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Length Bias (Different natural history bias). Screening picks up prevalent disease Prevalence = incidence x duration Slowly growing tumors have greater duration in presymptomatic phase, therefore greater prevalence

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length bias different natural history bias
Length Bias (Different natural history bias)
  • Screening picks up prevalent disease
  • Prevalence = incidence x duration
  • Slowly growing tumors have greater duration in presymptomatic phase, therefore greater prevalence
  • Therefore, cases picked up by screening will be disproportionately those that are slow growing
length bias
Length bias

Source: EDITORIAL: Finding and Redefining Disease.Effective Clinical Practice, March/April 1999. Available at: ACP- Online http://www.acponline.org/journals/ecp/marapr99/primer.htm

length bias1
Length Bias

Slower growing tumor with better prognosis


Early detection

Higher cure rate

avoiding length bias
Avoiding Length Bias

Only present when

  • survival from diagnosis is compared
  • AND disease is heterogeneous

Lead time bias usually present as well

Avoiding length bias:

  • Compare mortality in the ENTIRE screened group to the ENTIRE unscreened group
stage migration bias
Stage migration bias

Old tests

New tests

stage migration bias1
Stage migration bias
  • Also called the "Will Rogers Phenomenon"
    • "When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states."

-- Will Rogers

  • Documented with colon cancer at Yale
  • Other examples abound – the more you look for disease, the higher the prevalence and the better the prognosis

Best reference on this topic: Black WC and Welch HG. Advances in diagnostic imaging and overestimation of disease prevalence and the benefits of therapy. NEJM 1993;328:1237-43.

a more general example of stage migration bias
A more general example of Stage Migration Bias
  • VLBW (< 1500 g), LBW (1500-2499 g) and NBW (> 2500 g) newborns exposed to Factor X in utero have decreased mortality compared with those not exposed
  • Is factor X good?
  • Maybe not! Factor X could be cigarette smoking!
    • Smoking moves babies to lower birthweight strata
    • Compared with other causes of LBW (i.e., prematurity) it is not as bad
stage migration bias2
Stage Migration Bias







Unexposed to smoke

Exposed to smoke

avoiding stage migration bias
Avoiding Stage Migration Bias

The harder you look for disease, and the more advanced the technology

the higher the prevalence, the higher the stage, and the better the (apparent) outcome for the stage

Beware of stage migration in any stratified analysis

Check OVERALL survival in screened vs. unscreened group

More generally, do not stratify on factors distal in a causal pathway to the factor you wish to evaluate!

  • A condition that looks just like the disease, but never would have bothered the patient
    • Type I: Disease which would never cause symptoms
    • Type II: Preclinical disease in people who will die from another cause before disease presents
  • In an individual treated patient it is impossible to distinguish pseudodisease from successfully treated asymptomatic disease
  • The Problem:
    • Treating pseudodisease will always be successful
    • Treating pseudodisease can only cause harm
example mayo lung project
Example: Mayo Lung Project
  • RCT of lung cancer screening
  • Enrollment 1971-76
  • 9,211 male smokers randomized to two study arms
    • Intervention: chest x-ray and sputum cytology every 4 months for 6 years (75% compliance)
    • Usual care (control): at trial entry, then a recommendation to receive the same tests annually

*Marcus et al., JNCI 2000;92:1308-16

mayo lung project extended follow up results
Mayo Lung Project Extended Follow-up Results*
  • Among those with lung cancer, intervention group had more cancers diagnosed at early stage and better survival

*Marcus et al., JNCI 2000;92:1308-16

mlp extended follow up results
MLP Extended Follow-up Results*
  • Intervention group: slight increase in lung-cancer mortality (P=0.09 by 1996)

*Marcus et al., JNCI 2000;92:1308-16

what happened
What happened?

Black W. Overdiagnosis: an underrecognized cause of confusion and harm in cancer screening. JNCI 2000;92:1308-16

After 20 years of follow up, there was a significant increase (29%) in the total number of lung cancers in the screened group

  • Excess of tumors in early stage
  • No decrease in late stage tumors

Overdiagnosis (pseudodisease)

looking for pseudodisease
Looking for Pseudodisease

Appreciate the varying natural history of disease, and limits of diagnosis

Impossible to distinguish from successful cure of (asymptomatic) disease in individual patient

Few compelling stories of pseudodisease…

Clues to pseudodisease:

Higher cumulative incidence of disease in screened group

No difference in overall mortality between screened and unscreened groups

what happened1
What happened?
  • Lead-time bias?
  • Length bias?
  • Volunteer bias?
  • Overdiagnosis (pseudodisease)

Black, WC. Overdiagnosis: An unrecognized cause of confusion and harm in cancer screening. JNCI 2000;92:1280-1


Each year, 182,000 women are diagnosed with breast cancer and 43,300 die. One woman in eight either has or will develop breast cancer in her lifetime...

