A new three-step method for using inverse propensity weighting with latent class analysis

F. J. Clouth*, S. Pauws, F. Mols, J. K. Vermunt

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Bias-adjusted three-step latent class analysis (LCA) is widely popular to relate covariates to class membership. However, if the causal effect of a treatment on class membership is of interest and only observational data is available, causal inference techniques such as inverse propensity weighting (IPW) need to be used. In this article, we extend the bias-adjusted three-step LCA to incorporate IPW. This approach separates the estimation of the measurement model from the estimation of the treatment effect using IPW only for the later step. Compared to previous methods, this solves several conceptual issues and more easily facilitates model selection and the use of multiple imputation. This new approach, implemented in the software Latent GOLD, is evaluated in a simulation study and its use is illustrated using data of prostate cancer patients.

Original languageEnglish
Number of pages21
JournalAdvances in Data Analysis and Classification
DOIs
Publication statusE-pub ahead of print - 2021

Keywords

  • Latent class analysis
  • Causal inference
  • Propensity score
  • Average treatment effect
  • Three-step modelling
  • QUALITY-OF-LIFE
  • PROSTATE-CANCER SURVIVORS
  • ACTIVE SURVEILLANCE
  • CAUSAL INFERENCE
  • MODELS
  • REGRESSION
  • OUTCOMES
  • SCORE

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