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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. [ABSTRACT FROM AUTHOR]

One of the primary objectives of a dose-finding trial for novel anti-cancer agent combination therapies, such as molecular targeted agents and immune-oncology therapies, is to identify optimal dose combinations that are tolerable and therapeutically beneficial for subjects in subsequent clinical trials. The goal differs from that of a dose-finding trial for traditional cytotoxic agents, in which the goal is to determine the maximum tolerated dose combinations. This paper proposes the new design, named 'BOIN-ETC' design, to identify optimal dose combinations based on both efficacy and toxicity outcomes using the waterfall approach. The BOIN-ETC design is model-assisted, so it is expected to be robust, and straightforward to implement in actual oncology dose-finding trials. These characteristics are quite valuable from a practical perspective. Simulation studies show that the BOIN-ETC design has advantages compared with the other approaches in the percentage of correct optimal dose combination selection and the average number of patients allocated to the optimal dose combinations across various realistic settings. [ABSTRACT FROM AUTHOR]

Traditional methods based on the assumption that the treatment distribution is a pure shift of the control distribution may not always hold. The possibility that an individual from the treatment group may not respond to the treatment motivates the use of a mixture distribution for the treatment group. This paper considers two test procedures based on the Wilcoxon rank-sum statistic for a group sequential design to detect the one-sided mixture alternative. Error spending functions are used for the allocation of error rates at each stage. The two tests are evaluated individually in determination of critical values and arm sizes and asymptotic multivariate normality is shown to hold for both. Upon comparison, the tests are presented to be asymptotically equivalent. Both test statistics maintain the Type I error rate even if F is misspecified in the design alternative. A more general definition of the treatment effect is used with the mixture distribution. Method of moments estimators and constrained k -means estimators for the treatment effect are evaluated. [ABSTRACT FROM AUTHOR]

Comparative Poisson trials of an experimental treatment versus a control typically condition on the total number of events that occur across both arms (Design A). Inference is based on the binomial distribution. Recently, an approach termed Design C to compare K experimental treatments to the same control was introduced. Under Design C without curtailment, the trial continues until a prespecified number of events occur in the control arm, leading to inference based on the negative multinomial distribution. The question remains of how advantageous it is to conduct one Design C trial comparing K experimental treatment arms to the same control arm as opposed to conducting K separate Design A trials each comparing one experimental treatment arm to a different control arm. This paper, therefore, compares the expected number of subjects to enroll for the two designs under uncurtailed and curtailed settings. The designs are evaluated when the null hypothesis and various assumptions for the alternative hypothesis hold. We simulate a variety of combinations for the Type 1 error, power, and ratio of the incidence rate of events in the experimental treatment to control arms. Design C frequently offers significant savings in terms of sample size relative to Design A. [ABSTRACT FROM AUTHOR]

Factorial allow for the simultaneous evaluation of more than one treatment, by randomizing patients to their possible combinations, including control. However, the statistical power of one treatment can be influenced by the effectiveness of the other, a matter that has not been widely recognized. In this paper, we evaluate the relationship between the observed effectiveness of one treatment and the implied power for a second treatment in the same trial, under a range of conditions. We provide analytic and numerical solutions for a binary outcome, under the additive, multiplicative, and odds ratio scales for treatment interaction. We demonstrate how the minimum required sample size for a trial depends on the two treatment effects. Relevant factors include the event rate in the control group, sample size, treatment effect sizes, and Type-I error rate thresholds. We show that that power for one treatment decreases as a function of the observed effectiveness of the other treatment if there is no multiplicative interaction. A similar pattern is observed with the odds ratio scale at low control rates, but at high control rates, power may increase if the first treatment is moderately more effective than its planned value. When treatments do not interact additively, power may either increase or decrease, depending on the control event rate. We also determine where the maximum power occurs for the second treatment. We illustrate these ideas with data from two actual factorial trials. These results can benefit investigators in planning the analysis of factorial clinical trials, in particular, to alert them to the potential for losses in power when one observed treatment effect differs from its originally postulated value. Updating the power calculation and modifying the associated required sample size can then ensure sufficient power for both treatments. [ABSTRACT FROM AUTHOR]

There has been an increased interest in borrowing information from historical control data to improve the statistical power for hypothesis testing, therefore reducing the required sample sizes in clinical trials. To account for the heterogeneity between the historical and current trials, power priors are often considered to discount the information borrowed from the historical data. However, it can be challenging to choose a fixed power prior parameter in the application. The modified power prior approach, which defines a random power parameter with initial prior to control the amount of historical information borrowed, may not directly account for heterogeneity between the trials. In this paper, we propose a novel approach to pick a power prior based on some direct measures of distributional differences between historical control data and current control data under normal assumptions. Simulations are conducted to investigate the performance of the proposed approach compared with current approaches (e.g. commensurate prior, meta-analytic-predictive, and modified power prior). The results show that the proposed power prior improves the study power while controlling the type I error within a tolerable limit when the distribution of the historical control data is similar to that of the current control data. The method is developed for both superiority and non-inferiority trials and is illustrated with an example from vaccine clinical trials. [ABSTRACT FROM AUTHOR]

This paper introduces the event-probability function, a measure of occurrence of an event of interest over time, defined as the instantaneous probability of an event at a given time point conditional on having survived until that point. Unlike the hazard function, the event-probability function is a proper probability. This paper describes properties and interpretation of the event-probability function, presents its connection with other popular functions, such as the hazard and survival functions, proposes practical flexible proportional-odds models for estimating conditional event-probabilities given covariates with possibly censored and truncated observations, discusses the theoretical and computational aspects of parameter estimation, and applies the proposed models for assessing mortality in patients with metastatic renal carcinoma from a randomized clinical trial. [ABSTRACT FROM AUTHOR]

