Search

Background: Considering multiple endpoints in clinical trials provide a more comprehensive understanding of treatment effects and may lead to increased power or reduced sample size, which may be beneficial in rare diseases. Besides the small sample sizes, allocation bias is an issue that affects the validity of these trials. We investigate the impact of allocation bias on testing decisions in clinical trials with multiple endpoints and offer a tool for selecting an appropriate randomization procedure (RP)., Methods: We derive a model for quantifying the effect of allocation bias depending on the RP in the case of two-arm parallel group trials with continuous multiple endpoints. We focus on two approaches to analyze multiple endpoints, either the Šidák procedure to show efficacy in at least one endpoint and the all-or-none procedure to show efficacy in all endpoints., Results: To evaluate the impact of allocation bias on the test decision we propose a biasing policy for multiple endpoints. The impact of allocation on the test decision is measured by the family-wise error rate of the Šidák procedure and the type I error rate of the all-or-none procedure. Using the biasing policy we derive formulas to calculate these error rates. In simulations we show that, for the Šidák procedure as well as for the all-or-none procedure, allocation bias leads to inflation of the mean family-wise error and mean type I error, respectively. The strength of this inflation is affected by the choice of the RP., Conclusion: Allocation bias should be considered during the design phase of a trial to increase validity. The developed methodology is useful for selecting an appropriate RP for a clinical trial with multiple endpoints to minimize allocation bias effects., (© 2024. The Author(s).)

Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package MAMS , requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr's method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power.

Sequential, multiple assignment, randomized trial (SMART) designs are appropriate for comparing adaptive treatment interventions, in which intermediate outcomes (called tailoring variables) guide subsequent treatment decisions for individual patients. Within a SMART design, patients may be re-randomized to subsequent treatments following the outcomes of their intermediate assessments. In this paper, we provide an overview of statistical considerations necessary to design and implement a two-stage SMART design with a binary tailoring variable and a survival final endpoint. A chronic lymphocytic leukemia trial with a final endpoint of progression-free survival is used as an example for the simulations to assess how design parameters, including, choice of randomization ratios for each stage of randomization, and response rates of the tailoring variable affect the statistical power. We assess the choice of weights from restricted re-randomization on data analyses and appropriate hazard rate assumptions. Specifically, for a given first-stage therapy and prior to the tailoring variable assessment, we assume equal hazard rates for all patients randomized to a treatment arm. After the tailoring variable assessment, individual hazard rates are assumed for each intervention path. Simulation studies demonstrate that the response rate of the binary tailoring variable impacts power as it directly impacts the distribution of patients. We also confirm that when the first stage randomization is 1:1, it is not necessary to consider the first stage randomization ratio when applying the weights. We provide an R-shiny application for obtaining power for a given sample size for SMART designs.

As part of the drug development process, interim analysis is frequently used to design efficient phase II clinical trials. A stochastic curtailment framework is often deployed wherein a decision to continue or curtail the trial is taken at each interim look based on the likelihood of observing a positive or negative treatment effect if the trial were to continue to its anticipated end. Thus, curtailment can take place due to evidence of early efficacy or futility. Traditionally, in the case of time-to-event endpoints, interim monitoring is conducted in a two-arm clinical trial using the log-rank test, often with the assumption of proportional hazards. However, when this is violated, the log-rank test may not be appropriate, resulting in loss of power and subsequently inaccurate sample sizes. In this paper, we propose stochastic curtailment methods for two-arm phase II trial with the flexibility to allow non-proportional hazards. The proposed methods are built utilizing the concept of relative time assuming that the survival times in the two treatment arms follow two different Weibull distributions. Three methods - conditional power, predictive power and Bayesian predictive probability - are discussed along with corresponding sample size calculations. The monitoring strategy is discussed with a real-life example.

This manuscript consists of two topics. Firstly, we explore the utility of internal pilot study (IPS) approach for reestimating sample size at an interim stage when a reliable estimate of the nuisance shape parameter of the Weibull distribution for modeling survival data is unavailable during the planning phase of a study. Although IPS approach can help rescue the study power, it is noted that the adjusted sample size can be as much as twice the initially planned sample size, which may put substantial practical constraints to continue the study. Secondly, we discuss Bayesian predictive probability for conducting interim analyses to obtain preliminary evidence of efficacy or futility of an experimental treatment warranting early termination of a clinical trial. In the context of single-arm clinical trials with time-to-event endpoints following Weibull distribution, we present the calculation of the Bayesian predictive probability when the shape parameter of the Weibull distribution is unknown. Based on the data accumulated at the interim, we propose two approaches which rely on the posterior mode or the entire posterior distribution of the shape parameter. To account for uncertainty in the shape parameter, it is recommended to incorporate its entire posterior distribution in our calculation.

When the primary endpoints in randomized clinical trials require long term follow-up or are costly to measure, it is often desirable to assess treatment effects on surrogate instead of clinical endpoints. Prior to adopting a surrogate endpoint for such purposes, the extent of its surrogacy on the primary endpoint must be assessed. There is a rich statistical literature on assessing surrogacy in the overall population, much of which is based on quantifying the proportion of treatment effect on the primary endpoint that is explained by the treatment effect on the surrogate endpoint. However, the surrogacy of an endpoint may vary across different patient subgroups according to baseline demographic characteristics, and limited methods are currently available to assess overall surrogacy in the presence of potential surrogacy heterogeneity. In this paper, we propose methods that incorporate covariates for baseline information, such as age, to improve overall surrogacy assessment. We use flexible semi-non-parametric modeling strategies to adjust for covariate effects and derive a robust estimate for the proportion of treatment effect of the covariate-adjusted surrogate endpoint. Simulation results suggest that the adjusted surrogate endpoint has greater proportion of treatment effect compared to the unadjusted surrogate endpoint. We apply the proposed method to data from a clinical trial of infliximab and assess the adequacy of the surrogate endpoint in the presence of age heterogeneity., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change as the trial progresses. Determining optimal allocation rates to allocate patients to the treatment and control arms in platform trials is challenging because the optimal allocation depends on the number of arms in the platform and the latter typically varies over time. In addition, the optimal allocation depends on the analysis strategy used and the optimality criteria considered. In this article, we derive optimal treatment allocation rates for platform trials with shared controls, assuming that a stratified estimation and a testing procedure based on a regression model are used to adjust for time trends. We consider both, analysis using concurrent controls only as well as analysis methods using concurrent and non-concurrent controls and assume that the total sample size is fixed. The objective function to be minimized is the maximum of the variances of the effect estimators. We show that the optimal solution depends on the entry time of the arms in the trial and, in general, does not correspond to the square root of k allocation rule used in classical multi-arm trials. We illustrate the optimal allocation and evaluate the power and type 1 error rate compared to trials using one-to-one and square root of k allocations by means of a case study., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Multicentre RCTs are widely used by critical care researchers to answer important clinical questions. However, few trials evaluating mortality outcomes report statistically significant results. We hypothesised that the low proportion of trials reporting statistically significant differences for mortality outcomes is plausibly explained by lower-than-expected effect sizes combined with a low proportion of participants who could realistically benefit from studied interventions., Methods: We reviewed multicentre trials in critical care published over a 10-yr period in the New England Journal of Medicine, the Journal of the American Medical Association, and the Lancet. To test our hypothesis, we analysed the results using a Bayesian model to investigate the relationship between the proportion of effective interventions and the proportion of statistically significant results for prior distributions of effect size and trial participant susceptibility., Results: Five of 54 trials (9.3%) reported a significant difference in mortality between the control and the intervention groups. The median expected and observed differences in absolute mortality were 8.0% and 2.0%, respectively. Our modelling shows that, across trials, a lower-than-expected effect size combined with a low proportion of potentially susceptible participants is consistent with the observed proportion of trials reporting significant differences even when most interventions are effective., Conclusions: When designing clinical trials, researchers most likely overestimate true population effect sizes for critical care interventions. Bayesian modelling demonstrates that that it is not necessarily the case that most studied interventions lack efficacy. In fact, it is plausible that many studied interventions have clinically important effects that are missed., (Copyright © 2021 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Composite endpoints reveal the tendency for statistical convention to arise locally within subfields. Composites are familiar in cardiovascular trials, yet almost unknown in sepsis. However, the VITAMINS trial in patients with septic shock adopted a composite of mortality and vasopressor-free days, and an ordinal scale describing patient status rapidly became standard in COVID studies. Aware that recent use could incite interest in such endpoints, we are motivated to flag their potential value and pitfalls for sepsis research and COVID studies., (© 2020 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

