By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
Health Works CollectiveHealth Works CollectiveHealth Works Collective
  • Health
    • Mental Health
    Health
    Healthcare organizations are operating on slimmer profit margins than ever. One report in August showed that they are even lower than the beginning of the…
    Show More
    Top News
    medicare part d benefits
    Everything that You Need to Know About Medicare Part D
    August 15, 2022
    Best Ways to Boost Your Immune System this Winter
    Best Ways to Boost Your Immune System this Winter
    November 15, 2022
    back pain issues
    Ways to Treat Constant Back Pain
    August 21, 2023
    Latest News
    How Probate Planning Shapes the Future of Your Estate and Family Care
    July 17, 2025
    Beyond Nutrition: Everyday Foods That Support Whole-Body Health
    June 15, 2025
    The Wide-Ranging Benefits of Magnesium Supplements
    June 11, 2025
    The Best Home Remedies for Migraines
    June 5, 2025
  • Policy and Law
    • Global Healthcare
    • Medical Ethics
    Policy and Law
    Get the latest updates about Insurance policies and Laws in the Healthcare industry for different geographical locations.
    Show More
    Top News
    More On Wellness Programs To Improve Health and Reduce Costs
    January 25, 2012
    Privatizing Social Security and Medicare: Who Can Defuse Political Dynamite?
    June 12, 2011
    Study: Risk of Death in Elderly Patients with Dementia Doubled with Some Antipsychotic Medications
    February 26, 2012
    Latest News
    How IT and Marketing Teams Can Collaborate to Protect Patient Trust
    July 17, 2025
    How Health Choices and Legal Actions Intersect After an Injury
    July 17, 2025
    How communities and healthcare providers can address slip and fall injuries with legal awareness
    July 17, 2025
    Let Your Lawyer Handle the Work Before You Pay Medical Costs
    July 6, 2025
  • Medical Innovations
  • News
  • Wellness
  • Tech
Search
© 2023 HealthWorks Collective. All Rights Reserved.
Reading: Estimating Lifetime or Episode-of-Illness Costs Under Censoring
Share
Notification Show More
Font ResizerAa
Health Works CollectiveHealth Works Collective
Font ResizerAa
Search
Follow US
  • About
  • Contact
  • Privacy
© 2023 HealthWorks Collective. All Rights Reserved.
Health Works Collective > Policy & Law > Public Health > Estimating Lifetime or Episode-of-Illness Costs Under Censoring
Public Health

Estimating Lifetime or Episode-of-Illness Costs Under Censoring

JasonShafrin
JasonShafrin
Share
5 Min Read
SHARE

How can you estimate an individual’s total lifetime cost of medical care? For people who die in your sample, this is simple. In most data sets, however, not all individuals will die during the period of observation. Thus, the data set is censored for those who do not die. In addition, many standard hazard models do not allow for researchers to disaggregate the effects of covariates on survival and the intensity of utilization. Both factors have an effect on cost. Assuming that censoring is random, Basu and Manning (2010) describe a method to calculate expected lifetime costs for each individual as follows:

  1. Estimate Survival Probabilities. Use a flexible survival model, such as an accelerated failure time model based on the generalized gamma distribution for time, to estimate the individual’s survival function after taking into account censoring. Let Sj(X) and hj(X) be the estimated survivor function and the hazard function for an interval indexed by j. The observation The predictions are obtained for all time periods for all patients.
  2. Estimate cost among patients who died. Among those subject intervals, (aj-1, aj], where we observe the subject to die, estimate a generalized linear model (or models if a two-part specification is necessary) for the observed costs after conditioning on covariates X. One can also condition on the time of death within the interval as well. Use parameter estimates from this model to predict costs, μ1j(X), for every subject-interval in the data. To account for the stochastic nature of U within that interval (i.e. to account for what would the costs be if the patient died inside that interval but at different times), one simply averages the predictions that are conditional of each value of U after weighting with the observed distribution of U among intervals where patients are observed to die. Therefore, μ1j(X)=∫μ1j(X,U) dF(U|abobsb+1).
  3. Estimate the cost among subjects not observed to die. Next, among those subject intervals, (aj-1, aj], where patients are not observed to die but excluding those where we only observe costs over a partial duration due to censoring, estimate a generalized linear model (or models if a two-part specification is necessary) for the observed cost functions after conditioning on covariates X. We use parameter estimates from this model to predict costs, μ2j(X), for every subject-interval in our data. We do not use the subject intervals where censoring occurs in our estimation in this part. This allows us effectively to allow for continuous censoring times.

  Thus, the resulting cost function for interval j for any individual is given by:

  • μj(X)=Sj(X)*[hj(X)*μ1j(X) + (1-hj(X))*μ2j(X)]

  There are a number of benefits of using this framework. First, Basu and Manning show that this estimator can “decompose the covariate effects on total costs into part mediated by survival effects and another mediated by intensity of use.” Second, this method allows for death to take place any time during each interval rather than solely at the end of an interval. Third, the model “allows for separate estimators to be used for end-of-life and non-end-of-life periods.” The separate estimators are especially useful in cases where end-of-life cost differs significantly from the regular course of care. For instance, one study demonstrated the there is a U-shaped pattern of cost history among cancer patients with the left side of the U corresponding to initial treatment and the right side reflecting a substantial spike in costs during the last 6 months of life. For those interested, Basu and Manning also provide a simulation and empirical application to demonstrate the utility of their econometric specification compared to earlier models.

TAGGED:medical costs
Share This Article
Facebook Copy Link Print
Share

Stay Connected

1.5kFollowersLike
4.5kFollowersFollow
2.8kFollowersPin
136kSubscribersSubscribe

Latest News

Grounded Healing: A Natural Ally for Sustainable Healthcare Systems
How IT and Marketing Teams Can Collaborate to Protect Patient Trust
Global Healthcare Policy & Law
July 17, 2025
paramedics in surgical gloves and masks
How Health Choices and Legal Actions Intersect After an Injury
Health care
July 16, 2025
a woman giving a key
How Probate Planning Shapes the Future of Your Estate and Family Care
Health
July 16, 2025
a woman with kinesio tapes on her back arm
How communities and healthcare providers can address slip and fall injuries with legal awareness
Health care
July 16, 2025

You Might also Like

ePatients
Global HealthcareHealth ReformHospital AdministrationMedical EducationMedical InnovationsMedical RecordsMobile HealthPublic HealthRemote DiagnosticsSocial Media

ePatients: What’s the Big Deal?

March 15, 2013

Promising New Patient Recovery Science

December 20, 2013

Dog Versus Treadmill: No Contest

March 16, 2011
health app
Public Health

Race and Sex Matter

March 15, 2011
Subscribe
Subscribe to our newsletter to get our newest articles instantly!
Follow US
© 2008-2025 HealthWorks Collective. All Rights Reserved.
  • About
  • Contact
  • Privacy
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?