Evaluating how clear the questions being investigated in randomised trials are: systematic review of estimands

A Causal Mediation Approach to Account for Interaction of Treatment and Intercurrent Events: Using Hypothetical Strategy

Hypothetical strategy is a common strategy for handling intercurrent events (IEs). No current guideline or study considers treatment-IE interaction to target the estimand in any one IE-handling strategy. Based on the hypothetical strategy, we aimed to (1) assess the performance of three estimators with different considerations for the treatment-IE interaction in a simulation and (2) compare the estimation of these estimators in a real trial. Simulation data were generalized based on realistic clinical trials of Alzheimer's disease. The estimand of interest was the effect of treatment with no IE occurring under the hypothetical strategy. Three estimators, namely, G-estimation with and without interaction and IE-ignored estimation, were compared in scenarios where the treatment-IE interaction effect was set as -50% to 50% of the main effect. Bias was the key performance measure. The real case was derived from a randomized trial of methadone maintenance treatment. Only G-estimation with interaction exhibited unbiased estimations regardless of the existence, direction or magnitude of the treatment-IE interaction in those scenarios. Neglecting the interaction and ignoring the IE would introduce a bias as large as 0.093 and 0.241 (true value, -1.561) if the interaction effect existed. In the real case, compared with G-estimation with interaction, G-estimation without interaction and IE-ignored estimation increased the estimand of interest by 33.55% and 34.36%, respectively. This study highlights the importance of considering treatment-IE interaction in the estimand framework. In practice, it would be better to include the interaction in the estimator by default.

Application of the Estimand Framework to Anesthesia Trials

SUMMARY

Events occurring after randomization, such as use of rescue medication, treatment discontinuation, or death, are common in randomized trials. These events can change either the existence or interpretation of the outcome of interest. However, appropriate handling of these intercurrent events is often unclear. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9(R1) addendum introduced the estimand framework, which aligns trial objectives with the design, conduct, statistical analysis, and interpretation of results. This article describes how the estimand framework can be used in anesthesia trials to precisely define the treatment effect to be estimated, key attributes of an estimand, common intercurrent events in anesthesia trials with strategies for handling them, and use of the estimand framework in a hypothetical anesthesia trial on postoperative delirium. When planning anesthesia trials, clearly defining the estimand is vital to ensure that what is being estimated is clearly understood, is clinically relevant, and helps answer the clinical questions of interest.

Reporting of Factorial Randomized Trials: Extension of the CONSORT 2010 Statement

Importance

Transparent reporting of randomized trials is essential to facilitate critical appraisal and interpretation of results. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, reporting of factorial trials is suboptimal.

Objective

To develop a consensus-based extension to the Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement for factorial trials.

Design

Using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework, the CONSORT extension for factorial trials was developed by (1) generating a list of reporting recommendations for factorial trials using a scoping review of methodological articles identified using a MEDLINE search (from inception to May 2019) and supplemented with relevant articles from the personal collections of the authors; (2) a 3-round Delphi survey between January and June 2022 to identify additional items and assess the importance of each item, completed by 104 panelists from 14 countries; and (3) a hybrid consensus meeting attended by 15 panelists to finalize the selection and wording of items for the checklist.

Findings

This CONSORT extension for factorial trials modifies 16 of the 37 items in the CONSORT 2010 checklist and adds 1 new item. The rationale for the importance of each item is provided. Key recommendations are (1) the reason for using a factorial design should be reported, including whether an interaction is hypothesized, (2) the treatment groups that form the main comparisons should be clearly identified, and (3) for each main comparison, the estimated interaction effect and its precision should be reported.

Conclusions and Relevance

This extension of the CONSORT 2010 Statement provides guidance on the reporting of factorial randomized trials and should facilitate greater understanding of and transparency in their reporting.

Consensus Statement for Protocols of Factorial Randomized Trials: Extension of the SPIRIT 2013 Statement

Importance

Trial protocols outline a trial's objectives as well as the methods (design, conduct, and analysis) that will be used to meet those objectives, and transparent reporting of trial protocols ensures objectives are clear and facilitates appraisal regarding the suitability of study methods. Factorial trials, in which 2 or more interventions are assessed in the same set of participants, have unique methodological considerations. However, no extension of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Statement, which provides guidance on reporting of trial protocols, for factorial trials is available.

Objective

To develop a consensus-based extension to the SPIRIT 2013 Statement for factorial trials.

Evidence Review

The SPIRIT extension for factorial trials was developed using the Enhancing the Quality and Transparency of Health Research (EQUATOR) methodological framework. First, a list of reporting recommendations was generated using a scoping review of methodological articles identified using a MEDLINE search (inception to May 2019), which was supplemented with relevant articles from the personal collections of the authors. Second, a 3-round Delphi survey (January to June 2022, completed by 104 panelists from 14 countries) was conducted to assess the importance of items and identify additional recommendations. Third, a hybrid consensus meeting was held, attended by 15 panelists to finalize selection and wording of the checklist.

Findings

This SPIRIT extension for factorial trials modified 9 of the 33 items in the SPIRIT 2013 checklist. Key reporting recommendations were that the rationale for using a factorial design should be provided, including whether an interaction is hypothesized; the treatment groups that will form the main comparisons should be identified; and statistical methods for each main comparison should be provided, including how interactions will be assessed.

