GPOWER LOGISTIC REGRESSION FULL

To further explore the independent correlation of serum immune regulatory factors with OP in RA patients, logistic regression model was established using forward stepwise procedure and LR test.

Normal data distribution was validated by Shapiro-Wilk test. I am only interested in records with a headache diagnosis, but I want to determine what set size I require measuring an increase or decrease in odds given the presence (exposure) to hypertension. GPower 3, (2) statistical tests comparing both dependent and independent Pearson correlations, and statistical tests for (3) simple linear regression coefficients, (4) multiple linear regression coefficients for both the fixed- and random-predictors models, (5) logistic regression coef-ficients, and (6) Poisson regression coefficients. Sample size estimation before the study was conducted using Gpower software. Is this what I need to add into the probability textboxes, and what do I add into the X param pi (I think this is the proportion of some distribution)? I assume this is my alternative hypothesis P(headache=1|hypertension=1). The prevalence of a patient having a headache with hypertension if 0.332. I assume this is my null hypothesis P(headache=1|hypertension=1) i.e., hypertension does not impact the outcome of a headache. The prevalence of having a headache, irrespective of hypertension is 0.004 (from ). I am trying to see if hypertension (my binary independent) makes a difference on having a headache (my binary dependent).

The main analysis To open the main Logistic Regression dialog box select.

GPOWER LOGISTIC REGRESSION FULL

I need some help understanding what values I need to put into a two-tailed logistic regression power analysis are in G*Power. There is a logistic regression option under z-tests in my g-power version (v 3.1), but I do not think that there is an option for the full model you describe. Chapter 19: Logistic regression Self-test answers SELF-TEST Rerun this analysis using a stepwise method (Forward: LR) entry method of analysis.

Gpower logistic regression