Blinded and unblinded sample size reestimation in crossover trials balanced for period

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under‐ or overpowered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re‐estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multitreatment crossover trials. Specifically, regulators favor reestimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing three motivating examples, including one based on a recently completed four‐treatment four‐period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.     

Bayesian Adaptive Randomization and Trial Monitoring with Predictive Probability for Time-to-Event Endpoint

There has been much development in Bayesian adaptive designs in clinical trials. In the Bayesian paradigm, the posterior predictive distribution characterizes the future possible outcomes given the currently observed data. Based on the interim time-to-event data, we develop a new phase II trial design by combining the strength of both Bayesian adaptive randomization and the predictive probability. By comparing the mean survival times between patients assigned to two treatment arms, more patients are assigned to the better treatment on the basis of adaptive randomization. We continuously monitor the trial using the predictive probability for early termination in the case of superiority or futility. We conduct extensive simulation studies to examine the operating characteristics of four designs: the proposed predictive probability adaptive randomization design, the predictive probability equal randomization design, the posterior probability adaptive randomization design, and the group sequential design. Adaptive randomization designs using predictive probability and posterior probability yield a longer overall median survival time than the group sequential design, but at the cost of a slightly larger sample size. The average sample size using the predictive probability method is generally smaller than that of the posterior probability design.     

Maximum likelihood estimation of generalized linear models for adaptive designs: Applications and asymptotics

Due to increasing discoveries of biomarkers and observed diversity among patients, there is growing interest in personalized medicine for the purpose of increasing the well‐being of patients (ethics) and extending human life. In fact, these biomarkers and observed heterogeneity among patients are useful covariates that can be used to achieve the ethical goals of clinical trials and improving the efficiency of statistical inference. Covariate‐adjusted response‐adaptive (CARA) design was developed to use information in such covariates in randomization to maximize the well‐being of participating patients as well as increase the efficiency of statistical inference at the end of a clinical trial. In this paper, we establish conditions for consistency and asymptotic normality of maximum likelihood (ML) estimators of generalized linear models (GLM) for a general class of adaptive designs. We prove that the ML estimators are consistent and asymptotically follow a multivariate Gaussian distribution. The efficiency of the estimators and the performance of response‐adaptive (RA), CARA, and completely randomized (CR) designs are examined based on the well‐being of patients under a logit model with categorical covariates. Results from our simulation studies and application to data from a clinical trial on stroke prevention in atrial fibrillation (SPAF) show that RA designs lead to ethically desirable outcomes as well as higher statistical efficiency compared to CARA designs if there is no treatment by covariate interaction in an ideal model. CARA designs were however more ethical than RA designs when there was significant interaction.     

Sensitivity analyses comparing time-to-event outcomes only existing in a subset selected postrandomization and relaxing monotonicity

In randomized studies researchers may be interested in the effect of treatment assignment on a time-to-event outcome that only exists in a subset selected after randomization. For example, in preventative HIV vaccine trials, it is of interest to determine whether randomization to vaccine affects the time from infection diagnosis until initiation of antiretroviral therapy. Earlier work assessed the effect of treatment on outcome among the principal stratum of individuals who would have been selected regardless of treatment assignment. These studies assumed monotonicity, that one of the principal strata was empty (e.g., every person infected in the vaccine arm would have been infected if randomized to placebo). Here, we present a sensitivity analysis approach for relaxing monotonicity with a time-to-event outcome. We also consider scenarios where selection is unknown for some subjects because of noninformative censoring (e.g., infection status k years after randomization is unknown for some because of staggered study entry). We illustrate our method using data from an HIV vaccine trial.     

Ethical Issues in Field Trials of Genetically Modified Disease‐Resistant Mosquitoes

Mosquito‐borne diseases take a tremendous toll on human populations, especially in developing nations. In the last decade, scientists have developed mosquitoes that have been genetically modified to prevent transmission of mosquito‐borne diseases, and field trials have been conducted. Some mosquitoes have been rendered infertile, some have been equipped with a vaccine they transmit to humans, and some have been designed to resist diseases. This article focuses on ethical issues raised by field trials of disease‐resistant, genetically modified mosquitoes. Some of these issues include: protecting the public and the environment from harm, balancing benefits and risks, collaborating with the local community, avoiding exploitation, and safeguarding the rights and welfare of research subjects. One of the most difficult problems involves protecting the welfare of community members who will be impacted by the release of mosquitoes but who are not enrolled in the study as research subjects. To address this concern, field trials should take place only when the targeted disease is a significant public health problem in an isolated area, the benefits of the trial for the community are likely to outweigh the risks, community leaders approve of the trial, and there are measures in place to protect the welfare of un‐enrolled community members, such as informing the community about the study and offering free treatment to people who contract mosquito‐borne diseases. Since the justification of any field trial depends on a careful examination of the scientific and ethical issues, proposed studies should be evaluated on a case‐by‐case basis.     

