treatment effects in target populations are gaining attention. This paper enumerates steps recommended for undertaking such extended inference, discusses currently viable choices for each one, and provides recommendations. We demonstrate a complete extended inference from a clinical trial studying a pharmaceutical treatment for Alzheimer's disease (AD) to a realistic target population of European residents diagnosed with AD. This case study highlights approaches to overcoming practical difficulties and demonstrates limitations of reliably extending inference from a trial to a real-world population. [ABSTRACT FROM AUTHOR]

Over the past decades, the primary interest in vaccine efficacy or immunogenicity evaluation mostly focuses on the biological effect of immunization in complying with the vaccination schedule in a targeted population. The safety questions, which are essential for vaccines as they are generally given to large healthy populations, need to be clearly defined to reflect the risk assessment of interest. ICH E9 (R1) provides a structured framework to clarify the clinical questions and formulate the treatment effect as an estimand. This paper applies the estimand framework to vaccine clinical trials on common clinical questions regarding efficacy, immunogenicity, and safety. [ABSTRACT FROM AUTHOR]

ICH E9(R1) introduces the estimand framework to strengthen dialogues between sponsors and regulators during drug development. A well-structured benefit-risk assessment (BRA) framework also intends to facilitate communication among stakeholders. However, the estimand in ICH E9(R1) is written mainly from the perspective of a single measure of treatment effect in clinical trials. There is lack of systematic discussion on estimand in the context of BRA. This paper initiates the BRA discussion under the estimand framework. By identifying two types of BRA approaches, we summarize and discuss completed clinical trials, using the estimand language for BRA. Benefits and challenges of using estimand for BRA are also discussed. [ABSTRACT FROM AUTHOR]

Returning to baseline (RTB) has been a practical method for handling missing data. Here we consider longitudinal clinical trials with daily patient reported outcomes (PROs), where efficacy endpoints are often defined as the average daily values in a cycle (such as a month or a week). The conventional method treats data at cycle level and ignores daily values. In this paper, we build a two-level constrained longitudinal data analysis (cLDA) model on daily values and propose two-level RTB method to impute daily values. Standard multiple imputation (MI) approach and likelihood-based approach are proposed and evaluated by simulations. [ABSTRACT FROM AUTHOR]

Clustered data frequently occur in biomedical research fields and clinical trials. The log-rank tests are widely used for two-independent samples of clustered data tests. The randomized block design and truncated binomial design are used for forcing balance in clinical trials and reducing selection bias. In this paper, survival clustered data are randomized by generalized randomized block, and subsequently clustered data in each block are randomized by truncated binomial design. Consequently, the p-values of the null permutation distribution of log-rank tests for clustered data are approximated via the double saddlepoint approximation method. Comprehensive numerical studies are carried out to assess the accuracy of the saddlepoint approximation. This approximation has a great accuracy over the asymptotic normal approximation. [ABSTRACT FROM AUTHOR]

We consider single-arm phase II cancer clinical trials with tumor response as the primary outcome. Oftentimes, the patient population of a phase II clinical trial consists of subpopulations with different expected response rates. A well-accepted design in this case is to specify the response rate and the prevalence of each subpopulation, to compute the response rate of the whole population using the weighted (by prevalence) average of the response rates across subpopulations, and to find a standard phase II design, such as Simon's minimax or optimal design, for testing on the response rate of the whole population based on the unstratified binomial test. In such trials, while the response rate is the primary parameter and the prevalence of each subpopulation is a nuisance parameter, the validity of an unstratified statistical test for deciding acceptance or rejection of the experimental treatment is influenced by observed prevalence. In order to avoid bias due to the discrepancy between observed and specified values of the nuisance parameter, we have to use stratified test for such trials. In this paper, we propose optimal and minimax designs for stratified binomial test. We also develop a user-friendly interactive software to visualize the optimal designs and help users make correct statistical decisions. [ABSTRACT FROM AUTHOR]

