Lead time bias

A distortion overestimating the apparent time surviving with a disease caused by bringing forward the time of its diagnosis


The premise of screening is that it allows earlier detection and treatment of a disease or health condition, leading to a greater chance of cure or at least longer survival. A disease or condition is clinically diagnosed after an individual display’s certain signs and symptoms. Individuals with disease detected through population screening receive a diagnosis earlier before signs and symptoms appear. As a consequence, estimates of differences in survival time between people diagnosed from screening and those whose disease is detected after symptoms develop can be biased, as survival time will appear to be longer in screen-detected people if early detection has no effect on the course of disease (figure 1) or if survival time is extended (figure 2). Lead time bias is not the exception but the rule that comes with any successful effort to detect disease early.


Figure 1.  Lead time bias where health outcome is the same in someone whose disease is detected by screening compared with someone whose disease is detected from symptoms, but survival time from the time of diagnosis is longer in the screened patient.


Figure 2. Lead time bias where the screened patient lives longer than the unscreened patient, but overall survival time is still exaggerated by the lead time from earlier diagnosis.


Badgwell and colleagues compared survival in women with breast cancer, aged 80 years or older that had accessed mammography screening regularly, irregularly or not at all in the five years prior to their diagnosis. Using a Medicare linked database, they reported that statistically significant improvements in overall and breast cancer-specific survival were associated with increasing use of mammography screening. Breast cancer–specific 5-year survival was 82% among women not screened, 88% among women with irregular and 94% among regular users of screening. In response, Berry et al noted that although the authors had recognised that their study was subject to healthy person bias (where healthy patients tend to access screening), they had failed to take into account lead time bias. As lead time bias adds to the survival time of all women whose tumours were detected by screening, an increase in survival in the screened cohort compared to unscreened group is expected, and without appropriate adjustment, the observed difference could not be concluded as a survival benefit from screening. Media coverage of the mammography study conveyed stronger conclusions than perhaps the authors intended noted Berry et al, aided by a “misleading” press release from the American Society of Clinical Oncology which also failed to account for lead time bias.


The benefits of early detection are often communicated to doctors and patients in the form of extended survival times. Extended survival may occur because early detection is effective but some of the observed benefit will be due to lead time bias. Therefore, without correcting for lead time, longer survival is not necessarily proof of the benefit of early detection. This does not seem widely understood, even amongst health professionals and educators. In a survey of 297 primary care doctors presented with results from two hypothetical screening tests, 76% considered better survival as evidence that screening works. An observational study assessing statistical literacy in medical education settings found 50% of the 16 university professors and senior medical educators included failed to identify lead-time bias when presented.

Preventive steps

In randomised control trials evaluating screening, lead time bias can be countered by taking the time origin as the point of randomisation, not the point of diagnosis, or by comparing the number of deaths occurring in a given period of time instead or as well as the number of people surviving. In observation settings, an alternative time origin to that of diagnosis may not be possible and comparisons of survival require adjustment for lead time bias. One such method is given by Duffy and colleagues. This method attempts to estimate the lead time bias for each patient and subtract this from the observed survival or censoring time to create a bias-adjusted overall survival time (figure 3). The authors applied their method to data from the Swedish Two-County study of breast cancer screening. After correction for lead time bias, the survival curve is lower than the uncorrected curve, suggesting that the positive effect of screening would otherwise have been overestimated.

Figure 3. Hypothetical example showing survival with unscreened (symptomatic) and screen-detected cases before and after correction for lead time.


Morrison AS. The effects of early treatment, lead time and length bias on the mortality experienced by cases detected by screening. Int J Epidemiol. 1982;11(3):261-7.

Welch HG, Woloshin S, Schwartz LM, Gordis L, Gotzsche PC, Harris R, et al. Overstating the evidence for lung cancer screening: the International Early Lung Cancer Action Program (I-ELCAP) study. Arch Intern Med. 2007;167(21):2289-95.

Badgwell BD, Giordano SH, Duan ZZ, Fang S, Bedrosian I, Kuerer HM, et al. Mammography Before Diagnosis Among Women Age 80 Years and Older With Breast Cancer. Journal of Clinical Oncology. 2008;26(15):2482-8.

Berry DA, Baines CJ, Baum M, Dickersin K, Fletcher SW, Gøtzsche PC, et al. Flawed Inferences About Screening Mammography’s Benefit Based on Observational Data. Journal of Clinical Oncology. 2009;27(4):639-40.

Wegwarth O, Schwartz LM, Woloshin S, Gaissmaier W, Gigerenzer G. Do Physicians Understand Cancer Screening Statistics? A National Survey of Primary Care Physicians in the United States. Ann Intern Med. 2012;156(5):340-U152.

Jenny MA, Keller N, Gigerenzer G. Assessing minimal medical statistical literacy using the Quick Risk Test: a prospective observational study in Germany. BMJ Open 2018;8:e020847.

Duffy SW, Nagtegaal ID, Wallis M, Cafferty FH, Houssami N, Warwick J, et al. Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. Am J Epidemiol. 2008;168(1):98-104.

These sources are retrieved dynamically from PubMed

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