When a clinical trial stops early for benefit, the maximum likelihood estimate (MLE) of the treatment effect may be subject to overestimation bias. Several authors have proposed adjusting for this bias using the conditional MLE, which is obtained by conditioning on early stopping. However, this approach has a fundamental problem in that the adjusted estimate may not be in the direction of benefit, even though the study has stopped early due to benefit. In this paper, we address this problem by embedding both the MLE and the conditional MLE within a broader class of penalised likelihood estimates, and choosing a member of the class that is a favourable compromise between the two. This penalised MLE, and its associated confidence interval, always lie in the direction of benefit when the study stops early for benefit. We study its properties using both simulations and analyses of the ENZAMET trial in metastatic prostate cancer. Conditional on stopping early for benefit, the method is found to have good unbiasedness and coverage properties, along with very favourable efficiency at earlier interim analyses. We recommend the penalised MLE as a supplementary analysis to a conventional primary analysis when a clinical trial stops early for benefit. [ABSTRACT FROM AUTHOR]

In sequential testing with binary data, sample size and time to detect a signal are the key performance measures to optimize. While the former should be optimized in Phase III clinical trials, minimizing the latter is of major importance in post-market drug and vaccine safety surveillance of adverse events. The precision of the relative risk estimator on termination of the analysis is a meaningful design criterion as well. This paper presents a linear programming framework to find the optimal alpha spending that minimizes expected time to signal, or expected sample size as needed. The solution enables (a) to bound the width of the confidence interval following the end of the analysis, (b) designs with outer signaling thresholds and inner non-signaling thresholds, and (c) sequential designs with variable Bernoulli probabilities. To illustrate, we use real data on the monitoring of adverse events following the H1N1 vaccination. The numerical results are obtained using the R Sequential package. [ABSTRACT FROM AUTHOR]

Recurrent event responses are frequently encountered during clinical trials of treatments for certain diseases, such as asthma. The recurrence rates of different treatments are often compared by applying the negative binomial model. In addition, a balanced treatment-allocation procedure that assigns the same number of patients to each treatment is often applied. Recently, a response-adaptive treatment-allocation procedure has been developed for trials with recurrent event data, and has been shown to be superior to balanced treatment allocation. However, this response-adaptive treatment allocation procedure is only applicable for the comparison of two treatments. In this paper, we derive response-adaptive treatment-allocation procedures for trials which comprise several treatments. As pairwise comparisons and multiple comparisons with a control are two common multiple-testing scenarios in trials with more than two treatments, corresponding treatment-allocation procedures for these scenarios are also investigated. The redesign of two clinical studies illustrates the clinical benefits that would be obtained from our proposed response-adaptive treatment-allocation procedures. [ABSTRACT FROM AUTHOR]

The amount of data collected from patients involved in clinical trials is continuously growing. All baseline patient characteristics are potential covariates that could be used to improve clinical trial analysis and power. However, the limited number of patients in phases I and II studies restricts the possible number of covariates included in the analyses. In this paper, we investigate the cost/benefit ratio of including covariates in the analysis of clinical trials with a continuous outcome. Within this context, we address the long-running question "What is the optimum number of covariates to include in a clinical trial?" To further improve the benefit/cost ratio of covariates, historical data can be leveraged to pre-specify the covariate weights, which can be viewed as the definition of a new composite covariate. Here we analyze the use of a composite covariate to improve the estimated treatment effect in small clinical trials. A composite covariate limits the loss of degrees of freedom and the risk of overfitting. [ABSTRACT FROM AUTHOR]

Many clinical trials incorporate stopping rules to terminate early if the clinical question under study can be answered with a high degree of confidence. While common in later-stage trials, these rules are rarely implemented in dose escalation studies, due in part to the relatively smaller sample size of these designs. However, even with a small sample size, this paper shows that easily implementable stopping rules can terminate dose-escalation early with minimal loss to the accuracy of maximum tolerated dose estimation. These stopping rules are developed when the goal is to identify one or two dose levels, as the maximum tolerated dose and co-maximum tolerated dose. In oncology, this latter goal is frequently considered when the study includes dose-expansion cohorts, which are used to further estimate and compare the safety and efficacy of one or two dose levels. As study protocols do not typically halt accrual between escalation and expansion, early termination is of clinical importance as it either allows for additional patients to be treated as part of the dose expansion cohort to obtain more precise estimates of the study endpoints or allows for an overall reduction in the total sample size. [ABSTRACT FROM AUTHOR]

The present paper deals with the problem of designing randomized multiarm clinical trials for treatment comparisons in order to achieve a suitable trade-off among inferential precision and ethical concerns. Although the large majority of the literature is focused on the estimation of the treatment effects, in particular for the case of two treatments with binary outcomes, the present paper takes into account the inferential goal of maximizing the power of statistical tests to detect correct conclusions about the treatment effects for normally response trials. After discussing the allocation optimizing the power of the classical multivariate test of homogeneity, we suggest a multipurpose design methodology, based on constrained optimization, which maximizes the power of the test under a suitable ethical constraint reflecting the effectiveness of the treatments. The ensuing optimal allocation depends in general on the unknown model parameters but, contrary to the unconstrained optimal solution or to some targets proposed in the literature, it is a non-degenerate continuous function of the treatment contrasts, and therefore it can be approached by standard response-adaptive randomization procedures. The properties of this constrained optimal allocation are described both theoretically and through suitable examples, showing good performances both in terms of ethical gain and statistical efficiency, taking into account estimation precision as well. [ABSTRACT FROM AUTHOR]