According to the International Conference on Harmonisation E14 Q&As R3, concentration-QTc analysis can serve as an alternative to the by-time-point analysis or intersection-union test as the primary basis for decisions to classify the QTc risk of a drug. In a recent scientific white paper on concentration-QTc analysis, a pre-specified linear mixed effect model was suggested to study a QTc prolongation effect. The model assumes a direct time-concordant relationship (direct effect) between QTc interval and drug-concentrations. In the case of lagged drug QTc effects, also called 'hysteresis', a linear direct effect model may yield a biased QTc estimate. In this work, we estimate the bias of QTc effects via simulations when hysteresis is not accounted for in the linear mixed effect model analysis. Simulations are performed to compare different methods of identifying hysteresis when two physiologically plausible QT drug mechanisms are considered. The focus of this paper is to provide hysteresis identification methods and assess the influence of hysteresis in estimating the QT prolongation for most commonly observed QT-Concentration profiles. [ABSTRACT FROM AUTHOR]

Shrinkage estimators for exploratory subgroup analyses are intuitively appealing and can greatly improve estimation over standard analysis approaches; however, adoption of these estimators has been limited by reliance on the exchangeability assumption. This paper describes a new shrinkage estimator that does not rely on this assumption. Rather than assuming that treatment effect sizes within subgroups are randomly distributed around an overall mean, this new estimator assumes that the difference between the effect sizes in any given pair of subgroups is randomly distributed around zero. The estimator is illustrated using data from a clinical trial in which the treatment effect size in one region was substantially different from the sizes in other regions. Simulation results show that the estimator has properties that are comparable to or superior to a standard shrinkage estimator when exchangeability is assumed, while allowing the flexibility to handle situations where exchangeability cannot be assumed. [ABSTRACT FROM AUTHOR]

Multisource exchangeability models (MEMs), a BayeTsian approach for dynamically integrating information from multiple clinical trials, are a promising approach for gaining efficiency in randomized controlled trials. When the supplementary trials are considerably larger than the primary trial, care must be taken when integrating supplementary data to avoid overwhelming the primary trial. In this paper, we propose "capping priors," which controls the extent of dynamic borrowing by placing an a priori cap on the effective supplemental sample size. We demonstrate the behavior of this technique via simulation, and apply our method to four randomized trials of very low nicotine content cigarettes. [ABSTRACT FROM AUTHOR]

Reference-based imputation (RBI) is a popular method for missing data. The methodology is well established for continuous end points but less well developed for repeated binary end points due to the lack of natural multivariate conditional distributions for such end points. In this paper, we propose RBI methods for repeated binary end points based on a multivariate probit model and a logistic model, including jump-to-reference (J2R), copy-reference (CR) and copy-increment-in-reference (CIR). We explore the distribution of the missing binary end points under RBI and propose efficient algorithms to implement the proposed RBI methods. We evaluate the proposed methods by simulations and a data set from a clinical trial. [ABSTRACT FROM AUTHOR]

This paper proposes two new approximate confidence limit methods for the common odds ratio from multiple 2 × 2 tables. The two new procedures, based on the asymptotic distribution of Woolf estimator and Mantel-Haenszel estimator, associate with inverse sinh transformation. We employ three pseudo-frequency methods to calculate confidence intervals in order to avoid the interval failure caused by the presence of zero cells in multiple 2 × 2 tables. We develop the modified inverse sinh intervals for the common odds ratio which add one pseudo-frequency (c1) to all the cells before computing the point estimate of common odds ratio and another pseudo-frequency (c2) to all the cells before computing the standard error estimate. The simulation is to evaluate the 22 confidence intervals, including Woolf, Mantel-Haenszel, their inverse sinh intervals, and their pseudo-frequency modified inverse sinh intervals, in terms of their coverage probabilities and average log lengths. Simulation results demonstrate that the adjusted inverse sinh intervals by two different pseudo-frequencies perform quite well when c2 is slightly greater than c1 since the coverage probabilities of them are closer to confidence level of 95%. Larger values of c2 lead to narrow intervals and low coverage probabilities. We also find that inverse sinh intervals are shorter than untransformed intervals based on Woolf estimator and Mantel-Haenszel estimator, respectively. These procedures were illustrated with two clinical trials. [ABSTRACT FROM AUTHOR]

