I am interested in learning whether a diet can help people lose weight. I randomly assigned participants to receive the new diet. Weight was monitored at baseline and again at the beginning of every month for 4 months following enrollment into the study.
I want to investigate if the new diet affect one’s weight over time differently for those on the new diet versus those not receiving the new diet.
I have the following data: id, treatment, age, outcome (the weight of a particular individual recorded at a given visit), and visitnumber.
I am not able to understand which model is appropriate for my question specifically, If I should include baseline age as a time varying variable (level 1 predictor) or as a covariate.
I have made the following model
adding adding visit number as level 1 predictor, treatment as level 2 predictor , visittreatment interaction, and baseline age as covariate in random intercept (by id) and random slope model (by visit). Yij=b00 +b01X+ b10 tij+b1 X1 * tij+r0i+ r1i* tij + b2age+ eij
b00 – average time 0 weight for Placebo group ( X= 0) b10 – average weight improvement for the placebo group for each visit ( X=0) b01 – average time 0 weight difference for treatment group patients b11 – average weight improvement difference for treatment patients for each visit r0i – individual deviation from average intercept r1i – individual deviation from average slope
- does this model look appropriate to my research question, or should I include age as level 2 predictor with treatment?
- I have age recorded as decimals ( for example 62.81341) as well as outcome recorded as (149.1255) will rounding them to 62 and 149.13 make any difference to my results?
- should I include interaction term age*treatment in my proposed model ?