RANDOMISATION, BIAS AND BLINDING IN CLINICAL TRIALS Dipesh Mistry & Jessica Smith September 2010

1. RANDOMISATION, BIAS AND BLINDING IN CLINICAL TRIALS Dipesh Mistry & Jessica Smith September 2010

2. OVERVIEW • Randomisation -What, why, how and when • Allocation Concealment • Bias • Blinding

3. WHAT IS RANDOMISATION? DEFINITION Randomisation is a process by which each participant has the same chance of being randomly assigned to one of two or more groups e.g. Group A & Group B.

4. WHY RANDOMISE? Possible explanations for a difference would include: 1.the intervention exhibits a real effect; 2.the outcome difference is solely due to chance 3.there is a systematic difference (bias) between the groups due to factors other than the intervention Group A Allocate Patients Follow up Eligible patients Analysis Group B

5. WHAT IS ALLOCATION BIAS? More Severe Patients Less Severe Patients LBP Patients

6. WHAT IS ALLOCATION BIAS? • Is there a difference because: • the treatment actually works • of the way he has allocated patients

7. RANDOMISATION DEPENDS ON: • the generation of an unpredictable allocation sequence 2. the concealment of that sequence

8. ALLOCATION RATIO’S • Equal Allocation Participants have the same chance of being assigned to trial arms • Easily accommodates multiple arm studies • Generally the most efficient design • Easy to implement 2. Unequal Allocation • Assigns more participants to intervention • e.g. BEST – 2:1 allocation

9. TYPES OF RANDOMISATION • Simple Randomisation • Permuted Block Randomisation • Stratified Block Randomisation • Minimisation Method

10. SIMPLE RANDOMISATION Definition Most basic form of randomisation where each treatment assignment is ”memory less” - made without regard to previous assignments Some examples: • Unbiased coin toss for each trial participant • Roll an unbiased die • Sequence of Random Numbers from statistical textbooks • Computer generated random sequence

11. ILLUSTRATION A computer generated random sequence: 4,8,3,2,7,2,6,6,3,4,2,1,6,2,0,……. • Two Groups (criteria: {2,4,6,8,0}=A, {1,3,5,7,9}=B):

12. ILLUSTRATION A computer generated random sequence: 4,8,3,2,7,2,6,6,3,4,2,1,6,2,0,……. Two Groups: different randomisation ratios (e.g. 1:2) (criteria:{1,2,3}=A, {4,5,6,7,8,9}=B; ignore 0’s)

13. ILLUSTRATION A computer generated random sequence: 4,8,3,2,7,2,6,6,3,4,2,1,6,2,0,……. Three Groups: (criteria:{1,2,3}=A, {4,5,6}=B, {7,8,9}=C; ignore 0’s)

14. SIMPLE RANDOMISATION SUMMARY Pros and Cons + Simplistic implementation + Allocation is random and unpredictable − Can produce unbalanced allocation − Can lead to analysis complications e.g. interim analyses Imbalance Solution Replace with another allocation list (criteria: >10) Restrict the randomisation

15. BLOCK/PERMUTED BLOCK RANDOMISATION Definition • Allocation list is comprised of “blocks” • Each block contains one possible combination (permutation) of possible treatment allocations • Allocation is balanced at end of each block

16. 4,8,3,2,7,2,6,6,3,4,2,1,6,2,0,……. Basic Implementation 1 Block size is an multiple of treatment arms i.e. 2 arms → b = {2, 4, 6, . . .}, 3 arms → b = {3, 6, 9, . . .} 2 List all possible combinations e.g. For block size of 2: AB or BA 3 Choose a criteria for each block of patients Digits 0-4 = AB Digits 5-9 = BA

17. ILLUSTRATION 4,8,3,2,7,2,6,6,3,4,2,1,6,2,0,……. 1 Lets say: - want to compare 2 groups e.g. Grp A & Grp B - choose block size of 4 patients 2 List allocation combinations for block size of 4 AABB BAAB ABBA ABAB BABA BBAA 3 Choose criteria for each block of patients 1 - AABB 2 - BAAB 3 - ABBA 4 - ABAB 5 - BABA 6 – BBAA ignore 0,7,8,9 ABAB ABBA BAAB BAAB BBAA BBAA …

18. BLOCK RANDOMISATION SUMMARY Pros and Cons + Balance between arms is guaranteed by block’s end + Interim analysis can still have balance − Bias can occur if allocation sequence is determined Recommendations Do NOT use block size of 2 Use reasonably large blocks to avoid predictability Not too large if interim analysis intended

19. STRATIFIED BLOCK RANDOMISATION Definition Balancing treatment groups with respect to prognostic factors which may be related with participant response in order to prospectively achieve treatment group comparability

