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Table 1 Suggested strategies for defining estimands for core COVID-19 outcomesa

From: Treatment estimands in clinical trials of patients hospitalised for COVID-19: ensuring trials ask the right questions

Objective (outcome in bold). Objectives relate to the effect of treatment if introduced into a healthcare system Treatment effect Truncation by death Treatment discontinuation
Evaluate the effect of treatment on mortality Difference in proportion dying by a specific time point (or risk ratio or odds ratio) NA Treatment policy strategyb
Evaluate the effect of treatment on the requirement for ventilation/oxygen/ICU as a measure of patient benefit Difference in proportion affected by a specific time point (or risk ratio or odds ratio) Composite strategy: death is set as failure Treatment policy strategyb
Evaluate the effect of treatment on the requirement for ventilation/oxygen/ICU from a healthcare systems perspective Difference in proportion affected by a specific time point (or risk ratio or odds ratio) While-alive strategy: data from when the patient is alive is used (e.g. did they require ventilation prior to death?) Treatment policy strategyb
Evaluate the effect of treatment on the number of days in hospital/on a ventilator/on oxygen/in ICU as a measure of patient benefit Difference in means or restricted mean time Composite strategy: outcome is defined as the number of days alive and out of hospital/off a ventilator/off oxygen/out of ICU within a given time period Treatment policy strategyb
Evaluate the effect of treatment on the number of days in hospital/on a ventilator/on oxygen/in ICU from a healthcare systems perspective Difference in means or restricted mean time While-alive strategy: data from when the patient is alive is used (e.g. patients are counted as not on a ventilator from point of death) Treatment policy strategyb
  1. aOther estimand aspects (treatment, population, other intercurrent events) also need to be specified in order to have fully defined estimands
  2. bCan be implemented using intention-to-treat analysis, where all randomised patients are included, and analysed according to their randomised group
  3. Effect to individual patients or to healthcare systems as a whole on mortality