1. PART 2

3. Celebrity/Political Endorsements  Dartmouth study (2005)* found that >50% of respondents had seen celebrity endorsements of screening for breast, colon and prostate cancer  Celebrities can be victims too: Liz Hurley was featured in a campaign for HPV screening by the company that makes the test * Larson R. Journal of the National Cancer Institute 2005;97:693-5

4. Public and Healthcare Forces  Disease research and advocacy groups  Academics who study the condition  Clinicians doing or interpreting the test  Clinicians who treat the condition  Managed care organizations  The public

5. Healthcare Forces  Disease research and advocacy groups  Academics who study the condition  Clinicians doing or interpreting the test  Clinicians who treat the condition  Managed care organizations

6. Recommendations Against Screening  2008: USPSTF updated of its prostate cancer screening recommendations  Risks of screening outweigh benefits for men age >75 years (as well as those with life expectancy <10 years  For these men, the USPSTF is now recommending against prostate cancer screening.

7. Forces Behind Excessive Screening  Economic  Political  Health care providers  Public/cultural

8. Copyright restrictions may apply. Schwartz, L. M. et al. JAMA 2004;291:71-78. Screening as an Obligation

9. E-mail Excerpt, 1998 “…Please, please, please tell all your female friends and relatives to insist on a CA-125 blood test every year as part of their annual physical exams. Be forewarned that their doctors might try to talk them out of it, saying, “it isn’t necessary." …Insist on the CA-125 blood test; do not take "NO" for an answer!”  Revised version sent out in 2000

11. Top Ten Countries’ Per Capita Healthcare Spending, 1997 ($) 010002000300040005000 Norway Netherlands Denmark Iceland France Canada Germany Luxembourg Switzerland United States Anderson GF and Poullier JP Health Affairs 18;178-88 May/June 1999

12. Potential Years of Life Lost*/100,000 population, top 10 spending Countries, 1995 0200040006000800010000 Norway Netherlands Denmark Iceland France Canada Germany Luxembourg Switzerland United States Male Female Before age 70. From Anderson GF and Poullier JP Health Affairs 18;178-88 May/June 1999

13. Outline  Overview and definitions  Can screening be bad?  Evaluating studies of screening tests Observational studies of screening Randomized trials of screening  Conclusion

15. Evaluating Studies of Screening Screening test Detect disease early Treat disease Patient outcome

16. Screening test Detect disease early Treat disease Patient outcome Evaluating Studies of Screening

17. Screening test Detect disease early Treat disease Patient outcome Evaluating Studies of Screening

18. Outline  Overview and definitions  Can screening be bad?  Evaluating studies of screening tests Observational studies of screening Randomized trials of screening  Conclusion

19. Evaluating Studies of Screening  Ideal Study: Randomize patients to screened/ unscreened Compares outcomes in ENTIRE screened group to ENTIRE unscreened group

20. Screened Not screened Survival from Randomization R D+ D- D+ Survival from Randomization Evaluating Studies of Screening  Ideal Study: Randomize patients to screened/ unscreened Compares outcomes in ENTIRE screened group to ENTIRE unscreened group

21. Observational studies: Patients are not randomized  Compare outcomes in patients screened vs unscreened  OR among patients with disease: Compare those diagnosed by screening vs those diagnosed by symptoms

22. Screened Not screened Survival from Randomization R Not screened Screened D+ D- D+ R Survival from Randomization Survival from Entry

23. Screened Not screened Survival from Randomization R Diagnosed by symptoms Diagnosed by screening Not screened Screened Survival from Diagnosis D+ D- D+ Patients with Disease D+ R Survival from Diagnosis Survival from Randomization Survival from Entry

24. Possible Biases in Observational Studies of Screening Tests  Volunteer bias  Lead time bias  Length bias  Stage migration bias  Pseudodisease

25. Possible Biases in Observational Studies of Screening Tests  Volunteer bias  Lead time bias  Length bias  Stage migration bias  Pseudodisease

26. Volunteer Bias  People who volunteer for studies differ from those who do not  Examples HIP Mammography study: ○ Women who volunteered for mammography had lower heart disease death rates Coronary drug project: ○ RCT of medications for secondary prevention of CAD ○ Men who took their medicine (drug or placebo!) had half the mortality of men who didn't

27. Avoiding Volunteer Bias  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

28. Possible Biases in Observational Studies of Screening Tests  Volunteer bias  Lead time bias  Length bias  Stage migration bias  Pseudodisease

29. Screening test Detect disease early Treat disease Patient outcome Evaluating Studies of Screening

30. 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

31. Latent Phase Onset of symptoms Death Detectable by screening Detected by screening Biological Onset Survival After Diagnosis Lead Time Lead Time Bias

32. 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

33. 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  Avoiding lead time bias Measure survival from time of randomization or entry into study

34. How Much Lead Time is Present?  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

35. 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

36. How Much Lead Time is Present?  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

37. 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

38. Possible Biases in Observational Studies of Screening Tests  Volunteer bias  Lead time bias  Length bias  Stage migration bias  Pseudodisease

39. Screening test Detect disease early Treat disease Patient outcome Evaluating Studies of Screening

40. Length Bias (Different Natural History Bias)  Slowly progressive cases spend more time in presymptomatic phase Disproportionately picked up by screening  Higher proportion of less aggressive disease in group detected by screening creates appearance of reduced mortality even if treatment is ineffective

