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Analyzing disease recurrence with missing at risk information.

Tomaž Štupnik1, Maja Pohar Perme2

  • 1Department of Thoracic Surgery, Univerzitetni Klinični Center Ljubljana, Zaloška 7, SI-1000, Ljubljana, Slovenia.

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Summary
This summary is machine-generated.

This study addresses biased survival analysis in disease recurrence data lacking death information. New methods, including iterative imputation, offer unbiased estimation by adjusting for general population mortality.

Keywords:
competing riskmortality tablesmultiple imputationrisk adjustmentsurvival

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Informatics

Background:

  • Survival analysis for time to disease recurrence can be biased when patient death data is missing.
  • This issue is common in studies of benign diseases or medical device registries.
  • Bias arises when recurrence time is long relative to patient survival.

Purpose of the Study:

  • To develop methods for unbiased survival analysis when death information is absent.
  • To evaluate the limitations of simple imputation and cumulative incidence functions in such scenarios.
  • To introduce and validate novel statistical approaches for handling missing mortality data.

Main Methods:

  • Utilized general population mortality tables to adjust for potential bias.
  • Proposed an iterative imputation method for missing death data.
  • Introduced a mortality-adjusted at-risk function for survival analysis.
  • Conducted simulations to assess method properties.
  • Applied methods to a real-world case study.

Main Results:

  • Simple imputation of expected survival time leads to biased estimates.
  • Cumulative incidence function analysis requires no additional assumptions on general population mortality.
  • The proposed iterative imputation and mortality-adjusted at-risk function provide unbiased estimates.
  • Simulations and real-world data confirm the effectiveness of the new methods.

Conclusions:

  • Missing mortality data in disease recurrence studies can cause significant bias.
  • Novel statistical frameworks, including iterative imputation and mortality adjustment, enable unbiased survival analysis.
  • These methods improve the reliability of estimates in epidemiological and clinical research with incomplete survival data.