Apps

🧪 This platform is in early beta. Features may change and you might encounter bugs. We appreciate your patience!

Lead-Time Bias

Also Known As: Zero-time shift bias
Aspect ID: lead_time_bias

Definition

Lead-time bias occurs when earlier detection of a disease through screening appears to extend survival time even when it does not actually change the date of death. If a disease would have been detected at age 60 clinically but is detected by screening at age 55, the patient now appears to survive 10 years (to age 65) instead of 5, even if they die at exactly the same age 65.

Examples

A lung cancer screening study reports that screened patients live an average of 15 months after diagnosis compared to 9 months for unscreened patients. But if the disease was caught 6 months earlier through screening, the actual survival benefit may be zero.

A new blood test for pancreatic cancer is celebrated because patients who test positive live an average of 18 months after diagnosis, versus 8 months for those diagnosed after symptoms. However, autopsies and disease modeling suggest the cancer's biological course is identical in both groups — the test simply detects the disease 10 months earlier, advancing the diagnosis clock without extending actual life.

A dementia screening program reports that patients identified early live with the diagnosis for an average of 9 years, compared to 5 years for those diagnosed after cognitive decline becomes obvious to family members. Neurologists caution that the disease progression timeline appears unchanged and that the extra 4 years largely represent time the patient spent labeled as having dementia without any alteration in the disease's ultimate course.

Verification Steps
Verification Steps
Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.
Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

Binary (yes/no) questions an LLM must answer to identify this aspect:

  1. 1

    Is survival measured from time of diagnosis rather than from symptom onset or death?

    Type: binary
  2. 2

    Does earlier detection via screening appear to extend survival time?

    Type: binary
  3. 3

    Is there evidence that the earlier diagnosis actually changed the date of death?

    Type: binary
  4. 4

    Are mortality rates (deaths per population per year) used alongside survival rates?

    Type: binary
Deep Dive
The expandable detail section on each aspect page with examples, psychology, and counter-strategies.
The Deep Dive section provides in-depth information about each aspect: a real-world example showing the pattern in action, an explanation of why it works psychologically, practical advice on how to counter it, alternative names, and links to related aspects.