2019
DOI: 10.21105/joss.01593
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ccostr: An R package for estimating mean costs with censored data

Abstract: Censoring is a frequent obstacle when working with time to event data, as e.g. not all patients in a medical study can be observed until death. For estimating the distribution of time to event the Kaplan-Meier estimator is useful, but when estimating mean costs it is not, since costs, as opposed to time, typically don't accumulate at a constant rate. Often costs accumulate at a higher rate at the beginning (e.g. at diagnosis) and end (e.g. death) of the study.

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Cited by 4 publications
(4 citation statements)
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“…Patients who were still attending the pediatric rheumatology clinic by the end of the study observation period, moved to another province, or were lost to follow-up were considered right censored in the analysis. To account for censoring in the healthcare cost estimates, we used the Zhao and Tian estimator (or weighted available sample estimator) for censored data with cost history [ 26 ]. The total cumulative mean cost including 95% confidence intervals was calculated for overall and JIA-associated costs for a period of 6 years.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Patients who were still attending the pediatric rheumatology clinic by the end of the study observation period, moved to another province, or were lost to follow-up were considered right censored in the analysis. To account for censoring in the healthcare cost estimates, we used the Zhao and Tian estimator (or weighted available sample estimator) for censored data with cost history [ 26 ]. The total cumulative mean cost including 95% confidence intervals was calculated for overall and JIA-associated costs for a period of 6 years.…”
Section: Methodsmentioning
confidence: 99%
“…The cumulative mean costs using the Zhao and Tian estimator are calculated by weighting the costs of the whole available sample, including censored cases, and partitioning the time over the observation period. The Zhao and Tian estimator has been shown to be more accurate than other estimators when cost history is available [ 26 , 27 ].…”
Section: Methodsmentioning
confidence: 99%
“…All analyses were run using R version 3.6.0. In particular, the package survival was used for Cox regression model (Therneau & Lumley, 2015) and cmprsk for competing risk (Gray & Gray, 2020), ipw for inverse probability of treatment weighting (van der Wal et al, 2011), MatchIt for propensity score matching (Ho, 2027) and ccostr for Kaplan-Meier sample average (Børty, Brøndum, & Bøgsted, 2019).…”
Section: Kaplan-meier Sample Averagementioning
confidence: 99%
“…The package ccostr [39] was used in RStudio (RStudio Team (2021), version 1.4.1103) for estimation of costs for the average sample (mean total cost, AS) (base-case) and Zhao and Tian estimator. Incremental cost-effectiveness ratios (ICERs) were calculated for all treatment lines and the combined sequences.…”
Section: Costsmentioning
confidence: 99%