I aim to estimate one populations mean blood pressure during different years. Here is the setting: (1) 7400 onservations; repeated measurements. Unbalanced. (2) measurements are undertaken anually from 2001 to 2012. Eachindividual maybe measured several times. (3) one can expect a time-bias, i.e as time progress, there will be made advances in therapy, which will lead to more efficient blood pressure lowering. These factors will not be taken into account. Deliberately.
Aim: estimate population mean blood pressure each year. Including confidence interval.
Methods: use lme4 package (linear outcome, blood pressure) to account for repeated measurements and lsmeans package to estimate the population mean each year.
fit <- lmer(bloodpressure ~ year + age + sex + (1 | patient_id), data=data) 'year' is the factor variable for which I'd like to obtsin the means. lsmeans(fit, ~ year) # not tried
Questions - is this method OK? - will the covariance be respected by using lsmeans, or should I use lmerTest package which has a built in function for estimating lsmeans? - each individual has a random slope in the call above, should I adjust it to include random effects for year also?
Thanks for any advice on this