Based on comments a previous question, I have changed my question.
I am wanting to see if the addition of "novel" variables to a model containing "traditional" variables improves the prediction of levels of the DV. For example, age and blood pressure are established predictors of atherosclerosis, amongst others. I include these in my base model. I then want to add kidney function and physical activity and compare the two models. I will compare the models by looking at AIC.
My question is about power, sample sizes and number of predictors. Using G power there are two options for calculating sample size in linear regression models: r squared deviation from zero, and r squared increase. I achieved a smaller sample size than planned, so now trying to calculate how many variables I can include in my models. What is the right approach here? Do I need to worry about sample size when designing models, or can I test multiple and choose the best fitting, lowest AIC, regardless of the number of variables. Which of the two options in G power is an appropriate method of seeing how many variables I can include in a model, based on desired power and sample size?