Estimating lead-time bias in lung cancer diagnosis of patients with previous cancers

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2 Scopus citations

Abstract

Surprisingly, survival from a diagnosis of lung cancer has been found to be longer for those who experienced a previous cancer than for those with no previous cancer. A possible explanation is lead-time bias, which, by advancing the time of diagnosis, apparently extends survival among those with a previous cancer even when they enjoy no real clinical advantage. We propose a discrete parametric model to jointly describe survival in a no-previous-cancer group (where, by definition, lead-time bias cannot exist) and in a previous-cancer group (where lead-time bias is possible). We model the lead time with a negative binomial distribution and the post-lead-time survival with a linear spline on the logit hazard scale, which allows for survival to differ between groups even in the absence of bias; we denote our model Logit-Spline/Negative Binomial. We fit Logit-Spline/Negative Binomial to a propensity-score matched subset of the Surveillance, Epidemiology, and End Results-Medicare linked data set, conducting sensitivity analyses to assess the effects of key assumptions. With lung cancer-specific death as the end point, the estimated mean lead time is roughly 11 months for stage I&II patients; with overall survival, it is roughly 3.4 months in stage I&II. For patients with higher-stage lung cancers, the mean lead time is 1 month or less for both outcomes. Accounting for lead-time bias reduces the survival advantage of the previous-cancer group when one exists, but it does not nullify it in all cases.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - Jan 1 2018

Keywords

  • Cancer screening
  • Cancer survivorship
  • Convolution
  • Discrete survival distribution
  • Spline

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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