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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
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Unreliable Continuous Treatment Indicators in Propensity Score Analysis.

Gail A Fish1, Walter L Leite2

  • 1Strategic Research Development, UF Research, University of Florida.

Multivariate Behavioral Research
|July 31, 2023
PubMed
Summary

Low composite reliability in propensity score analyses (PSA) can underestimate treatment effects. Using factor scores estimated with covariates may help mitigate this bias in generalized propensity score (GPS) models.

Keywords:
Monte Carlo simulation studyPropensity score analysiscontinuous latent treatmentsfactor scoresgeneralized propensity scorereliability

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Area of Science:

  • Statistics
  • Epidemiology
  • Educational Research

Background:

  • Propensity score analyses (PSA) commonly use composite measures for continuous treatments, often without reporting composite reliability.
  • The impact of unreliability in these composite indicators on treatment effect estimation in PSA is not well understood.
  • Latent variable or factor score approaches, which account for measurement error, are infrequently used as alternatives.

Purpose of the Study:

  • To investigate the effects of indicator unreliability in latent continuous treatments within propensity score analyses using the generalized propensity score (GPS).
  • To assess how composite reliability influences the bias, Root Mean Squared Error (RMSE), and coverage rates of Average Treatment Effect (ATE) estimates.

Main Methods:

  • A Monte Carlo simulation study was employed, manipulating factors such as composite reliability, treatment representation, factor loading variability, sample size, and the number of treatment indicators.
  • Generalized propensity scores (GPS) were utilized to model the relationship between covariates and the continuous treatment.
  • ATE estimates were evaluated based on relative bias, RMSE, and coverage rates under various simulation conditions.

Main Results:

  • Lower composite reliability was found to systematically underestimate the ATE for latent continuous treatments.
  • The number of treatment indicators and variability in factor loadings had minimal impact on ATE estimates once overall composite reliability was accounted for.
  • In correctly specified GPS models, using factor scores that incorporated covariates helped to reduce the bias caused by low composite reliability.

Conclusions:

  • The reliability of composite indicators is a critical factor in obtaining accurate ATE estimates in PSA for continuous treatments.
  • Researchers should consider the implications of measurement unreliability and explore latent variable approaches or factor scores, particularly when covariates can be integrated into their estimation.
  • Findings have implications for the design and analysis of observational studies involving complex or multi-indicator treatment variables.