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Estimation in discrete time coarsened multivariate longitudinal models.

Marcus Westerberg1

  • 1Department of Mathematics and Department of Surgical Sciences, Uppsala University, Regional Cancer Center Midsweden, Uppsala University Hospital, Uppsala, Sweden.

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Summary
This summary is machine-generated.

This study addresses missing event data in longitudinal studies by comparing statistical methods. Maximum likelihood and Monte Carlo Expectation Maximization methods are effective for analyzing coarsened event data.

Keywords:
Coarsened dataMonte Carlo expectation maximizationlongitudinal datamaximum likelihoodmissing datamulti-state modeling

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Longitudinal studies often encounter missing data due to event coarsening, where event details like onset or cause are not fully observed.
  • This missingness can occur in various scenarios, including grouped observations or unknown causes of death, impacting the analysis of event histories.

Purpose of the Study:

  • To develop and evaluate statistical methods for analyzing longitudinal data with independent and non-informative coarsening.
  • To compare the performance of different estimation strategies, including maximum likelihood and Expectation Maximization, against naive approaches.

Main Methods:

  • Derivation of likelihood function, score, and observed information under coarsening assumptions.
  • Simulation studies to assess bias, standard errors, and confidence interval coverage of various estimators.
  • Application to longitudinal data of prostate cancer patients, analyzing drug prescriptions and survival outcomes.

Main Results:

  • Direct maximum likelihood and Monte Carlo Expectation Maximization demonstrated robust performance in handling coarsened event data.
  • Methods that ignored coarsening or used artificial censoring showed significant bias and poor confidence interval coverage.
  • The performance of estimators was influenced by factors such as sample size and the specific type of data coarsening.

Conclusions:

  • Statistical methods like maximum likelihood and Monte Carlo Expectation Maximization are crucial for accurate analysis of longitudinal data with missing event information.
  • Naive approaches to handling coarsened data can lead to misleading results, emphasizing the need for appropriate statistical techniques.
  • The choice of method should consider the characteristics of the data, including sample size and the nature of the coarsening mechanism.