Basket trials are a recent and innovative approach in oncological clinical trial design. A basket trial is a type of clinical trial for which eligibility is based on the presence of a specific genomic alteration, irrespective of cancer type. Additionally, basket trials are often used to evaluate the response rate of an investigational therapy across several types of cancer. Recently developed statistical methods for evaluating the response rate in basket trials can be generally categorized into two groups: (a) those that account for the degrees of homogeneity/heterogeneity of response rates among subpopulations, and (b) those using borrowed response rate information across subpopulations to improve the statistical efficiency using Bayesian hierarchical models. In this study, we developed a new basket trial design that accounts for the uncertainties of homogeneity and heterogeneity of response rates among subpopulations using the Bayesian model averaging approach. We demonstrated the utility of the proposed method by comparing our approach against other methods for the two methodological groups using simulated and actual data. On an average, the proposed methods offered an intermediate performance between the BHM-weak and BHM-strong methods. The proposed method would be useful for "signal-finding" basket trials without prior information on the treatment effect of an investigational drug, in part because the proposed method does not require specifications regarding prior distributions of homogeneity response rates among subpopulations., (© 2020 John Wiley & Sons Ltd.)

In clinical development, there is a trade-off between investment and level of confidence in the potential of the drug before going into phase III. Reduced investment requires the use of short-term endpoints. On new compounds, only limited information about the relationship between treatment effects of short- and long-term endpoints is usually available. Therefore, decision-making solely based on short-term endpoints does not seem desirable. Our goal is to plan an efficient development program, which uses short- and long-term endpoints data for decision-making. We found that with limited prior information and restrictions on maximum sample size, decision-making after phase II cannot be substantially improved. We follow the concept of a "phase 2+" design where after a go-to-phase-III-decision, further follow-up data from phase II are employed to make interim decisions on phase III. The program will be stopped early when additional phase II and/or available phase III data lead to a low probability of success (PoS). We utilize information from a multi-categorical short-term endpoint (response status) and a long-term endpoint (overall survival (OS)) to determine the PoS in phase III with OS as the primary endpoint. Optimal combinations of decision boundaries and time points are demonstrated in a simulation study. Our results show that the proposed second look using additional follow-up data from phase II/III improves PoS estimates compared to the first look, especially when prior data about the control arm is available. The proposed planning strategy allows a customized compromise between the quality of decision-making and program duration., (© 2020 John Wiley & Sons Ltd.)

We propose a Bayesian optimal phase II (BOP2) design for clinical trials with a time-to-event endpoint (eg, progression-free survival [PFS]) or co-primary endpoints consisted of a time-to-event endpoint and a categorical endpoint (eg, PFS and toxicity). We use an exponential-inverse gamma model to model the time to event. At each interim, the go/no-go decision is made by comparing the posterior probabilities of the event of interest with an adaptive probability cutoff. The BOP2 design is flexible in the number of interim looks and applicable to both single-arm and two-arm trials. The design maximizes the power for detecting effective treatments, with a well-controlled type I error, thereby bridging the gap between Bayesian designs and frequentist designs. The BOP2 design is easy to implement. Its stopping boundary can be enumerated and included in study protocol before the onset of the trial for single-arm studies. Simulation studies show that the BOP2 design has favorable operating characteristics, with higher power and lower risk of incorrectly terminating the trial than some Bayesian phase II designs. The software to implement the BOP2 design will be freely available at www.trialdesign.org., (© 2020 John Wiley & Sons Ltd.)

Background: The ICH E9(R1) addendum states that the strategy to account for intercurrent events should be included when defining an estimand, the treatment effect to be estimated based on the study objective. The estimator used to assess the treatment effect needs to be aligned with the estimand that accounted for intercurrent events. Regardless of the strategy, missing data resulting from patient premature withdrawal could undermine the robustness of the study results. Informative censoring due to dropouts in an events-based study is one such example. Sensitivity analyses using imputation methods are useful to examine the uncertainty due to informative censoring and address the robustness and strength of the study results., Methods: We assessed the effect of premature patient withdrawal in the PRECISION study, a randomized non-inferiority clinical trial of patients with chronic arthritic pain that compared the cardiovascular safety of three nonsteroidal anti-inflammatory drugs-based treatment policies or paradigms. The protocol-defined use of concomitant or rescue medications was permitted since changes in pain medications due to insufficient analgesia were expected in patients in this long-term study. Anticipating that premature study discontinuations could potentially lead to informative censoring, a supplementary analysis was pre-specified in which censored outcomes due to the premature study discontinuation were imputed based on adverse events that were clinically associated with the primary endpoint (cardiovascular outcome based on the Antiplatelet Trialists Collaboration composite endpoint). Furthermore, tipping point analyses were conducted to test the robustness of the primary analysis results by assuming data censored not at random. The level of increase at which the primary study conclusion would change was estimated., Results: For the analysis of time to first primary endpoint event through 30 months, 4065 out of the 24,081 enrolled patients were lost to follow-up, withdrew consent, or were no longer willing to participate in the study. These withdrawals occurred gradually and resulted in a cumulative total of 5893 censored patient-years of observation (10.2%). The rate of discontinuation and the baseline characteristics of the discontinued patients were similar across the three treatment groups. The non-inferiority conclusion from the primary analysis was confirmed in the supplementary analysis incorporating relevant adverse events. Furthermore, tipping point analyses demonstrated that in order to lose non-inferiority in the primary analysis, the risk of primary endpoint events during the censored observation time would have to increase by more than 2.7-fold in the celecoxib group while remaining constant in the other nonsteroidal anti-inflammatory drugs groups, demonstrating that the scenarios where the study results are invalid appear not plausible., Conclusions: Supplementary and sensitivity analyses presented to address informative censoring in PRECISION helped to further interpret and strengthen the study results.

