BIOM_Presentation - Biometrics India Pfizer Global R D The Concept of Randomization and Blinding in Clinical Trials Suraj P Anand Randomization

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon
The Concept of Randomization and Blinding in Clinical Trials Suraj P Anand
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Randomization Randomization is the process of assigning clinical trial participants to treatment groups. Randomization gives each participant a known (usually equal) chance of being assigned to any of the groups. Successful randomization requires that group assignment cannot be predicted in advance.
Background image of page 2
Why Randomize? If, at the end of a clinical trial, a difference in outcomes occurs between two treatment groups (say, intervention and control) possible explanations for this difference would include: the intervention exhibits a real effect; the outcome difference is solely due to chance there is a systematic difference (or bias) between the groups due to factors other than the intervention. Randomization aims to obviate the third possibility.
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Forms of Randomization Simple Randomization Permuted Block Randomization Stratified Block Randomization Dynamic (adaptive) random allocation
Background image of page 4
Simple Randomization Coin Tossing for each trial participant Sequence of Random Numbers from statistical textbooks Computer generated sequence
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Illustrations The computer generated sequence: 4,8,3,2,7,2,6,6,3,4,2,1,6,2,0,……. Two Groups (criterion:even-odd): AABABAAABAABAAA…… Three Groups: (criterion:{1,2,3}~A, {4,5,6}~B, {7,8,9}~C; ignore 0’s) BCAACABBABAABA…… Two Groups: different randomisation ratios(eg.,2:3): (criterion:{0,1,2,3}~A, {4,5,6,7,8,9}~B) BBAABABBABAABAA……. .
Background image of page 6
Permuted Block Randomization Used for small studies to maintain reasonably good balance among groups In a two group design, Blocks having equal numbers of As and Bs (A = intervention and B = control, for example) are used, with the order of treatments within the block being randomly permuted
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Illustration With a block size of 4 for two groups(A,B), there are 6 possible permutations and they can be coded as: 1=AABB, 2=ABAB, 3=ABBA, 4=BAAB, 5=BABA, 6=BBAA Each number in the random number sequence in turn selects the next block, determining the next four participant allocations (ignoring numbers 0,7,8 and 9). e.g., The sequence 67126814…. will produce BBAA AABB ABAB BBAA AABB BAAB. In practice, a block size of four is too small since researchers may crack the code and risk selection bias. Mixing block sizes of between 6 and 12 is better with the size kept unknown to the investigator. This precaution maintains concealment. Simple randomization should determine which block size to use next.
Background image of page 8
Stratified Block Randomization Stratified block randomization can further restrict chance imbalances to ensure the treatment groups are as alike as possible for selected prognostic variables or other patient factors. A set of permuted blocks is generated for each combination of prognostic factors Typical examples of such factors are age group, severity of condition, and treatment centre. Stratification simply means having separate block randomisation schemes for each combination of characteristics (‘stratum’)
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 10
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 05/02/2010 for the course BIOSTAT ST520 taught by Professor Kim during the Spring '10 term at Yonsei University.

Page1 / 30

BIOM_Presentation - Biometrics India Pfizer Global R D The Concept of Randomization and Blinding in Clinical Trials Suraj P Anand Randomization

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online