A 25-year review of sequential methodology in clinical studies

Group sequential design for time-to-event outcome with non-proportional hazards using the concept of relative time utilizing two different Weibull distributions

A group sequential design allows investigators to sequentially monitor efficacy and safety as part of interim testing in phase III trials. Literature is well developed in the case of continuous and binary outcomes, however, in case of trials with a time-to-event outcome, popular methods of sample size calculation often assume proportional hazards. In situations where the proportional hazards assumption is inappropriate as indicated by historical data, these popular methods are very restrictive. In this paper, a novel simulation-based group sequential design is proposed for a two-arm randomized phase III clinical trial with a survival endpoint for the non-proportional hazards scenario. By assuming that the survival times for each treatment arm follow two different Weibull distributions, the proposed method utilizes the concept of Relative Time to calculate the efficacy and safety boundaries at selected interim testing points. The test statistic used to generate these boundaries is asymptotically normal, allowing p-value calculation at each boundary. Many design features specific to time-to-event data can be incorporated with ease. Additionally, the proposed method allows the flexibility of having the accelerated failure time model and the proportional hazards model as constrained special cases. Real life applications are discussed demonstrating the practicality of the proposed method.

Bayesian analysis for nurse and midwifery research: statistical, practical and ethical benefits

BACKGROUND

The statistical shortcomings of null hypothesis significance testing (NHST) are well documented, yet it continues to be the default paradigm in quantitative healthcare research. This is due partly to unfamiliarity with Bayesian statistics.

AIM

To highlight some of the theoretical and practical benefits of using Bayesian analysis.

DISCUSSION

A growing body of literature demonstrates that Bayesian analysis offers statistical and practical benefits that are unavailable to researchers who rely solely on NHST. Bayesian analysis uses prior information in the inference process. It tests a hypothesis and yields the probability of that hypothesis, conditional on the observed data; in contrast, NHST checks observed data - and more extreme unobserved data - against a hypothesis and yields the long-term probability of the data based on repeated hypothetical experiments. Bayesian analysis provides quantification of the evidence for the null and alternative hypothesis, whereas NHST does not provide evidence for the null hypothesis. Bayesian analysis allows for multiplicity of testing without corrections, whereas NHST multiplicity requires corrections. Finally, it allows sequential data collection with variable stopping, whereas NHST sequential designs require specialised statistical approaches.

CONCLUSION

The Bayesian approach provides statistical, practical and ethical advantages over NHST.

IMPLICATIONS FOR PRACTICE

The quantification of uncertainty provided by Bayesian analysis - particularly Bayesian parameter estimation - should better inform evidence-based clinical decision-making. Bayesian analysis provides researchers with the freedom to analyse data in real time with optimal stopping when the data is persuasive and continuing when data is weak, thereby ensuring better use of the researcher's time and resources.

Group sequential design for randomized trials using "first hitting time" model

Group sequential design (GSD) has become a popular choice in recent clinical trials as it improves trial efficiency by providing options for early termination. The implementation of traditional tests for survival analysis (eg, the log-rank test and the Cox proportional hazard (PH) model) in the GSD setting has been widely discussed. The PH assumption is required for conventional (sequential) design, it is, however, often violated in practice. As an alternative, some generalized tests have been proposed (eg, the Max-Combo test) and their efficacies have been established. In this article, we explore the application of a more flexible, "first hitting time" based threshold regression (TR) model to GSD. TR assumes that subjects' health status is a latent (unobservable) process, and the clinical event of interest occurs when the latent health process hits a pre-specified boundary. The simulation results supported our findings that, in most cases, this comparable new method can successfully control type I error while providing higher early stopping opportunities in the sequential design, even when non-proportional hazard presents.