A clinical trial often has primary and secondary endpoints and comparisons of high and low doses of a study drug to a control. Multiplicity is not only caused by the multiple comparisons of study drugs versus the control, but also from the hierarchical structure of the hypotheses. Closed test procedures were proposed as general methods to address multiplicity. Two commonly used tests for intersection hypotheses in closed test procedures are the Simes test and the average method. When the treatment effect of a less efficacious dose is not much smaller than the treatment effect of a more efficacious dose for a specific endpoint, the average method has better power than the Simes test for the comparison of two doses versus control. Accordingly, for inferences for primary and secondary endpoints, the matched parallel gatekeeping procedure based on the Simes test for testing intersection hypotheses is extended here to allow the average method for such testing. This procedure is further extended to clinical trials with more than two endpoints as well as to clinical trials with more than two active doses and a control.

Purpose of Review: Clinical-trial design, analysis, and interpretation entails the use of efficient and reliable endpoints. Statistical issues related to endpoints warrant continued attention, as they may have a substantial impact on the conduct of clinical trials and on interpretation of their results., Recent Findings: We review concepts and discuss recent developments related to the use of time-to-event endpoints in studies on adjuvant and neoadjuvant therapy for colon, pancreatic, and gastric adenocarcinomas. The definition of endpoints has varied to a considerable extent in these settings. Although these variations are relevant in interpreting results from individual trials, they probably have a small impact when considered in aggregate. In terms of surrogacy, most published reports so far have used aggregated data. A few studies based on the preferred method of a metaanalysis of individual-patient data have shown that disease-free survival (DFS) is a surrogate for overall survival in the adjuvant therapy of stage III colon cancer and in gastric cancer, whereas DFS with a landmark of six months is a surrogate for overall survival in the neoadjuvant therapy of adenocarcinoma of the esophagus, gastroesophageal junction, or stomach., Summary: Testing novel agents in gastrointestinal cancer requires continued attention to statistical issues related to endpoints.

In this paper, we propose a Bayesian design framework for a biosimilars clinical program that entails conducting concurrent trials in multiple therapeutic indications to establish equivalent efficacy for a proposed biologic compared to a reference biologic in each indication to support approval of the proposed biologic as a biosimilar. Our method facilitates information borrowing across indications through the use of a multivariate normal correlated parameter prior (CPP), which is constructed from easily interpretable hyperparameters that represent direct statements about the equivalence hypotheses to be tested. The CPP accommodates different endpoints and data types across indications (eg, binary and continuous) and can, therefore, be used in a wide context of models without having to modify the data (eg, rescaling) to provide reasonable information-borrowing properties. We illustrate how one can evaluate the design using Bayesian versions of the type I error rate and power with the objective of determining the sample size required for each indication such that the design has high power to demonstrate equivalent efficacy in each indication, reasonably high power to demonstrate equivalent efficacy simultaneously in all indications (ie, globally), and reasonable type I error control from a Bayesian perspective. We illustrate the method with several examples, including designing biosimilars trials for follicular lymphoma and rheumatoid arthritis using binary and continuous endpoints, respectively., (© 2019 The International Biometric Society.)

The design of a clinical trial is often complicated by the multi-systemic nature of the disease; a single endpoint often cannot capture the spectrum of potential therapeutic benefits. Multi-domain outcomes which take into account patient heterogeneity of disease presentation through measurements of multiple symptom/functional domains are an attractive alternative to a single endpoint. A multi-domain test with adaptive weights is proposed to synthesize the evidence of treatment efficacy over numerous disease domains. The test is a weighted sum of domain-specific test statistics with weights selected adaptively via a data-driven algorithm. The null distribution of the test statistic is constructed empirically through resampling and does not require estimation of the covariance structure of domain-specific test statistics. Simulations show that the proposed test controls the type I error rate, and has increased power over other methods such as the O'Brien and Wei-Lachin tests in scenarios reflective of clinical trial settings. Data from a clinical trial in a rare lysosomal storage disorder were used to illustrate the properties of the proposed test. As a strategy of combining marginal test statistics, the proposed test is flexible and readily applicable to a variety of clinical trial scenarios., (© 2019 John Wiley & Sons Ltd.)

A placebo-controlled randomized clinical trial is required to demonstrate that an experimental treatment is superior to its corresponding placebo on multiple coprimary endpoints. This is particularly true in the field of neurology. In fact, clinical trials for neurological disorders need to show the superiority of an experimental treatment over a placebo in two coprimary endpoints. Unfortunately, these trials often fail to detect a true treatment effect for the experimental treatment versus the placebo owing to an unexpectedly high placebo response rate. Sequential parallel comparison design (SPCD) can be used to address this problem. However, the SPCD has not yet been discussed in relation to clinical trials with coprimary endpoints. In this article, our aim was to develop a hypothesis-testing method and a method for calculating the corresponding sample size for the SPCD with two coprimary endpoints. In a simulation, we show that the proposed hypothesis-testing method achieves the nominal type I error rate and power and that the proposed sample size calculation method has adequate power accuracy. In addition, the usefulness of our methods is confirmed by returning to an SPCD trial with a single primary endpoint of Alzheimer disease-related agitation., (© 2019 John Wiley & Sons, Ltd.)

One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials., (© 2019 John Wiley & Sons Ltd.)

A challenge arising in cancer immunotherapy trial design is the presence of a delayed treatment effect wherein the proportional hazard assumption no longer holds true. As a result, a traditional survival trial design based on the standard log-rank test, which ignores the delayed treatment effect, will lead to substantial loss of statistical power. Recently, a piecewise weighted log-rank test is proposed to incorporate the delayed treatment effect into consideration of the trial design. However, because the sample size formula was derived under a sequence of local alternative hypotheses, it results in an underestimated sample size when the hazard ratio is relatively small for a balanced trial design and an inaccurate sample size estimation for an unbalanced design. In this article, we derived a new sample size formula under a fixed alternative hypothesis for the delayed treatment effect model. Simulation results show that the new formula provides accurate sample size estimation for both balanced and unbalanced designs., (© 2019 John Wiley & Sons, Ltd.)

