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Analysis of pre- and post-intervention outcomes with floor and ceiling effects

Adrian Sayers, Michael R. Whitehouse, Andrew Judge, Alexander MacGregor, Ashley W. Blom and Yoav Ben-Shlomo
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Adrian Sayers: Musculoskeletal Research Unit, University of Bristol
Michael R. Whitehouse: Musculoskeletal Research Unit, University of Bristol
Andrew Judge: Musculoskeletal Research Unit, University of Bristol
Alexander MacGregor: Musculoskeletal Medicine Research Group, University of East Anglia
Ashley W. Blom: Musculoskeletal Research Unit, University of Bristol
Yoav Ben-Shlomo: Bristol Population Health Science Institute, University of Bristol

London Stata Conference 2019 from Stata Users Group

Abstract: Background: Analysis of pre- and post-intervention change in observational studies using Patient Reported Outcome Measures (PROMs) is often believed to be a trivial exercise, and guidance for analysis of data from randomized control trials is often applied. This is often inappropriate, and that analysis of change scores may be preferable. However, it is unclear if this is suitable in outcomes with floor and ceiling effects. I investigate the association between body mass index (BMI) and the efficacy of primary hip replacement. Methods: Using a Monte Carlo simulation study and data from a national joint replacement register (162,513 patients with pre- and post-surgery PROMs) I investigate simple approaches for the analysis of outcomes with floor and ceiling effects that are measured at two occasions: linear and tobit regression (baseline adjusted ANCOVA, change-score analysis, postscore analysis) in addition to linear and multilevel Tobit models. Results: Analysis of data with floor and ceiling effects with models that fail to account for these features induce substantial bias. Single-level tobit models correct only for floor or ceiling effects when the exposure of interest is not associated with the baseline score. In observational data scenarios, only multilevel tobit models are capable of providing unbiased inferences. Conclusions: Inferences from pre- and post-studies that fail to account for floor and ceiling effects may induce spurious associations with substantial risk of bias. Multilevel tobit models indicate the efficacy of total hip replacement is independent of BMI. Restricting access to total hip replacement based on a patient's BMI cannot be supported by the data.

Date: 2019-09-15
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