Related Experiment Video
Updated: Jun 4, 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
The role of the c-statistic in variable selection for propensity score models
Daniel Westreich1, Stephen R Cole, Michele Jonsson Funk
1Department of Obstetrics and Gynecology and Duke Global Health Institute, Duke University, Durham, NC, USA. daniel.westreich@duke.edu
Pharmacoepidemiology and Drug Safety
|February 26, 2011
Summary
The c-statistic is often misused to assess propensity scores for confounding control. Relying on it can harm study precision and generalizability, making it an unreliable metric for observational research.
Area of Science:
- Epidemiology
- Biostatistics
- Observational Research Methods
Background:
- Propensity scores are used to control confounding in observational studies.
- The c-statistic is frequently cited as a measure of propensity score performance.
Purpose of the Study:
- To evaluate the utility of the c-statistic in assessing propensity score models.
- To highlight the limitations of using the c-statistic for confounding control.
Main Methods:
- Review of applied literature on propensity score methodology.
- Conceptual analysis of the c-statistic's role in propensity score construction.
Main Results:
- A high c-statistic does not guarantee effective confounding control.
- Using the c-statistic may reduce overlap between treated and untreated groups.
- This reduction can necessitate population restrictions, impacting precision and generalizability.
Conclusions:
- The c-statistic is an inadequate metric for evaluating propensity scores.
- Variable selection should prioritize subject matter knowledge, empirical data, and sensitivity analyses for robust confounding control.
Related Concept Videos
01:09Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
01:20Biostatistics: Overview
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
01:10Statgraphics
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
01:20Comparing the Survival Analysis of Two or More Groups
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 Cox...
01:19The Mantel-Cox Log-Rank Test
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.