The win ratio has been studied methodologically and applied in data analysis and in designing clinical trials. Researchers have pointed out that the results depend on follow-up time and censoring time, which are sometimes used interchangeably. In this article, we distinguish between follow-up time and censoring time, show theoretically the impact of censoring on the win ratio, and illustrate the impact of follow-up time. We then point out that, if the treatment has long-term benefit from a more important but less frequent endpoint (eg, death), the win ratio can show that benefit by following patients longer, avoiding masking by more frequent but less important outcomes, which occurs in conventional time-to-first-event analyses. For the situation of nonproportional hazards, we demonstrate that the win ratio can be a good alternative to methods such as landmark survival rate, restricted mean survival time, and weighted log-rank tests., (© 2019 John Wiley & Sons, Ltd.)

Recently, molecularly targeted agents and immunotherapy have been advanced for the treatment of relapse or refractory cancer patients, where disease progression-free survival or event-free survival is often a primary endpoint for the trial design. However, methods to evaluate two-stage single-arm phase II trials with a time-to-event endpoint are currently processed under an exponential distribution, which limits application of real trial designs. In this paper, we developed an optimal two-stage design, which is applied to the four commonly used parametric survival distributions. The proposed method has advantages compared with existing methods in that the choice of underlying survival model is more flexible and the power of the study is more adequately addressed. Therefore, the proposed two-stage design can be routinely used for single-arm phase II trial designs with a time-to-event endpoint as a complement to the commonly used Simon's two-stage design for the binary outcome., (© 2019 John Wiley & Sons, Ltd.)

The large number of failures in phase III clinical trials, which occur at a rate of approximately 45%, is studied herein relative to possible countermeasures. First, the phenomenon of failures is numerically described. Second, the main reasons for failures are reported, together with some generic improvements suggested in the related literature. This study shows how statistics explain, but do not justify, the high failure rate observed. The rate of failures due to a lack of efficacy that are not expected, is considered to be at least 10%. Expanding phase II is the simplest and most intuitive way to reduce phase III failures since it can reduce phase III false negative findings and launches of phase III trials when the treatment is positive but suboptimal. Moreover, phase II enlargement is discussed using an economic profile. As resources for research are often limited, enlarging phase II should be evaluated on a case-by-case basis. Alternative strategies, such as biomarker-based enrichments and adaptive designs, may aid in reducing failures. However, these strategies also have very low application rates with little likelihood of rapid growth., (© 2019 John Wiley & Sons, Ltd.)

Covariate adjustment for the estimation of treatment effect for randomized controlled trials (RCT) is a simple approach with a long history, hence, its pros and cons have been well-investigated and published in the literature. It is worthwhile to revisit this topic since recently there has been significant investigation and development on model assumptions, robustness to model mis-specification, in particular, regarding the Neyman-Rubin model and the average treatment effect estimand. This paper discusses key results of the investigation and development and their practical implication on pharmaceutical statistics. Accordingly, we recommend that appropriate covariate adjustment should be more widely used for RCTs for both hypothesis testing and estimation., (© 2019 John Wiley & Sons, Ltd.)

Percentile is ubiquitous in statistics and plays a significant role in the day-to-day statistical application. FDA Guidance for Industry: Assay Development for Immunogenicity Testing of Therapeutic Protein Products (2016) recommends the use of a lower confidence limit of the percentile of the negative subject population as the cut point to guarantee a pre-specified false-positive rate with high confidence. Shen proposed and compared an exact t approach with some approximated approaches. However, the exact t approach might be compromised by computational time and complexity. In this article, we proposed to use a UMOVER method as a potential alternative for percentile estimation for one application to screening and confirmatory cut point determination due to its easy implementation and similar performance to the exact t approach. The applications and performance comparison with different approaches are investigated and discussed. Furthermore, we extended the proposed method for the comparison of the percentile of the test product and percentile of the reference product followed by numerical studies.

We propose a novel response-adaptive randomization procedure for multi-armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non-myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response-adaptive algorithm based on the Gittins index for the multi-armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969-978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi-armed setting, there are efficiency and patient benefit gains of using a response-adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response-adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi-armed trial context., (© 2019 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.)

Background and Objective: The use of valid surrogate endpoints can accelerate the development of phase III trials. Numerous validation methods have been proposed with the most popular used in a context of meta-analyses, based on a two-step analysis strategy. For two failure time endpoints, two association measures are usually considered, Kendall's τ at individual level and adjusted R2 ([Formula: see text]) at trial level. However, [Formula: see text] is not always available mainly due to model estimation constraints. More recently, we proposed a one-step validation method based on a joint frailty model, with the aim of reducing estimation issues and estimation bias on the surrogacy evaluation criteria. The model was quite robust with satisfactory results obtained in simulation studies. This study seeks to popularize this new surrogate endpoints validation approach by making the method available in a user-friendly R package., Methods: We provide numerous tools in the frailtypack R package, including more flexible functions, for the validation of candidate surrogate endpoints using data from multiple randomized clinical trials., Results: We implemented the surrogate threshold effect which is used in combination with [Formula: see text] to make decisions concerning the validity of the surrogate endpoints. It is also possible thanks to frailtypack to predict the treatment effect on the true endpoint in a new trial using the treatment effect observed on the surrogate endpoint. The leave-one-out cross-validation is available for assessing the accuracy of the prediction using the joint surrogate model. Other tools include data generation, simulation study and graphic representations. We illustrate the use of the new functions with both real data and simulated data., Conclusion: This article proposes new attractive and well developed tools for validating failure time surrogate endpoints., Competing Interests: The authors have declared that no competing interests exist.

In the analysis of composite endpoints in a clinical trial, time to first event analysis techniques such as the logrank test and Cox proportional hazard test do not take into account the multiplicity, importance, and the severity of events in the composite endpoint. Several generalized pairwise comparison analysis methods have been described recently that do allow to take these aspects into account. These methods have the additional benefit that all types of outcomes can be included, such as longitudinal quantitative outcomes, to evaluate the full treatment effect. Four of the generalized pairwise comparison methods, ie, the Finkelstein-Schoenfeld, the Buyse, unmatched Pocock, and adapted O'Brien test, are summarized. They are compared to each other and to the logrank test by means of simulations while specifically evaluating the effect of correlation between components of the composite endpoint on the power to detect a treatment difference. These simulations show that prioritized generalized pairwise comparison methods perform very similarly, are sensitive to the priority rank of the components in the composite endpoint, and do not measure the true treatment effect from the second priority-ranked component onward. The nonprioritized pairwise comparison test does not suffer from these limitations and correlation affects only its variance., (© 2019 John Wiley & Sons, Ltd.)

