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Statistics for the Terrified: Trial Design and Sample Size. Andrea Marshall Mark Williams. By the end of this session you will be aware of The different types of trial designs and why we use them Importance of sample size and requirements needed to be able calculate it
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Trial Design and Sample Size
Sample: People with Rheumatoid Arthritis of the hand
Usual care + Exercise
1. Looking at role of sunscreen
2. Looking at role of Betacarotene
Sunscreen + betacarotene tabs (n=404)
Sunscreen + placebo tabs (n=408)
Placebo tabs only (n=393)
Betacarotene tabs only (n=416)
No sunscreen (n=809)
BC and Placebo tabs only
Sunscreen+BC and Sunscreen+placebo
No BC (n=801)
Sunscreen+placebo and placebo tabs only
Sunscreen+BC and BC tabs only
Intervention – Physical activation programme
Control – Depression Awareness
Primary outcome and how it will be analysed
Hypothesis being tested – superiority/non-inferiority/equivalence
Significance level (generally set to 5%)
Power (generally 80-90%.)Sample Size
“Absence of evidence is not evidence of absence”
80% power for standard 2-arm parallel group trial with a 1:1 allocation and P0 = 0.25 and P1 = 0.40, i.e. 15% absolute differences in proportion of responses
Sample size required is 152 patients in each arm giving a minimum total of 304 patients
Sample size required is 211 patients in each arm
giving a minimum total of 422 patients
E.g. In COUGAR-2,
Sample size required is 146 patients in each arm giving a minimum total of 292 patients
A total sample size of 343 is needed for a patient randomised trial
Cluster designWhich type of trial would you need a washout period incorporated into the design?
Phase II trial
Phase III (parallel) trial
Phase III (cluster) trialWhich type of trial is likely to require the largest sample size?
90%What is the generally acceptedminimum level of power for a trial?
Significance levelWhich of the following information do you NOT need to calculate the sample size for a binary outcome?
Use 85% power
Use 1% significance level
All of the above
None of above
If sample size to detect 10% differences in a binary outcome with 5% significance level and 80% power is not obtainable, how could you decrease the sample size required?
More patients with Trial B
More patients with Trial C
Same for all trials
If wanting to detect with a continuous outcome: Trial A: difference in means of 5, SD = 10Trial B: difference in means of 4, SD = 8 Trial C: Standardised difference of 0.5would you need ... ?