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  3. Cost-effectiveness Analyses For Sequential Multiple Assignment Randomized Trials.
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  3. Cost-effectiveness Analyses For Sequential Multiple Assignment Randomized Trials.

Related Experiment Video

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

Cost-Effectiveness Analyses for Sequential Multiple Assignment Randomized Trials.

Lina M Montoya1,2, Elvin H Geng3, Harriet F Adhiambo4

  • 1School of Data Science and Society, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Statistics in Medicine
|June 10, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new method for assessing the cost-effectiveness of adaptive treatment strategies in Sequential Multiple Assignment Randomized Trials (SMARTs). The findings provide crucial insights for optimizing healthcare interventions and resource allocation in complex clinical trial designs.

Keywords:
cost‐effectiveness analysisincremental cost‐effectiveness ratiosequential multiple assignment randomized trialtargeted maximum likelihood estimation

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Area of Science:

  • Biostatistics
  • Health Economics
  • Clinical Trial Design

Background:

  • Sequential Multiple Assignment Randomized Trials (SMARTs) are increasingly used in clinical research.
  • Cost-effectiveness analysis (CEA) is often proposed for adaptive strategies within SMARTs.
  • Incremental Cost-Effectiveness Ratios (ICERs) are commonly used to assess cost-effectiveness.

Purpose of the Study:

  • To present an estimation and inference procedure for cost-effectiveness measures of embedded dynamic treatment regimes in SMART designs.
  • To introduce a targeted maximum likelihood estimator (TMLE) for ICERs in SMARTs.
  • To demonstrate the application of these methods using a real-world HIV care adherence trial.

Main Methods:

  • Development of a targeted maximum likelihood estimator (TMLE) for ICERs.
  • Utilization of influence curve-based inference for statistical analysis.
  • Simulation studies to evaluate the performance of the proposed methods.
  • Application to the Adaptive Strategies for Preventing and Treating Lapses of Retention in HIV Care (ADAPT-R) trial.

    • Main Results:

    • The study presents an estimation and inference procedure for ICERs in SMARTs.
    • The proposed TMLE with influence curve-based inference is illustrated through simulations.
    • Estimated ICERs with inference are presented for embedded regimes in the ADAPT-R trial.
    • This work contributes novel cost-effectiveness analysis results from a SMART.

    Conclusions:

    • The developed methods provide a robust framework for cost-effectiveness analysis in SMARTs.
    • This research facilitates better decision-making regarding adaptive treatment strategies.
    • The findings are particularly relevant for optimizing interventions in HIV care adherence programs.