Centronuclear (myotubular) myopathy

In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, adaptive designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective adaptive designs. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered adaptive designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of adaptive design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing adaptive design methods in clinical trials are also discussed.     

Propensity Score Matching in Randomized Clinical Trials

Summary Cluster randomization trials with relatively few clusters have been widely used in recent years for evaluation of health‐care strategies. On average, randomized treatment assignment achieves balance in both known and unknown confounding factors between treatment groups, however, in practice investigators can only introduce a small amount of stratification and cannot balance on all the important variables simultaneously. The limitation arises especially when there are many confounding variables in small studies. Such is the case in the INSTINCT trial designed to investigate the effectiveness of an education program in enhancing the tPA use in stroke patients. In this article, we introduce a new randomization design, the balance match weighted (BMW) design, which applies the optimal matching with constraints technique to a prospective randomized design and aims to minimize the mean squared error (MSE) of the treatment effect estimator. A simulation study shows that, under various confounding scenarios, the BMW design can yield substantial reductions in the MSE for the treatment effect estimator compared to a completely randomized or matched‐pair design. The BMW design is also compared with a model‐based approach adjusting for the estimated propensity score and Robins‐Mark‐Newey E‐estimation procedure in terms of efficiency and robustness of the treatment effect estimator. These investigations suggest that the BMW design is more robust and usually, although not always, more efficient than either of the approaches. The design is also seen to be robust against heterogeneous error. We illustrate these methods in proposing a design for the INSTINCT trial.     

A Bayesian seamless phase I–II trial design with two stages for cancer clinical trials with drug combinations

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as “optimal,” which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Under these circumstances, it is common practice to implement two-stage designs where a set of maximum tolerated dose combinations is selected in a first stage, and then studied in a second stage for treatment efficacy. In this article we present a new two-stage design for early phase clinical trials with drug combinations. In the first stage, binary toxicity data is used to guide the dose escalation and set the maximum tolerated dose combinations. In the second stage, we take the set of maximum tolerated dose combinations recommended from the first stage, which remains fixed along the entire second stage, and through adaptive randomization, we allocate subsequent cohorts of patients in dose combinations that are likely to have high posterior median time to progression. The methodology is assessed with extensive simulations and exemplified with a real trial.     

An alternative metric for evaluating the potential patient benefit of response-adaptive randomization procedures

When planning a two-arm group sequential clinical trial with a binary primary outcome that has severe implications for quality of life (e.g., mortality), investigators may strive to find the design that maximizes in-trial patient benefit. In such cases, Bayesian response-adaptive randomization (BRAR) is often considered because it can alter the allocation ratio throughout the trial in favor of the treatment that is currently performing better. Although previous studies have recommended using fixed randomization over BRAR based on patient benefit metrics calculated from the realized trial sample size, these previous comparisons have been limited by failures to hold type I and II error rates constant across designs or consider the impacts on all individuals directly affected by the design choice. In this paper, we propose a metric for comparing designs with the same type I and II error rates that reflects expected outcomes among individuals who would participate in the trial if enrollment is open when they become eligible. We demonstrate how to use the proposed metric to guide the choice of design in the context of two recent trials in persons suffering out of hospital cardiac arrest. Using computer simulation, we demonstrate that various implementations of group sequential BRAR offer modest improvements with respect to the proposed metric relative to conventional group sequential monitoring alone.     

Mind the gap: An empirical study of post‐trial access in HIV biomedical prevention trials

The principle of providing post‐trial access for research participants to successful products of that research is widely accepted and has been enshrined in various declarations and guidelines. While recent ethical guidelines recognise that the responsibility to provide post‐trial access extends to sponsors, regulators and government bodies as well as to researchers, it is the researchers who have the direct duty of care to participants. Researchers may thus need to act as advocates for trial participants, especially where government bodies, sponsors, and regulatory bodies have complex interests vested in decisions about whether or not new interventions are made available, how, and to whom. This paper provides an empirical account of post‐trial access in the context of HIV prevention research. It describes both access to the successful products of research and the provision antiretroviral drugs for trial participants who acquire HIV. First, we provide evidence that, in the current system, there is considerable variation in the duration and timeliness of access. We then argue that by analysing the difficulties faced by researchers to this point, and their efforts to meet this obligation, much can be learned about how to secure post‐trial access in HIV biomedical preventions trials. While researchers alone have a limited obligation, their advocacy on behalf of trial participants may be necessary to call the other parties to account.     