We introduce an improved Bonferroni method for testing two primary endpoints in clinical trial settings using a new data-adaptive critical value that explicitly incorporates the sample correlation coefficient. Our methodology is developed for the usual Student's t-test statistics for testing the means under normal distributional setting with unknown population correlation and variances. Specifically, we construct a confidence interval for the unknown population correlation and show that the estimated type-1 error rate of the Bonferroni method with the population correlation being estimated by its lower confidence limit can be bounded from above less conservatively than using the traditional Bonferroni upper bound. We also compare the new procedure with other procedures commonly used for the multiple testing problem addressed in this paper. [ABSTRACT FROM AUTHOR]

While current guidelines generally recommend single endpoints for primary analyses of confirmatory clinical trials, it is recognized that certain settings require inference on multiple endpoints for comprehensive conclusions on treatment effects. Furthermore, combining treatment effect estimates from several outcome measures can increase the statistical power of tests. Such an efficient use of resources is of special relevance for trials in small populations. This paper reviews approaches based on a combination of test statistics or measurements across endpoints as well as multiple testing procedures that allow for confirmatory conclusions on individual endpoints. We especially focus on feasibility in trials with small sample sizes and do not solely rely on asymptotic considerations. A systematic literature search in the Scopus database, supplemented by a manual search, was performed to identify research papers on analysis methods for multiple endpoints with relevance to small populations. The identified methods were grouped into approaches that combine endpoints into a single measure to increase the power of statistical tests and methods to investigate differential treatment effects in several individual endpoints by multiple testing. [ABSTRACT FROM AUTHOR]

In a comparative longitudinal clinical study, multiple clinical events of interest are typically collected in timing and occurrence during the follow-up period. These clinical events are often indicative of disease burden over the study period and provide overall evidence of benefit/risk of one treatment relative to another. While these clinical events are usually used to form a composite endpoint, only the first occurrence of the composite endpoint event is considered in primary efficacy analysis. This type of analysis is commonly performed but it may not be ideal. Most of the existing methods for analyzing multiple event-time data were developed, relying on certain model assumptions. However, the assumptions may greatly affect the inferences for treatment effect. In this paper, we propose a simple, non-parametric estimator of conditional mean survival time for multiple events to quantify treatment effect which has clinically meaningful interpretation. We use simulation studies to evaluate the performance of the new method. Further, we apply this method to analyze the data from a cardiovascular clinical trial as an illustration. [ABSTRACT FROM AUTHOR]

Clinical trials with data-driven decision rules often pursue multiple clinical objectives such as the evaluation of several endpoints or several doses of an experimental treatment. These complex analysis strategies give rise to “multivariate” multiplicity problems with several components or sources of multiplicity. A general framework for defining gatekeeping procedures in clinical trials with adaptive multistage designs is proposed in this paper. The mixture method is applied to build a gatekeeping procedure at each stage and inferences at each decision point (interim or final analysis) are performed using the combination function approach. An advantage of utilizing the mixture method is that it enables powerful gatekeeping procedures applicable to a broad class of settings with complex logical relationships among the hypotheses of interest. Further, the combination function approach supports flexible data-driven decisions such as a decision to increase the sample size or remove a treatment arm. The paper concludes with a clinical trial example that illustrates the methodology by applying it to develop an adaptive two-stage design with a mixture-based gatekeeping procedure. [ABSTRACT FROM PUBLISHER]

Cox proportional hazards (PH) model evaluates the effects of interested covariates under PH assumption without specified the baseline hazard. In clinical trial applications, however, the explicitly estimated hazard or cumulative survival function for each treatment group helps to assess and interpret the meaning of treatment difference. In this paper, we propose to use a flexible mixture model under the PH constraint to fit the underline survival functions. Simulations are conducted to evaluate its performance and show that the proposed mixture PH model is very similar to the Cox PH model in terms of estimating the hazard ratio, bias, confidence interval coverage, type-I error and testing power. Application to several real clinical trial examples demonstrates that the results from this approach are almost identical to the results from Cox PH model. The explicitly estimated hazard function for each treatment group provides additional useful information and helps the interpretation of hazard comparisons. [ABSTRACT FROM AUTHOR]

