Volunteer biasLead time biasLength biasStage migration biasPseudodisease

People who volunteer for screening differ from those who do not (generally healthier)

Examples HIP Mammography study:

▪ Women who volunteered for mammography had lower heart disease death rates

Multicenter Aneurysm Screening Study (Problem 6.3)

Men aged 65-74 were randomized to either receive an invitation for an abdominal ultrasound scan or not

Randomize patients to screened and unscreened

Control for factors (confounders) which might be associated with receiving screening AND the outcome eg: family history, level of health

concern, other health behaviors

Screening test

Detect disease early

Treat disease

Patient outcome

(Survival)

Latent Phase

Onset of symptoms DeathDetectable by screening

Detected by screening

Biological Onset

Survival After Diagnosis

Survival After Diagnosis

Lead Time

Lead Time Bias

Latent Phase

Onset of symptoms DeathDetectable by screening

Detected by screening

Biological Onset

Survival After Diagnosis

Survival After Diagnosis

Lead Time

Lead Time Bias

Contribution of lead time to survival measured from diagnosis

Only present when survival from diagnosis is compared between diseased persons Screened vs not screened Diagnosed by screening vs by

symptomsAvoiding lead time bias

Measure outcome from time of randomization or entry into study

Depends on relative lengths of latent phase (LP) and screening interval (S)

Screening interval shorter than LP:

ScreenScreen Screen Screen

Depends on relative lengths of latent phase (LP) and screening interval (S)

Screening interval shorter than LP: Maximum false increase in survival = LP Minimum = LP – S

Screening interval longer than LP: Max = LP Proportion of disease dx by screening =

LP/S

Figure 2: Maximum lead time bias possible when screening interval is longer than latent phase

Max = LPProportion of disease diagnosed by screening: P = LP/S

SLP

Max

Screen ScreenScreen

Screening test

Detect disease early

Treat disease

Patient outcome

(Survival)

Slowly progressive cases spend more time in presymptomatic phase Disproportionately picked up by

screeningHigher proportion of less aggressive

disease in screened group creates appearance of improved survival even if treatment is ineffective

TIME

TIME

Disease onset Symptomatic disease

Screen 1 Screen 2TIME

Screen 1 Screen 2TIME

Screen 1 Screen 2TIME

Survival in patients detected by screening

Survival in patients detected by symptoms

Only present when survival from diagnosis is compared AND disease is heterogeneous

Lead time bias usually present as wellAvoiding length bias:

Compare mortality in the ENTIRE screened group to the ENTIRE unscreened group

New test

Stage disease

Treat disease

“Stage-specific”patient outcome

(stratified analysis)

Also called the "Will Rogers Phenomenon” "When the Okies left Oklahoma and moved

to California, they raised the average intelligence level in both states.”

Can occur when New test classifies severity of disease

differently AND outcomes are stratified by severity of

disease (ie: stage-specific survival)

Stage 1

Stage 2

Stage 3

Stage 4

Stage 0

Old test

Stage 1

Stage 2

Stage 3

Stage 4

Stage 0

Old test New test

Stage 1

Stage 2

Stage 3

Stage 4

Stage 0Stage 0

Stage 2

Stage 3

Stage 4

Stage 1

Old test New test

Stage 1

Stage 2

Stage 3

Stage 4

Stage 0Stage 0

Stage 2

Stage 3

Stage 4

Stage 1

Old test New test

You are evaluating a new policy to admit COPD patients with CO2> 50 to the ICU rather than ward

Deaths in both ICU and ward go DOWN

Is this policy effective?

Admitted to ICU

Admitted to ward

Admitted to ward

Admitted to ICU

Before new policy After new policy

You are evaluating a new policy to admit COPD patients with CO2> 50 to the ICU rather than ward

Deaths in both ICU and ward go DOWN

Is this policy effective?

You want to know overall survival, before and after the policy…

Looking harder for disease, with more advanced technology, results in: Higher disease prevalence Higher disease stage (severity) Better (apparent) outcome for each stage

Stage migration bias does NOT affect Mortality in entire population Survival in ENTIRE screened group vs

ENTIRE unscreened group

Screening test

Detect disease early

Treat disease

Patient outcome(Survival)

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 presentsThe Problem:

Treating pseudodisease will always be successful Treating pseudodisease can only cause harm

Screening test negative -> Clinical FU (1st gold standard)

Screening test positive ->Biopsy (2nd gold standard)

If pseudodisease exists Sensitivity of screening falsely increased

▪ Why? Biopsy is not a “gold standard”… Screening will appear to prolong survival

▪ Why? Patients with pseudodisease always do well!

RCT of lung cancer screening9,211 male smokers randomized

to two study arms Intervention: CXR and sputum

cytology every 4 months for 6 years (75% compliance)

Usual care: recommendation to receive same tests annually

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

Marcus et al., 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)

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

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

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

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

Clues to pseudodisease: Higher cumulative incidence in screened

group No difference in overall mortality between

screened and unscreened groups Schwartz, 2004: 56% said they would

want to be tested for pseudodisease !

Screened group Decreased mortality

Screened group Decreased mortality

Better health behaviors

Volunteer Bias

Disease Detected by Screening

Prolonged survival

Prolonged survival

Earlier “zero time”

Lead Time Bias

Disease Detected by Screening

Prolonged survival(Higher cure rate)

Slower growing tumor with better prognosis

Length Bias

Disease Detected by Screening

Prolonged stage-specific survival

Higher stage assignment

Stage Migration Bias

Disease Detected by Screening or New Test

Prolonged survival(Higher cure rate)

“Disease” is Pseudodisease

Overdiagnosis

Disease Detected by Screening or New Test

Diagnosed by symptoms

Diagnosed by screening

Not screened

Screened

Survival after Diagnosis

D-

D-

Patients with Disease

D+

D+

R

Survival after Diagnosis

Survival from Randomization

Survival from Randomization

Screened

Not screened

Survival from Randomization

R

D+D-

D-D+ Survival from

Randomization

What about the “Ideal Study”? Quality of randomization Cause-specific vs total mortality

Edinburgh mammography trial (1994) Randomization by healthcare practice

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

Problems: Assignment of cause of death is

subjective Screening and/or treatment may have

important effects on other causes of death

Bias introduced can make screening appear better or worse!

Meta-analysis of 40 RCT’s of radiation therapy for early breast cancer* Breast cancer mortality reduced in

patients receiving radiation (20-yr ARR 4.8%; P = .0001)

BUT mortality from “other causes” increased (20-yr ARR -4.3%; P = 0.003)

Does radiation help women?*Early Breast Cancer Trialists Collaborative Group. Lancet 2000;355:1757

“Sticky diagnosis” bias: If pt has a cancer, death more often

attributed to cancer Effect: overestimates cancer mortality in

screened group “Slippery linkage” bias:

Linkage lost between death and screening/diagnosis (eg: death from complications of screening result)

Effect: underestimates cancer mortality in screened group

Mortality from other causes generally exceeds screening or cancer-related mortality

Effect on condition of interest more difficult to detect

Screening may be promoted due to economic, political or public interest rather than evidence

We must consider: size of effect and balance of benefits/harms to patient and society

Studies of screening efficacy: Ideal comparison: RCT of screened vs

unscreened population Biases possible when survival measured in

diseased patients only Mortality less subject to bias than survival