Sample size derivation is a crucial element of planning any confirmatory trial. The required sample size is typically derived based on constraints on the maximal acceptable Type I error rate and minimal desired power. Power depends on the unknown true effect and tends to be calculated either for the smallest relevant effect or a likely point alternative. The former might be problematic if the minimal relevant effect is close to the null, thus requiring an excessively large sample size, while the latter is dubious since it does not account for the a priori uncertainty about the likely alternative effect. A Bayesian perspective on sample size derivation for a frequentist trial can reconcile arguments about the relative a priori plausibility of alternative effects with ideas based on the relevance of effect sizes. Many suggestions as to how such "hybrid" approaches could be implemented in practice have been put forward. However, key quantities are often defined in subtly different ways in the literature. Starting from the traditional entirely frequentist approach to sample size derivation, we derive consistent definitions for the most commonly used hybrid quantities and highlight connections, before discussing and demonstrating their use in sample size derivation for clinical trials.

Background: Platform trials allow adding new experimental treatments to an ongoing trial. This feature is attractive to practitioners due to improved efficiency. Nevertheless, the operating characteristics of a trial that adds arms have not been well-studied. One controversy is whether just the concurrent control data (i.e. of patients who are recruited after a new arm is added) should be used in the analysis of the newly added treatment(s), or all control data (i.e. non-concurrent and concurrent). Methods: We investigate the benefits and drawbacks of using non-concurrent control data within a two-stage setting. We perform simulation studies to explore the impact of a linear and a step trend on the inference of the trial. We compare several analysis approaches when one includes all the control data or only concurrent control data in the analysis of the newly added treatment. Results: When there is a positive trend and all the control data are used, the marginal power of rejecting the corresponding hypothesis and the type one error rate can be higher than the nominal value. A model-based approach adjusting for a stage effect is equivalent to using concurrent control data; an adjustment with a linear term may not guarantee valid inference when there is a non-linear trend. Conclusions: If strict error rate control is required then non-concurrent control data should not be used; otherwise it may be beneficial if the trend is sufficiently small. On the other hand, the root mean squared error of the estimated treatment effect can be improved through using non-concurrent control data.

Background The relationship between late clinical outcomes after injury and early dynamic changes between fibrinolytic states is not fully understood. The authors hypothesized that temporal transitions in fibrinolysis states using rotational thromboelastometry (ROTEM) would aid stratification of adverse late clinical outcomes and improve understanding of how tranexamic acid modulates the fibrinolytic response and impacts mortality. Methods The authors conducted a secondary analysis of previously collected data from trauma patients enrolled into an ongoing prospective cohort study (International Standard Randomised Controlled Trial Number [ISRCTN] 12962642) at a major trauma center in the United Kingdom. ROTEM was performed on admission and at 24 h with patients retrospectively grouped into three fibrinolysis categories: tissue factor–activated ROTEM maximum lysis of less than 5% (low); tissue factor–activated ROTEM maximum lysis of 5 to 15% (normal); or tissue factor–activated ROTEM maximum lysis of more than 15% (high). Primary outcomes were multiorgan dysfunction syndrome and 28-day mortality. Results Seven-hundred thirty-one patients were included: 299 (41%) were treated with tranexamic acid and 432 (59%) were untreated. Two different cohorts with low-maximum lysis at 24 h were identified: (1) severe brain injury and (2) admission shock and hemorrhage. Multiple organ dysfunction syndrome was greatest in those with low-maximum lysis on admission and at 24 h, and late mortality was four times higher than in patients who remained normal during the first 24 h (7 of 42 [17%] vs. 9 of 223 [4%]; P = 0.029). Patients that transitioned to or remained in low-maximum lysis had increased odds of organ dysfunction (5.43 [95% CI, 1.43 to 20.61] and 4.85 [95% CI, 1.83 to 12.83], respectively). Tranexamic acid abolished ROTEM hyperfibrinolysis (high) on admission, increased the frequency of persistent low-maximum lysis (67 of 195 [34%]) vs. 8 of 79 [10%]; P = 0.002), and was associated with reduced early mortality (28 of 195 [14%] vs. 23 of 79 [29%]; P = 0.015). No increase in late deaths, regardless of fibrinolysis transition patterns, was observed. Conclusions Adverse late outcomes are more closely related to 24-h maximum lysis, irrespective of admission levels. Tranexamic acid alters early fibrinolysis transition patterns, but late mortality in patients with low-maximum lysis at 24 h is not increased. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Background Platform trials improve the efficiency of the drug development process through flexible features such as adding and dropping arms as evidence emerges. The benefits and practical challenges of implementing novel trial designs have been discussed widely in the literature, yet less consideration has been given to the statistical implications of adding arms. Main We explain different statistical considerations that arise from allowing new research interventions to be added in for ongoing studies. We present recent methodology development on addressing these issues and illustrate design and analysis approaches that might be enhanced to provide robust inference from platform trials. We also discuss the implication of changing the control arm, how patient eligibility for different arms may complicate the trial design and analysis, and how operational bias may arise when revealing some results of the trials. Lastly, we comment on the appropriateness and the application of platform trials in phase II and phase III settings, as well as publicly versus industry-funded trials. Conclusion Platform trials provide great opportunities for improving the efficiency of evaluating interventions. Although several statistical issues are present, there are a range of methods available that allow robust and efficient design and analysis of these trials.