The delayed outcome issue is common in early phase dose-finding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and efficacy responses are subject to the delayed outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity–efficacy distribution. In this paper, we propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient's actual follow-up time. The CWL method makes no parametric model assumption on either the dose–response curve or the toxicity–efficacy correlation and therefore can be applied to any existing phase I/II trial design. Numerical trial applications show that the proposed CWL method yields desirable operating characteristics. [ABSTRACT FROM AUTHOR]

In reliability theory, diagnostic accuracy, and clinical trials, the quantity P (X > Y) + 1 / 2 P (X = Y) , also known as the Probabilistic Index (PI), is a common treatment effect measure when comparing two groups of observations. The quantity P (X > Y) − P (Y > X) , a linear transformation of PI known as the net benefit, has also been advocated as an intuitively appealing treatment effect measure. Parametric estimation of PI has received a lot of attention in the past 40 years, with the formulation of the Uniformly Minimum-Variance Unbiased Estimator (UMVUE) for many distributions. However, the non-parametric Mann–Whitney estimator of the PI is also known to be UMVUE in some situations. To understand this seeming contradiction, in this paper a systematic comparison is performed between the non-parametric estimator for the PI and parametric UMVUE estimators in various settings. We show that the Mann–Whitney estimator is always an unbiased estimator of the PI with univariate, completely observed data, while the parametric UMVUE is not when the distribution is misspecified. Additionally, the Mann–Whitney estimator is the UMVUE when observations belong to an unrestricted family. When observations come from a more restrictive family of distributions, the loss in efficiency for the non-parametric estimator is limited in realistic clinical scenarios. In conclusion, the Mann–Whitney estimator is simple to use and is a reliable estimator for the PI and net benefit in realistic clinical scenarios. [ABSTRACT FROM AUTHOR]

Multi-arm multi-stage clinical trials in which more than two drugs are simultaneously investigated provide gains over separate single- or two-arm trials. In this paper we propose a generic Bayesian adaptive decision-theoretic design for multi-arm multi-stage clinical trials with K (K ≥ 2) arms. The basic idea is that after each stage a decision about continuation of the trial and accrual of patients for an additional stage is made on the basis of the expected reduction in loss. For this purpose, we define a loss function that incorporates the patient accrual costs as well as costs associated with an incorrect decision at the end of the trial. An attractive feature of our loss function is that its estimation is computationally undemanding, also when K > 2. We evaluate the frequentist operating characteristics for settings with a binary outcome and multiple experimental arms. We consider both the situation with and without a control arm. In a simulation study, we show that our design increases the probability of making a correct decision at the end of the trial as compared to nonadaptive designs and adaptive two-stage designs. [ABSTRACT FROM AUTHOR]

Recent work has shown that outcomes in clinical trials can be affected by which treatment the trial participants would select if they were allowed to do so, and if they do or do not actually receive that treatment. These influences are known as selection and preference effects, respectively. Unfortunately, they cannot be evaluated in conventional, parallel group trials because patient preferences remain unknown. However, several alternative designs have been proposed, to measure and take account of patient preferences. In this paper, we discuss three preference-based designs (the two-stage, fully randomised, and partially randomised designs). In conventional trials, only the treatment effect is estimable, while the preference-based designs have the potential to estimate some or all of the selection and preference effects. The relative efficiency of these designs is affected by several factors, including the proportion of participants who are undecided about treatments, or who are unable or unwilling to state a preference; the relative preference rate between the treatments being compared, among patients who do have a preference; and the ratio of patients randomised to each treatment. We also discuss the advantages and disadvantages of these designs under different scenarios. [ABSTRACT FROM AUTHOR]

Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all outcome(s); and lacks a compensatory mechanism that allows large positive effects on one or multiple outcome(s) to outweigh small negative effects on other outcomes. To address these shortcomings, this paper proposes (1) a Bayesian model for the analysis of correlated binary outcomes based on the multivariate Bernoulli distribution; and (2) a flexible decision criterion with a compensatory mechanism that captures the relative importance of the outcomes. A simulation study demonstrates that efficient and unbiased decisions can be made while Type I error rates are properly controlled. The performance of the framework is illustrated for (1) fixed, group sequential, and adaptive designs; and (2) non-informative and informative prior distributions. [ABSTRACT FROM AUTHOR]

Identification of a subset of patients who may be sensitive to a specific treatment is an important problem in clinical trials. In this paper, we consider the case where the treatment effect is measured by longitudinal outcomes, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. A threshold linear mixed model is introduced, and a smoothing maximum likelihood method is proposed to obtain the estimation of the parameters in the model. Broyden-Fletcher-Goldfarb-Shanno algorithm is employed to maximize the proposed smoothing likelihood function. The proposed procedure is evaluated through simulation studies and application to the analysis of data from a randomized clinical trial on patients with advanced colorectal cancer. [ABSTRACT FROM AUTHOR]