Bland–Altman Limits of Agreement from a Bayesian and Frequentist Perspective

Bland–Altman agreement analysis has gained widespread application across disciplines, last but not least in health sciences, since its inception in the 1980s. Bayesian analysis has been on the rise due to increased computational power over time, and Alari, Kim, and Wand have put Bland–Altman Limits of Agreement in a Bayesian framework (Meas. Phys. Educ. Exerc. Sci. 2021, 25, 137–148). We contrasted the prediction of a single future observation and the estimation of the Limits of Agreement from the frequentist and a Bayesian perspective by analyzing interrater data of two sequentially conducted, preclinical studies. The estimation of the Limits of Agreement θ1 and θ2 has wider applicability than the prediction of single future differences. While a frequentist confidence interval represents a range of nonrejectable values for null hypothesis significance testing of H0: θ1 ≤ −δ or θ2 ≥ δ against H1: θ1 > −δ and θ2 < δ, with a predefined benchmark value δ, Bayesian analysis allows for direct interpretation of both the posterior probability of the alternative hypothesis and the likelihood of parameter values. We discuss group-sequential testing and nonparametric alternatives briefly. Frequentist simplicity does not beat Bayesian interpretability due to improved computational resources, but the elicitation and implementation of prior information demand caution. Accounting for clustered data (e.g., repeated measurements per subject) is well-established in frequentist, but not yet in Bayesian Bland–Altman analysis.

6G Enabled Industrial Internet of Everything: Towards a Theoretical Framework

Currently, the deficiencies of the 5G mobile system as an enabler of Internet of Everything (IoE) applications are stimulating global research activities to focus on the sixth generation (6G) wireless system. The potential of IoE is enormous and growing exponentially. With the dawn of the fifth industrial revolution, IoE is transposing into industrial Internet of Everything (IIoE) projects which are complex and are eventuating to become a prominent technology for all industries offering new opportunities. This study embodies a synthesis of 6G, IoT, IoE, IIoE exhaustive literature review advancing knowledge to facilitate theory development. For the first time, a novel theoretical framework for the 6G-enabled IIoE (henceforth referred to as 6GIIoE) system was developed. Judiciously, sequential methodology is best suited for this emerging discipline research to create significant new knowledge in the literature contributing eternal insights to expound valuable contexts to ruminate significant findings. The theoretical framework developed recognizes 6GIIoE priority areas, challenges, and applications bestowing a guide for 6G-enabled IIoE initiatives divulging opportunities for future research activities.

Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country

Starting from historic reflections, the current SARS-CoV-2 induced COVID-19 pandemic is examined from various perspectives, in terms of what it implies for the implementation of non-pharmaceutical interventions, the modeling and monitoring of the epidemic, the development of early-warning systems, the study of mortality, prevalence estimation, diagnostic and serological testing, vaccine development, and ultimately clinical trials. Emphasis is placed on how the pandemic had led to unprecedented speed in methodological and clinical development, the pitfalls thereof, but also the opportunities that it engenders for national and international collaboration, and how it has simplified and sped up procedures. We also study the impact of the pandemic on clinical trials in other indications. We note that it has placed biostatistics, epidemiology, virology, infectiology, and vaccinology, and related fields in the spotlight in an unprecedented way, implying great opportunities, but also the need to communicate effectively, often amidst controversy.

The world is waiting, use sequential analysis and get us the evidence-based treatment we need for COVID-19

In spite of the relatively high morbidity and mortality, there is no approved medication yet for COVID-19. There are more than 200 ongoing trials on different drugs or vaccines, but new medications may take until 2021 to develop. Defining the optimal number of patients to be included in a study is a considerable challenge in these interventional researches. Ethical considerations prompt researchers to minimize the number of patients included in a trial. This gains particular importance when the disease is rare or lethal which is particularly so in the case of COVID-19. It is of paramount importance to explore some of the available tools that could help accelerate the adoption of any or some of the many proposed modalities for the treatment of diseases. These tools should be effective, yet efficient, for rapid testing of such treatments. Sequential analysis has not been frequently used in many clinical trials where it should have been used. None of the authors in published literature, as far as we know, used sequential analysis techniques to test potential drugs for COVID-19. In addition to its usefulness when the results of new forms of treatment are quickly needed, other important benefit of sequential analysis includes the ability to reach a similar conclusion about the utility of a new drug without unduly exposing more patients to the side effect of the old drug, in particularly, for the treatment of a rare disease.

