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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Simulating longitudinal data from marginal structural models using the additive hazard model.

Ruth H Keogh1, Shaun R Seaman2, Jon Michael Gran3

  • 1Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK.

Biometrical Journal. Biometrische Zeitschrift
|May 13, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a simulation method for evaluating marginal structural models (MSMs) in longitudinal studies with time-dependent confounding. It provides a way to derive correct MSM forms from conditional data generation, aiding causal inference research.

Keywords:
additive hazard modelcausal inferencecongenial modelslongitudinal datamarginal structural modelsimulation studysurvival analysistime-dependent confounding

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Causal Inference

Background:

  • Observational longitudinal data are crucial for treatment effect investigation but face time-dependent confounding.
  • Marginal structural models (MSMs) are common for addressing this, but their evaluation via simulation is challenging.
  • Assessing advanced causal inference methods requires robust simulation techniques.

Purpose of the Study:

  • To develop a simulation framework for evaluating marginal structural models (MSMs) in longitudinal studies with time-to-event outcomes.
  • To provide methods for deriving correctly specified MSMs from conditional data-generating processes.
  • To facilitate the assessment of causal inference methods in complex longitudinal settings.

Main Methods:

  • Derivation of general results linking conditional data generation to marginal structural models.
  • Application of these results to Aalen additive hazard and Cox proportional hazard models.
  • Description and illustration of a simulation algorithm for evaluating MSMs.

Main Results:

  • General results enable the derivation of correctly specified MSMs from conditional data generation.
  • Conditional additive hazard models simplify MSM specification and fitting using standard software.
  • A practical simulation algorithm is described and demonstrated.

Conclusions:

  • The proposed simulation approach aids researchers in effectively evaluating causal inference methods.
  • This work enhances the reliability of simulation studies for assessing MSMs in longitudinal data.
  • Improved simulation techniques will guide the application of advanced causal inference methods.