In longitudinal clinical trials with daily patient-reported outcomes, the analysis endpoints are often defined as the averaged daily diary outcomes in a treatment cycle (such as a month or a week). Conventional methods often deal with missing data at the cycle level by imputing the average, and the cycle average is treated as missing if the number of days with available outcomes in the treatment cycle is less than a certain number. This was the method used for a case study of a phase 3 clinical trial evaluating a treatment for insomnia with daily patient-reported outcomes. Such methods may introduce bias. Motivated by this, we propose methods to impute missing daily outcomes in this paper. Specifically, we define a two-level missing pattern for clinical trials with daily patient-reported outcomes, and propose two-level methods to impute missing data at daily base. Other than the standard methods by multiple imputations, we derive analytic formulas for the proposed two-level methods to reduce computational intensity and improve the estimates of variances. The proposed two-level methods provide more powerful approaches to estimate the treatment difference compared to the conventional cycle-level methods, which are evaluated by theoretical development and simulation studies. In addition, the methods are applied to the motivating phase 3 trial evaluating a treatment for insomnia with daily patient-reported outcomes. [ABSTRACT FROM AUTHOR]

Drug combination trials are often motivated by the fact that individual drugs target the same disease but via different routes. A combination of such drugs may then have an overall better effect than the individual treatments which has to be verified by clinical trials. Several statistical methods have been explored that discuss the problem of comparing a fixed-dose combination therapy to each of its components. But an extension of these approaches to multiple dose combinations can be difficult and is not yet fully investigated. In this paper, we propose two approaches by which one can provide confirmatory assurance with familywise error rate control, that the combination of two drugs at differing doses is more effective than either component doses alone. These approaches involve multiple comparisons in multilevel factorial designs where the type 1 error can be controlled first, by bootstrapping tests, and second, by considering the least favorable null configurations for a family of union intersection tests. The main advantage of the new approaches is that their implementation is simple. The implementation of these new approaches is illustrated with a real data example from a blood pressure reduction trial. Extensive simulations are also conducted to evaluate the new approaches and benchmark them with existing ones. We also present an illustration of the relationship between the different approaches. We observed that the bootstrap provided some power advantages over the other approaches with the disadvantage that there may be some error rate inflation for small sample sizes. [ABSTRACT FROM AUTHOR]

Background: Among various design aspects, the choice of randomization procedure have to be agreed on, when planning a clinical trial stratified by center. The aim of the paper is to present a methodological approach to evaluate whether a randomization procedure mitigates the impact of bias on the test decision in clinical trial stratified by center.Methods: We use the weighted t test to analyze the data from a clinical trial stratified by center with a two-arm parallel group design, an intended 1:1 allocation ratio, aiming to prove a superiority hypothesis with a continuous normal endpoint without interim analysis and no adaptation in the randomization process. The derivation is based on the weighted t test under misclassification, i.e. ignoring bias. An additive bias model combing selection bias and time-trend bias is linked to different stratified randomization procedures.Results: Various aspects to formulate stratified versions of randomization procedures are discussed. A formula for sample size calculation of the weighted t test is derived and used to specify the tolerated imbalance allowed by some randomization procedures. The distribution of the weighted t test under misclassification is deduced, taking the sequence of patient allocation to treatment, i.e. the randomization sequence into account. An additive bias model combining selection bias and time-trend bias at strata level linked to the applied randomization sequence is proposed. With these before mentioned components, the potential impact of bias on the type one error probability depending on the selected randomization sequence and thus the randomization procedure is formally derived and exemplarily calculated within a numerical evaluation study.Conclusion: The proposed biasing policy and test distribution are necessary to conduct an evaluation of the comparative performance of (stratified) randomization procedure in multi-center clinical trials with a two-arm parallel group design. It enables the choice of the best practice procedure. The evaluation stimulates the discussion about the level of evidence resulting in those kind of clinical trials. [ABSTRACT FROM AUTHOR]

Comparisons of survival times between screen-detected and symptomatically detected breast cancer cases are subject to lead time and length biases. Whilst the existence of these biases is well known, correction procedures for these are not always clear, as are not the interpretation of these biases. In this paper we derive, based on a recently developed continuous tumour growth model, conditional lead time distributions, using information on each individual's tumour size, screening history and percent mammographic density. We show how these distributions can be used to obtain an individual-based (conditional) procedure for correcting survival comparisons. In stratified analyses, our correction procedure works markedly better than a previously used unconditional lead time correction, based on multi-state Markov modelling. In a study of postmenopausal invasive breast cancer patients, we estimate that, in large (>12 mm) tumours, the multi-state Markov model correction over-corrects five-year survival by 2-3 percentage points. The traditional view of length bias is that tumours being present in a woman's breast for a long time, due to being slow-growing, have a greater chance of being screen-detected. This gives a survival advantage for screening cases which is not due to the earlier detection by screening. We use simulated data to share the new insight that, not only the tumour growth rate but also the symptomatic tumour size will affect the sampling procedure, and thus be a part of the length bias through any link between tumour size and survival. We explain how this has a bearing on how observable breast cancer-specific survival curves should be interpreted. We also propose an approach for correcting survival comparisons for the length bias. [ABSTRACT FROM AUTHOR]

The ability to produce a long-lasting, or durable, immune response is a crucial characteristic of many highly effective vaccines. A goal of early-phase vaccine trials is often to compare the immune response durability of multiple tested vaccine regimens. One parameter for measuring immune response durability is the area under the mean post-peak log immune response profile. In this paper, we compare immune response durability across vaccine regimens within and between two phase I trials of DNA-primed HIV vaccine regimens, HVTN 094 and HVTN 096. We compare four estimators of this durability parameter and the resulting statistical inferences for comparing vaccine regimens. Two of these estimators use the trapezoid rule as an empirical approximation of the area under the marginal log response curve, and the other two estimators are based on linear and nonlinear models for the marginal mean log response. We conduct a simulation study to compare the four estimators, provide guidance on estimator selection, and use the nonlinear marginal mean model to analyze immunogenicity data from the two HIV vaccine trials. [ABSTRACT FROM AUTHOR]