Adaptive trial designs in diagnostic accuracy research

The aim of diagnostic accuracy studies is to evaluate how accurately a diagnostic test can distinguish diseased from nondiseased individuals. Depending on the research question, different study designs and accuracy measures are appropriate. As the prior knowledge in the planning phase is often very limited, modifications of design aspects such as the sample size during the ongoing trial could increase the efficiency of diagnostic trials. In intervention studies, group sequential and adaptive designs are well established. Such designs are characterized by preplanned interim analyses, giving the opportunity to stop early for efficacy or futility or to modify elements of the study design. In contrast, in diagnostic accuracy studies, such flexible designs are less common, even if they are as important as for intervention studies. However, diagnostic accuracy studies have specific features, which may require adaptations of the statistical methods or may lead to specific advantages or limitations of sequential and adaptive designs. In this article, we summarize the current status of methodological research and applications of flexible designs in diagnostic accuracy research. Furthermore, we indicate and advocate future development of adaptive design methodology and their use in diagnostic accuracy trials from an interdisciplinary viewpoint. The term "interdisciplinary viewpoint" describes the collaboration of experts of the academic and nonacademic research.

Group sequential design for time-to-event data using the concept of proportional time

Sequential monitoring of efficacy and safety is an important part of clinical trials. A Group Sequential design allows researchers to perform interim monitoring after groups of patients have completed the study. Statistical literature is well developed for continuous and binary outcomes and relies on asymptotic normality of the test statistic. However, in the case of time-to-event data, existing methods of sample size calculation are done either assuming proportional hazards or assuming exponentially distributed lifetimes. In scenarios where these assumptions are not true, as evidenced from historical data, these traditional methods are restrictive and cannot always be used. As interim monitoring is driven by ethical, financial, and administrative considerations, it is imperative that sample size calculations be done in an efficient manner keeping in mind the specific needs of a clinical trial with a time-to-event outcome. To address these issues, a novel group sequential design is proposed using the concept of Proportional Time. This method utilizes the generalized gamma ratio distribution to calculate the efficacy and safety boundaries and can be used for all distributions that are members of the generalized gamma family using an error spending approach. The design incorporates features specific to survival data such as loss to follow-up, administrative censoring, varying accrual times and patterns, binding or non-binding futility rules with or without skips, and flexible alpha and beta spending mechanisms. Three practical examples are discussed, followed by discussion of the important aspects of the proposed design.

A two-stage phase II clinical trial design with nested criteria for early stopping and efficacy

We propose a two-stage design for a single arm clinical trial with an early stopping rule for futility. This design employs different endpoints to assess early stopping and efficacy. The early stopping rule is based on a criteria determined more quickly than that for efficacy. These separate criteria are also nested in the sense that efficacy is a special case of, but usually not identical to, the early stopping endpoint. The design readily allows for planning in terms of statistical significance, power, expected sample size, and expected duration. This method is illustrated with a phase II design comparing rates of disease progression in elderly patients treated for lung cancer to rates found using a historical control. In this example, the early stopping rule is based on the number of patients who exhibit progression-free survival (PFS) at 2 months post treatment follow-up. Efficacy is judged by the number of patients who have PFS at 6 months. We demonstrate our design has expected sample size and power comparable with the Simon two-stage design but exhibits shorter expected duration under a range of useful parameter values.

Transfer to the Local Stroke Center versus Direct Transfer to Endovascular Center of Acute Stroke Patients with Suspected Large Vessel Occlusion in the Catalan Territory (RACECAT): Study protocol of a cluster randomized within a cohort trial

RATIONALE

Optimal pre-hospital delivery pathways for acute stroke patients suspected to harbor a large vessel occlusion have not been assessed in randomized trials.

AIM

To establish whether stroke subjects with rapid arterial occlusion evaluation scale based suspicion of large vessel occlusion evaluated by emergency medical services in the field have higher rates of favorable outcome when transferred directly to an endovascular center (endovascular treatment stroke center), as compared to the standard transfer to the closest local stroke center (local-SC).

DESIGN

Multicenter, superiority, cluster randomized within a cohort trial with blinded endpoint assessment.

PROCEDURE

Eligible patients must be 18 or older, have acute stroke symptoms and not have an immediate life threatening condition requiring emergent medical intervention. They must be suspected to have intracranial large vessel occlusion based on a pre-hospital rapid arterial occlusion evaluation scale of ≥5, be located in geographical areas where the default health authority assigned referral stroke center is a non-thrombectomy capable hospital, and estimated arrival at a thrombectomy capable stroke hospital in less than 7 h from time last seen well. Cluster randomization is performed according to a pre-established temporal sequence (temporal cluster design) with three strata: day/night, distance to the endovascular treatment stroke center, and week/week-end day.

