Related Experiment Video
Updated: Jan 9, 2026
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
15.0K
Mean Cost and Cost-Effectiveness Ratios with Censored Data: a Tutorial and SAS® Macros
Eduard Poltavskiy1, Dingning Liu2, Shuai Chen2,3,4
1Independent researcher, Sacramento, CA, USA.
Biostatistics & Epidemiology
|December 5, 2025
Summary
Censoring is a critical challenge in medical cost and survival data analysis. This tutorial explains how to estimate mean costs and cost-effectiveness ratios using censored data, offering practical SAS code examples.
Area of Science:
- Health Economics
- Biostatistics
- Survival Analysis
Background:
- Censoring is a significant challenge in analyzing medical cost and survival data.
- Medical costs can be treated as survival data, accruing until an endpoint like death.
- Censored cost data have been extensively studied since landmark papers in 1997 and 1998.
Purpose of the Study:
- To provide a tutorial on estimating mean cost and cost-effectiveness ratios with censored data.
- To address two common data scenarios: total cost data and longitudinal cost history.
- To offer an updated literature review on censored cost data analysis.
Main Methods:
- Explanation of methods for estimating mean cost with censored data.
- Guidance on calculating cost-effectiveness ratios in the presence of censoring.
- Illustrative examples using both total cost and longitudinal cost data.
Main Results:
- Demonstration of cost and cost-effectiveness estimation techniques for censored data.
- Practical application examples for different data structures.
- Provision of SAS code for practitioners and data analysts.
Conclusions:
- Accurate estimation of medical costs and cost-effectiveness is feasible despite data censoring.
- The tutorial provides valuable methods and tools for researchers analyzing healthcare costs.
- Updated literature review and practical SAS code enhance the utility for data analysts.
Related Concept Videos
01:09Censoring Survival Data
505
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
505
01:20Comparing the Survival Analysis of Two or More Groups
538
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
538
01:14Statistical Analysis System (SAS)
796
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
796
01:24Kaplan-Meier Approach
537
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
537
01:18Introduction To Survival Analysis
714
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
714
01:15Assumptions of Survival Analysis
385
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
385