Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetesBMJ 2010; 340 doi: https://doi.org/10.1136/bmj.b5087 (Published 12 March 2010) Cite this as: BMJ 2010;340:b5087
All rapid responses
Rapid responses are electronic comments to the editor. They enable our users to debate issues raised in articles published on bmj.com. A rapid response is first posted online. If you need the URL (web address) of an individual response, simply click on the response headline and copy the URL from the browser window. A proportion of responses will, after editing, be published online and in the print journal as letters, which are indexed in PubMed. Rapid responses are not indexed in PubMed and they are not journal articles. The BMJ reserves the right to remove responses which are being wilfully misrepresented as published articles or when it is brought to our attention that a response spreads misinformation.
From March 2022, the word limit for rapid responses will be 600 words not including references and author details. We will no longer post responses that exceed this limit.
The word limit for letters selected from posted responses remains 300 words.
We congratulate the authors on an excellent work which highlights a
serious source of bias in cohort studies. The aim of this letter is to
further emphasize the importance of the work by Levesque et al.,noting
that immortal time bias is neither restricted to the field of
pharmacoepidemiology nor to specific data examples. In fact, immortal time
bias, which is sometimes also called "time-dependent bias", has
been found to be a common phenomenon in a variety of medical fields (1).
In addition, simple algebraic considerations show that immortal time bias
inevitably leads to underestimating hazard ratios (comparing exposed
versus unexposed) because of time distorted risk sets (2). If the endpoint
is death, this implies that any beneficial effect of time-dependent
treatment / exposure is overestimated. Any harmful effect will be subdued
or even reversed. Levesque et al. give an impressive example of such a
Another field that is prone to immortal time bias is, e.g., hospital
epidemiology. Here, interest often focuses on the impact of some time-
dependent event such as hospital-acquired infection on hospital outcome
(3). E.g., policy makers are interested in subsequent hospital stay. Then,
the endpoint is discharge and the underestimated discharge hazard ratio
corresponds to an overestimation of extra length of stay due to the
hospital-acquired infection. Hence, policy makers often receive
exaggerative values if patients who acquire a nosocomial infection during
their hospital stay are incorrectly classified as infected between
admission and infection.
The paper by Lévesque et al. highlights an important issue, and we
join the authors in advocating a proper analysis which takes the time-
dependency of treatment / exposure into account.
(1) van Walraven, C., Davis, D., Forster, A., and Wells, G. (2004),
“Time-dependent bias was common
in survival analyses published in leading clinical journals,” J Clin
Epidemiol , 57, 672–682.
(2) Beyersmann, J., Gastmeier, P., Wolkewitz, M., and Schumacher, M.
(2008), “An easy mathematical
proof showed that time-dependent bias inevitably leads to biased effect
estimation,” J Clin
Epidemiol , 61, 1216–1221.
(3) Samore M and Harbarth S., “A Methodologically Focused Review of
the Literature in Hospital Epidemiology and Infection Control”, In:
Mayhall, C.G. “Hospital Epidemiology and Infection Control”, 3th ed.
Lippincott Williams & Wilkins, 2004, chapter 93
Competing interests: No competing interests