Optimizing covariate selection and results inference in anchored matched adjusted indirect comparison method | Health Decision

Optimizing covariate selection and results inference in anchored matched adjusted indirect comparison method

Authors

  • Mingjun Rui
  • Hongchao Li
  • Yingcheng Wang

DOI:

https://doi.org/10.54844/hd.2024.0014

Keywords:

matched adjusted indirect comparison, covariate selection, results inference

Abstract

Indirect comparison methods become particularly prominent in pharmacoeconomic evaluations. This study delves into the

anchored matched adjusted indirect comparison (MAIC) method, spotlighting the challenges of selecting appropriate covariates

and distinguishing between predictive and prognostic factors. In addition, our research bridges the gap of MAIC results application

inference, enhancing the methodological rigor and applicability of MAIC analyses. Through theoretical exploration and a detailed

case study of toripalimab and pembrolizumab in the neoadjuvant treatment of NSCLC, we demonstrate the significant impact of

covariate selection on the outcomes of pharmacoeconomic evaluations. Analyzing the individual patient data by using statistical

methods alone is insufficient to identify all potential prognostic factors. Instead, a combination of previously published related

research and expert consultations is necessary. The individual patient data network meta-analysis should be employed if the

shared effect modifier assumption is not met to make the MAIC results be inferred for the real-world decision-making population.

Key words: matched adjusted indirect comparison, covariate selection, results inference

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Published

2024-07-12

How to Cite

1.
Rui M, Li H, Wang Y. Optimizing covariate selection and results inference in anchored matched adjusted indirect comparison method. Health Decision. 2024;2(S1). doi:10.54844/hd.2024.0014

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ABSTRACT

Optimizing covariate selection and results inference in anchored matched adjusted indirect comparison method


Mingjun Rui1, Hongchao Li2,3*, Yingcheng Wang1

1School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

2Center for Pharmacoeconomics and Outcomes Research, China Pharmaceutical University, Nanjing, Jiangsu, China

3School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, Jiangsu, Jiangsu, China


*Corresponding Author:

Hongchao Li, E-mail: lihongchao@cpu.edu.cn


Received: 15 June 2024 Published: 15 July 2024


ABSTRACT

Indirect comparison methods become particularly prominent in pharmacoeconomic evaluations. This study delves into the anchored matched adjusted indirect comparison (MAIC) method, spotlighting the challenges of selecting appropriate covariates and distinguishing between predictive and prognostic factors. In addition, our research bridges the gap of MAIC results application inference, enhancing the methodological rigor and applicability of MAIC analyses. Through theoretical exploration and a detailed case study of toripalimab and pembrolizumab in the neoadjuvant treatment of NSCLC, we demonstrate the significant impact of covariate selection on the outcomes of pharmacoeconomic evaluations. Analyzing the individual patient data by using statistical methods alone is insufficient to identify all potential prognostic factors. Instead, a combination of previously published related research and expert consultations is necessary. The individual patient data network meta-analysis should be employed if the shared effect modifier assumption is not met to make the MAIC results be inferred for the real-world decision-making population.

Key words: matched adjusted indirect comparison, covariate selection, results inference