Table 1.

Methodological features that may improve clinical trials benefits and challenges

Feature	Benefits and advantages	Challenges and disadvantages
Larger trials (increased sample sizes)	Decreased uncertainty, increased precision Easier to detect potential subgroup differences Less chance of inconclusive results (i.e., greater precision and less uncertainty); results from fewer large RCTs are easier to compare than results from many smaller RCTs Easier to address safety concerns if properly monitored, as larger trials have higher chances of detecting rare adverse events Increased generalisability/external validity in multicentre/international RCTs	Economic: trial cost and optimal use of overall research resources Collaboration: increased workload coordinating, different regulatory requirements including different handling of consent procedures and reporting of adverse events, challenges with coordination due to language and time zone differences Comparability: potential differences in standard of care/available resources in international trials Academic challenges: less individually led projects due to increased collaboration – group authorships may be less attractive in settings where individual author positions are valued (e.g., grant applications)
Standardisation and meta-analyses	Increased comparability/less heterogeneity Less competition between trials Meta-analysis may be more sensible – less statistical inconsistency may lead to more precise results Prospective meta-analyses or meta-trials may provide quicker answers than individual conventional RCTs and meta-analyses, especially if trialists share data earlier, and adequate certainty is obtained before individual trials finish	Agreement between investigators on the design and variables could be challenging and time-consuming; compromises may be necessary for standardisations; core outcome sets may improve this Data not routinely collected in one setting may be required due to standardisation, potentially increasing workload in some centres If adequate standardisation is not possible, comparisons in meta-analyses may be difficult Differences in populations, interventions, comparators, outcomes, concurrent treatments and changes over time may hamper interpretation of meta-analyses
Research programmes	Complete research programmes including multiple study types may lead to better RCTs focussing on more relevant questions Evidence synthesis prior to trial conduct puts trials into context and may help identify the largest knowledge gaps or where new trials are not necessary	Research programmes may require substantial resources and time until an eventual trial can start; in most situations this will be sensible, but may not be possible during pandemics or emergencies and may require additional resources and funding
Outcome choices	Choosing non-dichotomous or non-mortality outcomes carrying more information may lead to more efficient or conclusive trials and smaller sample size requirements Outcomes with more levels than just dead/alive may convey important information on how well survivors fare	Definition and handling of death is challenging, including appropriate “weighting” of death, and clinical interpretation if mortality is treated in a special manner (e.g., if days alive without life support is analysed as an ordinal variable with death treated as worse than 0 days) Many non-mortality patient-important outcomes have skewed distributions complicating many common statistical (parametric) analyses and estimations of differences on an interpretable scale (including in meta-analyses) Difficulties in interpretation if effects on mortality and other parts of the outcome are in different directions (for composite outcomes and days alive without life support and similar outcomes) Risk of choosing less patient-important outcomes or surrogate outcomes
Avoiding dichotomisation of results, probabilistic interpretations	Nuanced conclusions; assessing evidence as a continuum avoids risk of incorrect “absence of evidence interpreted as evidence of absence” errors Using Bayesian methods allow incorporation of previous results or scepticism and easier propagation of uncertainty to subsequent calculations The same level of evidence may not be required to change clinical practice for all interventions—this depends on price, risk of adverse events, availability, character of intervention, invasiveness, etc.; these considerations apply to both trials and clinical practice guidelines	Probabilistic interpretations do not solve the primary issues of many trials; lack of dichotomisation does not in itself increase the certainty of evidence While conventional significance thresholds are arbitrary, they are widely used; changing methods may lead some researchers to opt for less strict thresholds or allow increased “spin” in conclusions. Disagreements in interpretation may increase if there is no standard threshold and pre-specified criteria for success for e.g. approving new interventions and standardised policy responses may be warranted Non-dichotomous and more detailed interpretations of trial results may be more difficult to communicate to non-researchers and non-experts Prior selection in Bayesian analyses adds additional complexity; results may be unduly influenced by strong(er) priors not shared by other researchers. Sensible priors (often non- or weakly informative priors are used in the primary Bayesian analyses of critical care trials), transparently reported, ideally pre-specified, and with adequate sensitivity analyses performed is warranted
