Open Science
Introduction
Methods
Results
Discussion
Objectives
Item 7: Specific objectives related to benefits and harms
Example
“To evaluate whether a structured exercise programme improved functional and health related quality of life outcomes compared with usual care for women at high risk of upper limb disability after breast cancer surgery” [123].
Explanation
Objectives are the questions that the trial was designed to answer. Adequate reporting of the research question is essential to allow readers to appraise and interpret the trial results. The PICO framework, which requires defining the patient population (P); the experimental intervention (I); the comparator intervention or condition (C); and the outcome or outcomes (O) of interest, has been proposed to help define the research question. PICO is sometimes styled as PICOTS, to include T (the timeframe) and/or S (the setting) [124].
Treatment decisions require an evaluation of the balance between benefit and harm; however, information about harms is frequently omitted or incompletely reported in published reports of trial results [125-128]. Trials whose primary objective is to evaluate benefits of an intervention may not be powered to detect harms, but authors should still report whether they considered harms outcomes when planning the trial [20].
Authors should clarify whether the aim is to establish superiority of the experimental intervention, or non-inferiority or equivalence, as compared with the comparator intervention [129]. Authors should also report whether the trial is intended to provide preliminary data (a pilot or feasibility trial), [130] explore pharmacokinetic properties, or generate confirmatory results.
For multi-arm trials, authors should clarify which treatment group comparisons are of interest (eg, A v B; A v C). If authors planned to readjust the objective during the trial (eg, in some platform trials or basket trials [131]), this should be reported. Finally, trials can be designed to study the effect of the experimental intervention under different conditions, often described on a spectrum from ideal conditions (explanatory trial) to standard clinical care conditions (pragmatic trial) [132].
The objectives should be phrased using neutral wording (eg, “to compare the effect of treatment A versus treatment B on outcome X for persons with condition Y”) rather than in terms of a particular direction of effect [133]. The trial objectives should align with what was specified in the trial registry and protocol; any changes to the trial objectives after it commenced should be reported with reasons (item 10).
Recently, some trials have been designed using the estimands framework to define the research question and trial objectives. While the terminology surrounding estimands may be new to some investigators, it is expected that the use of this framework will become more widespread. Box 1 provides more information about the estimands framework and how it is being used.
Box start
Box 1: Estimands
Concerns have been raised that the precise research questions that randomised trials set out to answer are often unclear [134] In particular, there is often ambiguity around how events occurring after randomisation (termed intercurrent events) are handled. Specifying the research question using an estimands framework is increasingly used to improve clarity. Despite calls for estimands to be included in the CONSORT 2025 statement, [134 135] their inclusion did not reach consensus. However, we provide a brief overview of estimands and introduce terminology, so they can be applied and reported if used. A more detailed primer on the estimand framework which provides practical guidance on estimands in studies of healthcare interventions can be found elsewhere [135].
ICH E9(R1) defines an estimand as “a precise description of the treatment effect reflecting the clinical question posed by a given clinical trial objective” [136]. The estimands framework provides a structured description of the objectives in an attempt to bring clarity in specifying the research question, which can be used to guide the study design, data collection, and statistical analysis methods. In brief, an estimand comprises five key attributes: population, treatment groups, endpoint, summary measure, and handling of intercurrent events (table 3). A separate estimand should be defined for each study outcome, and for some outcomes, more than one estimand may be defined.