It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced analysis of covariance models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present paper, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general analysis of covariance model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far. We prove that these assumptions are sufficient to ensure the asymptotic validity of the combined HCCME-wild bootstrap analysis of covariance. The results of our simulation study indicate that our proposed test remedies the problems of the analysis of covariance F test and its heteroskedasticity-consistent alternatives in small to moderate sample size scenarios. Our test only requires very mild conditions, thus being applicable in a broad range of real-life settings, as illustrated by the detailed discussion of a dataset from preclinical research on spinal cord injury. Our proposed method is ready-to-use and allows for valid hypothesis testing in frequently encountered settings (e.g., comparing group means while adjusting for baseline measurements in a randomized controlled clinical trial). [ABSTRACT FROM AUTHOR]

Individualizing treatment according to patients' characteristics is central for personalized or precision medicine. There has been considerable recent research in developing statistical methods to determine optimal personalized treatment strategies by modeling the outcome of patients according to relevant covariates under each of the alternative treatments, and then relying on so-called predicted individual treatment effects. In this paper, we use potential outcomes and principal stratification frameworks and develop a multinomial model for left and right-censored data to estimate the probability that a patient is a responder given a set of baseline covariates. The model can apply to RCT or observational study data. This method is based on the monotonicity assumption, which implies that no patients would respond to the control treatment but not to the experimental one. We conduct a simulation study to evaluate the properties of the proposed estimation method. Results showed that the predictions of the probability of being a responder were well calibrated even if we observed variability and a small bias when many parameters were estimated. We finally applied the method to a cohort study on the selection of patients for additional radiotherapy after resection of a soft-tissue sarcoma. [ABSTRACT FROM AUTHOR]

We examine a class of multivariate meta-regression models in the presence of individual patient data. The methodology is well motivated from several studies of cholesterol-lowering drugs where the goal is to jointly analyze the multivariate outcomes, low density lipoprotein cholesterol, high density lipoprotein cholesterol, and triglycerides. These three continuous outcome measures are correlated and shed much light on a subject's lipid status. One of the main goals in lipid research is the joint analysis of these three outcome measures in a meta-regression setting. Since these outcome measures are not typically multivariate normal, one must consider classes of distributions that allow for skewness in one or more of the outcomes. In this paper, we consider a new general class of multivariate skew distributions for multivariate meta-regression and examine their theoretical properties. Using these distributions, we construct a Bayesian model for the meta-data and develop an efficient Markov chain Monte Carlo computational scheme for carrying out the computations. In addition, we develop a multivariate L measure for model comparison, Bayesian residuals for model assessment, and a Bayesian procedure for detecting outlying trials. The proposed multivariate L measure, Bayesian residuals, and Bayesian outlying trial detection procedure are particularly suitable and computationally attractive in the multivariate meta-regression setting. A detailed case study demonstrating the usefulness of the proposed methodology is carried out in an individual patient data multivariate meta-regression setting using 26 pivotal Merck clinical trials that compare statins (cholesterol-lowering drugs) in combination with ezetimibe and statins alone on treatment-naïve patients and those continuing on statins at baseline. [ABSTRACT FROM AUTHOR]

In recent years, there has been a prominent discussion in the literature about the potential for overestimation of the treatment effect when a clinical trial stops at an interim analysis due to the experimental treatment showing a benefit over the control. However, there has been much less attention paid to the converse issue, namely, that sequentially monitored clinical trials which did not stop early for benefit tend to underestimate the treatment effect. In meta-analyses of many studies, these two sources of bias will tend to balance each other to produce an unbiased estimate of the treatment effect. However, for the interpretation of a single study in isolation, underestimation due to interim analysis may be an important consideration. In this paper, we discuss the nature of this underestimation, including theoretical and simulation results demonstrating that it can be substantial in some contexts. Furthermore, we show how a conditional approach to estimation, in which we condition on the study reaching its final analysis, may be used to validly inflate the observed treatment difference from a sequentially monitored clinical trial. Expressions for the conditional bias and information are derived, and these are used in supplied R code that computes the bias-adjusted estimate and an associated confidence interval. As well as simulation results demonstrating the validity of the methods, we present a data analysis example from a pivotal clinical trial in cardiovascular disease. The methods will be most useful when an unbiased treatment effect estimate is critical, such as in cost-effectiveness analysis or risk prediction. [ABSTRACT FROM AUTHOR]

Random effects two-part models have been applied to longitudinal studies for zero-inflated (or semi-continuous) data, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include monthly medical costs, daily alcohol drinks, relative abundance of microbiome, etc. With the advance of information technology for data collection and storage, the number of variables available to researchers can be rather large in such studies. To avoid curse of dimensionality and facilitate decision making, it is critically important to select covariates that are truly related to the outcome. However, owing to its intricate nature, there is not yet a satisfactory variable selection method available for such sophisticated models. In this paper, we seek a feasible way of conducting variable selection for random effects two-part models on the basis of the recently proposed "minimum information criterion" (MIC) method. We demonstrate that the MIC formulation leads to a reasonable formulation of sparse estimation, which can be conveniently solved with SAS Proc NLMIXED. The performance of our approach is evaluated through simulation, and an application to a longitudinal alcohol dependence study is provided. [ABSTRACT FROM AUTHOR]

