Methodological Challenges and Statistical Approaches in the COMprehensive Post-Acute Stroke Services Study.
Cluster Analysis
Data Interpretation, Statistical
Health Services Research
/ methods
Humans
Intention to Treat Analysis
/ methods
Patient Outcome Assessment
Pragmatic Clinical Trials as Topic
Randomized Controlled Trials as Topic
Stroke
/ therapy
Stroke Rehabilitation
/ statistics & numerical data
Treatment Outcome
Journal
Medical care
ISSN: 1537-1948
Titre abrégé: Med Care
Pays: United States
ID NLM: 0230027
Informations de publication
Date de publication:
01 08 2021
01 08 2021
Historique:
entrez:
6
7
2021
pubmed:
7
7
2021
medline:
19
11
2021
Statut:
ppublish
Résumé
The COMprehensive Post-Acute Stroke Services study was a cluster-randomized pragmatic trial designed to evaluate a comprehensive care transitions model versus usual care. The data collected during this trial were complex and analysis methodology was required that could simultaneously account for the cluster-randomized design, missing patient-level covariates, outcome nonresponse, and substantial nonadherence to the intervention. The objective of this study was to discuss an array of complementary statistical methods to evaluate treatment effectiveness that appropriately addressed the challenges presented by the complex data arising from this pragmatic trial. We utilized multiple imputation combined with inverse probability weighting to account for missing covariate and outcome data in the estimation of intention-to-treat effects (ITT). The ITT estimand reflects the effectiveness of assignment to the COMprehensive Post-Acute Stroke Services intervention compared with usual care (ie, it does not take into account intervention adherence). Per-protocol analyses provide complementary information about the effect of treatment, and therefore are relevant for patients to inform their decision-making. We describe estimation of the complier average causal effect using an instrumental variables approach through 2-stage least squares estimation. For all preplanned analyses, we also discuss additional sensitivity analyses. Pragmatic trials are well suited to inform clinical practice. Care should be taken to proactively identify the appropriate balance between control and pragmatism in trial design. Valid estimation of ITT and per-protocol effects in the presence of complex data requires application of appropriate statistical methods and concerted efforts to ensure high-quality data are collected.
Sections du résumé
BACKGROUND
The COMprehensive Post-Acute Stroke Services study was a cluster-randomized pragmatic trial designed to evaluate a comprehensive care transitions model versus usual care. The data collected during this trial were complex and analysis methodology was required that could simultaneously account for the cluster-randomized design, missing patient-level covariates, outcome nonresponse, and substantial nonadherence to the intervention.
OBJECTIVE
The objective of this study was to discuss an array of complementary statistical methods to evaluate treatment effectiveness that appropriately addressed the challenges presented by the complex data arising from this pragmatic trial.
METHODS
We utilized multiple imputation combined with inverse probability weighting to account for missing covariate and outcome data in the estimation of intention-to-treat effects (ITT). The ITT estimand reflects the effectiveness of assignment to the COMprehensive Post-Acute Stroke Services intervention compared with usual care (ie, it does not take into account intervention adherence). Per-protocol analyses provide complementary information about the effect of treatment, and therefore are relevant for patients to inform their decision-making. We describe estimation of the complier average causal effect using an instrumental variables approach through 2-stage least squares estimation. For all preplanned analyses, we also discuss additional sensitivity analyses.
DISCUSSION
Pragmatic trials are well suited to inform clinical practice. Care should be taken to proactively identify the appropriate balance between control and pragmatism in trial design. Valid estimation of ITT and per-protocol effects in the presence of complex data requires application of appropriate statistical methods and concerted efforts to ensure high-quality data are collected.
Identifiants
pubmed: 34228017
doi: 10.1097/MLR.0000000000001580
pii: 00005650-202108001-00007
pmc: PMC8263146
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
S355-S363Informations de copyright
Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.
Déclaration de conflit d'intérêts
R.B.D.: Ownership Interest, Care Directions. The remaining authors declare no conflict of interest.
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