STUDY OUTCOME

The primary endpoint is the modified Rankin Scale score at 90 days. The primary safety outcome is mortality at 90 days.

ANALYSIS

The primary endpoint based on the modified intention-to-treat population is the distribution of modified Rankin Scale scores at 90 days analyzed under a sequential triangular design. The maximum sample size is 1754 patients, with two planned interim analyses when 701 (40%) and 1227 patients have completed follow-up. Hypothesized common odds ratio is 1.35.

Adaptive designs in clinical trials: from scientific advice to marketing authorisation to the European Medicine Agency

Background

In recent years, experience on the application of adaptive designs in confirmatory clinical trials has accumulated. Although planning such trials comes at the cost of additional operational complexity, adaptive designs offer the benefit of flexibility to update trial design and objectives as data accrue. In 2007, the European Medicines Agency (EMA) provided guidance on confirmatory clinical trials with adaptive (or flexible) designs. In order to better understand how adaptive trials are implemented in practice and how they may impact medicine approval within the EMA centralised procedure, we followed on 59 medicines for which an adaptive clinical trial had been submitted to the EMA Scientific Advice (SA) and analysed previously in a dedicated EMA survey of scientific advice letters. We scrutinized in particular the submission of the corresponding medicines for a marketing authorisation application (MAA). We also discuss the current regulatory perspective as regards the implementation of adaptive designs in confirmatory clinical trials.

Methods

Using the internal EMA MAA database, the AdisInsight database and related trial registries, we analysed how many of these 59 trials actually started, the completion status, results, the time to trial start, the adaptive elements finally implemented after SA, their possible influence on the success of the trial and corresponding product approval.

Results

Overall 31 trials out of 59 (53%) were retrieved. Thirty of them (97%) have been started and 23 (74%) concluded. Nine of these trials (39% out of 23) demonstrated a significant treatment effect on their primary endpoint and 4 (17% out of 23) supported a marketing authorisation (MA). An additional two trials were stopped using pre-defined criteria for futility, efficiently identifying trials on which further resources should not be spent. Median time to trial start after SA letter was given by EMA was 5 months. In the investigated trial registries, at least 18 trial (58% of 31 retrieved trials) designs were implemented with adaptive elements, which were predominantly dose selection, sample size reassessment (SSR) and stopping for futility (SFF). Among the 11 completed trials including adaptive elements, 6 demonstrated a significant treatment effect on their primary endpoint (55%).

Conclusions

Adaptive designs are now well established in the drug development landscape. If properly pre-planned, adaptations can play a key role in the success of some of these trials, for example to help successfully select the most promising dose regimens for phase II/III trials. Interim analyses can also enable stopping of trials for futility when they do not hold their promises. Type I error rate control, trial integrity and results consistency between the different stages of the analyses are fundamental aspects to be discussed thoroughly. Engaging early dialogue with regulators and implementing the scientific advice received is strongly recommended, since much experience in discussing adaptive designs and assessing their results has been accumulated.

A literature review of applied adaptive design methodology within the field of oncology in randomised controlled trials and a proposed extension to the CONSORT guidelines

Background

The application of adaptive design methodology within a clinical trial setting is becoming increasingly popular. However the application of these methods within trials is not being reported as adaptive designs hence making it more difficult to capture the emerging use of these designs. Within this review, we aim to understand how adaptive design methodology is being reported, whether these methods are explicitly stated as an ‘adaptive design’ or if it has to be inferred and to identify whether these methods are applied prospectively or concurrently.

Methods

Three databases; Embase, Ovid and PubMed were chosen to conduct the literature search. The inclusion criteria for the review were phase II, phase III and phase II/III randomised controlled trials within the field of Oncology that published trial results in 2015. A variety of search terms related to adaptive designs were used.

Results

A total of 734 results were identified, after screening 54 were eligible. Adaptive designs were more commonly applied in phase III confirmatory trials. The majority of the papers performed an interim analysis, which included some sort of stopping criteria. Additionally only two papers explicitly stated the term ‘adaptive design’ and therefore for most of the papers, it had to be inferred that adaptive methods was applied. Sixty-five applications of adaptive design methods were applied, from which the most common method was an adaptation using group sequential methods.