Phase II clinical trials are concerned with making decision of whether a treatment is sufficiently efficacious to be worth further investigations in late large scale Phase III trials. In oncology Phase II trials, frequentist single-arm two-stage group-sequential designs with a binary endpoint are commonly used. To allow for more flexibility, adaptive versions of these designs have been proposed. In this paper, we propose point and interval estimation for adaptive designs in which the second stage sample size is a pre-specified function of first stage's number of responses. Our approach is based on sample space orderings, from which we derive p-values, and point and interval estimates. Simulation studies show that our proposed methods perform better, in terms of bias and root mean square error, than the fixed-sample maximum likelihood estimator. [ABSTRACT FROM AUTHOR]

Marginal structural models are causal models designed to adjust for time-dependent confounders in observational studies with dynamically adjusted treatments. They are robust tools to assess causality in complex longitudinal data. In this paper, a marginal structural model is proposed with an innovative dose-delay joint-exposure model for Inverse-Probability-of-Treatment Weighted estimation of the causal effect of alterations to the therapy intensity. The model is motivated by a precise clinical question concerning the possibility of reducing dosages in a regimen. It is applied to data from a randomised trial of chemotherapy in osteosarcoma, an aggressive primary bone-tumour. Chemotherapy data are complex because their longitudinal nature encompasses many clinical details like composition and organisation of multi-drug regimens, or dynamical therapy adjustments. This manuscript focuses on the clinical dynamical process of adjusting the therapy according to the patient's toxicity history, and the causal effect on the outcome of interest of such therapy modifications. Depending on patients' toxicity levels, variations to therapy intensity may be achieved by physicians through the allocation of either a reduction or a delay of the next planned dose. Thus, a negative feedback is present between exposure to cytotoxic agents and toxicity levels, which acts as time-dependent confounders. The construction of the model is illustrated highlighting the high complexity and entanglement of chemotherapy data. Built to address dosage reductions, the model also shows that delays in therapy administration should be avoided. The last aspect makes sense from the cytological point of view, but it is seldom addressed in the literature. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Covariate-adaptive designs are widely used to balance covariates and maintain randomization in clinical trials. Adaptive designs for discrete covariates and their asymptotic properties have been well studied in the literature. However, important continuous covariates are often involved in clinical studies. Simply discretizing or categorizing continuous covariates can result in loss of information. The current understanding of adaptive designs with continuous covariates lacks a theoretical foundation as the existing works are entirely based on simulations. Consequently, conventional hypothesis testing in clinical trials using continuous covariates is still not well understood. In this paper, we establish a theoretical framework for hypothesis testing on adaptive designs with continuous covariates based on linear models. For testing treatment effects and significance of covariates, we obtain the asymptotic distributions of the test statistic under null and alternative hypotheses. Simulation studies are conducted under a class of covariate-adaptive designs, including the p-value-based method, the Su's percentile method, the empirical cumulative-distribution method, the Kullback-Leibler divergence method, and the kernel-density method. Key findings about adaptive designs with independent covariates based on linear models are (1) hypothesis testing that compares treatment effects are conservative in terms of smaller type I error, (2) hypothesis testing using adaptive designs outperforms complete randomization method in terms of power, and (3) testing on significance of covariates is still valid. [ABSTRACT FROM AUTHOR]

Randomized trials with patient-reported outcomes are commonly plagued by missing data. The analysis of such trials relies on untestable assumptions about the missing data mechanism. To address this issue, it has been recommended that the sensitivity of the trial results to assumptions should be a mandatory reporting requirement. In this paper, we discuss a recently developed methodology (Scharfstein et al., Biometrics, 2018) for conducting sensitivity analysis of randomized trials in which outcomes are scheduled to be measured at fixed points in time after randomization and some subjects prematurely withdraw from study participation. The methodology is explicated in the context of a placebo-controlled randomized trial designed to evaluate a treatment for bipolar disorder. We present a comprehensive data analysis and a simulation study to evaluate the performance of the method. A software package entitled SAMON (R and SAS versions) that implements our methods is available at www.missingdatamatters.org . [ABSTRACT FROM AUTHOR]

Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection-union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Just over half of publicly funded trials recruit their target sample size within the planned study duration. When recruitment targets are missed, the funder of a trial is faced with the decision of either committing further resources to the study or risk that a worthwhile treatment effect may be missed by an underpowered final analysis. To avoid this challenging situation, when there is insufficient prior evidence to support predicted recruitment rates, funders now require feasibility assessments to be performed in the early stages of trials. Progression criteria are usually specified and agreed with the funder ahead of time. To date, however, the progression rules used are typically ad hoc. In addition, rules routinely permit adaptations to recruitment strategies but do not stipulate criteria for evaluating their effectiveness. In this paper, we develop a framework for planning and designing internal pilot studies which permit a trial to be stopped early if recruitment is disappointing or to continue to full recruitment if enrolment during the feasibility phase is adequate. This framework enables a progression rule to be pre-specified and agreed upon prior to starting a trial. The novel two-stage designs stipulate that if neither of these situations arises, adaptations to recruitment should be made and subsequently evaluated to establish whether they have been successful. We derive optimal progression rules for internal pilot studies which minimise the expected trial overrun and maintain a high probability of completing the study when the recruitment rate is adequate. The advantages of this procedure are illustrated using a real trial example. [ABSTRACT FROM AUTHOR]