In early phase clinical trial, finding maximum-tolerated dose (MTD) is a very important goal. Many researches show that finding a correct MTD can improve drug efficacy and safety significantly. Usually, dose-finding trials start from very low doses, so in many cases, more than 50% patients or cohorts do not have dose-limiting toxicity (DLT), but DLT may occur suddenly and increase fast along with just two or three doses. Although some fantastic models were built to find MTD, little consideration was given to those '0 DLTs' and the 'jump' of DLTs. In this paper, we developed a Bayesian zero-inflated binomial regression for dose-finding study, which analyses dose-finding data from two aspects: 1) observation of only zeros, 2) number of DLTs based on binomial distribution, so it can help us analyse if the cohorts without DLT have potential possibility to have DLT and fit the 'jump' of DLTs. [ABSTRACT FROM AUTHOR]

Delayed separation in survival curves has been observed in immuno-oncology clinical trials. Under this situation, the classic log-rank test may confront high power loss. In this paper, we consider a Zmax test, which is the maximum of the log-rank test and a Fleming–Harrington test. Simulation studies indicate that the Zmax test not only controls the Type I error rate but also maintains good power under different delayed effect models. The asymptotic properties of the Zmax test are also established, which further supports its robustness. We apply the Zmax test to two data sets reported in recent immuno-oncology clinical trials, in which Zmax has exhibited remarkable improvement over the conventional log-rank test. [ABSTRACT FROM AUTHOR]

It is desirable to work efficiently and cost effectively to evaluate new therapies in a time-sensitive and ethical manner without compromising the integrity and validity of the development process. The seamless phase II/III clinical trial has been proposed to meet this need, and its efficient, ethical and economic advantages can be strengthened by its combination with innovative response adaptive randomization (RAR) procedures. In particular, well-designed frequentist RAR procedures can target theoretically optimal allocation proportions, and there are explicit asymptotic results. However, there has been little research into seamless phase II/III clinical trials with frequentist RAR because of the difficulty in performing valid statistical inference and controlling the type I error rate. In this paper, we propose the framework for a family of frequentist RAR designs for seamless phase II/III trials, derive the asymptotic distribution of the parameter estimators using martingale processes and offer solutions to control the type I error rate. The numerical studies demonstrate our theoretical findings and the advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

A Sequential Parallel Comparison Design has two stages, the first comparing drug to placebo and the second comparing drug to placebo among patients who did not respond to placebo in the first stage. The paper, Statistical Inference Problems in Sequential Parallel Comparison Designs, claims that the estimate of the treatment difference in the second stage is biased and that under certain circumstances, a suggested hypothesis test will reject the null hypothesis when it should be accepted. This rejoinder argues that the estimate in the second stage is not biased when the true target of estimation (estimand) is properly understood. Further, the null hypothesis that the authors posit is not the correct null hypothesis for clinical trials, and in the situation, they describe that the treatment should be considered to be effective. [ABSTRACT FROM AUTHOR]

