Sample size for binary outcome and continuous (%) predictor

[sas_question123]

I have a project in which I have 60-70 samples.

Outcome = binary (positive or negative case)
Exposure = % of the time participants followed new guidelines (0-100%)
Question - would it be correct to use logistic regression to analyze this?
I am guessing a sample size of 60 or 70 is too small.

What would be the best way to calculate the sample size? Difficult to find examples where the predictor is continuous and outcome is binary.
Also generally, how would the proportion of positive cases affect the analysis? I don't expect there'll be much, if any, but would like to know how to calculate the sample size in both scenarios (regular number of positive cases and low number of positive cases). Thank you.

[StatDave]

Use the LOGISTIC statement in PROC POWER.

[sas_question123]

Thank you! I'm very new to power/sample size calculations. I pasted my code below. If I expect the predictor to be really skewed to the left, would exponential be a good vardist to choose?

proc power ;
logistic
vardist ("predictor") = exponential ()
testpredictor ="predictor"
power = 0.8
ntotal=.;
run ;

[StatDave]

Exponential and gamma are both reasonable distributions for positively valued and right skewed data