Length Bias (Different natural history bias)

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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
• Therefore, cases picked up by screening will be
  disproportionately those that are slow growing

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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/pri
mer.htm
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Length Bias
Slower growing tumor with better prognosis
?
Early detection
Higher cure rate
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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

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Stage migration bias
Old tests
New tests
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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 19933281237-43.
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A more general example of Stage Migration Bias

• VLBW (lt 1500 g), LBW (1500-2499 g) and NBW (gt
  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

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Stage Migration Bias
NBW
NBW
LBW
LBW
VLBW
VLBW
Unexposed to smoke
Exposed to smoke
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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!

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Pseudodisease

• 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

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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 2000921308-16
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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 2000921308-16
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MLP Extended Follow-up Results

• Intervention group slight increase in
  lung-cancer mortality (P0.09 by 1996)

Marcus et al., JNCI 2000921308-16
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What happened?

• 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)

Black W. Overdiagnosis an underrecognized cause
of confusion and harm in cancer screening. JNCI
2000921308-16
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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

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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
2000921280-1
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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)
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Why is this misleading

• Each year 43,000 die, 182,000 new cases suggests
  mortality is 24
• 5-year survival gt 95 with early detection
  suggests lt 5 mortality, suggesting about 80 of
  these deaths preventable
• Actual efficacy is closer lt 20 for breast cancer
  mortality (lower for total mortality)

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Issues with RCTs of cancer screening

• Quality of randomization
• Choice of outcome variable cause-specific vs.
  total mortality

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Poor Quality Randomization. Example Edinburgh
trial

• Randomization by practice (N87?), 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) 542548.
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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

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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 20003551757
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Cancer mortality vs. Total mortality in RCTs
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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 lt 0.05 from a
  meta-analysis of RCTs

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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 1997127955-65
(Based on optimistic assumptions about
mammography.) Cromwell J et al. JAMA
19972781759-66 Marseille E et al. Lancet
2002 359 1851-56
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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
1995332744-7
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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

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Spiral CT Screening for Lung Cancer
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Source http//www.lbl.gov/Education/ELSI/pollutio
n-main.html
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Questions?
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Extra slides
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D
Mortality from disease
Screened
D-
R
D
Mortality from disease
Not screened
D-
Mortality from disease
D
Screened
D-
R
D
Not screened
Mortality from disease
D-
Survival from Diagnosis
Diagnosed by screening
Patients with Disease
Diagnosed by symptoms
Survival from Diagnosis
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Disease vs. Risk factor screening. 1
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Disease vs. Risk factor screening. 2
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Disease vs. Risk factor screening. 3
May be political as well as scientific decision
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NHLBI National Lung Screening Trial

• 46,000 participants randomized in 2 years
• Equal randomization
• Three annual screens
• Spiral CT versus chest x-ray!

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Problem psuedodisease doesnt make a good story

• Hard to understand
• Cant identify any victims