Background During the past two decades, the number and complexity of clinical trials have risen dramatically increasing the difficulty of choosing sites for inspection. FDA's resources are limited and so sites should be chosen with care. Purpose To determine if data mining techniques and/or unsupervised statistical monitoring can assist with the process of identifying potential clinical sites for inspection. Methods Five summary-level clinical site datasets from four new drug applications (NDA) and one biologics license application (BLA), where the FDA had performed or had planned site inspections, were used. The number of sites inspected and the results of the inspections were blinded to the researchers. Five supervised learning models from the previous two years (2016–2017) of an on-going research project were used to predict site inspections results, i.e., No Action Indicated (NAI), Voluntary Action Indicated (VAI), or Official Action Indicated (OAI). Statistical Monitoring Applied to Research Trials (SMARTTM) software for unsupervised statistical monitoring software developed by CluePoints (Mont-Saint-Guibert, Belgium) was utilized to identify atypical centers (via a p-value approach) within a study.Finally, Clinical Investigator Site Selection Tool (CISST), developed by the Center for Drug Evaluation and Research (CDER), was used to calculate the total risk of each site thereby providing a framework for site selection. The agreement between the predictions of these methods was compared. The overall accuracy and sensitivity of the methods were graphically compared. Results Spearman's rank order correlation was used to examine the agreement between the SMARTTM analysis (CluePoints' software) and the CISST analysis. The average aggregated correlation between the p-values (SMARTTM) and total risk scores (CISST) for all five studies was 0.21, and range from −0.41 to 0.50. The Random Forest models for 2016 and 2017 showed the highest aggregated mean agreement (65.1%) amongst outcomes (NAI, VAI, OAI) for the three available studies. While there does not appear to be a single most accurate approach, the performance of methods under certain circumstances is discussed later in this paper. Limitations Classifier models based on data mining techniques require historical data (i.e., training data) to develop the model. There is a possibility that sites in the five-summary level datasets were included in the training datasets for the models from the previous year's research which could result in spurious confirmation of predictive ability. Additionally, the CISST was utilized in three of the five site selection processes, possibly biasing the data. Conclusion The agreement between methods was lower than expected and no single method emerged as the most accurate. [ABSTRACT FROM AUTHOR]

Recruitment of patients in concurrent control arms can be very challenging for clinical trials for pediatric and rare diseases. Innovative approaches, such as platform trial designs, including shared internal control arm(s), can potentially reduce the needed sample size, improving the efficiency and speed of the drug development program. Furthermore, historical borrowing, which involves leveraging information from control arms in previous relevant clinical trials, may further enhance a clinical trial's efficiency. In this paper, we discuss platform trials highlighting their advantages and limitations. We then compare various strategies that borrow historical data or information, such as pooling data from different studies, analyzing data from studies separately, test-then-pool, dynamic pooling, and Bayesian hierarchical modeling, which focuses on the meta-analytic-predictive (MAP) prior. We further propose a procedure to illustrate the feasibility of utilizing historical controls under a platform setting and describe the statistical performance of our method via simulations. [ABSTRACT FROM AUTHOR]

Precision medicine, in the sense of tailoring the choice of medical treatment to patients' pretreatment characteristics, is nowadays gaining a lot of attention. Preferably, this tailoring should be realized in an evidence-based way, with key evidence in this regard pertaining to subgroups of patients that respond differentially to treatment (i.e., to subgroups involved in treatment–subgroup interactions). Often a-priori hypotheses on subgroups involved in treatment–subgroup interactions are lacking or are incomplete at best. Therefore, methods are needed that can induce such subgroups from empirical data on treatment effectiveness in a post hoc manner. Recently, quite a few such methods have been developed. So far, however, there is little empirical experience in their usage. This may be problematic for medical statisticians and statistically minded medical researchers, as many (nontrivial) choices have to be made during the data-analytic process. The main purpose of this paper is to discuss the major concepts and considerations when using these methods. This discussion will be based on a systematic, conceptual, and technical analysis of the type of research questions at play, and of the type of data that the methods can handle along with the available software, and a review of available empirical evidence. We will illustrate all this with the analysis of a dataset comparing several anti-depressant treatments. [ABSTRACT FROM AUTHOR]

Repeated measurements are widely encountered in medical or pharmaceutical studies, which can be analyzed by both longitudinal data and functional data analysis methods, particularly when the underlying process is continuous and the number of measurement points is not too small. Motivated by real problems of clustering patient profiles in clinical trials, this paper gives an overview of the clustering methods for repeated measurement data and compares three longitudinal data methods and two functional data methods with extensive simulation studies. Methods with appropriate properties are applied to the real data to produce interpretable results. [ABSTRACT FROM AUTHOR]

