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The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.


IMPACT FACTOR 2018: 1.309

CiteScore 2018: 1.11

SCImago Journal Rank (SJR) 2018: 1.325
Source Normalized Impact per Paper (SNIP) 2018: 0.715

Mathematical Citation Quotient (MCQ) 2018: 0.03

Online
ISSN
1557-4679
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Simple Quasi-Bayes Approach for Modeling Mean Medical Costs

Grace YoonORCID iD: https://orcid.org/0000-0003-3263-1352 / Wenxin Jiang / Lei Liu / Ya-Chen Tina Shih
Published Online: 2019-06-05 | DOI: https://doi.org/10.1515/ijb-2018-0122

Abstract

Several statistical issues associated with health care costs, such as heteroscedasticity and severe skewness, make it challenging to estimate or predict medical costs. When the interest is modeling the mean cost, it is desirable to make no assumption on the density function or higher order moments. Another challenge in developing cost prediction models is the presence of many covariates, making it necessary to apply variable selection methods to achieve a balance of prediction accuracy and model simplicity. We propose Spike-or-Slab priors for Bayesian variable selection based on asymptotic normal estimates of the full model parameters that are consistent as long as the assumption on the mean cost is satisfied. In addition, the scope of model searching can be reduced by ranking the Z-statistics. This method possesses four advantages simultaneously: robust (due to avoiding assumptions on the density function or higher order moments), parsimonious (feature of variable selection), informative (due to its Bayesian flavor, which can compare posterior probabilities of candidate models) and efficient (by reducing model searching scope with the use of Z-ranking). We apply this method to the Medical Expenditure Panel Survey dataset.

Keywords: Spike-or-Slab prior; variable selection; sandwich variance estimator; health econometrics

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About the article

Received: 2018-01-05

Accepted: 2019-04-26

Published Online: 2019-06-05


This work was supported by the Agency for Healthcare Research and Quality (Grant Number: R01 HS 020263, Funder Id: http://dx.doi.org/10.13039/100000133) and National Cancer Institute (Grant Number: T32-CA090301, Funder Id: http://dx.doi.org/10.13039/100000054).


Citation Information: The International Journal of Biostatistics, 20180122, ISSN (Online) 1557-4679, DOI: https://doi.org/10.1515/ijb-2018-0122.

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