Improved HTE analyses	Predictive HTE analyses and other approaches considering multiple patient characteristics simultaneously or overall risk may better reflect clinical reality than one-variable-at-a-time subgroup analyses Hierarchical models may limit the risk of exaggerated results and chance findings in smaller subgroups and increase precision due to borrowing of information Assessment of HTE according to variables of interest on the continuous scale may better detect dose–response relationships than categorised subgroup analyses	Subgroup or HTE analyses, regardless of approach, generally requires more patients—trials may still be underpowered to detect differences The more analyses conducted, the greater risk of chance findings—this may be mitigated, but not completely solved, by the discussed approaches Requires careful consideration of whether HTE analyses should be conducted on the absolute or relative scales; when the baseline risk differs between groups, there will always be HTE on either the relative or the absolute scale (often, intervention effects are most consistent on the relative scale)
Adaptation	Adaptive sample sizes/stopping rules may lead to optimally sized trials, more likely to reach conclusive evidence Adaptive arm adding/dropping may increase overall trial efficiency Adaptive randomisation may increase chance of getting better interventions in some situations, which may make trial participation more attractive to patients Adaptive enrichment may enable trials to better detect differences in responses and tailor interventions to different subpopulations or phenotypes	Logistic and economic challenges in planning and funding trials without fixed sample sizes; alternative financing models may be necessary Planning may be more difficult; instead of simple sample size calculations, advanced statistical simulation may be necessary to estimate required sizes and risk of random errors, requiring increased collaboration with statisticians and increased training of clinician-researchers Pre-specified criteria for stopping/adaptation necessary; may be difficult to define Adaptation requires more real-time data collection and verification, increasing data registration burden on individual sites Adaptations may be complex to implement and communicate Outcomes with longer follow-up lead to slower adaptation compared to shorter-term outcomes, which may add additional complexity. Consequently, the use of shorter-term outcomes to guide adaptive trials instead of the outcome of primary interest may be considered in some situations Risk of adaptations based on chance findings/fluctuations may require restraints of adaptation to avoid random errors, which is difficult to plan and handle While adaptive trials may be more likely to reach conclusive evidence if continued until a stopping rule is reached, they may need to be substantially larger to confirm or refute all clinically relevant effects (as is the case for conventional trials, too)
Adaptive platform trials	Increased efficiency, and potentially similar advantages as for adaptive conventional trials May decrease time to clinical adaptation and enable “learning while doing” Reuse of trial infrastructure and embedding in electronic health records and clinical practice may increase efficiency and decrease cost Potential improvement of informed consent procedures compared to consent when co-enrolment in multiple trials occurs Familiarity and consistency with a common platform design may be easier in practice than repeated conduction of independent RCTs	Same challenges as adaptive trials in general Potential regulatory issues; less well-known design may complicate approvals May take longer time to setup and implement than regular trials More complex – may be more difficult to implement and train staff, more difficult to explain to patients/potential complication of consent procedures, relatives and other stakeholders, may be more difficult to work with for non-researcher clinicians Standards for conducting and reporting less developed; may be more difficult to report and explain results Additional complexity with time drift/temporal variation and response-adaptive randomisation and potential re-use of non-concurrent controls requires adequate statistical handling to avoid bias Potential challenges with workload/stress of perpetual trials
Embedding trials in clinical practice and registers	Tighter integration of clinical practice and clinical trials may lead to faster improvements in patient care Embedding clinical trials in electronic health records may reduce data-collection burden and cost and alert clinicians and researchers of eligible patients and clinical events Register-based trials (including register-based cluster-randomised trials) may reduce data-collection burden and trial cost by using clinical registers already in place	Register-based data-collection may not be as easily standardised without changing individual registers; compromises based on availability in registers may be necessary Embedding trials in registers or electronic health records poses additional challenges with different electronic health record software and across borders Data quality and completeness in registers may not be as good as when data are prospectively collected for all variables Limited long-term outcome data generally available in registers due to additional complexity of data collection

HTE heterogeneity of treatment effects; RCT randomised clinical trial