This paper deals with the methods to augment concurrent controls (CC) in a randomized controlled trial with available historical data in clinical studies. In their article, Matching with multiple control groups and adjusting for group differences, Stuart and Rubin proposed a matching method where the primary/local control and the secondary/non-local control are both included in the propensity score estimates. The authors discuss a similar approach taking the CC as the primary and the historical control as the secondary, and find that this approach does not save the sample size of the randomized trial compared to the traditional randomized design without supplementation of historical data. A new matching method that saves sample size is proposed, where propensity scores are estimated without the concurrent randomized control patients. A two-stage design is proposed, which allows one to examine the assumption of the new matching method before a commitment of using the matching method in the second stage. Previous clinical trials data is used as an example to illustrate the feasibility of the proposed methods. Simulation studies have been used to investigate operating characteristics of the proposed method. [ABSTRACT FROM AUTHOR]

Multi-arm multi-stage designs, in which multiple active treatments are compared to a control and accumulated information from interim data are used to add or remove arms from the trial, may reduce development costs and shorten the drug development timeline. As such, this adaptive update is a natural complement to Bayesian methodology in which the prior clinical belief is sequentially updated using the observed probability of success. Simulation is often required for planning clinical trials to accommodate the complexity of the design and to optimize key design characteristics. This paper addresses two key limiting factors in simulations, namely the computational burden and the time needed to obtain results. We first introduce a generic process for simulating Bayesian multi-arm multi-stage designs with binary endpoints. Then, to address the computational burden and time, we optimize the method for calculating the posterior probability and posterior predictive probability of success. [ABSTRACT FROM AUTHOR]

This review article sets out to examine the Type I error rates used in noninferiority trials. Most papers regarding noninferiority trials only state Type I error rate without mentioning clearly which Type I error rate is evaluated. Therefore, the Type I error rate in one paper is often different from the Type I error rate in another paper, which can confuse readers and makes it difficult to understand papers. Which Type I error rate should be evaluated is related directly to which paradigm is employed in the analysis of noninferiority trial, and to how the historical data are treated. This article reviews the characteristics of the within-trial Type I error rate and the unconditional across-trial Type I error rate which have frequently been examined in noninferiority trials. The conditional across-trial Type I error rate is also briefly discussed. In noninferiority trials comparing a new treatment with an active control without a placebo arm, it is argued that the within-trial Type I error rate should be controlled in order to obtain approval of the new treatment from the regulatory agencies. I hope that this article can help readers understand the difference between two paradigms employed in noninferiority trials. [ABSTRACT FROM PUBLISHER]

The article discusses clinical trial optimization problems in the context of mid- to late-stage drug development. Using the Clinical Scenario Evaluation approach, main objectives of clinical trial optimization are formulated, including selection of clinically relevant optimization criteria, identification of sets of optimal and nearly optimal values of the parameters of interest, and sensitivity assessments. The paper focuses on a class of optimization criteria arising in clinical trials with several competing goals, termed tradeoff-based optimization criteria, and discusses key considerations in constructing and applying tradeoff-based criteria. The clinical trial optimization framework considered in the paper is illustrated using two case studies based on a clinical trial with multiple objectives and a two-stage clinical trial which utilizes adaptive decision rules. [ABSTRACT FROM AUTHOR]

The analysis and planning methods for competing risks model have been described in the literature in recent decades, and noninferiority clinical trials are helpful in current pharmaceutical practice. Analytical methods for noninferiority clinical trials in the presence of competing risks (NiCTCR) were investigated by Parpia et al., who indicated that the proportional sub-distribution hazard (SDH) model is appropriate in the context of biological studies. However, the analytical methods of the competing risks model differ from those appropriate for analyzing noninferiority clinical trials with a single outcome; thus, a corresponding method for planning such trials is necessary. A sample size formula for NiCTCR based on the proportional SDH model is presented in this paper. The primary endpoint relies on the SDH ratio. A total of 120 simulations and an example based on a randomized controlled trial verified the empirical performance of the presented formula. The results demonstrate that the empirical power of sample size formulas based on the Weibull distribution for noninferiority clinical trials with competing risks can reach the targeted power. [ABSTRACT FROM AUTHOR]