Conclusions

This review indicated that the reporting of adaptive design methodology within clinical trials needs improving. The proposed extension to the current CONSORT 2010 guidelines could help capture adaptive design methods. Furthermore provide an essential aid to those involved with clinical trials.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-017-0393-6) contains supplementary material, which is available to authorized users.

Sequential designs with small samples: Evaluation and recommendations for normal responses

Sequential monitoring is a well-known methodology for the design and analysis of clinical trials. Driven by the lower expected sample size, recent guidelines and published research suggest the use of sequential methods for the conduct of clinical trials in rare diseases. However, the vast majority of the developed and most commonly used sequential methods relies on asymptotic assumptions concerning the distribution of the test statistics. It is not uncommon for trials in (very) rare diseases to be conducted with only a few decades of patients and the use of sequential methods that rely on large-sample approximations could inflate the type I error probability. Additionally, the setting of a rare disease could make the traditional paradigm of designing a clinical trial (deciding on the sample size given type I and II errors and anticipated effect size) irrelevant. One could think of the situation where the number of patients available has a maximum and this should be utilized in the most efficient way. In this work, we evaluate the operational characteristics of sequential designs in the setting of very small to moderate sample sizes with normally distributed outcomes and demonstrate the necessity of simple corrections of the critical boundaries. We also suggest a method for deciding on an optimal sequential design given a maximum sample size and some (data driven or based on expert opinion) prior belief on the treatment effect.

Simultaneous sequential monitoring of efficacy and safety led to masking of effects

OBJECTIVE

Usually, sequential designs for clinical trials are applied on the primary (=efficacy) outcome. In practice, other outcomes (e.g., safety) will also be monitored and influence the decision whether to stop a trial early. Implications of simultaneous monitoring on trial decision making are yet unclear. This study examines what happens to the type I error, power, and required sample sizes when one efficacy outcome and one correlated safety outcome are monitored simultaneously using sequential designs.

STUDY DESIGN AND SETTING

We conducted a simulation study in the framework of a two-arm parallel clinical trial. Interim analyses on two outcomes were performed independently and simultaneously on the same data sets using four sequential monitoring designs, including O'Brien-Fleming and Triangular Test boundaries. Simulations differed in values for correlations and true effect sizes.

RESULTS

When an effect was present in both outcomes, competition was introduced, which decreased power (e.g., from 80% to 60%). Futility boundaries for the efficacy outcome reduced overall type I errors as well as power for the safety outcome.

CONCLUSION

Monitoring two correlated outcomes, given that both are essential for early trial termination, leads to masking of true effects. Careful consideration of scenarios must be taken into account when designing sequential trials. Simulation results can help guide trial design.

Recent developments in clinical trial designs for HIV vaccine research

HIV vaccine strategies are expected to be a crucial component for controlling the HIV epidemic. Despite the large spectrum of potential candidate vaccines for both prophylactic and therapeutic use, the overall development process of an efficacious HIV vaccine strategy is lengthy. The design of clinical trials and the progression of a candidate strategy through the different clinical development stages remain methodologically challenging, mainly due to the lack of validated correlates of protection. In this review, we describe recent advances in clinical trial designs to increase the efficiency of the clinical development of candidate HIV vaccine strategies. The methodological aspects of the designs for early- (phase I and II) and later -stage (phase IIB and III) development are discussed, taking into account the specificities of both prophylactic and therapeutic HIV vaccine development.

Cross-sector surveys assessing perceptions of key stakeholders towards barriers, concerns and facilitators to the appropriate use of adaptive designs in confirmatory trials

Background

Appropriately conducted adaptive designs (ADs) offer many potential advantages over conventional trials. They make better use of accruing data, potentially saving time, trial participants, and limited resources compared to conventional, fixed sample size designs. However, one can argue that ADs are not implemented as often as they should be, particularly in publicly funded confirmatory trials. This study explored barriers, concerns, and potential facilitators to the appropriate use of ADs in confirmatory trials among key stakeholders.

