Understanding Biostatistics: Applications in Medical Research

1. Biostatistics Carsten Dahl Mørch Fredrik Bajersvej 7 A2-212 Tel:  9635 8757 Mail: cdahl@hst.aau.dk Web: http://www.hst.aau.dk/~cdahl/biostat_9BME/

2. Biostatistics • Biostatistics is statistics applied to biology • Design of experiments • The limitations when working with human subjects • Non-normality

3. Biostatistics

4. Proofs • Mathematical proofs: • Emperical proofs: F = m*a

5. Medical research • Observational studies • Survey • Clinical case report • Experimental studies • Usually comparing treatments • Design

6. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

7. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

8. Concurrent treatment • The wonders of the CT scanner • Patients treated in 1978 for stroke scanned by CT scanner • Patched paired with stroke patients from 1974 (before the scanner)

9. Concurrent treatment • The wonders of the CT scanner • Patients treated in 1978 for stroke NOT scanned by CT scanner • Patched paired with stroke patients from 1974 (before the scanner)

10. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

11. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

12. Random allocation • Doctors chose for them self if tuberculosis should receive a BCG vaccine or be in the control group

13. Random allocation • Tuberculosis patients were allocated randomly to receive BCG vaccine or be in the control group

14. Random allocation • OK: • Toss a coin • Throw dices • Computer programs • http://www.random.org/ • Not OK • Alternation • By date

15. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

16. Investigator Bias • Do not allocate by date of arrival: • Patients arriving at even days should be treated, and patients arriving at odd days should act as control.

17. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

18. Volunteer Bias • People volunteering for experiments differs form the general population • Obviously more compliant • Volunteers for experiments at AAU?

19. Volunteer Bias • Salk poliomyelitis with two different designs: • Randomized controls 2nd grades were invited to participate and randomized to either vaccine or saline injection • Observed controls 2nd graters was offered vaccine. 1st and 3rd graders were unvaccinated controls

20. Volunteer Bias

21. Volunteer Bias • If vaccine was as ineffective as saline we would expect 200.745*57/100.000 = 114 cases. • In the entire randomized control we would expect 114+114+121 = 350 cases or 46 pr. 100.00 subjects.

22. Selection of subjects • Low variability makes it easier to detect differences in the treatments • Inclusion and exclusion criterions • Acute bilateral pulmonary tuberculosis • Bacteriologically proved • Between 15 and 30 years • Unsuitable for other treatment • Narrow inclusion criterion makes it difficult to conclude on the general population

23. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

24. Intention to treat • Analyzing the data as we had intention to treat the subjects although they may refuse treatment. • All 2nd graders in the observed control study were intended to be vaccinated: (38+43)/(221998+123605)=23 • Conservative estimate

25. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

26. Cross-over design

27. Cross-over design

28. Cross-over design • Each subject acts as his/hers own control • Randomization • Carry-over effect of treatment or testing • Treating ‘symptoms’

29. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

30. Placebo (response bias) • Any treatment may help • The subject should be blinded to the kind of treatment • Effective treatment should be better than a sham treatment

31. Placebo • Take the red pill

32. Double blind studies • When the assessment is made the investigator should be blind to the treatment. • Can all studies be blinded? • Communication between subjects

33. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid

34. Ethical considerations • The Declaration of Helsinki • Reduce suffering of the subjects • Local ethics comity • Den videnskabsetiske kommité consists of both experts and laymen. • Secures that the rights of the subjects are being followed, especially as to the information given to the subjects.

35. Ethical considerations • Informed Consent • Subjects must be informed about the nature, purpose, risks etc. in the study. • The subjects should give their consent before being enrolled and have the right to withdraw from the study at any time.

36. Stanley Milgrams "watershed" experiment

37. Comparing Treatments • The treatments must be: • Applied concurrently • Treated by the same investigator • Random allocation into treatment groups • Avoid cheating (investigator bias) • Volunteer bias • Intention to treat • Use cross-over design if possible • Double blinded • Ethically solid