TY - JOUR
T1 - Integration of elicited expert information via a power prior in Bayesian variable selection
T2 - Application to colon cancer data
AU - Boulet, Sandrine
AU - Ursino, Moreno
AU - Thall, Peter
AU - Landi, Bruno
AU - Lepère, Céline
AU - Pernot, Simon
AU - Burgun, Anita
AU - Taieb, Julien
AU - Zaanan, Aziz
AU - Zohar, Sarah
AU - Jannot, Anne Sophie
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the French National Cancer Institut (INCa) [grant number 10801, 9539, SHSE SP 16-031].
Publisher Copyright:
© The Author(s) 2019.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Background: Building tools to support personalized medicine needs to model medical decision-making. For this purpose, both expert and real world data provide a rich source of information. Currently, machine learning techniques are developing to select relevant variables for decision-making. Rather than using data-driven analysis alone, eliciting prior information from physicians related to their medical decision-making processes can be useful in variable selection. Our framework is electronic health records data on repeated dose adjustment of Irinotecan for the treatment of metastatic colorectal cancer. We propose a method that incorporates elicited expert weights associated with variables involved in dose reduction decisions into the Stochastic Search Variable Selection (SSVS), a Bayesian variable selection method, by using a power prior. Methods: Clinician experts were first asked to provide numerical clinical relevance weights to express their beliefs about the importance of each variable in their medical decision making. Then, we modeled the link between repeated dose reduction, patient characteristics, and toxicities by assuming a logistic mixed-effects model. Simulated data were generated based on the elicited weights and combined with the observed dose reduction data via a power prior. We compared the Bayesian power prior-based SSVS performance to the usual SSVS in our case study, including a sensitivity analysis using the power prior parameter. Results: The selected variables differ when using only expert knowledge, only the usual SSVS, or combining both. Our method enables one to select rare variables that may be missed using only the observed data and to discard variables that appear to be relevant based on the data but not relevant from the expert perspective. Conclusion: We introduce an innovative Bayesian variable selection method that adaptively combines elicited expert information and real world data. The method selects a set of variables relevant to model medical decision process.
AB - Background: Building tools to support personalized medicine needs to model medical decision-making. For this purpose, both expert and real world data provide a rich source of information. Currently, machine learning techniques are developing to select relevant variables for decision-making. Rather than using data-driven analysis alone, eliciting prior information from physicians related to their medical decision-making processes can be useful in variable selection. Our framework is electronic health records data on repeated dose adjustment of Irinotecan for the treatment of metastatic colorectal cancer. We propose a method that incorporates elicited expert weights associated with variables involved in dose reduction decisions into the Stochastic Search Variable Selection (SSVS), a Bayesian variable selection method, by using a power prior. Methods: Clinician experts were first asked to provide numerical clinical relevance weights to express their beliefs about the importance of each variable in their medical decision making. Then, we modeled the link between repeated dose reduction, patient characteristics, and toxicities by assuming a logistic mixed-effects model. Simulated data were generated based on the elicited weights and combined with the observed dose reduction data via a power prior. We compared the Bayesian power prior-based SSVS performance to the usual SSVS in our case study, including a sensitivity analysis using the power prior parameter. Results: The selected variables differ when using only expert knowledge, only the usual SSVS, or combining both. Our method enables one to select rare variables that may be missed using only the observed data and to discard variables that appear to be relevant based on the data but not relevant from the expert perspective. Conclusion: We introduce an innovative Bayesian variable selection method that adaptively combines elicited expert information and real world data. The method selects a set of variables relevant to model medical decision process.
KW - Bayesian variable selection
KW - clinical relevance weights elicitation
KW - electronic health record
KW - power prior method
KW - repeated measures
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U2 - 10.1177/0962280219841082
DO - 10.1177/0962280219841082
M3 - Article
C2 - 30963815
AN - SCOPUS:85064190602
SN - 0962-2802
VL - 29
SP - 541
EP - 567
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 2
ER -