If detected early, the five-year survival rate exceeds 95%. Mammograms are among the best early detection methods, yet 13 million women in the U.S. are 40 years old or older and have never had a mammogram.

39,800 Clicks per mammogram (Sept, ’04)

why is this misleading
Why is this misleading
  • Each year 43,000 die, 182,000 new cases suggests mortality is ~24%
  • 5-year survival > 95% with early detection suggests < 5% mortality, suggesting about 80% of these deaths preventable
  • Actual efficacy is closer < 20% for breast cancer mortality (lower for total mortality)
issues with rcts of cancer screening
Issues with RCTs of cancer screening
  • Quality of randomization
  • Choice of outcome variable: cause-specific vs. total mortality
poor quality randomization example edinburgh trial
Poor Quality Randomization. Example: Edinburgh trial
  • Randomization by practice (N=87?), not by woman
  • 7 practices changed allocation status
  • Highest SES
    • 26% of women in control group
    • 53% of women in screening group
  • 26% reduction in cardiovascular mortality in mammography group

Br J Cancer. 1994 September; 70(3): 542–548.

problems with cause specific mortality as an endpoint
Problems with cause-specific mortality as an endpoint
  • Assignment of cause of death is subjective
    • Sticky diagnosis bias: deaths of unclear cause attributed to cancer if previously diagnosed
    • Slippery linkage bias: late deaths due to complications of screening or treatment will not be counted in cause specific mortality
  • Treatment may have effects on other causes of death
meta analysis of radiotherapy for early breast cancer
Meta-analysis of radiotherapy for early breast cancer*
  • Meta-analysis of 40 RCTs
  • Central review of individual-level data; N = 20,000
  • Breast cancer mortality reduced (20-yr absolute risk reduction 4.8%; P = .0001)
  • Mortality from other causes increased (20-yr absolute risk increase 4.3%; P = 0.003)

*Early Breast Cancer Trialists Collaborative Group. Lancet 2000;355:1757

tn conclusions on screening
TN Conclusions on Screening
  • Promotion of screening by entities with a vested interest and public enthusiasm for screening are challenges to EBM
  • High quality RCTs are needed
  • Cause-specific mortality is problematic, but total mortality usually not feasible
  • Effect size is relevant: decision to screen should not be based only on a P < 0.05 from a meta-analysis of RCTs
cost per qaly
Cost per QALY
  • Mammography, age 40-50: $105,000*
  • Mammography, age 50-69: $21,400*
  • Smoking cessation counseling: $2000**
  • HIV prevention in Africa: $1-20***

*Salzman P et al. Ann Int Med 1997;127:955-65 (Based on optimistic assumptions about mammography.)

**Cromwell J et al. JAMA 1997;278:1759-66

***Marseille E et al. Lancet 2002; 359: 1851-56

return to george annas
Return to George Annas*
  • Need to begin to think differently about health. Two dysfunctional metaphors:
    • Military metaphor – battle disease, no cost too high for victory, no room for uncertainty
    • Market metaphor -- medicine as a business; health care as a product; success measured economically

*Annas G. Reframing the debate on health care reform by replacing our metaphors. NEJM 1995;332:744-7

ecology metaphor
Ecology metaphor
  • Sustainability
  • Limited resources
  • Interconnectedness
  • More critical of technology
  • Move away from domination, buying, selling, exploiting
  • Focus on the big picture
    • Populations rather than individuals
    • Causes rather than symptoms

Source: http://www.lbl.gov/Education/ELSI/pollution-main.html



Mortality from disease





Mortality from disease

Not screened



Mortality from disease





Not screened

Mortality from disease


Survival from Diagnosis

Diagnosed by screening

Patients with Disease

Diagnosed by symptoms

Survival from Diagnosis

disease vs risk factor screening 3
Disease vs. Risk factor screening. 3

*May be political as well as scientific decision

nhlbi national lung screening trial
NHLBI National Lung Screening Trial
  • 46,000 participants randomized in 2 years
  • Equal randomization
    • Three annual screens
    • Spiral CT versus chest x-ray!
problem psuedodisease doesn t make a good story
Problem: psuedodisease doesn’t make a good story
  • Hard to understand
  • Can’t identify any victims