Outcome operationalisation
heterogenous, death, DAWOLS
Central methodological considerations when operationalising DAWOLS, DAOH, and similar outcomes are summarised in Fig. Flowchart illustrating the necessary decisions and considerations when using days alive without life support and similar outcomes in randomised clinical trials. Considerations related to individual steps are further elaborated in the textFirst, the outcome definition, including restrictions (e.g., due to maximum follow-up durations) and handling of death, is central. Most commonly, deceased patients are assigned the worst possible value (0 days or a categorical value worse than all possible actual values when analysed as an ordinal outcome) [Second, the expected outcome distribution should be considered, including whether inflation (peaks) at the minimum and/or maximum values is expected. This may be informed by data from similar, previous studies and will be affected by how death is handled. DAWOLS and DAOH typically have skewed distributions with substantial inflation at the minimum value (as a substantial proportion of the patients either die before getting off life support or discharged, or die within the follow-up period if death is penalised, which usually makes the minimum inflation higher) and sometimes at the maximum value (due to truncation of the follow-up period; consequently, shorter follow-up periods usually increase inflation at the maximum value).Third, the effect measure (estimand) of primary interest (e.g., mean difference, the difference in proportions of patients with the minimum/maximum/all values, etc.) must be specified. Most commonly, mean differences are used [Fourth, the need for individual predictions or assessment of heterogenous treatment effects (e.g., subgroup analyses) must also be considered, as this may require certain restrictions to prevent predictions beyond the scale of the outcome and may affect the choice of model. For example, if only mean values are of interest, models need not be able to reproduce a distribution of predicted values that resemble the actual outcome distribution.Finally, an analysis plan should be pre-specified, including which effect measure(s) will be used and how model adequacy will be assessed; whether any potential sensitivity analyses will be conducted; and which alternative approaches may be used if data are distributed differently than expected, if key assumptions are unexpectedly violated, or if the model fit is inadequate. If multiple models are outlined in the analysis plan due to limited knowledge about the distribution of the outcome data, the plan should include a clear strategy for selecting the primary analysis model.When comparing models and approaches in this study, we focus on DAWOLS defined as the total number of days alive without life support up to a specified maximum number of days, possibly re-scaled to a proportion of the maximum number of days and with dead patients assigned the worst possible value (0 days or a category worse than the lowest possible value when modelled as an ordinal variable, i.e., -1).