Methods

We conducted three cross-sectional, online parallel surveys between November 2014 and January 2015. The surveys were based upon findings drawn from in-depth interviews of key research stakeholders, predominantly in the UK, and targeted Clinical Trials Units (CTUs), public funders, and private sector organisations. Response rates were as follows: 30(55 %) UK CTUs, 17(68 %) private sector, and 86(41 %) public funders. A Rating Scale Model was used to rank barriers and concerns in order of perceived importance for prioritisation.

Results

Top-ranked barriers included the lack of bridge funding accessible to UK CTUs to support the design of ADs, limited practical implementation knowledge, preference for traditional mainstream designs, difficulties in marketing ADs to key stakeholders, time constraints to support ADs relative to competing priorities, lack of applied training, and insufficient access to case studies of undertaken ADs to facilitate practical learning and successful implementation. Associated practical complexities and inadequate data management infrastructure to support ADs were reported as more pronounced in the private sector. For funders of public research, the inadequate description of the rationale, scope, and decision-making criteria to guide the planned AD in grant proposals by researchers were all viewed as major obstacles.

Conclusions

There are still persistent and important perceptions of individual and organisational obstacles hampering the use of ADs in confirmatory trials research. Stakeholder perceptions about barriers are largely consistent across sectors, with a few exceptions that reflect differences in organisations’ funding structures, experiences and characterisation of study interventions. Most barriers appear connected to a lack of practical implementation knowledge and applied training, and limited access to case studies to facilitate practical learning.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-015-1119-x) contains supplementary material, which is available to authorized users.

Classification in two-stage screening

Decision theory is applied to the problem of setting thresholds in medical screening when it is organised in two stages. In the first stage that involves a less expensive procedure that can be applied on a mass scale, an individual is classified as a negative or a likely positive. In the second stage, the likely positives are subjected to another test that classifies them as (definite) positives or negatives. The second-stage test is more accurate, but also more expensive and more involved, and so there are incentives to restrict its application. Robustness of the method with respect to the parameters, some of which have to be set by elicitation, is assessed by sensitivity analysis.

An Investigation of the Shortcomings of the CONSORT 2010 Statement for the Reporting of Group Sequential Randomised Controlled Trials: A Methodological Systematic Review

BACKGROUND

It can be argued that adaptive designs are underused in clinical research. We have explored concerns related to inadequate reporting of such trials, which may influence their uptake. Through a careful examination of the literature, we evaluated the standards of reporting of group sequential (GS) randomised controlled trials, one form of a confirmatory adaptive design.

METHODS

We undertook a systematic review, by searching Ovid MEDLINE from the 1st January 2001 to 23rd September 2014, supplemented with trials from an audit study. We included parallel group, confirmatory, GS trials that were prospectively designed using a Frequentist approach. Eligible trials were examined for compliance in their reporting against the CONSORT 2010 checklist. In addition, as part of our evaluation, we developed a supplementary checklist to explicitly capture group sequential specific reporting aspects, and investigated how these are currently being reported.

RESULTS

Of the 284 screened trials, 68(24%) were eligible. Most trials were published in "high impact" peer-reviewed journals. Examination of trials established that 46(68%) were stopped early, predominantly either for futility or efficacy. Suboptimal reporting compliance was found in general items relating to: access to full trials protocols; methods to generate randomisation list(s); details of randomisation concealment, and its implementation. Benchmarking against the supplementary checklist, GS aspects were largely inadequately reported. Only 3(7%) trials which stopped early reported use of statistical bias correction. Moreover, 52(76%) trials failed to disclose methods used to minimise the risk of operational bias, due to the knowledge or leakage of interim results. Occurrence of changes to trial methods and outcomes could not be determined in most trials, due to inaccessible protocols and amendments.

DISCUSSION AND CONCLUSIONS

There are issues with the reporting of GS trials, particularly those specific to the conduct of interim analyses. Suboptimal reporting of bias correction methods could potentially imply most GS trials stopping early are giving biased results of treatment effects. As a result, research consumers may question credibility of findings to change practice when trials are stopped early. These issues could be alleviated through a CONSORT extension. Assurance of scientific rigour through transparent adequate reporting is paramount to the credibility of findings from adaptive trials. Our systematic literature search was restricted to one database due to resource constraints.

