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Regression analysis of health care charges with heteroscedasticity

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  • Xiao‐Hua Zhou
  • Kevin T. Stroupe
  • William M. Tierney

Abstract

We examine the effects of a prospective drug utilization review and patients' characteristics on total in‐patient and out‐patient health care charges. Our analysis of charges is complicated by the fact that the total health care charges are skewed. A log‐transformation of these charges can normalize their distribution but may not stabilize their variance. To handle these problems, we propose a linear regression model with a non‐constant variance (heteroscedasticity). Using results from a fitted linear regression model for log‐transformed charges, we also discuss interpreting the regression coefficients in the original scale and estimating the total health care charges to individual patients. Employing these methods, we analyse total health care charges for drug utilization review patients with hypertension and identify patients' factors that are related to their total health care charges.

Suggested Citation

  • Xiao‐Hua Zhou & Kevin T. Stroupe & William M. Tierney, 2001. "Regression analysis of health care charges with heteroscedasticity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 303-312.
    • Handle: RePEc:bla:jorssc:v:50:y:2001:i:3:p:303-312
    DOI: 10.1111/1467-9876.00235
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    Cited by:

    1. Adam Maidman & Lan Wang, 2018. "New semiparametric method for predicting high‐cost patients," Biometrics, The International Biometric Society, vol. 74(3), pages 1104-1111, September.
    2. Halvard Buhaug, 2010. "Dude, Where’s My Conflict?," Conflict Management and Peace Science, Peace Science Society (International), vol. 27(2), pages 107-128, April.
    3. Liu, Lei & Strawderman, Robert L. & Cowen, Mark E. & Shih, Ya-Chen T., 2010. "A flexible two-part random effects model for correlated medical costs," Journal of Health Economics, Elsevier, vol. 29(1), pages 110-123, January.
    4. Lan Wang & Xiao-Hua Zhou, 2007. "Assessing the Adequacy of Variance Function in Heteroscedastic Regression Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1218-1225, December.
    5. Sara Gustavsson & Björn Fagerberg & Gerd Sallsten & Eva M. Andersson, 2014. "Regression Models for Log-Normal Data: Comparing Different Methods for Quantifying the Association between Abdominal Adiposity and Biomarkers of Inflammation and Insulin Resistance," IJERPH, MDPI, vol. 11(4), pages 1-19, March.

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