Interim analyses are routinely used to monitor accumulating data in clinical trials. When the objective of the interim analysis is to stop the trial if the trial is deemed futile, it must ideally be conducted as early as possible. In trials where the clinical endpoint of interest is only observed after a long follow-up, many enrolled patients may therefore have no information on the primary endpoint available at the time of the interim analysis. To facilitate earlier decision-making, one may incorporate early response data that are predictive for the primary endpoint (eg, an assessment of the primary endpoint at an earlier time) in the interim analysis. Most attention so far has been given to the development of interim test statistics that include such short-term endpoints, but not to decision procedures. Existing tests moreover perform poorly when the information is scarce, eg, due to rare events, when the cohort of patients with observed primary endpoint data is small, or when the short-term endpoint is a strong but imperfect predictor. In view of this, we develop an interim decision procedure based on the conditional power approach that utilizes the short-term and long-term binary endpoints in a framework that is expected to provide reliable inferences, even when the primary endpoint is only available for a few patients, and has the added advantage that it allows the use of historical information. The operational characteristics of the proposed procedure are evaluated for the phase III clinical trial that motivated this approach, using simulation studies., (© 2019 John Wiley & Sons, Ltd.)

In the clinical development of some new infectious disease drugs, early clinical pharmacology trials may predict with high confidence that the efficacious doses are well below the range of the safety margin. In this case, a dose-ranging study may be unnecessary after a proof-of-concept (PoC) study testing the highest dose. A multi-stage adaptive design spanning both PoC and confirmatory stages is proposed in this context. The design incorporates two interim analyses allowing strategies for stopping, continuing, or expanding the study. A conditional power threshold for a binary endpoint is proposed to assess futility. Additional components of early efficacy and sample size adjustment are also included to enhance the design's flexibility and robustness. Design operating characteristics are evaluated by numerical calculation. We show that the proposed streamlined trial design has the same statistical rigor as a conventional phase 3 clinical trial with adequate power and a properly controlled type 1 error rate. Additional adaptive design options are also investigated and discussed.

In randomised trials, continuous endpoints are often measured with some degree of error. This study explores the impact of ignoring measurement error and proposes methods to improve statistical inference in the presence of measurement error. Three main types of measurement error in continuous endpoints are considered: classical, systematic, and differential. For each measurement error type, a corrected effect estimator is proposed. The corrected estimators and several methods for confidence interval estimation are tested in a simulation study. These methods combine information about error-prone and error-free measurements of the endpoint in individuals not included in the trial (external calibration sample). We show that, if measurement error in continuous endpoints is ignored, the treatment effect estimator is unbiased when measurement error is classical, while Type-II error is increased at a given sample size. Conversely, the estimator can be substantially biased when measurement error is systematic or differential. In those cases, bias can largely be prevented and inferences improved upon using information from an external calibration sample, of which the required sample size increases as the strength of the association between the error-prone and error-free endpoint decreases. Measurement error correction using already a small (external) calibration sample is shown to improve inferences and should be considered in trials with error-prone endpoints. Implementation of the proposed correction methods is accommodated by a new software package for R., (© 2019 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

In clinical endpoint bioequivalence studies, the observed per-protocol (PP) population (compliers and completers in general) is usually used in the primary analysis for equivalence assessment. However, intercurrent events, ie, missingness and noncompliance, are not properly handled. The resulting estimand is not causal. Previously, we proposed the first causal framework to assess equivalence in the presence of missing data and noncompliance. We proposed a causal survivor average causal effect (SACE) estimand for the difference of means (DOM). In equivalence assessment, DOM is not as widely used as the ratio of means (ROM). However, no existing formula links the observed PP estimand to the SACE estimand for ROM as exists for DOM. Herein, we propose a similar causal framework for ROM using the principal stratification approach, one of the strategies recommended by the International Conference on Harmonisation (ICH) E9 R1 addendum. We quantify the bias of the observed ROM PP estimand for the SACE estimand, which provides a basis to identify three conditions under which the two estimands are equal. We propose a sensitivity analysis method to evaluate the robustness of the current PP estimator to estimate the SACE estimand. We extend Fieller's confidence interval for the SACE estimand using ROM, which can be applied to many settings. Simulation demonstrates that the PP estimator is biased in either directions and may inflate type 1 error and/or change power when the three identified conditions are violated. Our work can be applied to comparative clinical biosimilar studies., (© 2019 John Wiley & Sons, Ltd.)

Objectives: To examine variation in outcomes, outcome measurement instruments (OMIs) and measurement times in clinical trials of non-valvular atrial fibrillation (NVAF) and to identify outcomes for prioritisation in developing a core outcome set (COS) in this field., Design: This study was a systematic review., Data Sources: Clinical trials published between January 2015 and March 2019 were obtained from PubMed, the Cochrane Library, Web of Science, Wanfang Database, the China National Knowledge Infrastructure and SinoMed., Eligibility Criteria: Randomised controlled trials (RCTs) and observational studies were considered. Interventions included traditional Chinese medicine and Western medicine. The required treatment duration or follow-up time was ≥4 weeks. The required sample size was ≥30 and≥50 in each group in RCTs and observational studies, respectively. We excluded trials that aimed to investigate the outcome of complications of NVAF, to assess the mechanisms or pharmacokinetics, or for which full text could not be acquired., Data Extraction and Synthesis: The general information and outcomes, OMIs and measurement times were extracted. The methodological and outcome reporting quality were assessed. The results were analysed by descriptive analysis., Results: A total of 218 articles were included from 25 255 articles. For clinical trials of antiarrhythmic therapy, 69 outcomes from 16 outcome domains were reported, and 28 (31.82%, 28/88) outcomes were reported only once; the most frequently reported outcome was ultrasonic cardiogram. Thirty-one outcomes (44.93%, 31/69) were provided definitions or OMIs; the outcome measurement times ranged from 1 to 20 with a median of 3. For clinical trials of anticoagulation therapy, 82 outcomes from 18 outcome domains were reported; 38 (29.23%, 38/130) outcomes were reported only once. The most frequently reported outcome was ischaemic stroke. Forty (48.78%, 40/82) outcomes were provided OMIs or definitions; and the outcome measurement times ranged from 1 to 27 with a median of 8., Conclusion: Outcome reporting in NVAF is inconsistent. Thus, developing a COS that can be used in clinical trials is necessary., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: In some randomised trials, the primary interest is in the mechanisms by which an intervention exerts its effects on health outcomes. That is, clinicians and policy-makers may be interested in how the intervention works (or why it does not work) through hypothesised causal mechanisms. In this article, we highlight the value of understanding causal mechanisms in randomised trials by applying causal mediation analysis to two randomised trials of complex interventions., Main Body: In the first example, we examine a potential mechanism by which an exercise programme for rheumatoid arthritis of the hand could improve hand function. In the second example, we explore why a rehabilitation programme for ankle fractures failed to improve lower-limb function through hypothesised mechanisms. We outline critical assumptions that are required for making valid causal inferences from these analyses, and provide results of sensitivity analyses that are used to assess the degree to which the estimated causal mediation effects could have been biased by residual confounding., Conclusion: This paper demonstrates how the application of causal mediation analyses to randomised trials can identify the mechanisms by which complex interventions exert their effects. We discuss methodological issues and assumptions that should be considered when mediation analyses of randomised trials are used to inform clinical practice and policy decisions.