A Practical Guide to Data Monitoring Committees in Adaptive Trials

Adaptive clinical trials require access to interim data to carry out trial modification as allowed by a prespecified adaptation plan. A data monitoring committee (DMC) is a group of experts that is charged with monitoring accruing trial data to ensure the safety of trial participants and that in adaptive trials may also play a role in implementing a preplanned adaptation. In this paper, we summarize current practices and viewpoints and provide guidance on evolving issues related to the use of DMCs in adaptive trials. We describe the common types of adaptive designs and point out some DMC-related issues that are unique to this class of designs. We include 3 examples of DMCs in late-stage adaptive trials that have been implemented in practice. We advocate training opportunities for researchers who may be interested in serving on a DMC for an adaptive trial since qualified DMC members are fundamental to the successful execution of DMC responsibilities.

Comparative effectiveness research and measuring the level of pharmaceutical innovation in the EU

Over the past 50 years, medicines have made a huge contribution to improving the health status of populations. However, the level of pharmaceutical innovation varies widely, ranging from breakthrough treatments for life-threatening diseases to minor modifications of medicines that have been on the market for some time. Once innovative medicines approach the market, a system of measuring the level of innovation should be established. This perspective describes the current situation, the challenges and initiatives, and formulates recommendations for a coordinated EU action to measure the level of pharmaceutical innovation. In our opinion, comparative effectiveness research needs to play a central role in measuring the level of pharmaceutical innovation.

Innovative trial designs are practical solutions for improving the treatment of tuberculosis

A growing number of new drugs for the treatment of tuberculosis are in clinical development. Confirmatory phase 3 trials are expensive and time-consuming and the question of whether one particular drug combination can be used to treat tuberculosis is less important from a public health perspective than the question of which are the shortest, simplest, most effective, and safest regimens. While preclinical and phase 1 studies provide some guidance in the selection of combinations for clinical evaluation, a large number of combinations will require phase 2 testing to ensure that only the best regimens advance to phase 3. The multi-arm multi-stage trial design is an example of a treatment selection-adaptive design where multiple experimental arms are each simultaneously compared with a common control and interim analyses allow for poor performing arms to be dropped early. Such designs, if designed and implemented correctly, require fewer patients, can be completed in a shorter time frame, and answer more relevant questions without any loss in statistical validity or scientific integrity. There are, however, practical issues that must be considered in applying this in tuberculosis treatment trials. More innovative trials designs should be considered to speed drug and regimen development for the treatment of tuberculosis.

Continuous safety monitoring for randomized controlled clinical trials with blinded treatment information. Part 3: Design considerations

Ongoing safety monitoring of clinical trials of investigational treatments must operate at levels that range from the minute and detailed - namely, mathematical treatment of trial data - to the philosophical and societal - namely, ethical concerns for individuals and populations. Between those two poles lies a realm of environmental and pragmatic considerations that reflect the goals, biases, risk-tolerance, and constraints of study sponsors and organizers. These factors, while more difficult to quantify or, at times, to justify, also have a meaningful impact on the approach to safety monitoring and the resulting actions and outcomes. This paper considers the influence and interaction of two such factors, study design and statistical framework, on continuous safety monitoring procedures. Group sequential designs have been generally preferred for clinical trials over continuous sequential designs because of practical considerations. The group means and greater time for deliberation when using a group sequential procedure, as opposed to a continuous sequential procedure, can improve the quality of the analyses with minimal loss in sensitivity. However, undertaking any sequential analysis within a frequentist framework provokes considerable theoretical and practical difficulties. Continuous monitoring with a likelihood based method, on the other hand, has the advantages that all available information, including new data, can be used; sample sizes need not be fixed; and decisions can be made at any time without statistical penalty, irrespective of trial design. Such responsive statistical rules are needed to provide guidance to the human beings charged with trial monitoring.

Changes in clinical trials methodology over time: a systematic review of six decades of research in psychopharmacology

Background

There have been many changes in clinical trials methodology since the introduction of lithium and the beginning of the modern era of psychopharmacology in 1949. The nature and importance of these changes have not been fully addressed to date. As methodological flaws in trials can lead to false-negative or false-positive results, the objective of our study was to evaluate the impact of methodological changes in psychopharmacology clinical research over the past 60 years.