The established molecular heterogeneity of human cancers has had profound effects on the design of cancer therapeutics. Most cancer drugs are today targeted to molecular alterations present in cancer cells. Tumors of the same primary site, however, often differ with regard to the alterations that they harbor. Consequently, this heterogeneity has required the development of new paradigms for clinical development. In this paper, we review some clinical trial designs finding active use in co-development of therapeutics and predictive biomarkers to inform their use in oncology. [ABSTRACT FROM AUTHOR]

It is increasingly common to encounter complex multiplicity problems with several multiplicity components in confirmatory Phase III clinical trials. These components are often based on several endpoints (primary and secondary endpoints) and several dose-control comparisons. When constructing a multiplicity adjustment in these settings, it is important to control the Type I error rate over all multiplicity components. An important class of multiple testing procedures, known as gatekeeping procedures, was derived using the mixture method that enables clinical trial sponsors to set up efficient multiplicity adjustments that account for clinically relevant logical relationships among the hypotheses of interest. An enhanced version of this mixture method is introduced in this paper to construct more powerful gatekeeping procedures for a specific type of logical relationships that rely on transitive serial restrictions. Restrictions of this kind are very common in Phase III clinical trials and the proposed method is applicable to a broad class of multiplicity problems. Several examples are provided to illustrate the new method and results of simulation trials are presented to compare the performance of gatekeeping procedures derived using this method and other available methods. [ABSTRACT FROM PUBLISHER]

Given the importance of addressing multiplicity issues in confirmatory clinical trials, several recent publications focused on the general goal of identifying most appropriate methods for multiplicity adjustment in each individual setting. This goal can be accomplished using the Clinical Scenario Evaluation approach. This approach encourages trial sponsors to perform comprehensive assessments of applicable analysis strategies such as multiplicity adjustments under all plausible sets of statistical assumptions using relevant evaluation criteria. This two-part paper applies a novel class of criteria, known as criteria based on multiplicity penalties, to the problem of evaluating the performance of several candidate multiplicity adjustments. The ultimate goal of this evaluation is to identify efficient and robust adjustments for each individual trial and optimally select parameters of these adjustments. Part II focuses on advanced settings with several sources of multiplicity, for example, clinical trials with several endpoints evaluated at two or more doses of an experimental treatment. A case study is given to illustrate a penalty-based approach to evaluating candidate multiple testing procedures in advanced multiplicity problems. [ABSTRACT FROM PUBLISHER]

Given the importance of addressing multiplicity issues in confirmatory clinical trials, several recent publications focused on the general goal of identifying most appropriate methods for multiplicity adjustment in each individual setting. This goal can be accomplished using the Clinical Scenario Evaluation approach. This approach encourages trial sponsors to perform comprehensive assessments of applicable analysis strategies such as multiplicity adjustments under all plausible sets of statistical assumptions using relevant evaluation criteria. This two-part paper applies a novel class of criteria, known as criteria based on multiplicity penalties, to the problem of evaluating the performance of several candidate multiplicity adjustments. The ultimate goal of this evaluation is to identify efficient and robust adjustments for each individual trial and optimally select parameters of these adjustments. Part I deals with traditional problems with a single source of multiplicity. Two case studies based on recently conducted Phase III trials are used to illustrate penalty-based approaches to evaluating candidate multiple testing methods and constructing optimization algorithms. [ABSTRACT FROM PUBLISHER]

In similarly designed clinical trials conducted in different regions or a multiregional trial, we sometimes observe different mean responses between trials or regions due to differences in distributions of an ethnic factor. Uesaka et al. (2003) used adjusted means for covariate, the distribution adjusted mean, to deal with such a situation. This paper examines distributional properties of the distribution adjusted mean and shows its applicability to comparison of mean responses from clinical trials where the distribution of an ethnic factor is different between regions. A real example and simulation results are shown. The results of this paper enable us to evaluate the similarity of different responses in clinical trials from different ethnic populations. [ABSTRACT FROM AUTHOR]

