European Obesity Academy Assmannshausen 2016 Statistics; power calculation and randomization Johan Bring Statisticon AB

Study process Planing Data collection Analysis Interpre- tation

ICH E9 – statistical principles ”This guidance is written primarily to attempt to harmonize the principles of statistical methodology applied to clinical trials for marketing applications submitted in Europe, Japan and the United States.” (p. 3)

Study objective Superiority A>B Equivalence A=B Non-inferiority A not worse than B

Overall Study Design One group Parallell groups Cross over Choice of control groups Placebo Active control Dose comparisons

Selection of study population Inclusion and exclusion criteria Methods of assigning patients to treatment groups Randomization Blinding

Primary efficacy variable(s) 1) ”The primary variable should be the variable capable of providing the most clinically relevant and convincing evidence directly related to the primary objective.” 2) ”There should generally be only one primary variable.” 3) ”The selection of the primary variable should reflect the accepted norms and standards in the field of research.” 4) ”The use of a reliable and validated variable with which experience has been gained is recommended.”

Cathegorised Variables It’s common to dichotomize continues response variables into responders/non-responders, e.g. -Reduction in BMI: more than 5%=1, less than 5%=0 -Obstructive sleep apnea: AHI>15 -Diastolic bloodpreasure, below 90mmHg=1, above 90mmHg=0 -Reduction in VAS-score, more than 30%=1, less than 30%=0 -Hb above 120=1, below 120=0.

Determination of sample size – hypothesis testing ’Accept’ H0 Reject H0 H0 true CorrectTyp-I error α H0 false Typ-II error β Correct H0: A = B H1: A ≠ B

How large risks should we take?

What does it mean that H1 is true?

An example Two surgical procedures (1 and 2) 1 is the old procedure and 2 is the new procedure We study the proportion of succesful operations. The old procedure has a succesrate of 0.20 (20%)

n1=100 n2=100

p1=0.20 p2=0.30

How determine the sample size Decide alfa (level of significance). Decide power. Decide smallest difference that is considered clinically relevant. (delta). (Note: that is not the same as the expected difference)

Studies with low power – are they unethical? Yes No

Subgroup analyses Significant effect for females Non-significant effect for females Significant effect for males Non-significant effect for males Probabilities for different outcomes