Journal of clinical epidemiology · 2025
Exploring the effect of COVID-19 restrictions on the social functioning scale in a clinical trial of antipsychotic reduction: using multiple imputation to target a hypothetical estimand.
СтатьяРандомизированное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Marston Louise, Moncrieff Joanna, Priebe Stefan, Cro Suzie, Cornelius Victoria R
Аннотация
OBJECTIVES: Many trials are affected by unforeseen events after recruitment has commenced. The aim of this study is to explore a hypothetical strategy for dealing with an intercurrent event that occurred during trial follow-up; COVID-19 restrictions.
STUDY DESIGN AND SETTING: Secondary analysis of a randomized controlled trial (RCT) in schizophrenia, comparing antipsychotic reduction vs maintenance medication on the social functioning scale (SFS) score at 12 months' follow-up. A hypothetical analysis strategy was used to estimate the treatment effect in a COVID-19 restriction-free world. Outcome data were set to missing, and multiple imputation was used to replace values affected by COVID-19.
RESULTS: The trial randomized 253 participants, 187 participants had an SFS score at 12 months, and 75 of those were collected during COVID-19 restrictions. In the original complete case regression analysis, targeting a treatment policy estimand, the treatment effect was estimated to be 0.51 (95% CI -1.33, 2.35) points higher in the reduction group. After multiple imputation, targeting the hypothetical estimand, the mean SFS score was -3.01 (95% CI -7.22, 1.20) points lower in the reduction group, but varied with different assumptions about the timing of events and in sensitivity analyses to increase the size of difference between randomized groups.
CONCLUSION: We demonstrated how the intervention effect can change when estimating the intervention effect in a pandemic world (treatment policy estimand) vs a pandemic restriction-free world (hypothetical estimand), and that estimates are sensitive to imputation and input assumptions. Trialists should be aware of potential intercurrent events and plan the analysis to take them into account.
PLAIN LANGUAGE SUMMARY: Many medical research studies that enable us to find out how well things work had to change due to COVID-19 restrictions. This may have altered the results. We used data from a randomized controlled trial (RCT) to examine whether there was evidence for this. The main outcome included questions on how often participants went to the cinema, swimming, to church or saw friends or relatives. Many of these activities were not possible during COVID-19 restrictions and became possible again over time. We used statistical methods to replace data that were collected during COVID-19 restrictions with the best possible estimate if COVID-19 had not happened. We found that the trial results were likely to have been different if the effect of COVID-19 restrictions were taken away. It is likely that most studies will be interested in results that do not include data collected during COVID-19.
STUDY DESIGN AND SETTING: Secondary analysis of a randomized controlled trial (RCT) in schizophrenia, comparing antipsychotic reduction vs maintenance medication on the social functioning scale (SFS) score at 12 months' follow-up. A hypothetical analysis strategy was used to estimate the treatment effect in a COVID-19 restriction-free world. Outcome data were set to missing, and multiple imputation was used to replace values affected by COVID-19.
RESULTS: The trial randomized 253 participants, 187 participants had an SFS score at 12 months, and 75 of those were collected during COVID-19 restrictions. In the original complete case regression analysis, targeting a treatment policy estimand, the treatment effect was estimated to be 0.51 (95% CI -1.33, 2.35) points higher in the reduction group. After multiple imputation, targeting the hypothetical estimand, the mean SFS score was -3.01 (95% CI -7.22, 1.20) points lower in the reduction group, but varied with different assumptions about the timing of events and in sensitivity analyses to increase the size of difference between randomized groups.
CONCLUSION: We demonstrated how the intervention effect can change when estimating the intervention effect in a pandemic world (treatment policy estimand) vs a pandemic restriction-free world (hypothetical estimand), and that estimates are sensitive to imputation and input assumptions. Trialists should be aware of potential intercurrent events and plan the analysis to take them into account.
PLAIN LANGUAGE SUMMARY: Many medical research studies that enable us to find out how well things work had to change due to COVID-19 restrictions. This may have altered the results. We used data from a randomized controlled trial (RCT) to examine whether there was evidence for this. The main outcome included questions on how often participants went to the cinema, swimming, to church or saw friends or relatives. Many of these activities were not possible during COVID-19 restrictions and became possible again over time. We used statistical methods to replace data that were collected during COVID-19 restrictions with the best possible estimate if COVID-19 had not happened. We found that the trial results were likely to have been different if the effect of COVID-19 restrictions were taken away. It is likely that most studies will be interested in results that do not include data collected during COVID-19.