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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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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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Explained variation for recurrent event data.

Refah Alotaibi1, Rosemeire Fiaccone2, Robin Henderson3

  • 1Princess Norah Bint Abdulrahman University, Riyadh 11635, Saudi Arabia.

Biometrical Journal. Biometrische Zeitschrift
|April 23, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces two novel statistics for measuring explained variation in recurrent event data, addressing a gap in statistical methods. These statistics, effective even with missing data, aid in comparing complex models for health event analysis.

Keywords:
C-indexCounting processCovariatesEvent historySurvival data

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Limited methods exist for quantifying explained variation in recurrent event data.
  • Existing measures for single-event data are not directly applicable to recurrent events.
  • Understanding explained variation is crucial for model selection and interpretation in health research.

Purpose of the Study:

  • To introduce and evaluate new statistics for explained variation in recurrent event data.
  • To propose and assess methods for handling missing data in these statistics.
  • To compare the performance of the proposed statistics in model selection.

Main Methods:

  • Described an existing rank-based measure for explained variation.
  • Investigated a new statistic based on observed and expected event counts.
  • Proposed and simulated adjustments for missing data.
  • Compared population values of the two statistics.

Main Results:

  • Both the existing and new statistics are applicable across various statistical models.
  • Simulations demonstrated the effectiveness of proposed missing data adjustments.
  • The statistics were successfully used to compare non-nested models for infant diarrhea recurrence.

Conclusions:

  • The developed statistics provide valuable tools for assessing explained variation in recurrent event data.
  • The methods are robust to missing data and useful for model comparison.
  • These advancements enhance the analysis of recurrent health events, such as infant diarrhea.