1. Evaluating Screening Programs Dr. Jørn Olsen Epi 200B January 19, 2010

2. Causal field Biological onset Detectable by testing Clinical Symptoms Diagnosing Disabling Primary prevention Secondary prevention Tertiary prevention

3. Definition of Screening  Last: The presumptive identification of unrecognized disease or defect by the application of tests, examinations or other procedures which can be applied rapidly. Screening tests sort out apparently well persons who probably have a disease from those who probably do not. A screening test is not intended to be diagnostic. Persons with positive or suspicious findings must be referred to their physicians for diagnosis and necessary treatment.

4.  Screening is not about diagnosing patients.  The aim is to identify people at high risk of having the disease.  The screening test is not a diagnostic test.  A positive screening test is a cue for using a diagnostic test-not a cue for treatment.

5.  Which conditions speak in favor of a screening program?

6.  A health problem that can be treated  An acceptable test with no or few side effects

7. Justification for screening: early treatment improves prognosis at reasonable cost  Screening requires a screening test.  We talk about a test’s sensitivity, specificity and predictive value.  What characterizes a good screening test?

8.  Safe  Acceptable  Inexpensive  Good predictive values

9. TestDD +-+- acac bdbd M3M4M3M4 M1M1 M2M2 N sens = a/M 1 spec = d/M 2 predictive value pos test = a/M 3 Parameters; sens = P (test+ D); spec = P (test- D) Predictive value pos test = P (D test+) Predictive value of a neg test = P (D test-) These are conditional probabilities A population

10. TestDD +-+- PP x sens PP x (1-sens) (1-PP) (1-spec) (1-PP) spec PP(1-PP) Predictive value of pos test P(D test+) = Predictive value of a negative test P(D test-) = PP x sens PP x sens + (1-PP) (1-spec) (1-PP) spec PP x (1-sens) + (1-PP) spec

11.  Bayes’ formula – predictive value depends upon sens, spec and PP, the prevalence proportion. From prior probability, PP, to a postereiori probability P(D test + )  1763 Richard Price presented a paper by Thomas Bayes “An essay toward solving a problem in the doctrine of chances”.

12. Is epoxy carcinogenic? Ame’s testCarcinogenic compounds Non carcinogenic compounds Positive Negative 157 18 14 94 175108

13. Sens. 157/18+157 = 0.90 Spec. 94/108 = 0.87 Epoxy had a positive Ame’s test 90% probability that it is carcinogenic? Depends upon the prior probability If 1% of all chemicals screened are carcinogenic

14. TestCC +-+- 900 100 12870 86130 13770 86230 100099000100,000 Predictive value of a positive test 900/13770 = 6.5% from 1% to 6.5% Predictive value of negative test 56130/86230= 99.9% from 99% to 99.9%

15. Benefits and side effects of screening a: True positives detected at screening – would benefit if detected before critical point c: False negatives diseased but not detected at screening. Screening may delay their diagnosing b: False positives are called in for diagnostic work up – are worried and diagnostic tests may carry risks d: True negatives are happy and like the program Main design issue: screening may have positive as well as negative effects. The sensitivity and specificity of the tests are key parameters together with the nature of the test, the disease and its treatment. testDD +-+- acac bdbd

16. Test values for HEME Select (colorectal cancer) population data testDD +-+- 22 10 418 7043 440 7053 3274617493 Sens = 22/32 = 0.688Spec = 7043/7461 = 0.944 Predictive value of post test = 22/440 = 0.050

17. In a clinical setting data could be testDD +-+- 688 312 56 944 744 1256 1000 2000 Predictive value of post test = 688/744 = 0.925

18. Sensitivity will often depend on the stage of the disease and may well be lower for early stages of the disease the predictive value of the test is closely dependent on the prevalence proportion of the disease. For this test, predictive value of pos test is 0.11 if colon cancer has a prevalence proportion of 0.01 and 0.01 if PP is 0.001.

19. Likelihood ratios (LR) LR + = = LR - = = An easy way to use Bayes’ theorem Prior odds = Posterior odds = prior odds x LR Posterior probability = Sens 1-spec 1-sens spec Prior probability 1-prior probability Posterior odds 1+ posterior odds P (test + D) P (test - D)

20. Screening for alcoholism; test sens = 0.90, spec = 0.60 Prior probability of alcoholism 0.30, then Prior odds = = 0.43 LR + = = 2.25 Posterior odds = 0.43 x 2.25 = 0.97 Posterior probability = = 0.49 You have increased your probability from 0.30 to 0.49 given the test was positive. 0.30 0.70 0.90 0.40 0.97 1+ 0.97

21. Screening may have negative as well as positive effects; a screening program should therefore be evaluated. It is not enough to show that those who were detected in a screening program had a longer survival than those not screened. For this patient, the clinical survival time is t d -t c and the screening survival time is t d -t s ; t c -t s longer. This time interval produces “lead time bias”. dead healthy tsts tctc tdtd time

22. IR after screening without screening screening

23. All classical designs have been used in evaluating screening programs. Main concerns: Since screening programs usually have both positive and negative effects, the case-control design may not be the best choice. Why is that? RCT:need to be large, may be out of date when finished, unbiased cause specific mortality may difficult to obtain, difficult to randomize at individual level. Does not address normal practice. No “confounding by indication” argument for doing a RCT. Follow-up: who comply to the program, high risk/low risk? Case-control: no possibility to include all effects of interest Ecological: ecological phallacy, but may be the best evidence after all

24. Additional design issues  Screening may address an early pre- disease lesion (adenoma) or cancer at an early stage. In the first situation, screening may reduce incidence but may have little impact on case fatality. In the second situation, screening should reduce incidence (and case fatality?). In both situations, cause specific mortality should be reduced (and total mortality?).

25. Additional design issues  A case-control addressing the first issue includes incident cases. For the second issue, cases are cause specific deaths.  The source population are those who are invited to be screened and belong to the population at risk.

26. Additional design issues  Incidence density sampling of controls is usually the only option.  Exposure is being screened in a given time interval up to case selection. D M

27. Gotzsche et al. Is screening for breast cancer with mammography justifiable? Lancet. 2000;355:129-34.

28. France In France BC cancer incidence is increasing cause specific mortality rates are stable BC screening is increasing. How could this be explained?

29. Randomized trials include 500,000 women but results differ and no conclusion has been reached. Review of trial according to: quality of randomization blinding of outcome assessment exclusion after randomization

30. New York trial Pairs of women were matched and the pairs were randomized but imbalance on previous lump in the breast, menopause, education

31. Edinburgh trial Cluster randomization of GPs difference in social conditions

32. Canadian trial Individual randomization

33. Stockholm trial Allocated according to data of birth Born 11-20 of any month allocated to the control group inconsistency in numbers

34. Gothenburg trial Data of birth and individual difference in age

35. Other Swedish trials Randomization of counties Differences in age

36. Peter C Gotzsche, et al. Public Health

38. Other quality criteria?

39. Conclusion: Screening for breast cancer with mammography is unjustified?