Cost prediction models for the comparison of two groups

For trial‐based economic evaluation where patient‐specific cost data are not routinely available, cost prediction models are commonly used to estimate total cost for each patient. Typically, multiple regression techniques are used on data from diagnosis‐matched, non‐trial patients (where patient‐lev...

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Bibliographic Details
Published inHealth economics Vol. 10; no. 4; pp. 363 - 366
Main Authors Willan, Andrew R., O'Brien, Bernie J.
Format Journal Article
LanguageEnglish
Published Chichester, UK John Wiley & Sons, Ltd 01.06.2001
SeriesHealth Economics
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ISSN1057-9230
1099-1050
DOI10.1002/hec.615

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Summary:For trial‐based economic evaluation where patient‐specific cost data are not routinely available, cost prediction models are commonly used to estimate total cost for each patient. Typically, multiple regression techniques are used on data from diagnosis‐matched, non‐trial patients (where patient‐level cost data are available) to model cost as a function of covariates that are observed on the trial subjects (e.g. length of hospital stay, procedures, etc.). The estimated beta coefficients provide a means of estimating the total cost for each patient in the trial. However, the variability of the beta coefficients due the measurement and sampling error is seldom included in the overall variance expression for mean costs by treatment group. In this paper we provide a method for estimating this variance and provide an example application Copyright © 2001 John Wiley & Sons, Ltd.
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ISSN:1057-9230
1099-1050
DOI:10.1002/hec.615