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Updated: Sep 18, 2025

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Two-stage targeted minimum-loss based estimation for non-negative two-part outcomes.

Nicholas T Williams1, Richard Liu2, Katherine L Hoffman1

  • 1Department of Epidemiology, Mailman School of Public Health, Columbia University, USA.

Statistical Methods in Medical Research
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method, the two-stage targeted minimum-loss based estimator (hTMLE), to better analyze healthcare data with zero-inflated positive outcomes. This approach improves causal effect estimation for non-negative two-part outcomes.

Keywords:
Hurdle modelscausal inferencedoubly-robustnonparametrictwo-part outcomes

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Area of Science:

  • Biostatistics
  • Health Services Research
  • Causal Inference

Background:

  • Non-negative two-part outcomes, common in healthcare (e.g., expenditure, length of stay), present unique analytical challenges.
  • Existing statistical methods often fail to fully leverage the semicontinuous nature of these outcomes.
  • There is a need for advanced methods to improve causal effect estimation in healthcare utilization research.

Purpose of the Study:

  • To develop and present a novel nonparametric two-stage targeted minimum-loss based estimator (hTMLE).
  • To address the estimation challenges posed by non-negative two-part outcomes in causal inference.
  • To provide a method applicable to a general class of interventions, including continuous, categorical, and binary exposures.

Main Methods:

  • Developed a nonparametric two-stage targeted minimum-loss based estimator (hTMLE).
  • The hTMLE method targets the intensity and binary components of the outcome sequentially.
  • The method is designed for general interventions and accommodates various exposure types.

Main Results:

  • The two-stage TMLE demonstrated potential for improved finite sample efficiency in simulations.
  • The method was successfully applied to estimate the effect of chronic pain and disability on opioid supply.
  • Efficiency gains were observed compared to existing methods in simulated scenarios.

Conclusions:

  • The developed hTMLE offers an improved approach for analyzing non-negative two-part outcomes in causal inference.
  • This method enhances the estimation of causal effects in healthcare research, particularly for utilization data.
  • The application to Medicaid data highlights the practical utility of hTMLE in real-world health studies.