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    Nearest neighbour propensity score matching and bootstrapping for estimating binary patient response in oncology: A Monte Carlo simulation

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    Publication type
    Journal article with impact factor
    Author
    Geldof, Tine
    Dusan, Popovic
    Van Damme, Nancy
    Huys, Isabelle
    Van Dyck, Walter
    Publication Year
    2020
    Journal
    Scientific Reports: A Nature Research Journal
    Publication Volume
    10
    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 diferent 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 efects.
    Keyword
    Oncology
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    Special Industries : Healthcare Management
    DOI
    10.1038/s41598-020-57799-w
    URI
    http://hdl.handle.net/20.500.12127/6425
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41598-020-57799-w
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