Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Projecting effectiveness after ending a randomized controlled trial: A two-state Markov microsimulation model
International Journal of Technology Assessment in Health Care, Volume 36, No. 4, Year 2020
Notification
URL copied to clipboard!
Description
Objective To investigate the behavior of restricted mean survival time (RMST) and designs of a two-state Markov microsimulation model through a 2 × 4 × 2 full factorial experiment.Method By projecting patient-wise 15-year-post-trial survival, we estimated life-year-gained between an intervention and a control group using data from the Cardiovascular Outcomes for People Using Anticoagulation Strategies Study (COMPASS). Projections considered either in-trial events or post-trial medications. They were compared based on three factors: (i) choice of probability of death, (ii) lengths of cycle, and (iii) usage of half-a-cycle age correction. Three-way analysis of variance and post-hoc Tukey's Honest Significant Difference test compared means among factors.Results When both in-trial events and post-trial study medications were considered, monthly, quarterly, or semiannually were not different from one other in projected life-year-gained. However, the annual one was different from the others: mean and 95 percent confidence interval 252.2 (190.5-313.9) days monthly, 251.8 (192.0-311.6) quarterly, 249.1 (189.7-308.5) semiannually, and 240.8 (178.5-303.1) annually. The other two factors also impacted life-year-gained: background probability (269.1 [260.3-277.9] days projected with REACH-based-probabilities, 227.7 [212.6-242.8] with a USA life table); half-a-cycle age correction (245.5 [199.0-292] with correction and 251.4 [209.1-293.7] without correction). When not considering post-trial medications, only the choice of probability of death appeared to impact life-year-gained.Conclusion For a large trial or cohort, to optimally project life-year-gained, one should consider using (i) annual projections, (ii) life table probabilities, (iii) in-trial events, and (iv) post-trial medication use. Copyright © The Author(s), 2020. Published by Cambridge University Press.
Authors & Co-Authors
Yuan, Fei
Canada, Hamilton
Population Health Research Institute, Ontario
Bangdiwala, Shrikant I.
Canada, Hamilton
Population Health Research Institute, Ontario
Canada, Hamilton
Mcmaster University
Tong, Wesley R.
Canada, Hamilton
Population Health Research Institute, Ontario
Lamy, Andre L.
Canada, Hamilton
Population Health Research Institute, Ontario
Canada, Hamilton
Mcmaster University
Canada, Hamilton
Hamilton Health Sciences
Statistics
Citations: 1
Authors: 4
Affiliations: 3
Identifiers
Doi:
10.1017/S0266462320000446
ISSN:
02664623
Research Areas
Health System And Policy
Noncommunicable Diseases
Study Design
Randomised Control Trial
Cohort Study
Study Approach
Quantitative