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Related Concept Videos

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Mendelian randomization using semiparametric linear transformation models.

Yen-Tsung Huang1

  • 1Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.

Statistics in Medicine
|December 28, 2019
PubMed
Summary
This summary is machine-generated.

Mendelian randomization (MR) provides a novel method to estimate the causal effect of smoking on lung cancer survival, accounting for confounding factors. This approach reveals a harmful prognostic impact of smoking pack-years, which crude associations might overlook.

Keywords:
estimating equationshazard ratioinstrumental variablesemiparametric probit modelsurvival analysis

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Genetic Epidemiology

Background:

  • Confounding factors complicate estimating causal effects in observational studies, particularly for survival outcomes.
  • Existing instrumental variable (IV) methods are limited for time-to-event data.
  • Cigarette smoking is a known risk factor for lung cancer, but its prognostic impact on survival requires robust causal inference.

Purpose of the Study:

  • To develop and validate a novel Mendelian randomization (MR) method for estimating causal effects on survival outcomes.
  • To assess the causal effect of cigarette smoking on lung cancer survival using this new methodology.
  • To address the limitations of existing IV analyses for time-to-event data.

Main Methods:

  • Proposed a novel IV analysis method using semiparametric linear transformation models for survival data.
  • Estimated causal effects on transformed survival time and survival probabilities.
  • Constructed estimating equations to handle unknown confounding and derived asymptotic properties of estimators.

Main Results:

  • The developed MR method effectively estimates causal effects in the survival context.
  • Analysis of a lung cancer study indicated a significant harmful prognostic effect of smoking pack-years.
  • The identified effect of smoking on survival was not apparent through crude association analyses.

Conclusions:

  • The proposed MR method offers a robust approach for causal inference in survival analysis.
  • Smoking pack-years have a detrimental effect on lung cancer survival.
  • This methodology enhances the ability to detect causal relationships in observational survival data, even with unmeasured confounding.