Then we calculate the absolute difference of the treatment counts for each factor, and sum those differences to give the imbalance for that treatment. The method 2 that Sealed Envelope uses proceeds by first calculating for each treatment the resulting counts for each prognostic factor assuming that that treatment was allocated next. Prognostic factorĬlearly in males and those under 30 there is an imbalance in favour of placebo so far. The next subject to be randomised is a man age 23, so before randomisation we have the following treatment counts for the strata. The treatment choice that would result in the smallest treatment imbalance for that combination of characteristics is then the preferred treatment for that subject.
#Red pill 44 453 trial#
To decide which treatment to allocate to the subject the balance of treatments in the trial is compared for subjects with the same characteristics as the subject to be randomised. The randomisations to the trial so far look like this: Number Here sex and age are prognostic factors for the trial. For similar reasons we would also like to balance subject age, so that younger subjects, who are expected to have a better outcome, are evenly distributed to the placebo and drug groups. It would be unfortunate if, by chance, more women received the new drug rather than placebo and more men were allocated to placebo rather than the new drug. Suppose it is important to balance subject sex in a trial of a new drug, because women are expected to respond more strongly to the drug. The method is best illustrated by example. It is effective even at small sample sizes and with multiple prognostic variables. Minimisation 1 is a method of randomisation that allocates subjects to the treatment group that best maintains balance in prognostic factors.
0 kommentar(er)
