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This study introduces a new causal inference method to analyze complex, time-varying behavioral data, improving understanding of smoking cessation and relapse risks. The approach enhances the analysis of dynamic processes in behavioral science.

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Area of Science:

  • Behavioral Science
  • Causal Inference
  • Longitudinal Data Analysis

Background:

  • Behavioral processes are dynamic and complex, necessitating intensive longitudinal data for accurate analysis.
  • Existing analytical methods struggle to capture time-varying causal effects in complex behavioral data, limiting understanding of dynamic processes.
  • Understanding dynamic elements is crucial for interventions, such as smoking cessation, to identify relapse risk factors.

Purpose of the Study:

  • To propose and evaluate a novel analytical approach, the augmented mediational g-formula, for estimating time-varying causal effects.
  • To assess the performance of the proposed method through simulation studies, comparing it with existing approaches.
  • To apply the optimal method to real-world data from the Wisconsin Smokers' Health Study II to examine smoking cessation dynamics.

Main Methods:

  • Developed an augmented mediational g-formula with a two-step estimation strategy to estimate time-varying causal (in)direct effects.
  • Conducted simulation studies to evaluate the proposed method's bias, precision, and alignment with the product-of-coefficients approach.
  • Applied the validated method to longitudinal data from the Wisconsin Smokers' Health Study II, incorporating time-varying confounders (daily stress) and mediators (daily treatment adherence).

Main Results:

  • Simulation results indicated the performance characteristics of the proposed augmented mediational g-formula.
  • The study successfully applied the method to assess the causal effect of pharmacological treatment on smoking cessation, mediated by adherence and confounded by stress.
  • The findings provide insights into the dynamic interplay of treatment, adherence, stress, and cessation outcomes over time.

Conclusions:

  • The augmented mediational g-formula offers a viable approach for estimating time-varying causal effects in intensive longitudinal behavioral data.
  • This method can elucidate complex dynamic behavioral processes, such as those in smoking cessation, by analyzing time-varying exposures, mediators, and confounders.
  • The study provides a preliminary framework for the causal analysis of time-varying bio-behavioral processes, paving the way for more sophisticated research.