The primary objective of a multiregional clinical trial (MRCT) is to assess the efficacy of all participating regions and evaluate the probability of applying the overall results to a specific region. The consistency assessment of the target region with the overall results is the most common way of evaluating the efficacy in a specific region. Recently, Huang et al. (2012) proposed an additional trial in the target region to an MRCT to evaluate the efficacy in the target ethnic (TE) population under the framework of simultaneous global drug development program (SGDDP). However, the operating characteristics of this statistical framework were not well considered. Therefore, a nested group sequential program for regional efficacy evaluation is proposed in this paper. It is an extension of Huang’s SGDDP framework and allows interim analysis after MRCT and in the course of local clinical trial (LCT) phase. It is able to well control the family-wise type I error in the program level and enhances the flexibility of the program. In LCT sample size estimation, we introduce virtual trial, which is transformed from the original program by using discounting factor, and an iteration method is employed to calculate the sample size and stopping boundaries of interim analyses. The proposed sample size estimation method is validated in the simulations and the effect of varied weight, effect size of TE population, and design setting is explored. Examples with normal end point, binary end point, and survival end point are shown to illustrate the application of the proposed nested group sequential program. [ABSTRACT FROM PUBLISHER]

Noninferiority multiregional clinical trials (MRCTs) have recently received increasing attention in drug development. While a major goal in an MRCT is to estimate the global treatment effect, it is also important to assess the consistency of treatment effects across multiple regions. In this paper, we propose an intuitive definition of consistency of noninferior treatment effects across regions under the random-effects modeling framework. Specifically, we quantify the consistency of treatment effects by the percentage of regions that meet a predefined treatment margin. This new approach enables us to achieve both goals in one modeling framework. We propose to use a signed likelihood ratio test for testing the global treatment effect and the consistency of noninferior treatment effects. In addition, we provide guidelines for the allocation rule to achieve optimal power for testing consistency among multiple regions. Extensive simulation studies are conducted to examine the performance of the proposed methodology. An application to a real data example is provided. [ABSTRACT FROM PUBLISHER]

To speed up the process of bringing a new drug to the market, more and more clinical trials are being conducted simultaneously in multiple regions. After demonstrating the overall drug’s efficacy across regions, the regulatory and drug sponsor may also want to assess the drug’s effect in specific region(s). Most of the recent approaches imposed a uniform criterion to assess the consistency of treatment effects between the interested region(s) and the entire study population regardless of the number of regions in multiregional clinical trials (MRCT). As a result, the needed sample size to achieve the desired probability of satisfying the regional requirement could be huge and implausible for the trial sponsors to implement. In this paper, we propose a unified additional requirement for regional approval by differing the parameters in the additional requirement depending on the number of planned regions. In particular, the values of the parameters are determined by a reasonable sample size increase with the desired probability satisfying the additional requirement. Considering the practicality of the global trial or sample size increase, we recommend specific values of the parameters for a different number of planned regions. We also introduce the assurance probability curve to evaluate the performance of different regional requirements. [ABSTRACT FROM PUBLISHER]

Pocock and Simon’s minimization method is a very popular covariate-adaptive randomization procedure intended to balance the allocations of two treatments across a set of covariates without compromising randomness. Additional covariate-adaptive schemes have been proposed in the literature, such as Atkinson’s -optimum Biased Coin Design and the Covariate-Adaptive Biased Coin Design (CA-BCD), and their properties were analyzed and compared in terms of imbalance and predictability. The aim of this paper is to push forward these comparisons by also taking into account other randomization methods, such as the Permuted Block Design, the Big Stick Design, a generalization of the CA-BCD that can be implemented when the covariate distribution is unknown, and the Covariate-Adaptive Dominant Biased Coin Design, which is a new class of stratified randomization methods that forces the balance increasingly as the joint imbalance grows and improves the degree of randomness as the size of every stratum increases. The performance of covariate-adaptive procedures is strictly related to the considered factors and the number of patients in the trial as well, which makes it hard to find a dominant rule, namely a design that is more balanced and less predictable with respect to other schemes. In general, stratified randomization methods perform very well when the number of strata is small, showing also some dominance structure with respect to the other designs. Nevertheless, the evolution and the performance of stratified designs are strictly related to the random entries of the subjects. Thus, these rules become less efficient in the case of both (i) limited samples and (ii) large number of factors/levels. [ABSTRACT FROM AUTHOR]