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Nested g‐computation: a causal approach to analysis of censored medical costs in the presence of time‐varying treatment

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  • Andrew J. Spieker
  • Emily M. Ko
  • Jason A. Roy
  • Nandita Mitra

Abstract

Rising medical costs are an emerging challenge in policy decisions and resource allocation planning. When cumulative medical cost is the outcome, right censoring induces informative missingness due to heterogeneity in cost accumulation rates across subjects. Inverse weighting approaches have been developed to address the challenge of informative cost trajectories in mean cost estimation, though these approaches generally ignore post‐baseline treatment changes. In post‐hysterectomy endometrial cancer patients, data from a linked database of Medicare records and the Surveillance, Epidemiology, and End Results programme of the National Cancer Institute reveal substantial within‐subject variation in treatment over time. In such a setting, the utility of existing intent‐to‐treat approaches is generally limited. Estimates of the population mean cost under a hypothetical time‐varying treatment regime can better assist with resource allocation when planning for a treatment policy change; such estimates must inherently take time‐dependent treatment and confounding into account. We develop a nested g‐computation approach to cost analysis to address this challenge, while accounting for censoring. We develop a procedure to evaluate sensitivity to departures from baseline treatment ignorability. We further conduct a variety of simulations and apply our nested g‐computation procedure to 2‐year costs from endometrial cancer patients.

Suggested Citation

  • Andrew J. Spieker & Emily M. Ko & Jason A. Roy & Nandita Mitra, 2020. "Nested g‐computation: a causal approach to analysis of censored medical costs in the presence of time‐varying treatment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1189-1208, November.
  • Handle: RePEc:bla:jorssc:v:69:y:2020:i:5:p:1189-1208
    DOI: 10.1111/rssc.12441
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    References listed on IDEAS

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    1. Bo Lu, 2005. "Propensity Score Matching with Time-Dependent Covariates," Biometrics, The International Biometric Society, vol. 61(3), pages 721-728, September.
    2. Andrew Spieker & Jason Roy & Nandita Mitra, 2018. "Analyzing medical costs with time‐dependent treatment: The nested g‐formula," Health Economics, John Wiley & Sons, Ltd., vol. 27(7), pages 1063-1073, July.
    3. Papanicolas, Irene & Woskie, Liana R. & Jha, Ashish K., 2018. "Health care spending in the United States and other high-income countries," LSE Research Online Documents on Economics 87362, London School of Economics and Political Science, LSE Library.
    4. Brent A. Johnson & Anastasios A. Tsiatis, 2005. "Semiparametric inference in observational duration-response studies, with duration possibly right-censored," Biometrika, Biometrika Trust, vol. 92(3), pages 605-618, September.
    5. Brent A. Johnson & Anastasios A. Tsiatis, 2004. "Estimating Mean Response as a Function of Treatment Duration in an Observational Study, Where Duration May Be Informatively Censored," Biometrics, The International Biometric Society, vol. 60(2), pages 315-323, June.
    6. Neugebauer, Romain & van der Laan, Mark J., 2006. "G-computation estimation for causal inference with complex longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1676-1697, December.
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    Cited by:

    1. Nicholas Illenberger & Nandita Mitra & Andrew J. Spieker, 2022. "A regression framework for a probabilistic measure of cost‐effectiveness," Health Economics, John Wiley & Sons, Ltd., vol. 31(7), pages 1438-1451, July.

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