Physiotherapy for Patients with Sciatica Awaiting Lumbar Micro-discectomy Surgery: A Nested, Qualitative Study of Patients' Views and Experiences

Background and Purpose Sciatica is a common clinical condition that can be extremely painful, disabling and life‐changing. Whether conservative or surgical treatment for sciatica secondary to an intervertebral disc prolapse is most effective is still much debated. An important component of conservative treatment is physiotherapy, which aims to promote physical and psychological health for the patient, whilst resorption of the disc takes place. This paper reports a qualitative study of patients' views and experiences of a bespoke physiotherapy intervention for the treatment of sciatica. Methods A qualitative study nested within a pilot randomized controlled trial of bespoke physiotherapy for the treatment of patients with sciatica awaiting lumbar microdiscectomy surgery. Patients randomized to receive bespoke physiotherapy in the intervention arm of the trial were invited to take part in semi‐structured interviews. Twenty‐one in‐depth, semi‐structured interviews took place. All interviews were recorded, fully transcribed and thematically analysed. Results Most patients in the sample found the physiotherapy valuable, appreciating the individual nature of the approach, the exercises to reduce pain and discomfort, techniques for improving functional spinal movement, walking and dynamic posture, and manual therapy and cardiovascular exercise. A small number did not find the physiotherapy of benefit. Sixteen patients in the sample went on to proceed with surgery, but most of these found value in having had the physiotherapy first. Discussion Many patients with sciatica appreciate the value of physiotherapy prior to surgery. Future research should examine patients' experiences of bespoke physiotherapy delivered within primary care

Reporting issues in group sequential randomised controlled trials: a systematic review protocol of published journal reports

Background: Adaptive designs are somewhat underused, despite prominence given to methodology in the statistical literature. Some concerns relates to robustness of adaptive designs in decision making, acceptability of trial findings to change practice, anxiety about early stopping of trials and worry about wrong decision making. These issues could be linked to inadequate reporting of the conduct of such clinical trials. We assess the reporting of group sequential randomised controlled trials (RCTs), which are one of the most well-understood adaptive designs in the confirmatory setting. Methods: We undertake a systematic review searching Ovid MEDLINE from 1st January 2001 to 23rd September 2014 and including parallel group confirmatory group sequential RCTs that were prospectively designed using the Frequentist approach. Eligible trials are screened for completeness in reporting against the CONSORT 2010 checklist with some proposed modifications to capture issues such as statistical bias correction following early stopping. Descriptive statistics aided with forest plots on CONSORT compliance are presented. Discussion: Reporting of the conduct of adaptive designs is an area which has not been fully explored. Hence, the findings from this study can enlighten us on the adequacy in reporting of well-understood group sequential RCTs as a class of adaptive designs and on ways to address some of the cited concerns. Most importantly, the study can inform policy makers on the adequacy of the current CONSORT statements in enhancing reporting of such adaptive designs

Adaptive designs in clinical trials: why use them, and how to run and report them

Abstract Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial’s course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented. We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice

Missing steps in a staircase: a qualitative study of the perspectives of key stakeholders on the use of adaptive designs in confirmatory trials

Background Despite the promising benefits of adaptive designs (ADs), their routine use, especially in confirmatory trials, is lagging behind the prominence given to them in the statistical literature. Much of the previous research to understand barriers and potential facilitators to the use of ADs has been driven from a pharmaceutical drug development perspective, with little focus on trials in the public sector. In this paper, we explore key stakeholders’ experiences, perceptions and views on barriers and facilitators to the use of ADs in publicly funded confirmatory trials. Methods Semi-structured, in-depth interviews of key stakeholders in clinical trials research (CTU directors, funding board and panel members, statisticians, regulators, chief investigators, data monitoring committee members and health economists) were conducted through telephone or face-to-face sessions, predominantly in the UK. We purposively selected participants sequentially to optimise maximum variation in views and experiences. We employed the framework approach to analyse the qualitative data. Results We interviewed 27 participants. We found some of the perceived barriers to be: lack of knowledge and experience coupled with paucity of case studies, lack of applied training, degree of reluctance to use ADs, lack of bridge funding and time to support design work, lack of statistical expertise, some anxiety about the impact of early trial stopping on researchers’ employment contracts, lack of understanding of acceptable scope of ADs and when ADs are appropriate, and statistical and practical complexities. Reluctance to use ADs seemed to be influenced by: therapeutic area, unfamiliarity, concerns about their robustness in decision-making and acceptability of findings to change practice, perceived complexities and proposed type of AD, among others. Conclusions There are still considerable multifaceted, individual and organisational obstacles to be addressed to improve uptake, and successful implementation of ADs when appropriate. Nevertheless, inferred positive change in attitudes and receptiveness towards the appropriate use of ADs by public funders are supportive and are a stepping stone for the future utilisation of ADs by researchers

Delphi consensus reached to produce a decision tool for SelecTing Approaches forRapid Reviews (STARR)

OBJECTIVES: There are many rapid review methods; however, there is little pragmatic guidance on which methods to select. This study aimed to reach consensus among international rapid review experts outlining areas to consider when selecting approaches for rapid reviews. STUDY DESIGN AND SETTING: A two-round modified online Delphi survey was conducted between May and July 2018. Participants were asked to rank the importance of a predefined list of 19 items. A consensus definition of at least 70% agreement for each item was decided a priori. RESULTS: Thirty experts from ten countries participated in Round 1 and 24 in Round 2. During Round 1, consensus was reached on all items. One additional item on quality assessment was suggested by respondents and comments suggested wording changes to improve clarity and understanding of the tool. Respondents in the second round indicated a high level of importance and all 20 items achieved consensus. These items addressed interaction with commissioners, scoping and searching the evidence-base, data extraction and synthesis methods, and reporting of rapid review methods. CONCLUSIONS: International consensus was reached to produce the STARR decision tool for planning rapid reviews and will lead to improved shared understanding between review teams and review commissioners

Response adaptive randomisation in clinical trials: Current practice, gaps and future directions.

