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Correcting population-based survival for DCOs - why a simple method works and when to avoid it.

Paul Silcocks1, Catherine S Thomson

  • 1Trent Cancer Registry, Sheffield S10 3TG, England, United Kingdom. paul.silcocks@nhs.net

European Journal of Cancer (Oxford, England : 1990)
|August 7, 2009
PubMed
Summary

High rates of cancer registrations based only on death certificates (DCOs) skew survival data. A proportional hazards model predicts trace-back effects, improving cancer survival estimates and data quality.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Cancer Research

Background:

  • A significant proportion of cancer registrations solely based on death certificates (DCOs) compromises data quality and biases cancer survival estimates.
  • Intensive trace-back of registrations initiated after death (DCIs) can improve data quality but is resource-intensive.

Purpose of the Study:

  • To introduce a proportional hazards model to predict the impact of trace-back on cancer survival estimates.
  • To provide a method for correcting survival estimates from sources with high DCO percentages.
  • To guide researchers in adjusting survival data for DCO proportions.

Main Methods:

  • Development of a proportional hazards model to assess DCOs relative to other cancer cases.
  • Application of the model to predict the effect of trace-back on survival curves.
  • Utilizing EUROCARE data to demonstrate correction methods for survival estimates.

Main Results:

  • The proposed model can predict the effect of trace-back on survival, justifying resource allocation.
  • The model facilitates correction of survival estimates, particularly for historical data with high DCOs.
  • A simple correction formula, (1-p) *S, where p is the DCO proportion and S is observed survival, is validated.

Conclusions:

  • Researchers should adjust cancer survival estimates considering DCO percentages.
  • The simple correction method is effective for 5-year survival estimates but less reliable for 1-year estimates, especially in cross-regional comparisons.
  • The study provides insights into hazard ratios and practical applications of survival data adjustment.