Evidence-Based Evaluation of Screening and Diagnostic Tests by mifei

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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
  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 Bias


           Slower growing
           tumor with
           better prognosis



Early detection   ?       Higher cure rate
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 0
   Stage 0
                   Stage 1
   Stage 1
                   Stage 2
   Stage 2
                   Stage 3
   Stage 3

   Stage 4         Stage 4

    Old tests          New tests
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
   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 Bias
                                      NBW
NBW




                                      LBW
LBW




                                      VLBW
VLBW



       Unexposed to      Exposed to
          smoke            smoke
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!
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
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*
   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*
   Intervention group: slight increase in lung-
    cancer mortality (P=0.09 by 1996)




         *Marcus et al., JNCI 2000;92:1308-16
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
2000;92:1308-16
    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 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
   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
   Quality of randomization
   Choice of outcome variable: cause-
    specific vs. total mortality
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
   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 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
Cancer mortality vs. Total mortality
in RCTs
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
   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*
   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
 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
Spiral CT Screening for Lung
Cancer
Source: http://www.lbl.gov/Education/ELSI/pollution-main.html
Questions?
Extra slides
        Screened        D+          Mortality from
R                       D-            disease
      Not screened      D+          Mortality from
                        D-            disease


        Screened        D+          Mortality from
                        D-            disease
R
      Not screened      D+           Mortality from
                        D-             disease


                     Diagnosed by     Survival from
                       screening       Diagnosis
    Patients with
      Disease        Diagnosed by     Survival from
                      symptoms         Diagnosis
Disease vs. Risk factor screening. 1
                    (Unrecognized)
                    Symptomatic
                    Disease
  # Labeled         Few
  # Treated         Few
  Duration of       Varies
  treatment
  NNT               Low
  Ease of showing   Easy
  benefit
  Potential for     False positives
  harm
Disease vs. Risk factor screening. 2
                   (Unrecognized)      Pre-
                   Symptomatic    symptomatic
                   Disease           Disease
 # Labeled         Few                 Few
 # Treated         Few                 Few
 Duration of           Varies     Varies, may be
 treatment                             short
 NNT               Low                 Low
 Ease of showing   Easy            Often difficult
 benefit
 Potential for     False positives   False positives,
 harm                                pseudodisease
Disease vs. Risk factor screening. 3
            (Unrecognized)       Pre-           Risk factor
            Symptomatic      symptomatic
            Disease            Disease
# Labeled Few                    Few               High*
# Treated   Few                  Few               High*
Duration of                 Varies, may be         Long
treatment                        short
NNT         Low                  Low               High
Ease of     Easy             Often difficult    Usually very
showing                                           difficult
benefit
Potential   False positives False positives,       Harmful
for harm                    pseudodisease        treatment,
                                               delayed effects
 *May be political as well as scientific decision
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
   Hard to understand
   Can’t identify any victims

								
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