Clinical trials may apply or use a sequential introduction of a new treatment to determine its efficacy or effectiveness with respect to a control treatment. The reasons for choosing a particular switch design have different origins. For instance, they may be implemented for ethical or logistic reasons or for studying disease-modifying effects. Large-scale pragmatic trials with complex interventions often use stepped wedge designs (SWDs), where all participants start at the control group, and during the trial, the control treatment is switched to the new intervention at different moments. They typically use cross-sectional data and cluster randomization. On the other hand, new drugs for inhibition of cognitive decline in Alzheimer's or Parkinson's disease typically use delayed start designs (DSDs). Here, participants start in a parallel group design and at a certain moment in the trial, (part of) the control group switches to the new treatment. The studies are longitudinal in nature, and individuals are being randomized. Statistical methods for these unidirectional switch designs (USD) are quite complex and incomparable, and they have been developed by various authors under different terminologies, model specifications, and assumptions. This imposes unnecessary barriers for researchers to compare results or choose the most appropriate method for their own needs. This paper provides an overview of past and current statistical developments for the USDs (SWD and DSD). All designs are formulated in a unified framework of treatment patterns to make comparisons between switch designs easier. The focus is primarily on statistical models, methods of estimation, sample size calculation, and optimal designs for estimation of the treatment effect. Other relevant open issues are being discussed as well to provide suggestions for future research in USDs. [ABSTRACT FROM AUTHOR]

Hida and Tango established a statistical testing framework for the three-arm non-inferiority trial including a placebo with a pre-specified non-inferiority margin to overcome the shortcomings of traditional two-arm non-inferiority trials (such as having to choose the non-inferiority margin). In this paper, we propose a new method that improves their approach with respect to two aspects. We construct our testing statistics based on the best unbiased pooled estimators of the homogeneous variance; and we use the principle of intersection-union tests to determine the rejection rule. We theoretically prove that our test is better than that of Hida and Tango for large sample sizes. Furthermore, when that sample size was small or moderate, our simulation studies showed that our approach performed better than Hida and Tango's. Although both controlled the type I error rate, their test was more conservative and the statistical power of our test was higher. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze the impact of response-adaptive randomization rules for normal response trials intended to test the superiority of one of two available treatments. Taking into account the classical Wald test, we show how response-adaptive methodology could induce a consistent loss of inferential precision. Then, we suggest a modified version of the Wald test which, by using the current allocation proportion to the treatments as a consistent estimator of the target, avoids some degenerate scenarios and so it should be preferable to the classical test. Furthermore, we show both analytically and via simulations how some target allocations may induce a locally decreasing power function. Thus, we derive the conditions on the target guaranteeing its monotonicity and we show how a correct choice of the initial sample size allows one to overcome this drawback regardless of the adopted target. [ABSTRACT FROM AUTHOR]

The purpose of a non-inferiority trial is to assert the efficacy of an experimental treatment compared with a reference treatment by showing that the experimental treatment retains a substantial proportion of the efficacy of the reference treatment. Statistical methods have been developed to test multiple experimental treatments in three-arm non-inferiority trials. In this paper, we report the development of procedures that simultaneously test the non-inferiority and the superiority of experimental treatments after the assay sensitivity has been established. The advantage of the proposed test procedures is the additional ability to identify superior treatments while retaining an non-inferiority testing power comparable to that of existing testing procedures. Single-step and stepwise procedures are derived and then compared with each other to determine their relative testing power and testing error in a simulation study. Finally, the suggested procedures are illustrated with two clinical examples. [ABSTRACT FROM AUTHOR]

The number of trials conducted and the number of patients per trial are typically small in paediatric clinical studies. This is due to ethical constraints and the complexity of the medical process for treating children. While incorporating prior knowledge from adults may be extremely valuable, this must be done carefully. In this paper, we propose a unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults. The dose-range is calculated under three extrapolation options, linear, allometry and maturation adjustment, using adult pharmacokinetic data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships are obtained using early-phase adult toxicity and efficacy data at several dose levels. Priors are integrated into the dose-finding process through Bayesian model selection or adaptive priors. This calibrates the model to adjust for misspecification, if the adult and pediatric data are very different. We performed a simulation study which indicates that incorporating prior adult information in this way may improve dose selection in children. [ABSTRACT FROM AUTHOR]

This paper investigates multiple imputation methods for regression models with interacting continuous and binary predictors when continuous variable may be missing. Usual implementations for parametric multiple imputation assume a multivariate normal structure for the variables, which is not satisfied for a binary variable nor its interaction with a continuous variable. To accommodate interactions, missing covariates are multiply imputed from conditional distribution in a manner consistent with the joint model. Alternative imputation methods under multivariate normal assumptions are also considered as candidate approximations and evaluated in a simulation study. The results suggest that the joint modeling procedure performs generally well across a wide range of scenarios and so do the approximation methods that incorporate interactions in the model appropriately by stratification. It is critical to include interactions in the imputation model as failure to do so may result in low coverage and bias. We apply the joint modeling approach and approximation methods in the study of childhood trauma with gender × trauma interaction. [ABSTRACT FROM AUTHOR]

The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms' Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model. [ABSTRACT FROM AUTHOR]

Drop-the-losers designs have been discussed extensively in the past decades, mostly focusing on two-stage models. The designs with more than two stages have recently received increasing attention due to their improved efficiency over the corresponding two-stage designs. In this paper, we consider the problem of estimating and testing the effect of selected treatment under the setting of three-stage drop-the-losers designs. A conservative interval estimator is proposed, which is proved to have at least the specified coverage probability using a stochastic ordering approach. The proposed interval estimator is also demonstrated numerically to have narrower interval width but higher coverage rate than the bootstrap method proposed by Bowden and Glimm (Biometrical Journal, vol. 56, pp. 332–349) in most cases. It is also a straightforward derivation from the stochastic ordering result that the family-wise error rate is strongly controlled with the maximum achieved at the global null hypothesis. [ABSTRACT FROM AUTHOR]

