2012
DOI: 10.1016/j.jval.2012.03.1388
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Inverse Probability Weighted Least Squares Regression in the Analysis of Time-Censored Cost Data: An Evaluation of the Approach Using SEER-Medicare

Abstract: 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.

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Cited by 20 publications
(20 citation statements)
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“…Costs were estimated by multiplying the quantity of each resource used by the corresponding unit cost of the resource. Inverse probability weighting was used to account for censoring and death when estimating total costs 32,33 . The intervals used for the weighting were two 6-month time intervals for the first year and annual intervals for years 2 and 3.…”
Section: End-points and Analysesmentioning
confidence: 99%
“…Costs were estimated by multiplying the quantity of each resource used by the corresponding unit cost of the resource. Inverse probability weighting was used to account for censoring and death when estimating total costs 32,33 . The intervals used for the weighting were two 6-month time intervals for the first year and annual intervals for years 2 and 3.…”
Section: End-points and Analysesmentioning
confidence: 99%
“…IPW was used to calculate mean costs (6), and IPW estimating equations were used to estimate the median, 25th percentile, and 75th percentile costs (7). To protect against potential differences in the censoring pattern between treatment groups, the probability weights were estimated separately for the two groups (8).…”
Section: Discussionmentioning
confidence: 99%
“…The propensity scores were used to create inverse probability weights (IPWs) for each individual in each year of inclusion in the data equal to 1/propensity score for those who were ever diagnosed with HCV, and 1/(1‐propensity score) for those who were not. The purpose of IPW weighting is to balance the sample on potentially confounding covariates before the estimation of coefficients . Each weight was then multiplied by the individual's Medicare weight to avoid confounding from over‐representation of SEER file individuals.…”
Section: Methodsmentioning
confidence: 99%