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Estimation of causal effect measures with the R-package stdReg.

Arvid Sjölander1

  • 1Karolinska Institute, Nobels väg 12 A, 171 77, Stockholm, Sweden. arvid.sjolander@ki.se.

European Journal of Epidemiology
|March 15, 2018
PubMed
Summary

This study demonstrates how logistic and Cox regression models, using the R-package stdReg, can estimate diverse causal effect measures beyond standard odds and hazard ratios. This enhances epidemiological research by providing richer insights into health outcomes.

Keywords:
Attributable fractionCausal effectCox proportional hazards regressionLogistic regressionNumber needed to treatRelative excess risk due to interaction

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Research Methodology

Background:

  • Causal effect measures are crucial in epidemiology.
  • Current research often limited to odds ratios and hazard ratios due to software and mathematical convenience.
  • A gap exists in accessible methods for estimating a broader range of causal effect measures.

Purpose of the Study:

  • To demonstrate the utility of logistic regression and Cox proportional hazards models for estimating diverse causal effect measures.
  • To introduce the R-package stdReg for facilitating these estimations.
  • To illustrate the application of these methods using real-world epidemiological data.

Main Methods:

  • Utilized logistic regression models for binary outcomes.
  • Employed Cox proportional hazards regression models for time-to-event data.
  • Applied the R-package stdReg to estimate causal effect measures including attributable fraction, number needed to treat, and relative excess risk due to interaction.

Main Results:

  • Successfully demonstrated the estimation of a wider range of causal effect measures using standard regression models.
  • The R-package stdReg provides a practical tool for implementing these advanced analyses.
  • Analyses on two public datasets (births and breast cancer) confirmed the feasibility and replicability of the methods.

Conclusions:

  • Logistic and Cox regression models are versatile tools for estimating various causal effect measures in epidemiology.
  • The stdReg R-package enhances the capability of researchers to conduct sophisticated causal inference.
  • Expanded causal effect estimation can lead to more nuanced understanding and targeted interventions in public health.