Correction of confounding bias in non-randomized studies by appropriate weighting

In non-randomized studies, the assessment of a causal effect of treatment or exposure on outcome is hampered by possible confounding. Applying multiple regression models including the effects of treatment and covariates on outcome is the well-known classical approach to adjust for confounding. In recent years other approaches have been promoted. One of them is based on the propensity score and considers the effect of possible confounders on treatment as a relevant criterion for adjustment. Another proposal is based on using an instrumental variable. Here inference relies on a factor, the instrument, which affects treatment but is thought to be otherwise unrelated to outcome, so that it mimics randomization. Each of these approaches can basically be interpreted as a simple reweighting scheme, designed to address confounding. The procedures will be compared with respect to their fundamental properties, namely, which bias they aim to eliminate, which effect they aim to estimate, and which parameter is modelled. We will expand our overview of methods for analysis of non-randomized studies to methods for analysis of randomized controlled trials and show that analyses of both study types may target different effects and different parameters. The considerations will be illustrated using a breast cancer study with a so-called Comprehensive Cohort Study design, including a randomized controlled trial and a non-randomized study in the same patient population as sub-cohorts. This design offers ideal opportunities to discuss and illustrate the properties of the different approaches.     

Ethics and HIV prevention research: An analysis of the early tenofovir PrEP trial in Nigeria

In 2004, the first ever multi‐sited clinical trials studied the prospect of HIV biomedical prevention (referred to as pre‐exposure prophylaxis—‘PrEP’). The trials were implemented at several international sites, but many officially closed down before they completed. At most sites, both scientists and community AIDS advocates raised concerns over the ethics and scientific rationales of the trial. Focusing on the Nigerian trial site, we detail the controversy that emerged among mostly Nigerian research scientists who scrutinized the research design and protocol. While some of the disputes, especially those pertaining to community engagement mechanisms, were ultimately resolved in international fora and implemented in later PrEP trials, concerns over science rationales and assumptions were never addressed. We argue that scientific rationales should be treated as ethical concerns and suggest that such concerns should be deliberated at host sites before the trial protocol is finalized.     

A Bayesian model with application for adaptive platform trials having temporal changes

Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed. However, temporal effects can be a serious obstacle for conducting clinical trials with complex designs, including the adaptive platform trials that are gaining popularity in recent medical product development. In this paper, we introduce a Bayesian robust prior for mitigating temporal effects based on a hidden Markov model, and propose a particle filtering algorithm for computation. We conduct simulation studies to evaluate the performance of the proposed method and provide illustration examples based on trials of Ebola virus disease therapeutics and hemostat in vascular surgery.     

Comparison of Closed-Ended,Open-Ended,and Perceived Informed Consent Comprehension Measures for a Mock HIV Prevention Trial among Women in Tanzania

Verifying participant comprehension continues to be a difficult ethical and regulatory challenge for clinical research. An increasing number of articles assessing methods to improve comprehension have been published, but they use a wide range of outcome measures including open-ended, closed-ended, and self-perceived measures of comprehension. Systematic comparisons of different measures have rarely been reported. This study evaluated the likely direction of bias observed when using open-ended, closed-ended, and perceived ease of comprehension measures among women administered a mock informed consent process in Mwanza, Tanzania. Participants were randomized to either a closed-ended or an open-ended assessment of comprehension, administered the consent process for a hypothetical HIV prevention trial in Kiswahili, and then administered a comprehension assessment, per their randomization. They were then asked how easy or hard it was to understand each of the informed consent components measured in the comprehension assessment. Women in the closed-ended arm had significantly higher overall comprehension scores than in the open-ended arm. Perceived scores were significantly higher when compared to both open-ended and close-ended scores within arms but were similar between arms. Findings highlight the importance of comprehension assessments in complex clinical trials that go beyond asking participants if they understand or have any questions. They also indicate the need for continued exploration of objective measures of comprehension in international clinical research settings, so that points in need of clarification can be efficiently and effectively identified and addressed. Such measures would reduce burdens on both staff and participants that result from well-intentioned but potentially unnecessary time spent explaining in unwarranted detail things already understood.     

