Hello,
I am planning on a longitudinal analysis of repeated cross-sectional measures using logistic regression with GEE and an independent correlation structure. The response variable is binary and the predictor an ordinal categorical variable (i.e. increasing levels of exposure so I know the sample size at each level of the predictor).
When I've used cross-sectional data in the past I've used the minimum detectable odds ratio as a standardized effect size. Initially, I calculated the minimum detectable OR using Pearson’s chi-squared tests to compare two independent proportions specifying a .05 significance level and an 80% power level and used the local prevalence of the outcome to be the prevalence in the reference group. Is this an appropriate approach for repeated measures data? Are there alternative power analyses would you recommend for this in STATA?
I would be grateful for any guidance or insight on the matter!
Sincerely,
Bill
I am planning on a longitudinal analysis of repeated cross-sectional measures using logistic regression with GEE and an independent correlation structure. The response variable is binary and the predictor an ordinal categorical variable (i.e. increasing levels of exposure so I know the sample size at each level of the predictor).
When I've used cross-sectional data in the past I've used the minimum detectable odds ratio as a standardized effect size. Initially, I calculated the minimum detectable OR using Pearson’s chi-squared tests to compare two independent proportions specifying a .05 significance level and an 80% power level and used the local prevalence of the outcome to be the prevalence in the reference group. Is this an appropriate approach for repeated measures data? Are there alternative power analyses would you recommend for this in STATA?
I would be grateful for any guidance or insight on the matter!
Sincerely,
Bill
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