Objectives: To determine the types of endpoints that were the basis for efficacy assessment of medicines used in particular groups of oncological indications. Changes in the endpoints applied in marketing authorization practice were also considered., Methods: The analysis included marketing authorization applications (MAAs) for medicines used in oncological indications that were first-time approved by the European Medicines Agency (EMA) between 2009 and 2017, and the extensions of the analyzed medicines., Results: The analysis covered 125 MAAs: first-time approved (62%) and extensions (38%). In the analyzed trials, the endpoints that were reported most frequently included overall survival (OS), progression-free survival (PFS), and overall response rate (in 94.4%, 92.8%, 87.2% of MAAs, respectively). The following trends were observed: decreased significance of OS as a primary endpoint and increased significance of PFS as a primary endpoint (hematological indications). An analysis of MAAs for which the OS results were immature confirms the increased significance of PFS and new efficacy indicators (ie, pathological complete response)., Conclusions: An analysis of EMA's marketing authorization practice proves that the use of surrogate endpoints is becoming increasingly common in evaluating oncological health technologies. EMA's guidelines underline the role played by surrogates in the process of assessing efficacy of new therapies. Results of an analysis demonstrate that protocols of clinical trials define surrogates as primary endpoints more and more often. Furthermore, a positive decision on granting marketing authorization is possible also in situations when only such clinical data are available., (Copyright © 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Perioperative investigators and professionals increasingly seek to evaluate whether implementing systematic practice changes improves outcomes compared to a previous routine. Cluster randomized trials are the optimal design to assess a systematic practice change but are often impractical; investigators, therefore, often select a before-after design. In this Statistical Grand Rounds, we first discuss biases inherent in a before-after design, including confounding due to periods being completely separated by time, regression to the mean, the Hawthorne effect, and others. Many of these biases can be at least partially addressed by using appropriate designs and analyses, which we discuss. Our focus is on segmented regression of an interrupted time series, which does not require a concurrent control group; we also present alternative designs including difference-in-difference, stepped wedge, and cluster randomization. Conducting segmented regression well requires a sufficient number of time points within each period, along with a robust set of potentially confounding variables. This method compares preintervention and postintervention changes over time, divergences in the outcome when an intervention begins, and trends observed with the intervention compared to trends projected without it. Difference-in-difference methods add a concurrent control, enabling yet stronger inference. When done well, the discussed methods permit robust inference on the effect of an intervention, albeit still requiring assumptions and having limitations. Methods are demonstrated using an interrupted time series study in which anesthesiologists took responsibility for an adult medical emergency team from internal medicine physicians in an attempt to improve outcomes.

In this Collection Review for the Novel Treatments for Tuberculosis Collection, Piero Olliaro and Michael Vaillant discuss the considerations when choosing a non-inferiority margin that is meaningful from statistical, ethical, clinical, and health standpoint., Competing Interests: The authors have declared that no competing interests exist.

Background: Cluster randomised trials with unequal sized clusters often have lower precision than with clusters of equal size. To allow for this, sample sizes are inflated by a modified version of the design effect for clustering. These inflation factors are valid under the assumption that randomisation is stratified by cluster size. We investigate the impact of unequal cluster size when that constraint is relaxed, with particular focus on the stepped-wedge cluster randomised trial, where this is more difficult to achieve., Methods: Assuming a multi-level mixed effect model with exchangeable correlation structure for a cross-sectional design, we use simulation methods to compare the precision for a trial with clusters of unequal size to a trial with clusters of equal size (relative efficiency). For a range of scenarios we illustrate the impact of various design features (the cluster-mean correlation - a function of the intracluster correlation and the cluster size, the number of clusters, number of randomisation sequences) on the average and distribution of the relative efficiency., Results: Simulations confirm that the average reduction in precision, due to varying cluster sizes, is smaller in a stepped-wedge trial compared to the parallel trial. However, the variance of the distribution of the relative efficiency is large; and is larger under the stepped-wedge design compared to the parallel design. This can result in large variations in actual power, depending on the allocation of clusters to sequences. Designs with larger variations in cluster sizes, smaller number of clusters and studies with smaller cluster-mean correlations (smaller cluster sizes or smaller intra-cluster correlation) are particularly at risk., Conclusion: The actual realised power in a stepped-wedge trial might be substantially higher or lower than that estimated. This is particularly important when there are a small number of clusters or the variability in cluster sizes is large. Constraining the randomisation on cluster size, where feasible, might mitigate this effect.

Background: The rationale for the use of composite time-to-event endpoints is to increase the number of expected events and thereby the power by combining several event types of clinical interest. The all-cause hazard ratio is the standard effect measure for composite endpoints where the all-cause hazard function is given as the sum of the event-specific hazards. However, the effect of the individual components might differ, in magnitude or even in direction, which leads to interpretation difficulties. Moreover, the individual event types often are of different clinical relevance which further complicates interpretation. Our working group recently proposed a new weighted effect measure for composite endpoints called the 'weighted all-cause hazard ratio'. By imposing relevance weights for the components, the interpretation of the composite effect becomes more 'natural'. Although the weighted all-cause hazard ratio seems an elegant solution to overcome interpretation problems, the originally published approach has several shortcomings: First, the proposed point estimator requires pre-specification of a parametric survival model. Second, no closed formula for a corresponding test statistic was provided. Instead, a permutation test was proposed. Third, no clear guidance for the choice of the relevance weights was provided. In this work, we will overcome these problems., Methods: Within this work a new non-parametric estimator and a related closed formula test statistic are presented. Performance of the new estimator and test is compared to the original ones by a Monte-Carlo simulation study., Results: The original parametric estimator is sensible to miss-specifications of the survival model. The new non-parametric estimator turns out to be very robust even if the required assumptions are not met. The new test shows considerably better power properties than the permutation test, is computationally much less expensive but might not preserve type one error in all situations. A scheme for choosing the relevance weights in the planning stage is provided., Conclusion: We recommend to use the non-parametric estimator along with the new test to assess the weighted all-cause hazard ratio. Concrete guidance for the choice of the relevance weights is now available. Thus, applying the weighted all-cause hazard ratio in clinical applications is both - feasible and recommended.

