TY - JOUR
T1 - Inverse probability weighted least squares regression in the analysis of time-censored cost data
T2 - An evaluation of the approach using SEER-medicare
AU - Griffiths, Robert I.
AU - Gleeson, Michelle L.
AU - Danese, Mark D.
AU - O'Hagan, Anthony
N1 - Funding Information:
Source of financial support: Amgen provided financial support for this study.
PY - 2012/7
Y1 - 2012/7
N2 - Objectives: To assess the accuracy and precision of inverse probability weighted (IPW) least squares regression analysis for censored cost data. Methods: By using Surveillance, Epidemiology, and End Results-Medicare, we identified 1500 breast cancer patients who died and had complete cost information within the database. Patients were followed for up to 48 months (partitions) after diagnosis, and their actual total cost was calculated in each partition. We then simulated patterns of administrative and dropout censoring and also added censoring to patients receiving chemotherapy to simulate comparing a newer to older intervention. For each censoring simulation, we performed 1000 IPW regression analyses (bootstrap, sampling with replacement), calculated the average value of each coefficient in each partition, and summed the coefficients for each regression parameter to obtain the cumulative values from 1 to 48 months. Results: The cumulative, 48-month, average cost was $67,796 (95% confidence interval [CI] $58,454-$78,291) with no censoring, $66,313 (95% CI $54,975-$80,074) with administrative censoring, and $66,765 (95% CI $54,510-$81, 843) with administrative plus dropout censoring. In multivariate analysis, chemotherapy was associated with increased cost of $25,325 (95% CI $17,549-$32,827) compared with $28,937 (95% CI $20,510-$37,088) with administrative censoring and $29,593 ($20,564-$39,399) with administrative plus dropout censoring. Adding censoring to the chemotherapy group resulted in less accurate IPW estimates. This was ameliorated, however, by applying IPW within treatment groups. Conclusion: IPW is a consistent estimator of population mean costs if the weight is correctly specified. If the censoring distribution depends on some covariates, a model that accommodates this dependency must be correctly specified in IPW to obtain accurate estimates.
AB - Objectives: To assess the accuracy and precision of inverse probability weighted (IPW) least squares regression analysis for censored cost data. Methods: By using Surveillance, Epidemiology, and End Results-Medicare, we identified 1500 breast cancer patients who died and had complete cost information within the database. Patients were followed for up to 48 months (partitions) after diagnosis, and their actual total cost was calculated in each partition. We then simulated patterns of administrative and dropout censoring and also added censoring to patients receiving chemotherapy to simulate comparing a newer to older intervention. For each censoring simulation, we performed 1000 IPW regression analyses (bootstrap, sampling with replacement), calculated the average value of each coefficient in each partition, and summed the coefficients for each regression parameter to obtain the cumulative values from 1 to 48 months. Results: The cumulative, 48-month, average cost was $67,796 (95% confidence interval [CI] $58,454-$78,291) with no censoring, $66,313 (95% CI $54,975-$80,074) with administrative censoring, and $66,765 (95% CI $54,510-$81, 843) with administrative plus dropout censoring. In multivariate analysis, chemotherapy was associated with increased cost of $25,325 (95% CI $17,549-$32,827) compared with $28,937 (95% CI $20,510-$37,088) with administrative censoring and $29,593 ($20,564-$39,399) with administrative plus dropout censoring. Adding censoring to the chemotherapy group resulted in less accurate IPW estimates. This was ameliorated, however, by applying IPW within treatment groups. Conclusion: IPW is a consistent estimator of population mean costs if the weight is correctly specified. If the censoring distribution depends on some covariates, a model that accommodates this dependency must be correctly specified in IPW to obtain accurate estimates.
KW - accuracy and precision
KW - cost analysis
KW - inverse probability weighting
KW - observational data
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U2 - 10.1016/j.jval.2012.03.1388
DO - 10.1016/j.jval.2012.03.1388
M3 - Article
C2 - 22867774
AN - SCOPUS:84864580199
VL - 15
SP - 656
EP - 663
JO - Value in Health
JF - Value in Health
SN - 1098-3015
IS - 5
ER -