Hello fellow members of this forum,
I am planning to perform a randomized trial for an intervention versus placebo, in a 1:1 ratio. The outcome is the binary presence of a side effect the intervention is supposed to prevent, calculated by either Chi squared or Fisher exact tests.
The problem is: I know for a fact that there is a potential confounder variable (presence of diabetes), that could increase the risk of this event and maybe where intervention could have a different effect. For this reason, i plan to stratify the randomization by taking into account the presence or absence of diabetes at baseline.
I created the sample calculations on a general randomization model, where the prevalence of de outcome in the control group is expected to be 72.3, in the intervention, 42.3, with a power of 80% and two-sided p value of 0.05:
. power twoprop 0.723 0.423, power (0.80) alpha (0.05)
Performing iteration ...
Estimated sample sizes for a two-sample proportions test
Pearson's chi-squared test
Ho: p2 = p1 versus Ha: p2 != p1
Study parameters:
alpha = 0.0500
power = 0.8000
delta = -0.3000 (difference)
p1 = 0.7230
p2 = 0.4230
Estimated sample sizes:
N = 84
N per group = 42
My question is: how can I account for stratification in the sample size calculations in Stata? I looked into the option and there was just cluster sampling, which is not the case...
Thank you,
Ligia Macedo
I am planning to perform a randomized trial for an intervention versus placebo, in a 1:1 ratio. The outcome is the binary presence of a side effect the intervention is supposed to prevent, calculated by either Chi squared or Fisher exact tests.
The problem is: I know for a fact that there is a potential confounder variable (presence of diabetes), that could increase the risk of this event and maybe where intervention could have a different effect. For this reason, i plan to stratify the randomization by taking into account the presence or absence of diabetes at baseline.
I created the sample calculations on a general randomization model, where the prevalence of de outcome in the control group is expected to be 72.3, in the intervention, 42.3, with a power of 80% and two-sided p value of 0.05:
. power twoprop 0.723 0.423, power (0.80) alpha (0.05)
Performing iteration ...
Estimated sample sizes for a two-sample proportions test
Pearson's chi-squared test
Ho: p2 = p1 versus Ha: p2 != p1
Study parameters:
alpha = 0.0500
power = 0.8000
delta = -0.3000 (difference)
p1 = 0.7230
p2 = 0.4230
Estimated sample sizes:
N = 84
N per group = 42
My question is: how can I account for stratification in the sample size calculations in Stata? I looked into the option and there was just cluster sampling, which is not the case...
Thank you,
Ligia Macedo
Comment