1 / 56

This piece starts at slide 50

This piece starts at slide 50. Possible Biases in Observational Studies of Screening Tests. Volunteer bias Lead time bias Length bias Stage migration bias Pseudodisease. Volunteer Bias. People who volunteer for screening differ from those who do not (generally healthier) Examples

romeo
Download Presentation

This piece starts at slide 50

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. This piece starts at slide 50

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

  3. Volunteer Bias • 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

  4. MASS Within Groups Result in Invited Group • 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

  5. 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

  6. Lead and Length Time Bias Screening test Detect disease early Treat disease Patient outcome (Survival)

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

  8. Latent Phase Biological Onset Detectable by screening Onset of symptoms Death Survival After Diagnosis Lead Time Survival After Diagnosis Detected by screening Lead Time Bias Contribution of lead time to survival measured from diagnosis

  9. Avoiding Lead Time Bias • Only present when survivalfrom 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

  10. How Much Lead Time is Present? • Depends on relative lengths of latent phase (LP) and screening interval (S) • Screening interval shorter than LP:

  11. Onset of symptoms Death Detectable by screening LP Detected by screening Max Min S Screen Figure 1: Maximum and minimum lead time bias possible when screening interval is shorter than latent phase Max = LP Min =LP – S Screen Screen Screen Screen

  12. 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

  13. S LP Max Screen Screen Screen 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

  14. Length Time Bias Screening test Detect disease early Treat disease Patient outcome (Survival)

  15. 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 screened group creates appearance of improved survival even if treatment is ineffective

  16. TIME

  17. Disease onset Symptomatic disease TIME

  18. Screen 1 Screen 2 TIME

  19. Screen 1 Screen 2 TIME

  20. Screen 1 Screen 2 TIME

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

  22. Avoiding Length Bias • Only present when • survivalfrom 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

  23. Stage Migration Bias New test Stage disease Treat disease “Stage-specific” patient outcome (stratified analysis)

  24. 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.” • Can occur when • New test classifies severity of disease differently • AND outcomes are stratified by severity of disease (ie: stage-specific survival)

  25. Stage Migration Bias Stage 0 Stage 1 Stage 2 Stage 3 Stage 4 Old test

  26. Stage Migration Bias Stage 0 Stage 1 Stage 2 Stage 3 Stage 4 Old test New test

  27. Stage Migration Bias Stage 0 Stage 0 Stage 1 Stage 1 Stage 2 Stage 2 Stage 3 Stage 3 Stage 4 Stage 4 Old test New test

  28. Stage Migration Bias Stage 0 Stage 0 Stage 1 Stage 1 Stage 2 Stage 2 Stage 3 Stage 3 Stage 4 Stage 4 Old test New test

  29. A non-cancer example • 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?

  30. Stage Migration Bias Admitted to ward Admitted to ward Admitted to ICU Admitted to ICU Before new policy After new policy

  31. A non-cancer example • 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…

  32. Identifying Stage Migration Bias • 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

  33. Pseudodisease Screening test Detect disease early Treat disease Patient outcome (Survival)

  34. 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

  35. Pseudodisease: Analogy to Double Gold Standard Bias • 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!

  36. 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

  37. MLP Extended Follow-up: Survival Curve Marcus et al., JNCI 2000;92:1308-16

  38. 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

  39. MLP Extended Follow-up: Mortality Marcus et al., JNCI 2000;92:1308-16

  40. 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 • 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 !

  41. Screened group Decreased mortality

  42. Better health behaviors Screened group Decreased mortality Volunteer Bias

  43. Disease Detected by Screening Prolonged survival

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

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

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

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

  48. Survival from Randomization D+ Screened D- R Volunteer Bias Survival from Randomization D+ Not screened D- Survival after Diagnosis Diagnosed by screening LengthBias Lead Time Bias Patients with Disease Diagnosed by symptoms Survival after Diagnosis Pseudodisease

  49. Survival from Randomization D+ Screened D- R D+ Survival from Randomization Not screened D- • What about the “Ideal Study”? • Quality of randomization • Cause-specific vs total mortality

  50. Poor Quality Randomization • 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

More Related