Methodology/Principal Findings

We performed a systematic review from 1949 to 2009 on MEDLINE and Web of Science electronic databases, and a hand search of high impact journals on studies of seven major drugs (chlorpromazine, clozapine, risperidone, lithium, fluoxetine and lamotrigine). All controlled studies published 100 months after the first trial were included. Ninety-one studies met our inclusion criteria. We analyzed the major changes in abstract reporting, study design, participants' assessment and enrollment, methodology and statistical analysis. Our results showed that the methodology of psychiatric clinical trials changed substantially, with quality gains in abstract reporting, results reporting, and statistical methodology. Recent trials use more informed consent, periods of washout, intention-to-treat approach and parametric tests. Placebo use remains high and unchanged over time.

Conclusions/Significance

Clinical trial quality of psychopharmacological studies has changed significantly in most of the aspects we analyzed. There was significant improvement in quality reporting and internal validity. These changes have increased study efficiency; however, there is room for improvement in some aspects such as rating scales, diagnostic criteria and better trial reporting. Therefore, despite the advancements observed, there are still several areas that can be improved in psychopharmacology clinical trials.

Pitfalls in the design and analysis of paediatric clinical trials: a case of a 'failed' multi-centre study, and potential solutions

Aim: To increase awareness of possible pitfalls in the design and analysis of a multi-centre randomized clinical trial and to give an overview of alternative study designs and their consequences for power analyses in case of limited availability of trial participants.

Methods: Investigation of the assumptions in the power calculation and re-analysis of the original data of a ‘failed’ trial on the effect of dexamethasone on the duration of mechanical ventilation in young children with respiratory syncytial virus infection. Use of ‘boundaries approach’ is explored using the data from this trial. A comprehensive overview of the various modern solutions for the design of a subsequent trial in this field is given.

Results: Two frequent major deficiencies of trial design and data analysis are reviewed in depth, i.e. too optimistic assumptions for the sample size calculation and failure to adjust for centre effects.

Conclusion: Critical review of trial assumptions and if necessary sample size recalculation based on an internal pilot by a data monitoring committee is recommended to maximize the probability of obtaining conclusive results.

Postregistration trials: should we? How do we?

Postregistration trials or postmarketing or phase IV trials are tools to investigate any treatment that is already approved and is, therefore, available for prescription use. The main objectives of these trials are typical effectiveness, more structured surveillance for uncommon or rare side effects, and the potential development of new indications. Several issues need to be addressed in the postregistration phase of the evaluation of a therapeutic treatment. Among those we will focus on two ethical challenges: reporting bias from industry-sponsored research and use of placebo as one of the arms in controlled studies where proven treatment already exists. The review of this topic is particularly relevant in the field of critical care because it is ongoing in the debate about the opportunity to perform a new placebo-controlled study with the recombinant human activated protein C (activated drotrecogin alfa-activated protein C). In fact, despite the approval of activated protein C for treatment of patients with severe sepsis and high risk of death, several issues regarding the efficacy and safety of its administration have been recognized by several investigators to the point that the European Medicines Agency asked for a new placebo-controlled study to further clarify the benefit/risk profile of activated protein C.

Group sequential and adaptive designs - a review of basic concepts and points of discussion

In recent times, group sequential and adaptive designs for clinical trials have attracted great attention from industry, academia and regulatory authorities. These designs allow analyses on accumulating data - as opposed to classical, "fixed-sample" statistics. The rapid development of a great variety of statistical procedures is accompanied by a lively debate on their potential merits and shortcomings. The purpose of this review article is to ease orientation in both respects. First, we provide a concise overview of the essential technical concepts, with special emphasis on their interrelationships. Second, we give a structured review of the current controversial discussion on practical issues, opportunities and challenges of these new designs.

Predictive checking for Bayesian interim analyses in clinical trials

Bayesian methodologies have been used for interim analyses of clinical trial data. In Bayesian interim analyses, decisions regarding the continuation of a trial are guided by a Bayesian model or indices, e.g., the predictive probability derived from it that specifies the conditions under which the clinical trial results might be judged sufficiently convincing to allow early stopping. Thus, its appropriateness for making such decisions depends on whether the model or the indices are reliable. In this paper we describe the use of both prior- and posterior- predictive checking approaches as a diagnostic tool for assessing the reliability of the model or indices on which the decision making is based. The proposed approach is illustrated with three examples, one of which is a simulation.