The enrichment design is an important clinical trial design to detect the treatment effect of the molecularly targeted agent (MTA) in personalized medicine. Under this design, patients are stratified into marker-positive and marker-negative subgroups based on their biomarker statuses and only the marker-positive patients are enrolled into the trial and randomized to receive either the MTA or a standard treatment. As the biomarker plays a key role in determining the enrollment of the trial, a misclassification of the biomarker can induce substantial bias, undermine the integrity of the trial, and seriously affect the treatment evaluation. In this paper, we propose a two-stage optimal enrichment design that utilizes the surrogate marker to correct for the biomarker misclassification. The proposed design is optimal in the sense that it maximizes the probability of correctly classifying each patient’s biomarker status based on the surrogate marker information. In addition, after analytically deriving the bias caused by the biomarker misclassification, we develop a likelihood ratio test based on the EM algorithm to correct for such bias. We conduct comprehensive simulation studies to investigate the operating characteristics of the optimal design and the results confirm the desirable performance of the proposed design. [ABSTRACT FROM AUTHOR]

Drop-the-losers designs have been discussed extensively in the past decades, mostly focusing on two-stage models. The designs with more than two stages have recently received increasing attention due to their improved efficiency over the corresponding two-stage designs. In this paper, we consider the problem of estimating and testing the effect of selected treatment under the setting of three-stage drop-the-losers designs. A conservative interval estimator is proposed, which is proved to have at least the specified coverage probability using a stochastic ordering approach. The proposed interval estimator is also demonstrated numerically to have narrower interval width but higher coverage rate than the bootstrap method proposed by Bowden and Glimm (Biometrical Journal, vol. 56, pp. 332-349) in most cases. It is also a straightforward derivation from the stochastic ordering result that the family-wise error rate is strongly controlled with the maximum achieved at the global null hypothesis. [ABSTRACT FROM AUTHOR]

The enrichment design is an important clinical trial design to detect the treatment effect of the molecularly targeted agent (MTA) in personalized medicine. Under this design, patients are stratified into marker-positive and marker-negative subgroups based on their biomarker statuses and only the marker-positive patients are enrolled into the trial and randomized to receive either the MTA or a standard treatment. As the biomarker plays a key role in determining the enrollment of the trial, a misclassification of the biomarker can induce substantial bias, undermine the integrity of the trial, and seriously affect the treatment evaluation. In this paper, we propose a two-stage optimal enrichment design that utilizes the surrogate marker to correct for the biomarker misclassification. The proposed design is optimal in the sense that it maximizes the probability of correctly classifying each patient's biomarker status based on the surrogate marker information. In addition, after analytically deriving the bias caused by the biomarker misclassification, we develop a likelihood ratio test based on the EM algorithm to correct for such bias. We conduct comprehensive simulation studies to investigate the operating characteristics of the optimal design and the results confirm the desirable performance of the proposed design. [ABSTRACT FROM AUTHOR]

This paper aims to develop diagnostic measures to assess the influence of data perturbations on estimates in AB-BA crossover studies with a Poisson distributed response. Generalised mixed linear models with normally distributed random effects are utilised. We show that in this special case, the model can be decomposed into two independent sub-models which allow to derive closed-form expressions to evaluate the changes in the maximum likelihood estimates under several perturbation schemes. The performance of the new influence measures is illustrated by simulation studies and the analysis of a real dataset. [ABSTRACT FROM AUTHOR]

The occurrence of an event of interest over time is often summarized by the incidence rate, defined as the average number of events per person-time. This type of rate applies to events that may occur repeatedly over time on any given subject, such as infections, and Poisson regression represents a natural regression method for modelling the effect of covariates on it. However, for events that can occur only once, such as death, the geometric rate may be a better summary measure. The geometric rate has long been utilized in demography for studying the growth of populations and in finance to compute compound interest on capital. This type of rate, however, is virtually unknown to medical research. This may be partly a consequence of the lack of a regression method for it. This paper describes a regression method for modelling the effect of covariates on the geometric rate. The described method is based on applying quantile regression to a transform of the time-to-event variable. The proposed method is used to analyze mortality in a randomized clinical trial and in an observational epidemiological study. [ABSTRACT FROM AUTHOR]

Split-mouth designs are frequently used in dental clinical research, where a mouth is divided into two or more experimental segments that are randomly assigned to different treatments. It has the distinct advantage of removing a lot of inter-subject variability from the estimated treatment effect. Methods of statistical analyses for split-mouth design have been well developed. However, little work is available on sample size consideration at the design phase of a split-mouth trial, although many researchers pointed out that the split-mouth design can only be more efficient than a parallel-group design when within-subject correlation coefficient is substantial. In this paper, we propose to use the generalized estimating equation (GEE) approach to assess treatment effect in split-mouth trials, accounting for correlations among observations. Closed-form sample size formulas are introduced for the split-mouth design with continuous and binary outcomes, assuming exchangeable and "nested exchangeable" correlation structures for outcomes from the same subject. The statistical inference is based on the large sample approximation under the GEE approach. Simulation studies are conducted to investigate the finite-sample performance of the GEE sample size formulas. A dental clinical trial example is presented for illustration. [ABSTRACT FROM AUTHOR]