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A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Quantile regression for incomplete longitudinal data with selection by death.

Hélène Jacqmin-Gadda1, Anaïs Rouanet2, Robert D Mba3

  • 1Bordeaux Population Health Research Center, University of Bordeaux, INSERM, Bordeaux, France.

Statistical Methods in Medical Research
|March 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel weighted estimating equation approach for quantile regression in longitudinal studies with missing data and mortality. The method accurately estimates population norms for cognitive scores in the elderly, accounting for complex data attrition.

Keywords:
Dropoutintermittent missing datamortal cohortpartly conditional estimatorquantile regressionweighted GEE

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Quantile regression is vital for establishing population norms for quantitative variables, especially cognitive scores in the elderly.
  • Existing methods struggle with longitudinal data complexities like mortality, dropout, and intermittent missing data.
  • Accurate population norms are crucial for identifying cognitive decline in routine examinations.

Purpose of the Study:

  • To extend the weighted estimating equation approach for estimating conditional quantiles in mortal cohorts with dropout and intermittent missing data.
  • To develop suitable weight estimation procedures for both monotone and intermittent missing data under different missing-at-random assumptions (p-MAR and u-MAR).
  • To provide a robust method for estimating cognitive score norms in the elderly population currently alive.

Main Methods:

  • Extension of the weighted estimating equation approach for longitudinal quantile regression.
  • Development of weight estimation procedures for monotone and intermittent missing data.
  • Implementation of subject-level bootstrap for statistical inference.
  • Validation through simulation studies and application to the French Paquid cohort.

Main Results:

  • The proposed method effectively estimates conditional quantiles in mortal cohorts with dropout and intermittent missing data.
  • The u-MAR analysis demonstrated robustness even when the true missingness mechanism was p-MAR, simplifying practical application.
  • The study highlighted the critical importance of using suitable weights for intermittent missing data, confirmed by simulations and real-data analysis.
  • The developed method is implemented in the R package 'weightQuant'.

Conclusions:

  • The weighted estimating equation approach provides a robust and flexible method for quantile regression in longitudinal studies with complex missing data patterns and mortality.
  • The findings offer improved tools for establishing accurate population norms, particularly for cognitive health in aging populations.
  • The R package 'weightQuant' makes this advanced statistical methodology accessible for researchers and practitioners.