Treatment allocation for nonlinear models in clinical trials: the logistic model

Many clinical trials have a binary outcome variable. If covariate adjustment is necessary in the analysis, the logistic-regression model is frequently used. Optimal designs for allocating treatments for this model, or for any nonlinear or heteroscedastic model, are generally unbalanced with regard to overall treatment totals and totals within strata. However, all treatment-allocation methods that have been recommended for clinical trials in the literature are designed to balance treatments within strata, either directly or asymptotically. In this paper, the efficiencies of balanced sequential allocation schemes are measured relative to sequential Ds-optimal designs for the logistic model, using as examples completed trials conducted by the Eastern Cooperative Oncology Group and systematic simulations. The results demonstrate that stratified, balanced designs are quite efficient, in general. However, complete randomization is frequently inefficient, and will occasionally result in a trial that is very inefficient.     

Equipoise,design bias,and randomized controlled trials: the elusive ethics of new drug development

The concept of 'equipoise', or the 'uncertainty principle', has been represented as a central ethical principle, and holds that a subject may be enrolled in a randomized controlled trial (RCT) only if there is true uncertainty about which of the trial arms is most likely to benefit the patient. We sought to estimate the frequency with which equipoise conditions were met in industry-sponsored RCTs in rheumatology, to explore the reasons for any deviations from equipoise, to examine the concept of 'design bias', and to consider alternative ethical formulations that might improve subject safety and autonomy. We studied abstracts accepted for the 2001 American College of Rheumatology meetings that reported RCTs, acknowledged industry sponsorship, and had clinical end-points (n = 45), and examined the proportion of studies that favored the registration or marketing of the sponsor's drug. In every trial (45/45) results were favorable to the sponsor, indicating that results could have been predicted in advance solely by knowledge of sponsorship (P < 0.0001). Equipoise clearly was being systematically violated. Publication bias appeared to be an incomplete explanation for this dramatic result; this bias occurs after a study is completed. Rather, we hypothesize that 'design bias', in which extensive preliminary data are used to design studies with a high likelihood of being positive, is the major cause of the asymmetric results. Design 'bias' occurs before the trial is begun and is inconsistent with the equipoise principle. However, design bias increases scientific efficiency, decreases drug development costs, and limits the number of subjects required, probably reducing aggregate risks to participants. Conceptual and ethical issues were found with the equipoise principle, which encourages performance of negative studies; ignores patient values, patient autonomy, and social benefits; is applied at a conceptually inappropriate decision point (after randomization rather than before); and is in conflict with the Belmont, Nuremberg, and other sets of ethical principles, as well as with US Food and Drug Administration procedures. We propose a principle of 'positive expected outcomes', which informs the assessment that a trial is ethical, together with a restatement of the priority of personal autonomy.     

Circulating serum vitamin D levels and total body bone mineral density: A Mendelian randomization study

Until recently, randomized controlled trials have not demonstrated convincing evidence that vitamin D, or vitamin D in combination with calcium supplementation could improve bone mineral density (BMD), osteoporosis and fracture. It remains unclear whether vitamin D levels are causally associated with total body BMD. Here, we performed a Mendelian randomization study to investigate the association of vitamin D levels with total body BMD using a large‐scale vitamin D genome‐wide association study (GWAS) dataset (including 79 366 individuals) and a large‐scale total body BMD GWAS dataset (including 66,628 individuals). We selected three Mendelian randomization methods including inverse‐variance weighted meta‐analysis (IVW), weighted median regression and MR‐Egger regression. All these three methods did not show statistically significant association of genetically increased vitamin D levels with total body BMD. Importantly, our findings are consistent with recent randomized clinical trials and Mendelian randomization study. In summary, we provide genetic evidence that increased vitamin D levels could not improve BMD in the general population. Hence, vitamin D supplementation alone may not be associated with reduced fracture incidence among community‐dwelling adults without known vitamin D deficiency, osteoporosis, or prior fracture.     

The large sample bounds on the principal strata effect with application to a prostate cancer prevention trial

Issues of post-randomization selection bias and truncation-by-death can arise in randomized clinical trials; for example, in a cancer prevention trial, an outcome such as cancer severity is undefined for individuals who do not develop cancer. Restricting analysis to a subpopulation selected after randomization can give rise to biased outcome comparisons. One approach to deal with such issues is to consider the principal strata effect (PSE, or equally, the survivor average causal effect). PSE is defined as the effect of treatment on the outcome among the subpopulation that would have been selected under either treatment arm. Unfortunately, the PSE cannot generally be estimated without the identifying assumptions; however, the bounds can be derived using a deterministic causal model. In this paper, we propose a number of assumptions for deriving the bounds with narrow width. The assumptions and bounds, which differ from those introduced by Zhang and Rubin (2003), are illustrated using data from a randomized prostate cancer prevention trial.