Andi Marmor, MD, MSEd Thomas B. Newman, MD, MPH October 18, 2012

 What are screening tests supposed to do?  Definition and spectrum of screening  What are the potential harms of screening?  Evaluating screening tests  Study designs  Survival vs mortality  Biases in studies of screening tests

 Common definition:  “Testing to detect asymptomatic disease”  A better definition?*:  “Application of a test to detect a potential disease or condition in people with no known signs or symptoms of that disease or condition”  “ Condition” includes a risk factor for a disease… *Common screening tests. David M. Eddy, editor. Philadelphia, PA: American College of Physicians, 1991

Risk factor Recognized symptomatic disease Presymptomatic disease Unrecognized symptomatic disease è Fewer people è Easier to demonstrate benefit è Less potential for harm to exceed benefit

 Risk factor treatment disease  Does risk factor predict disease?  Does treatment reduce risk factor?  Does identification/treatment of risk factor reduce disease?  Potential for harm exceeding benefit greatest when screening for risk factors!  Caution: risk factors as surrogate outcomes

 Are PVC’s after MI a risk factor for sudden death?  Yes  Do encainide and flecainide decrease PVCs?  Yes  Do these drugs save lives?  NO! RCT showed total mortality after 10 months higher in treated group vs placebo: 8.3% vs. 3.5% (P <0.0001) Echt DS et al. N Engl J Med. 1991;324:781-8 Moore TJ. Deadly Medicine. NY: Simon and Schuster, 1995

 Does screening detect risk factor?  Yes  Benefits to screening?  Not studied  Possible risks to children/society?  Cost, testing, distraction from other priorities

 Detect disease in earlier stage than would be detected by symptoms  Only possible if an early detectable phase is present (latent phase)  Begin treatment earlier  Only beneficial if earlier treatment is more effective than later treatment  Do this without greater harm than benefit

 Natural history heterogeneous  Screening test may pick up slower growing or less aggressive cancers  Not all patients diagnosed with cancer will become symptomatic  “Pseudodisease”  Diagnosis is subjective  There is no gold standard

Malignant Benign

Malignant Can’t tell Benign

Why Not?

 To those with a negative result  To those with a positive result  To all

 The general teaching:  Maximize sensitivity for screening tests  This is true IF  Goal is not to miss anyone with the disease  HOWEVER….  NPV already good in low- prevalence population

Copyright restrictions may apply. Schwartz, L. M. et al. JAMA 2004;291:  38% had experienced at least 1 false-positive;  >40% described that experience as "very scary”/"scariest time of my life.”  98% were glad they had had the screening test.  73% would prefer a total- body CT over $1000

 Organisation for Economic Co-operation and Development. “OECD Health Data: Health Expenditures and Financing”, OECD Health Statistics Data from internet subscription database. library.org, data accessed on 08/23/12.

 Economic  Political  Public/cultural  Health care providers

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 2009: USPSTF changed age for routine mammogram from 40 to 50  For women over 11 years of follow up: ▪ 1900 women invited, 20,000 visits, 2000 FP mammograms = one death prevented  Recommendations criticized by  Radiologists  American Cancer Society  The public Quanstrum, Hayward. Lessons from the mammography wars. NEJM, 2010

 What are screening tests supposed to do?  Definition and spectrum of screening  What are the potential harms of screening?  Evaluating screening tests  Study designs  Survival vs mortality  Biases in studies of screening tests

Screening test Detect disease early Treat disease Patient outcome

Screening test Detect disease early Treat disease Patient outcome

Screening test Detect disease early Treat disease Patient outcome

 Ideal Study:  Randomize patients to screened/ unscreened  Compares outcome (eg: mortality) in ENTIRE screened group to ENTIRE unscreened group Screened Not screened Mortality R D+ D- D+ Mortality

 Survival:  Denominator is patients with the disease  Introduces multiple biases  Mortality:  Denominator often a population (eg: those randomized to screening vs controls)  May include patients without the disease  Can be total or cause-specific…

 Survival (patients with disease)  Compare those diagnosed by screening vs those diagnosed by symptoms  Compare those with disease in screened group vs those with disease in unscreened group  Stage-specific survival in screened vs unscreened  Mortality (all enrolled)  Compare outcomes in all screened patients vs all unscreened patient

Screened Not screened Mortality R Not screened Screened D+ D- D+ R Mortality D- Screened Not screened R D+ Survival with disease

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

 People who volunteer for screening differ from those who do not (generally healthier)  Example of the effect  HIP Mammography study (1960’s): randomized 60,000 women ▪ 2/3 randomized to screening accepted  Among invited group, those who GOT mammography had lower cardiovascular 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 Ashton, et al 2002

 Randomize patients to screened and unscreened  Intent to treat – analyze as randomized  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 Screened Not screened R D+ Survival with disease D-

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 (in entire group)

 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

 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

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

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

 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 !

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 Stage 2 Stage 3 Stage 4 Stage 1 Old testNew test

 You are evaluating a new policy to admit COPD patients with CO2> 40 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> 40 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

D- Not screened Screened D- D+ R Mortality Screened Not screened R D+ D- D+ Mortality Screened Not screened R D+ Survival with disease

Screened Not screened R D+ D- D+  What about the “Ideal Study”?  Quality of randomization  Cause-specific vs total mortality Screened Not screened R D+ D- D+ 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  Evidence: 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!

 “Sticky diagnosis” bias:  If pt has a cancer, death more often attributed to cancer  Effect: overestimates cancer mortality in screened group (makes screening look WORSE)  “Slippery linkage” bias:  Linkage lost between death and screening/diagnosis (eg: death from complications of screening result)  Effect: underestimates cancer mortality in screened group (makes screening look BETTER)

 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

 Mortality from other causes generally exceeds disease-specific 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, or stratified by stage  Mortality less subject to bias than survival