Goal Attainment Scaling is an assessment instrument to evaluate interventions on the basis of individual, patient-specific goals. The attainment of these goals is mapped in a pre-specified way to attainment levels on an ordinal scale, which is common to all goals. This approach is patient-centred and allows one to integrate the outcomes of patients with very heterogeneous symptoms. The latter is of particular importance in clinical trials in rare diseases because it enables larger sample sizes by including a broader patient population. In this paper, we focus on the statistical analysis of Goal Attainment Scaling outcomes for the comparison of two treatments in randomised clinical trials. Building on a general statistical model, we investigate the properties of different hypothesis testing approaches. Additionally, we propose a latent variable approach to generate Goal Attainment Scaling data in a simulation study, to assess the impact of model parameters such as the number of goals per patient and their correlation, the choice of discretisation thresholds and the type of design (parallel group or cross-over). Based on our findings, we give recommendations for the design of clinical trials with a Goal Attainment Scaling endpoint. Furthermore, we discuss an application of Goal Attainment Scaling in a clinical trial in mastocytosis.

Composite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a composite binary endpoint to size a trial, one needs to specify the event rates and the effect sizes of the composite components as well as the correlation between them. In practice, the marginal parameters of the components can be obtained from previous studies or pilot trials; however, the correlation is often not previously reported and thus usually unknown. We first show that the sample size for composite binary endpoints is strongly dependent on the correlation and, second, that slight deviations in the prior information on the marginal parameters may result in underpowered trials for achieving the study objectives at a pre-specified significance level. We propose a general strategy for calculating the required sample size when the correlation is not specified and accounting for uncertainty in the marginal parameter values. We present the web platform CompARE to characterize composite endpoints and to calculate the sample size just as we propose in this paper. We evaluate the performance of the proposal with a simulation study and illustrate it by means of a real case study using CompARE., (© 2019 John Wiley & Sons, Ltd.)

Adaptive trial methodology for multiarmed trials and enrichment designs has been extensively discussed in the past. A general principle to construct test procedures that control the family-wise Type I error rate in the strong sense is based on combination tests within a closed test. Using survival data, a problem arises when using information of patients for adaptive decision making, which are under risk at interim. With the currently available testing procedures, either no testing of hypotheses in interim analyses is possible or there are restrictions on the interim data that can be used in the adaptation decisions as, essentially, only the interim test statistics of the primary endpoint may be used. We propose a general adaptive testing procedure, covering multiarmed and enrichment designs, which does not have these restrictions. An important application are clinical trials, where short-term surrogate endpoints are used as basis for trial adaptations, and we illustrate how such trials can be designed. We propose statistical models to assess the impact of effect sizes, the correlation structure between the short-term and the primary endpoint, the sample size, the timing of interim analyses, and the selection rule on the operating characteristics., (© 2019 John Wiley & Sons, Ltd.)

The individual causal association (ICA) has recently been introduced as a metric of surrogacy in a causal-inference framework. The ICA is defined on the unit interval and quantifies the association between the individual causal effect on the surrogate (ΔS) and true (ΔT) endpoint. In addition, the ICA offers a general assessment of the surrogate predictive value, taking value 1 when there is a deterministic relationship between ΔT and ΔS, and value 0 when both causal effects are independent. However, when one moves away from the previous two extreme scenarios, the interpretation of the ICA becomes challenging. In the present work, a new metric of surrogacy, the minimum probability of a prediction error (PPE), is introduced when both endpoints are binary, ie, the probability of erroneously predicting the value of ΔT using ΔS. Although the PPE has a more straightforward interpretation than the ICA, its magnitude is bounded above by a quantity that depends on the true endpoint. For this reason, the reduction in prediction error (RPE) attributed to the surrogate is defined. The RPE always lies in the unit interval, taking value 1 if prediction is perfect and 0 if ΔS conveys no information on ΔT. The methodology is illustrated using data from two clinical trials and a user-friendly R package Surrogate is provided to carry out the validation exercise., (© 2018 John Wiley & Sons, Ltd.)

The stratified Cox model is commonly used for stratified clinical trials with time-to-event endpoints. The estimated log hazard ratio is approximately a weighted average of corresponding stratum-specific Cox model estimates using inverse-variance weights; the latter are optimal only under the (often implausible) assumption of a constant hazard ratio across strata. Focusing on trials with limited sample sizes (50-200 subjects per treatment), we propose an alternative approach in which stratum-specific estimates are obtained using a refined generalized logrank (RGLR) approach and then combined using either sample size or minimum risk weights for overall inference. Our proposal extends the work of Mehrotra et al, to incorporate the RGLR statistic, which outperforms the Cox model in the setting of proportional hazards and small samples. This work also entails development of a remarkably accurate plug-in formula for the variance of RGLR-based estimated log hazard ratios. We demonstrate using simulations that our proposed two-step RGLR analysis delivers notably better results through smaller estimation bias and mean squared error and larger power than the stratified Cox model analysis when there is a treatment-by-stratum interaction, with similar performance when there is no interaction. Additionally, our method controls the type I error rate while the stratified Cox model does not in small samples. We illustrate our method using data from a clinical trial comparing two treatments for colon cancer., (© 2019 John Wiley & Sons, Ltd.)

Applied statisticians and pharmaceutical researchers are frequently involved in the design and analysis of clinical trials where at least one of the outcomes is binary. Treatments are judged by the probability of a positive binary response. A typical example is the noninferiority trial, where it is tested whether a new experimental treatment is practically not inferior to an active comparator with a prespecified margin δ. Except for the special case of δ = 0, no exact conditional test is available although approximate conditional methods (also called second-order methods) can be applied. However, in some situations, the approximation can be poor and the logical argument for approximate conditioning is not compelling. The alternative is to consider an unconditional approach. Standard methods like the pooled z-test are already unconditional although approximate. In this article, we review and illustrate unconditional methods with a heavy emphasis on modern methods that can deliver exact, or near exact, results. For noninferiority trials based on either rate difference or rate ratio, our recommendation is to use the so-called E-procedure, based on either the score or likelihood ratio statistic. This test is effectively exact, computationally efficient, and respects monotonicity constraints in practice. We support our assertions with a numerical study, and we illustrate the concepts developed in theory with a clinical example in pulmonary oncology; R code to conduct all these analyses is available from the authors., (© 2019 John Wiley & Sons, Ltd.)