20. WHAT DOES THIS MEAN? REMEMBER: randomisation is to ensure treatment group comparability HOWEVER: certain prognostic factors may be predictors of response e.g. age, sex, THEREFORE: we want to make certain that there is a balance in both groups for these factors HOW? – have a separate block randomisation scheme for each category

21. ILLUSTRATION • Single Strata: Age, (block size 4)

22. ILLUSTRATION • Two Strata: Gender & Age, (block size 4)

23. STRATIFIED RANDOMISATION SUMMARY • Pros and Cons • + Balance important variables between arms • + Improves power by reducing variance • − Complicates allocation process • − Too many strata can lead to sparse data

24. MINIMISATION METHOD Definition Minimisation (Adaptive randomisation) is an accepted statistical method to limit imbalance in randomised clinical trials in conditions with known important prognostic factors. Its called minimisation because imbalance in the prognostic factors is minimised

25. ILLUSTRATION- Lets say we have 40 patients already- Each factor (gender & age) should consist of 40 patients- Next patient (41st patient) is Female and <50.- For each treatment, add number of patients in the corresponding 2 rows: Group A = 9 + 8 = 17 Group B = 8 + 7 = 15 NEXT ASSIGNMENT TO GROUP B

26. MINIMISATION METHOD SUMMARY Pros and Cons + Cannot determine next allocation + Maintains balance across groups + Advantage over stratified block randomisation, as randomisation does not occur within strata − Can be technically challenging to implement

27. ALLOCATION CONCEALMENT- different to blinding- avoids selection bias

28. PRACTICAL CONSIDERATIONS

29. RECAP • Randomisation • Reduces allocation bias • Ensures comparability of Groups • Allocation Concealment • Reduces selection bias • Again, ensures comparability of Groups • OTHER FORMS OF BIAS AFTER RANDOMISATION….

30. TYPES OF BIAS • Patient bias • Care Provider bias • Assessor bias • Analysis and Interpretation bias

31. PATIENT BIAS DEFINITION The patients knowledge of the treatment being received may affect the outcome of the study. • patient’s knowledge that they are receiving a “new” treatment may substantially affect the patient’s subjective assessment.

32. CARE PROVIDER BIAS DEFINITION The care provider’s knowledge of which treatment a patient is receiving may affect the way the provider • Deals with the patient • Treats the patient • May give patient information about the treatment the patient is receiving

33. ASSESSOR BIAS DEFINITION Assessor’s knowledge of which treatment the patient is receiving may affect the way the assessor assesses outcome • Affect validity of conclusion of the study • If assessment conducted whilst patient still receiving treatment may indirectly provide patient with information about the treatment they are receiving.

34. ANALYSIS AND INTERPERTATION BIAS Knowledge of the treatment arm may affect resultant analysis of the data as • Seeking explanation of an “anomalous” finding when one is found contrary to the study hypothesis. • Accept a “positive” finding without fully exploring the data Knowledge of the treatment arm may affect decisions made by external monitors • Terminate the study due to adverse events • Terminate the study for superiority of treatment

35. BLINDING Blinding = masking treatment All of these potential biases can be avoided if everyone involved within a study is blinded to the treatment being given to the patient. HIERARCHY OF BLINDING • Un-blinded • Single Blind • Double Blind • Complete Blind

36. SINGLE BLIND The patient (or sometimes the clinician) is blinded to the treatment given. Often used when double blinding is impractical for reason such as • Need to adjust medication • Potential side effects

37. DOUBLE BLIND Neither the participant nor the physician conducting the study know which treatment is being given to the participant Minimises bothpotentialpatient biases and potentialassessor biases Should be used whenever possible

38. DOUBLE BLINDING TECHNIQUES Placebo for each possible treatment • Tablets identical in physical appearance • Tablets with similar taste and smell • Same carrier used for IV infusions Other treatments “shammed” as far as possible • Sham surgery

39. DOUBLE BLINDING- ALWAYS FEASIBLE? Double blinding may not be possible: • May not be ethically permissible to blind patient • Study using an exercise intervention

40. PROBLEMS WITH DOUBLE BLINDING SIDE EFFECTS If drug produces side effects = difficult to blind patient and ethically wrong to produce a placebo that induces side effects. EFFICACY If treatment truly effective may become clear as to which treatment the patient is receiving – rare!

41. COMPLETE BLINDING Effectively it is the best approach BUT • Requires two groups of people for data processing, one group to encode data and one group to perform the analysis. • Therefore not economically feasible. Used sometimes by major drug companies

42. COMPLETE BLINDING TECHNIQUES • Analysis uses coded treatment groups • Analysis uses coded side effects • Analysis uses coded laboratory tests

43. SUMMARY • BIAS different types of bias exist in all clinical trials • Randomisation deals with • Allocation concealment deals with • Blinding deals with other types of bias Allocation bias Selection bias

44. THANK YOU FOR LISTENING ANY QUESTIONS?

45. Next week… ANALYSIS Simple and to the point, defining terms