MASS Within Groups Result in Invited Group

Avoiding Volunteer Bias Randomize patients to screened and unscreened Otherwise, try to control for factors (confounders) associated with both screening and outcome –Examples: family history, level of health concern, other health behaviors

Lead Time Bias (zero-time bias) Screening identifies disease during a latent period before it becomes symptomatic If survival is measured from time of diagnosis, screening will always improve survival even if treatment is ineffective

Lead time bias Source: EDITORIAL: Finding and Redefining Disease. Effective Clinical Practice, March/April Available at: ACP- Online accessed 8/30/02

Avoiding Lead Time Bias Only present when survival from diagnosis is compared between diseased persons –Screened vs. not screened –Diagnosed by screening vs. by symptoms –Diagnosed at different stages Avoiding lead time bias –Measure mortality, not survival –Count from date of randomization

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 Available at: ACP- Online

Length Bias Early detectionHigher cure rate Slower growing tumor with better prognosis ?

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 Old testsNew 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:

A more general example of Stage Migration Bias VLBW ( 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 LBW VLBW NBW LBW VLBW Unexposed to smoke Exposed to 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 Randomized trial of lung cancer screening Enrollment ,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): same tests at trial entry, then a recommendation to receive them annually *Marcus et al., JNCI 2000;92:

Mayo Lung Project Extended Follow-up Results* Among those with lung cancer, intervention group had more cancers diagnosed at an early stage and better survival *Marcus et al., JNCI 2000;92:

Mayo Lun Project LP Extended Follow-up* Intervention group: slight increase in lung- cancer mortality (P=0.09 by 1996) *Marcus et al., JNCI 2000;92:

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:

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

Problem: psuedodisease doesn’t make a good story Hard to understand Can’t identify any victims “…ignorance is not bliss. It leaves the poor fellow with undetected high-grade cancer as collateral damage. The fact that his death might be statistically offset by the survival of someone who would have died from unneeded treatment is cold comfort.” --Editorial, USA Today. 10/10/11

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) Summary

Each year 43,000 die, 182,000 new cases suggests ~24% die 5-year survival > 95% with early detection suggests < 5% die, making about 80% of the deaths appear preventable Actual efficacy of mammography is < 20% for breast cancer mortality (lower or nonexistent for total mortality) Why this is misleading

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 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

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

Questions?

Extra slides

Screened Not screened Mortality from disease R Diagnosed by symptoms Diagnosed by screening Not screened Screened Survival from Diagnosis D+ D- D+ Patients with Disease D+ R Survival from Diagnosis Mortality from disease

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: (Based on optimistic assumptions about mammography.) **Cromwell J et al. JAMA 1997;278: ***Marseille E et al. Lancet 2002; 359:

Disease vs. Risk factor screening. 1

Disease vs. Risk factor screening. 2

Disease vs. Risk factor screening. 3 *May be political as well as scientific decision