Importance: Surrogate end points in oncology trade the advantage of reducing the time needed to conduct clinical trials for the disadvantage of greater uncertainty regarding the treatment effect on patient-centered end points, such as overall survival (OS) and quality of life., Objective: To quantify the amount of time saved through the acceptance of surrogate end points, including response rate (RR) and progression-free survival (PFS)., Design, Setting, and Participants: This retrospective study of US Food and Drug Administration (FDA) oncology approvals and their drug registration trials based on actual publication analyzed the original and updated clinical trials data that led to FDA-approved drug indications in oncology from 2006 to 2017 by using existing publications, conference abstracts, and package inserts from the FDA. Data related to cancer type, line of therapy (first-line, second-line, and third- or later-line treatment of advanced or metastatic disease), FDA approval type, end point basis for approval (RR, PFS, or OS/quality of life), sample size, accrual rate, and drug RR were extracted by March 23, 2018. All data were analyzed by July 13, 2018., Main Outcomes and Measures: The main outcome was the study duration needed to complete the primary end point analysis used for each drug indication approval. This was estimated from reported enrollment dates, analysis cutoff dates, time to response, median duration of response, median PFS, and median OS., Results: In total, 188 distinct indications among 107 cancer drugs were identified. The RR was more often used for FDA approval in subsequent lines of therapy (17 of 71 drug indications [24%] in first-line therapy vs 34 of 77 drug indications [44%] in second-line therapy vs 19 of 24 drug indications [79%] in third- or later-line therapy, P < .001). Study duration for PFS (median, 31 [range, 10-104] months) was similar to that for OS (median, 33 [range, 12-117] months; P = .31), whereas study duration for RR (median, 25 [range, 11-54] months) was shorter than that for OS (P = .001). In multivariate analysis, compared with using OS, use of PFS as the end point was associated with study durations that were shorter by a mean of 11 months (95% CI, 5-17 months), and the use of RR as the end point was associated with study durations that were shorter by a mean of 19 months (95% CI, 13-25 months)., Conclusions and Relevance: From the findings of this study, an estimated 11 months appeared to be needed (ie, approximately 12% longer in the drug development cycle) to assess the OS benefit of a cancer drug. This study's findings suggest that this must be weighed against the downside of increased uncertainty of clinical benefit arising from using surrogate end points.

Background: Proportional hazards (PH) is an assumption often made by researchers, despite evidence of nonproportionality in a significant proportion of clinical trials. In the presence of non-PH, the interpretation of hazard ratios, medians, and landmark survival as summary measures of treatment effect can become problematic. Several recent studies have recommended restricted mean survival time (RMST) as an alternative metric for survival analysis, particularly where non-PH may apply., Objectives: To determine the current approaches of health technology assessment (HTA) agencies to value assessment in the presence of non-PH, and the extent to which RMST is accepted as an alternative measure of treatment benefit., Methods: Methodological guidelines published by 10 HTA agencies were reviewed to establish recommended approaches for presenting survival benefit from clinical trials. Published HTA reports for 23 oncology agents approved by the US Food and Drug Administration and the European Medicines Agency since 2014 were reviewed to determine how guidelines are implemented in practice and identify instances where the PH assumption was tested and RMST analyses reported., Results: Testing for non-PH is not widely incorporated into HTA except by the UK National Institute for Health and Care Excellence. RMST is used infrequently but has been used in a number of countries, particularly by agencies that focus on cost effectiveness., Conclusions: HTA agencies vary in their approaches to non-PH. Most do not routinely check the PH assumption. RMST has played a role in assessing clinical benefit within HTA, although not consistently within countries (across drugs) or across countries (for the same drug)., (Copyright © 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Background: In proportionate or adaptive interventions, the dose or intensity can be adjusted based on individual need at predefined decision stages during the delivery of the intervention. The development of such interventions may require an evaluation of the effectiveness of the individual stages in addition to the whole intervention. However, evaluating individual stages of an intervention has various challenges, particularly the statistical design and analysis. This review aimed to identify the use of trials of proportionate interventions and how they are being designed and analysed in current practice., Methods: We searched MEDLINE, Web of Science and PsycINFO for articles published between 2010 and 2015 inclusive. We considered trials of proportionate interventions in all fields of research. For each trial, its aims, design and analysis were extracted. The data synthesis was conducted using summary statistics and a narrative format., Results: Our review identified 44 proportionate intervention trials, comprising 28 trial results, 13 protocols and three secondary analyses. These were mostly described as stepped care (n=37) and mainly focussed on mental health research (n=30). The other studies were aimed at finding an optimal adaptive treatment strategy (n=7) in a variety of therapeutic areas. Further terminology used included adaptive intervention, staged intervention, sequentially multiple assignment trial or a two-phase design. The median number of decision stages in the interventions was two and only one study explicitly evaluated the effect of the individual stages., Conclusions: Trials of proportionate staged interventions are being used predominantly within the mental health field. However, few studies consider the different stages of the interventions, either at the design or the analysis phase, and how they may interact with one another. There is a need for further guidance on the design, analyses and reporting across trials of proportionate interventions., Trial Registration: Prospero, CRD42016033781. Registered on 2 February 2016.

Background: When using a continuous outcome measure in a randomised controlled trial (RCT), the baseline score should be measured in addition to the post-intervention score, and it should be analysed using the appropriate statistical analysis., Methods: We derive the correlation between the change score and baseline score and show that there is always a correlation (usually negative) between the change score and baseline score. We discuss the following correlations and provide the mathematical derivations in the Appendix: Correlation between change score and baseline score Correlation between change score and post score Correlation between change score and average score. The setting here is a parallel, two-arm RCT, but the method discussed in this paper is applicable for any studies or trials that have a continuous outcome measure; it is not restricted to RCTs., Results: We show that using the change score as the outcome measure does not address the problem of regression to the mean, nor does it take account of the baseline imbalance. Whether the outcome is change score or post score, one should always adjust for baseline using analysis of covariance (ANCOVA); otherwise, the estimated treat effect may be biased. We show that these correlations also apply when comparing two measurement methods using Bland-Altman plots., Conclusions: The correlation between baseline and post-intervention scores can be derived using the variance sum law. We can then use the derived correlation to calculate the required sample size in the design stage. Baseline imbalance may occur in RCTs, and ANCOVA should be used to adjust for baseline in the analysis stage.

In the past, many clinical trials have withdrawn subjects from the study when they prematurely stopped their randomised treatment and have therefore only collected 'on-treatment' data. Thus, analyses addressing a treatment policy estimand have been restricted to imputing missing data under assumptions drawn from these data only. Many confirmatory trials are now continuing to collect data from subjects in a study even after they have prematurely discontinued study treatment as this event is irrelevant for the purposes of a treatment policy estimand. However, despite efforts to keep subjects in a trial, some will still choose to withdraw. Recent publications for sensitivity analyses of recurrent event data have focused on the reference-based imputation methods commonly applied to continuous outcomes, where imputation for the missing data for one treatment arm is based on the observed outcomes in another arm. However, the existence of data from subjects who have prematurely discontinued treatment but remained in the study has now raised the opportunity to use this 'off-treatment' data to impute the missing data for subjects who withdraw, potentially allowing more plausible assumptions for the missing post-study-withdrawal data than reference-based approaches. In this paper, we introduce a new imputation method for recurrent event data in which the missing post-study-withdrawal event rate for a particular subject is assumed to reflect that observed from subjects during the off-treatment period. The method is illustrated in a trial in chronic obstructive pulmonary disease (COPD) where the primary endpoint was the rate of exacerbations, analysed using a negative binomial model., (© 2018 John Wiley & Sons, Ltd.)