Conclusions and Relevance

In this consensus statement, 9 factorial-specific items were provided that should be addressed in all protocols of factorial trials to increase the trial's utility and transparency.

Management of partial-thickness rotator cuff tears with autologous adipose-derived regenerative cells is safe and more effective than injection of corticosteroid

Symptomatic, partial-thickness rotator cuff tears (sPTRCT) are problematic. This study tested the hypothesis that management of sPTRCT with injection of fresh, uncultured, unmodified, autologous, adipose-derived regenerative cells (UA-ADRCs) is safe and more effective than injection of corticosteroid even in the long run. To this end, subjects who had completed a former randomized controlled trial were enrolled in the present study. At baseline these subjects had not responded to physical therapy treatments for at least 6 weeks, and were randomly assigned to receive respectively a single injection of UA-ADRCs (n = 11) or a single injection of methylprednisolone (n = 5). Efficacy was assessed using the ASES Total score, pain visual analogue scale (VAS), RAND Short Form-36 Health Survey and range of motion at 33.2 ± 1.0 (mean ± SD) and 40.6 ± 1.9 months post-treatment. Proton density, fat-saturated, T2-weighted MRI of the index shoulder was performed at both study visits. There were no greater risks connected with injection of UA-ADRCs than those connected with injection of corticosteroid. The subjects in the UA-ADRCs group showed statistically significantly higher mean ASES Total scores than the subjects in the corticosteroid group. The MRI scans at 6 months post-treatment allowed to "watch the UA-ADRCs at work".

Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers

BACKGROUND

Considered one of the highest levels of evidence, results of randomized controlled trials (RCTs) remain an essential building block in mental health research. They are frequently used to confirm that an intervention "works" and to guide treatment decisions. Given their importance in the field, it is concerning that the quality of many RCT evaluations in mental health research remains poor. Common errors range from inadequate missing data handling and inappropriate analyses (e.g., baseline randomization tests or analyses of within-group changes) to unduly interpretations of trial results and insufficient reporting. These deficiencies pose a threat to the robustness of mental health research and its impact on patient care. Many of these issues may be avoided in the future if mental health researchers are provided with a better understanding of what constitutes a high-quality RCT evaluation.

METHODS

In this primer article, we give an introduction to core concepts and caveats of clinical trial evaluations in mental health research. We also show how to implement current best practices using open-source statistical software.

RESULTS

Drawing on Rubin's potential outcome framework, we describe that RCTs put us in a privileged position to study causality by ensuring that the potential outcomes of the randomized groups become exchangeable. We discuss how missing data can threaten the validity of our results if dropouts systematically differ from non-dropouts, introduce trial estimands as a way to co-align analyses with the goals of the evaluation, and explain how to set up an appropriate analysis model to test the treatment effect at one or several assessment points. A novice-friendly tutorial is provided alongside this primer. It lays out concepts in greater detail and showcases how to implement techniques using the statistical software R, based on a real-world RCT dataset.

DISCUSSION

Many problems of RCTs already arise at the design stage, and we examine some avoidable and unavoidable "weak spots" of this design in mental health research. For instance, we discuss how lack of prospective registration can give way to issues like outcome switching and selective reporting, how allegiance biases can inflate effect estimates, review recommendations and challenges in blinding patients in mental health RCTs, and describe problems arising from underpowered trials. Lastly, we discuss why not all randomized trials necessarily have a limited external validity and examine how RCTs relate to ongoing efforts to personalize mental health care.

Starting a conversation about estimands with public partners involved in clinical trials: a co-developed tool

BACKGROUND

Clinical trials aim to draw conclusions about the effects of treatments, but a trial can address many different potential questions. For example, does the treatment work well for patients who take it as prescribed? Or does it work regardless of whether patients take it exactly as prescribed? Since different questions can lead to different conclusions on treatment benefit, it is important to clearly understand what treatment effect a trial aims to investigate-this is called the 'estimand'. Using estimands helps to ensure trials are designed and analysed to answer the questions of interest to different stakeholders, including patients and public. However, there is uncertainty about whether patients and public would like to be involved in defining estimands and how to do so. Public partners are patients and/or members of the public who are part of, or advise, the research team. We aimed to (i) co-develop a tool with public partners that helps explain what an estimand is and (ii) explore public partner's perspectives on the importance of discussing estimands during trial design.

METHODS

An online consultation meeting was held with 5 public partners of mixed age, gender and ethnicities, from various regions of the UK. Public partner opinions were collected and a practical tool describing estimands, drafted before the meeting by the research team, was developed. Afterwards, the tool was refined, and additional feedback sought via email.

RESULTS

Public partners want to be involved in estimand discussions. They found an introductory tool, to be presented and described to them by a researcher, helpful for starting a discussion about estimands in a trial design context. They recommended storytelling, analogies and visual aids within the tool. Four topics related to public partners' involvement in defining estimands were identified: (i) the importance of addressing questions that are relevant to patients and public in trials, (ii) involving public partners early on, (iii) a need for education and communication for all stakeholders and (iv) public partners and researchers working together.

CONCLUSIONS

We co-developed a tool for researchers and public partners to use to facilitate the involvement of public partners in estimand discussions.

Eliminating Ambiguous Treatment Effects Using Estimands

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most authors do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is challenging, because many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings in which patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly.