Dear Statalisters, I am using mixedin Stata 13 to assess the effect of a treatment on four different measures of physical activity in a randomised trial. The problem is that the models run very slowly, often more than 45 minutes before rendering an output, if they report an output at all.
My database has over 1300 cases, and it is multiple imputed, with the multiple imputation command mi generating 50 imputed datasets. The dependent variable is Activity (continuous), and the model adjusts it by a number of covariates, both continuous and categorical, and the baseline value of the dependent variable Activity_b. The model also includes 3 fixed effects: treatment (with values 1,2,3), centre (with values 1,2,3,4 for the participating sites) and frailty (with categories frail, pre-frail and no frail), and one random effect defined by Couple - to account for cluster-randomisation of individuals in a couple to the same treatment.
The command is
where cov is the list of covariates defined as global:
I've tried it in two different computers (Stata 13 and Stata15.1), and the models run extremely slow in both. What can you suggest to increase the speed of estimation?
Thanks a lot !
Marta
My database has over 1300 cases, and it is multiple imputed, with the multiple imputation command mi generating 50 imputed datasets. The dependent variable is Activity (continuous), and the model adjusts it by a number of covariates, both continuous and categorical, and the baseline value of the dependent variable Activity_b. The model also includes 3 fixed effects: treatment (with values 1,2,3), centre (with values 1,2,3,4 for the participating sites) and frailty (with categories frail, pre-frail and no frail), and one random effect defined by Couple - to account for cluster-randomisation of individuals in a couple to the same treatment.
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input double Activity byte treatment double centre float frailty double Activity_b int Couple
. 2 1 2 682.0952380952385 1
. 2 1 1 732.4722222222217 2
739.7619047619044 2 1 1 669.7380952380956 2
853.2222222222217 1 1 1 870.5714285714286 4
622.2619047619044 3 1 1 588.6666666666671 5
. 1 1 2 699.5476190476186 6
. 3 1 1 631.305555555555 7
805.8809523809529 1 1 1 737.3095238095242 8
. 2 1 1 739.2380952380956 9
. 2 1 1 644.305555555555 10
. 2 1 1 697.2857142857143 10
788.5714285714286 3 1 1 768.8809523809529 12
766.7857142857143 1 1 0 761.0476190476186 13
640.4047619047615 2 1 0 657.7857142857143 14
. 3 1 0 695.6666666666671 15
. 3 1 2 714.7619047619044 16
793.3095238095242 3 1 0 776.2619047619044 17
746.97619047619 3 1 0 751.0714285714286 17
. 2 1 2 799.3333333333329 19
. 2 1 0 845 20
end
label values frailty frailty_lbl
label def frailty_lbl 0 "No frail", modify
label def frailty_lbl 1 "Pre-frail", modify
label def frailty_lbl 2 "Frail", modify
The command is
Code:
mi estimate: mixed Activity i.treatment i.centre i.frailty $cov Activity_b || Couple:, residuals (independent, by (centre))
Code:
global cov "covariate1 covariate2 covariate3"
Thanks a lot !
Marta