41. TIME

42. Disease onset Symptomatic disease

43. Screen 1Screen 2 TIME

44. Screen 1Screen 2 TIME

45. Screen 1Screen 2 TIME

46. Survival in patients detected by screening Survival in patients detected by symptoms

47. 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

48. Possible Biases in Observational Studies of Screening Tests  Volunteer bias  Lead time bias  Length bias  Stage migration bias  Pseudodisease

49. Screening test Detect disease early Treat disease Patient outcome Evaluating Studies of Screening Stratified Analysis

50. Stage Migration Bias  Feinstein and colleagues (1985) Apparent improved stage-specific survival in a 1977 cohort of lung cancer pts vs 1954 cohort Truth: New technology resulted in 1977 cohort diagnosed with more “advanced” lung cancer  Also called the "Will Rogers Phenomenon" "When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states."

51. Stage Migration Bias Stage 1 Stage 2 Stage 3 Stage 4 Stage 0 Stage 2 Stage 3 Stage 4 Stage 1 Old testNew test

52. A Non-Cancer Example  “Infants in each of 3 birthweight strata (VLBW, LBW and NBW) who are exposed to Factor X have decreased mortality compared with unexposed weight-matched infants”  Is factor X beneficial?  Maybe not! Factor X could be cigarette smoking!

53. Stage Migration Bias LBW VLBW NBW LBW VLBW Unexposed to smoke Exposed to smoke

54. 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

55. Possible Biases in Observational Studies of Screening Tests  Volunteer bias  Lead time bias  Length bias  Stage migration bias  Pseudodisease

56. 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  The Problem: Treating pseudodisease will always be successful Treating pseudodisease can only cause harm

57. Analogy to Double Gold Standard Bias  Screening (test) result negative Clinical FU (first gold standard)  Screening (test) result positive Biopsy (2 nd gold standard)  If pseudodisease exists Sensitivity (true positive rate) of screening falsely increased Screening will also prolong survival among diseased individuals

58. Example: Mayo Lung Project  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;92:1308-16

59. MLP 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

60. 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

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

62. 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

63. Screened groupBetter survival

64. Screened GroupProlonged survival Better health behaviors Volunteer Bias

65. Disease Detected by Screening Better survival

66. Prolonged survival Earlier “zero time” Lead Time Bias Disease Detected by Screening

67. Prolonged survival (Higher cure rate) Slower growing tumor with better prognosis Length Bias Disease Detected by Screening

68. Prolonged stage- specific survival Higher stage assignment Stage Migration Bias Disease Detected by Screening or New Test

69. Prolonged survival (Higher cure rate) “Disease” is Pseudodisease Overdiagnosis Disease Detected by Screening or New Test

70. Diagnosed by symptoms Diagnosed by screening Not screened Screened Survival after Diagnosis D- Patients with Disease D+ R Survival after Diagnosis Survival from Randomization

71. Diagnosed by symptoms Diagnosed by screening Not screened Screened Survival after Diagnosis D- Patients with Disease D+ R Survival after Diagnosis Survival from Randomization

72. Outline  Overview and definitions  Can screening be bad?  Evaluating studies of screening tests Observational studies of screening Randomized trials of screening  Conclusion

73. Screened Not screened Survival from Randomization R D+ D- D+Survival from Randomization Issues with RCTs of Cancer Screening o Quality of randomization o Cause-specific vs total mortality

74. Poor Quality Randomization  Edinburgh mammography trial  Randomization by healthcare practice  26% reduction in cardiovascular mortality in mammography group  7 practices changed allocation status  Highest SES 26% of women in control group 53% of women in screening group

75. Cause-Specific Mortality  Problems: Assignment of cause of death is subjective Screening or treatment may have important effects on other causes of death  Bias introduced can make screening appear better or worse!

76. Example  Meta-analysis of 40 RCT’s of radiation therapy for early breast cancer* Breast cancer mortality reduced in screened pts (20-yr ARR 4.8%; P =.0001) BUT mortality from “other causes” increased (20-yr ARR -4.3%; P = 0.003)  Were these additional deaths actually due to screening? *Early Breast Cancer Trialists Collaborative Group. Lancet 2000;355:1757

77. Biases in Cause-Specific Mortality  “Sticky diagnosis” bias: If pt has cancer, death more often attributed to cancer Effect: overestimates cancer mortality in screened group  “Slippery linkage” bias: Linkage lost between death and cancer diagnosis (eg: not recognized as due to screening or treatment) Effect: underestimates cancer mortality in screened group

78. The truth about total mortality  Mortality from other causes generally exceeds screening or cancer-related mortality  Effect on condition of interest more difficult to detect  Total mortality more important for some screening tests than others…

79. Outline  Overview and definitions  Can screening be bad?  Evaluating studies of screening tests Observational studies of screening Randomized trials of screening  Conclusion

80. Key Points  Screening may be promoted due to economic, political or public interest rather than evidence  High-quality RCT’s are ideal to make quality recommendations  Attention to study design, size of effect and costs to patient and society needed Look for studies that compare outcomes in screened vs unscreened Aware of biases present when disease dx by screening is compared to dx in other ways