Introduction: Adaptive designs (ADs) offer clinical trials flexibility to modify design aspects based on accumulating interim data. Response adaptive randomisation (RAR) adjusts treatment allocation according to interim results, favouring promising treatments. Despite scientific appeal, RAR adoption lags behind other ADs. Understanding methods and applications could provide insights and resources and reveal future research needs. This study examines RAR application, trial results and achieved benefits, reporting gaps, statistical tools and concerns, while highlighting examples of effective practices. Methods: RAR trials with comparative efficacy, effectiveness or safety objectives, classified at least phase I/II, were identified via statistical literature, trial registries, statistical resources and researcher-knowledge. Search spanned until October 2023, including results until February 2024. Analysis was descriptive and narrative. Results: From 652 articles/trials screened, 65 planned RAR trials (11 platform trials) were identified, beginning in 1985 and gradually increasing through to 2023. Most trials were in oncology (25%) and drug-treatments (80%), with 63% led by US teams. Predominantly Phase II (62%) and multi-arm (63%), 85% used Bayesian methods, testing superiority hypotheses (86%). Binary outcomes appeared in 55%, with a median observation of 56 days. Bayesian RAR algorithms were applied in 83%. However, 71% of all trials lacked clear details on statistical implementation. Subgroup-level RAR was seen in 23% of trials. Allocation was restricted in 51%, and 88% was included a burn-in period. Most trials (85%) planned RAR alongside other adaptations. Of trials with results, 92% used RAR, but over 50% inadequately reported allocation changes. A mean 22% reduction in sample size was seen, with none over-allocating to ineffective arms. Conclusion: RAR has shown benefits in conditions like sepsis, COVID-19 and cancer, enhancing effective treatment allocation and saving resources. However, complexity, costs and simulation need limit wider adoption. This review highlights RAR's benefits and suggests enhancing statistical tools to encourage wider adoption in clinical research

Reporting and communication of sample size calculations in adaptive clinical trials: a review of trial protocols and grant applications

Background An adaptive design allows modifying the design based on accumulated data while maintaining trial validity and integrity. The final sample size may be unknown when designing an adaptive trial. It is therefore important to consider what sample size is used in the planning of the study and how that is communicated to add transparency to the understanding of the trial design and facilitate robust planning. In this paper, we reviewed trial protocols and grant applications on the sample size reporting for randomised adaptive trials. Method We searched protocols of randomised trials with comparative objectives on ClinicalTrials.gov (01/01/2010 to 31/12/2022). Contemporary eligible grant applications accessed from UK publicly funded researchers were also included. Suitable records of adaptive designs were reviewed, and key information was extracted and descriptively analysed. Results We identified 439 records, and 265 trials were eligible. Of these, 164 (61.9%) and 101 (38.1%) were sponsored by industry and public sectors, respectively, with 169 (63.8%) of all trials using a group sequential design although trial adaptations used were diverse. The maximum and minimum sample sizes were the most reported or directly inferred (n = 199, 75.1%). The sample size assuming no adaptation would be triggered was usually set as the estimated target sample size in the protocol. However, of the 152 completed trials, 15 (9.9%) and 33 (21.7%) had their sample size increased or reduced triggered by trial adaptations, respectively. The sample size calculation process was generally well reported in most cases (n = 216, 81.5%); however, the justification for the sample size calculation parameters was missing in 116 (43.8%) trials. Less than half gave sufficient information on the study design operating characteristics (n = 119, 44.9%). Conclusion Although the reporting of sample sizes varied, the maximum and minimum sample sizes were usually reported. Most of the trials were planned for estimated enrolment assuming no adaptation would be triggered. This is despite the fact a third of reported trials changed their sample size. The sample size calculation was generally well reported, but the justification of sample size calculation parameters and the reporting of the statistical behaviour of the adaptive design could still be improved

Confidence intervals for adaptive trial designs I: a methodological review

Regulatory guidance notes the need for caution in the interpretation of confidence intervals (CIs) constructed during and after an adaptive clinical trial. Conventional CIs of the treatment effects are prone to undercoverage (as well as other undesirable properties) in many adaptive designs (ADs) because they do not take into account the potential and realized trial adaptations. This paper is the first in a two-part series that explores CIs for adaptive trials. It provides a comprehensive review of the methods to construct CIs for ADs, while the second paper illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. We describe several classes of techniques for constructing CIs for adaptive clinical trials before providing a systematic literature review of available methods, classified by the type of AD. As part of this, we assess, through a proposed traffic light system, which of several desirable features of CIs (such as achieving nominal coverage and consistency with the hypothesis test decision) each of these methods holds

Point estimation for adaptive trial designs II: practical considerations and guidance

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred