Hi,

I have a panel data of 30 subjects with 15 longitudinal observations each. These subjects were allocated to receive either the intervention A or B. I have measured a number of plasma biomarkers as continuous outcomes (non-normally distributed, so I use the log10 transformation) and have different covariates which are possible confounders.

I am using linear mixed models to compute the effect of the intervention on the outcomes at the different time-points. I would like to adjust the models by potential confounders, but I am not sure if the rule that generally applies to multivariate models (1 adjusting variable for every 10 observations or outcomes) applies here. I suppose that a dataset with a large number of observations per subject allows adjusting for more variables. Could you please help me with this?

My code is:

xtset id week

xtmixed log_biomarker confounder1 confouder2 confounder3 confounder4 confouder5 i.week##arm || idnum:, covariance(independent) vce(robust)

Thanks in advance,

Sergio

I have a panel data of 30 subjects with 15 longitudinal observations each. These subjects were allocated to receive either the intervention A or B. I have measured a number of plasma biomarkers as continuous outcomes (non-normally distributed, so I use the log10 transformation) and have different covariates which are possible confounders.

I am using linear mixed models to compute the effect of the intervention on the outcomes at the different time-points. I would like to adjust the models by potential confounders, but I am not sure if the rule that generally applies to multivariate models (1 adjusting variable for every 10 observations or outcomes) applies here. I suppose that a dataset with a large number of observations per subject allows adjusting for more variables. Could you please help me with this?

My code is:

xtset id week

xtmixed log_biomarker confounder1 confouder2 confounder3 confounder4 confouder5 i.week##arm || idnum:, covariance(independent) vce(robust)

Thanks in advance,

Sergio

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