Correcting for Lead Time and Length Bias in Estimating the Effect of Screen Detection on Cancer Survival

Abstract

Determination of survival time among persons with screen-detected cancer is subject to lead time and length biases. The authors propose a simple correction for lead time, assuming an exponential distribution of the preclinical screen-detectable period. Assuming two latent categories of tumors, one of which is more prone to screen detection and correspondingly less prone to death from the cancer in question, the authors have developed a strategy of sensitivity analysis for various magnitudes of length bias. Here they demonstrate these methods using a series of 25,962 breast cancer cases (1988-2004) from the West Midlands, United Kingdom.

Population estimates of survival in women with screen-detected and symptomatic breast cancer taking account of lead time and length bias

“…Applying a sensitivity analysis for length bias [11], the lead time adjusted 51% reduction in fatality (based on all subjects) was slightly attenuated to an estimated 49%. Our results suggest that the effect of length bias in terms of artificially inflating the survival advantage of screen detection is likely to be smaller than that of other biases such as lead time and self-selection for screening.…”

“…The length bias method applied in this study can be adapted to model overdiagnosis, the most extreme form of length bias [11]. If we assume that 25% of screen-detected breast cancers are overdiagnosed, which is considerably higher than our formal estimates [14][15][16], but is consistent with 10% overdiagnosis in the cohort as a whole (since 25% of 39% is approximately 10%) as observed in the Malmö randomised trial [17], the fatality reduction corrected for lead time and overdiagnosis was 34% [11].…”

“…Irrespective of assumptions made about potential bias there was a substantial improvement in survival associated with screening. The detailed methods are published separately [11] and can be used to assess the impact of breast screening on survival in other populations.…”

“…Survival was estimated by the Kaplan-Meier method [9], and both relative risks, which are the ratios of the cumulative probabilities of dying from the disease within a specified follow-up time [10], and Cox regression relative hazards, the ratios of the rates of fatality at individual points in time [9], were calculated. We adjusted for lead time and length bias using the methods of Duffy et al [11]. Briefly, the additional follow-up due to lead time was estimated individually for each screen-detected case, based on the observed follow-up time since diagnosis, the estimated 4-year average preclinical screen-detectable time estimated from the Swedish Two-County Trial data [12], and whether the patient had died of breast cancer or not.…”

Failure of researchers, reviewers, editors, and the media to understand flaws in cancer screening studies: Application to an article in Cancer

“…In particular and consistent with assumptions of some other modelers, I assumed that sojourn times have an exponential distribution. 11 However, in making the point of these simulations, the assumption regarding any particular distribution of sojourn times does not matter. All that matters is that sojourn times vary from 1 tumor to another.…”