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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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A Bayesian semiparametric latent variable approach to causal mediation.

Chanmin Kim1, Michael Daniels2, Yisheng Li3

  • 1Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.

Statistics in Medicine
|December 19, 2017
PubMed
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This study introduces a novel Bayesian method to estimate varying direct and indirect causal effects in individuals. The approach identifies distinct patient clusters, improving causal mediation analysis for personalized medicine.

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Bayesian nonparametricscausal inferencecluster-specific effectseffect modificationsequential ignorability

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Area of Science:

  • Biostatistics
  • Causal Inference
  • Health Services Research

Background:

  • Standard causal mediation analysis in randomized studies often averages direct and indirect effects, masking individual variations.
  • Heterogeneity in these effects arises from measured and unmeasured participant characteristics, limiting personalized interpretation.
  • Existing methods fail to capture the nuanced, participant-specific nature of causal pathways.

Purpose of the Study:

  • To develop and validate a Bayesian semiparametric method for estimating heterogeneous direct and indirect causal effects.
  • To address the limitation of population-averaged effects by identifying and characterizing individual-level effect heterogeneity.
  • To apply the novel method to analyze the impact of an expressive writing intervention on renal cell carcinoma patients.

Main Methods:

  • Proposed a Bayesian semiparametric clustering approach to group individuals based on covariate profiles and unmeasured characteristics.
  • Employed regression models with stick-breaking priors for coefficient clustering to estimate cluster-specific direct and indirect effects.
  • Introduced a data-dependent prior to enhance the clustering process by incorporating individual effect information.

Main Results:

  • Simulation studies demonstrated the proposed method's superior performance compared to existing approaches in estimating heterogeneous effects.
  • The method successfully identified distinct clusters, revealing variations in direct and indirect effects within the study population.
  • Analysis of the expressive writing intervention showed heterogeneous causal effects on renal cell carcinoma patients.

Conclusions:

  • The developed Bayesian clustering method effectively estimates heterogeneous direct and indirect causal effects, offering a more personalized approach.
  • This methodology advances causal inference by accounting for unobserved heterogeneity, crucial for understanding complex interventions.
  • The findings have implications for tailoring interventions and improving patient outcomes in oncology and other fields.