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

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.

Cancer cases detected in the prevention and control service of a private cancer clinic in Peru

“…Lead-time bias is a situation in which the early time of diagnosis extends survival even when they do not provide a clinical advantage to the patients. This bias artificially additions survival time to the screen-detected cancer cases [32]. Length bias is another situation when slow-growing tumors that do not develop many symptoms are more likely to be screen-detected.…”

Association of cancer screening and residing in a coal-polluted East Asian region with overall survival of lung cancer patients: a retrospective cohort study

“…First, we tried to subtract an estimated mean lead time from the survival time of patients diagnosed by screening. The mean lead time was estimated to be www.nature.com/scientificreports/ 3.4 months for OS of stages one and two lung cancer and ≤ 1 month for stages three and four 20 . By subtracting 100 days if stage 1 or 2 and subtracting 30 days if stage 3 or 4 from the survival time of patients diagnosed by screening, the same multivariate Cox regression model in Table 2 using the corrected dataset still showed that both XF residency status (HR = 0.789, 95% CI: 0.687-0.906, P < 0.001) and screening (HR = 0.677, 95% CI: 0.511-0.898, P = 0.007) were associated with improved OS, and this method of correcting for lead time did not nullify the survival advantage.…”

“…Lead time bias, however, is an important issue for in analysis of OS, which is defined as the time duration from diagnosis to death, because the lead time would artificially lengthen OS in screen-diagnosed cases. The mean lead time has been estimated to be 3.4 months for OS of early stage (I or II) lung cancer patients, and only ≤ 1 month for advanced stage (III or IV) lung cancer 20 . Given the large difference in median survival between XF and non-XF patients (> 3.5 years) and our simulation of lead time bias, lead time bias could not nullify the survival advantage of screening in our data set.…”

“…Surveillance bias primarily affect the analysis of cancer risk, and is difficult to mitigate in retrospective observational studies 19 . The estimated mean lead time for overall survival of lung cancer is 3.4 months for stages I and II and ≤ 1 month for stages III and IV 20 . We investigated whether cancer screening is still a significant factor for improved overall survival after correction for length bias or lead time bias.…”

Circular RNA circZFR promotes tumorigenic capacity of lung cancer via CCND1

“…Lung cancer (LC) is the third human malignant tumor and the leading cause of cancer-associated mortality around the world ( 1 , 2 ). Surprisingly, survival from a diagnosis of LC is longer for those who experienced a previous cancer than for those without previous cancer ( 3 ). LC is a serious cancer which can be cured if it is diagnosed at early stages, but its early diagnosis is not good ( 4 ).…”