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Related Experiment Video

Updated: May 16, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Prognostic score methods for the estimation of the average causal effect.

Chamika Porage¹, Ingeborg Waernbaum¹

¹Department of Statistics, Uppsala University, Kyrkogårdsgatan 10, 753 12, Uppsala, Sweden.

The International Journal of Biostatistics

|May 15, 2026

Summary

We introduce the full prognostic score (FPGS), an enhanced prognostic score (PGS), to improve causal inference by adjusting for confounding. FPGS demonstrated better performance in simulations and an NHANES study on smoking and blood lead levels.

Keywords:

confounding adjustment effect modification full prognostic score regression imputation targeted maximum likelihood estimation

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Published on: October 23, 2020

Area of Science:

Causal Inference
Biostatistics
Epidemiology

Background:

Prognostic scores (PGS) summarize covariate associations with outcomes.
Existing methods may not fully account for confounding, especially with effect modification.

Purpose of the Study:

To introduce the full prognostic score (FPGS) as an extension of PGS for robust causal inference.
To demonstrate FPGS's ability to meet sufficiency conditions for confounding adjustment.
To evaluate FPGS performance in estimating average causal effects.

Main Methods:

Developed a general algorithm for FPGS implementation using regression techniques (linear, random forest, XGBoost).
Integrated FPGS into semiparametric estimators: regression imputation, stratification, and targeted maximum likelihood estimation (TMLE).
Conducted simulation studies and an empirical analysis of NHANES data (smoking and blood lead levels).

Main Results:

FPGS estimators met sufficiency conditions for confounding adjustment under effect modification.
Simulations showed lower mean squared error for linear regression imputation and TMLE with FPGS compared to alternatives.
The empirical study assessed the effect of smoking on blood lead levels using FPGS.

Conclusions:

FPGS offers a robust approach for causal inference by enhancing confounding adjustment.
FPGS-based estimators, particularly with linear regression imputation and TMLE, show improved finite-sample properties.
The FPGS methodology is applicable to real-world epidemiological data analysis.