Double robust estimator of average causal treatment effect for censored medical cost data

A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies

HIGHLIGHTS

We illustrate the steps involved in carrying out cost-effectiveness analysis using net benefit regressions with possibly censored demo data by providing step-by-step guidance and code applied to a data set.We demonstrate the importance of these new methods by illustrating how naïve methods for handling censoring can lead to biased cost-effectiveness results.

An inverse probability weighted regression method that accounts for right-censoring for causal inference with multiple treatments and a binary outcome

Comparative effectiveness research often involves evaluating the differences in the risks of an event of interest between two or more treatments using observational data. Often, the post-treatment outcome of interest is whether the event happens within a pre-specified time window, which leads to a binary outcome. One source of bias for estimating the causal treatment effect is the presence of confounders, which are usually controlled using propensity score-based methods. An additional source of bias is right-censoring, which occurs when the information on the outcome of interest is not completely available due to dropout, study termination, or treatment switch before the event of interest. We propose an inverse probability weighted regression-based estimator that can simultaneously handle both confounding and right-censoring, calling the method CIPWR, with the letter C highlighting the censoring component. CIPWR estimates the average treatment effects by averaging the predicted outcomes obtained from a logistic regression model that is fitted using a weighted score function. The CIPWR estimator has a double robustness property such that estimation consistency can be achieved when either the model for the outcome or the models for both treatment and censoring are correctly specified. We establish the asymptotic properties of the CIPWR estimator for conducting inference, and compare its finite sample performance with that of several alternatives through simulation studies. The methods under comparison are applied to a cohort of prostate cancer patients from an insurance claims database for comparing the adverse effects of four candidate drugs for advanced stage prostate cancer.

Net-benefit regression with censored cost-effectiveness data from randomized or observational studies

Cost-effectiveness analysis is an essential part of the evaluation of new medical interventions. While in many studies both costs and effectiveness (eg, survival time) are censored, standard survival analysis techniques are often invalid due to the induced dependent censoring problem. We propose methods for censored cost-effectiveness data using the net-benefit regression framework, which allow covariate-adjustment and subgroup identification when comparing two intervention groups. The methods provide a straightforward way to construct cost-effectiveness acceptability curves with censored data. We also propose a more efficient doubly robust estimator of average causal incremental net benefit, which increases the likelihood that the results will represent a valid inference in observational studies. Lastly, we conduct extensive numerical studies to examine the finite-sample performance of the proposed methods, and illustrate the proposed methods with a real data example using both survival time and quality-adjusted survival time as the measures of effectiveness.

A comparison of parametric propensity score-based methods for causal inference with multiple treatments and a binary outcome

We consider comparative effectiveness research (CER) from observational data with two or more treatments. In observational studies, the estimation of causal effects is prone to bias due to confounders related to both treatment and outcome. Methods based on propensity scores are routinely used to correct for such confounding biases. A large fraction of propensity score methods in the current literature consider the case of either two treatments or continuous outcome. There has been extensive literature with multiple treatment and binary outcome, but interest often lies in the intersection, for which the literature is still evolving. The contribution of this article is to focus on this intersection and compare across methods, some of which are fairly recent. We describe propensity-based methods when more than two treatments are being compared, and the outcome is binary. We assess the relative performance of these methods through a set of simulation studies. The methods are applied to assess the effect of four common therapies for castration-resistant advanced-stage prostate cancer. The data consist of medical and pharmacy claims from a large national private health insurance network, with the adverse outcome being admission to the emergency room within a short time window of treatment initiation.

Hepatitis C Cure Is Associated with Decreased Healthcare Costs in Cirrhotics in Retrospective Veterans Affairs Cohort

BACKGROUND

Approximately 233,898 individuals in the Veterans Affairs healthcare network are hepatitis C virus (HCV)-infected, making the Veterans Affairs the single largest provider of HCV care in the USA. Direct-acting antiviral treatment regimens for HCV offer high cure rates. However, these medications pose an enormous financial burden, and whether HCV cure is associated with decreased healthcare costs is poorly defined.

AIMS

To measure downstream healthcare costs in a national population of HCV-infected patients up to 9 years post-HCV antiviral treatment, to compare downstream healthcare costs between cured and uncured patients, and to assess impact of cirrhosis status on cost differences.

METHODS

This is a retrospective cohort study (2004-2014) of hepatitis C-infected patients who initiated antiviral treatment within the United States Veterans Affairs healthcare system October 2004-September 2013. We measured inpatient, outpatient, and pharmacy costs after HCV treatment.

RESULTS

For the entire cohort, cure was associated with mean cumulative cost savings in post-treatment years three-six, but no cost savings by post-treatment year nine. By post-treatment year nine, cure in cirrhosis patients was associated with a mean cumulative cost savings of $9474 (- 32,666 to 51,614) per patient, while cure in non-cirrhotic patients was associated with a mean cumulative cost excess of $2526 (- 12,211 to 7159) per patient.

CONCLUSIONS

Among patients with cirrhosis at baseline, cure is associated with absolute cost savings up to 9 years post-treatment compared to those without cure. Among patients without cirrhosis, early post-treatment cost savings are counterbalanced by higher costs in later years.