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Adaptive stratified sampling design in two-phase studies for average causal effect estimation.

Min Zeng1,2, Qiyu Wang1,3, Zijian Sui2

  • 1Department of Biostatistics, City University of Hong Kong, Hong Kong, 999077, China.

Biometrics
|October 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive stratified sampling design (AdaStrat) for more efficient causal inference from observational data. AdaStrat minimizes confounding bias and improves average causal effect (ACE) estimation in two-phase studies.

Keywords:
causal inferencemissing confoundersampling designstratification strategy

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Area of Science:

  • Statistics
  • Epidemiology
  • Biomarker Research

Background:

  • Observational data analysis for causal inference is challenged by confounding effects.
  • Costly confounder data (e.g., genetic biomarkers, medical imaging) limit traditional studies.
  • Two-phase studies offer a resource-efficient approach by collecting expensive data on a subset of subjects.

Purpose of the Study:

  • To propose an adaptive stratified sampling design (AdaStrat) for efficient causal inference.
  • To minimize the variance of the average causal effect (ACE) estimator within a fixed second-phase sample size.
  • To improve upon existing fixed stratified sampling designs in two-phase studies.

Main Methods:

  • Developed an adaptive stratified sampling design (AdaStrat) for two-phase studies.
  • Utilized pilot data with costly confounder measures to create a stratification and sampling strategy.
  • Applied the AdaStrat strategy to select second-phase subjects for expensive confounder measurement.

Main Results:

  • AdaStrat demonstrated superior efficiency in ACE estimation compared to prefixed stratified designs.
  • Simulation studies showed AdaStrat outperformed fixed stratified sampling (FixStrat) with 20-30% relative efficiency gains.
  • The effectiveness of AdaStrat was validated using UK Biobank data.

Conclusions:

  • AdaStrat provides a more efficient method for causal inference in two-phase observational studies.
  • The adaptive nature of AdaStrat optimizes resource allocation for costly confounder data collection.
  • AdaStrat offers a statistically rigorous and practically efficient solution for confounding in observational research.