 Volunteer bias  Lead time bias  Length bias  Stage migration bias  Pseudodisease

 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 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 Death Detectable by screening Detected by screening Biological Onset Survival After Diagnosis Lead Time Lead Time Bias

Latent Phase Onset of symptoms Death Detectable by screening Detected by screening Biological Onset 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 symptoms  Avoiding 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:

Figure 1: Maximum and minimum lead time bias possible when screening interval is shorter than latent phase Max = LP Min =LP – S S LP Max Min Screen Onset of symptomsDeath Detectable by screening Detected by screening 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 = LP Proportion of disease diagnosed by screening: P = LP/S S LP Max Screen

Screening test Detect disease early Treat disease Patient outcome (Survival)

 Slowly progressive cases spend more time in presymptomatic phase  Disproportionately picked up by screening  Higher proportion of less aggressive disease in screened group creates appearance of improved survival even if treatment is ineffective

TIME

Disease onset Symptomatic disease

Screen 1Screen 2 TIME

Screen 1Screen 2 TIME

Screen 1Screen 2 TIME

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 well  Avoiding 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 testNew test

Stage 1 Stage 2 Stage 3 Stage 4 Stage 0 Stage 2 Stage 3 Stage 4 Stage 1 Old testNew test

Stage 1 Stage 2 Stage 3 Stage 4 Stage 0 Stage 2 Stage 3 Stage 4 Stage 1 Old testNew 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 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 presents  The Problem:  Treating pseudodisease will always be successful  Treating pseudodisease can only cause harm

 Screening test negative -> Clinical FU (1 st gold standard)  Screening test positive ->Biopsy (2 nd 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 screening  9,211 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:

Marcus et al., JNCI 2000;92:

 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:

 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 groupDecreased mortality

Screened groupDecreased mortality Better health behaviors Volunteer Bias

Disease Detected by Screening 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- Patients with Disease D+ R Survival after Diagnosis Survival from Randomization

Screened Not screened Survival from Randomization R 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