Nearest Neighbour Propensity Score Matching and Bootstrapping for Estimating Binary Patient Response in Oncology: A Monte Carlo Simulation
Geldof, Tine Popovic, Dusan Van Damme, Nancy Huys, Isabelle Van Dyck, Walter
Geldof, Tine
Popovic, Dusan
Van Damme, Nancy
Huys, Isabelle
Van Dyck, Walter
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Journal article with impact factor
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Supervisor
Publication Year
2020
Journal
Scientific Reports
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Publication Volume
10
Publication Issue
1
Publication Begin page
964
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Abstract
Nearest Neighbour (NN) propensity score (PS) matching methods are commonly used in pharmacoepidemiology to estimate treatment response using observational data. Unfortunately, there is limited evidence on the optimal approach for accurately estimating binary treatment response and, more so, to estimate its variance. Bootstrapping, although commonly used to accurately estimate variance, is rarely used together with PS matching. In this Monte Carlo simulation-based study, we examined the performance of bootstrapping used in conjunction with PS matching, as opposed to different NN matching techniques, on a simulated dataset exhibiting varying levels of real world complexity. Thus, an experimental design was set up that independently varied the proportion of patients treated, the proportion of outcomes censored and the amount of PS matches used. Simulation results were externally validated on a real observational dataset obtained from the Belgian Cancer Registry. We found all investigated PS methods to be stable and concordant, with k-NN matching to be optimally dealing with the censoring problem, typically present in chronic cancer-related datasets, whilst being the least computationally expensive. In contrast, bootstrapping used in conjunction with PS matching, being the most computationally expensive, only showed superior results in small patient populations with long-term largely unobserved treatment effects.
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Keywords
32 Biomedical and Clinical Sciences, 42 Health Sciences, 3211 Oncology and Carcinogenesis, Cancer, Cancer, Antineoplastic Agents, Computer Simulation, Humans, Monte Carlo Method, Neoplasms, Propensity Score, Registries, Treatment Outcome