Hello,
I am facing with an unusual problem of using aggregate data in order
to estimate the attributable cost of a disease, for different stages.
My data set consist of mean and std estimates of the cost outcome
corresponding to strata coming from cross-classification of a set of
factors (age group, gender, co morbidity etc.), as well as the number
of observations in those strata. Those estimates are separate for the
controls and cases (of more than one disease levels). Some strata have
only controls or only cases, and some have only one observation, so no
estimate for sd. So in most cases (except for the ?atomic? strata)
individual patient data are not available. For example, the data set
is something like
disease.level stratum cost.mean cost.sd n.cases
2 STR1 156359.070 NA 1
0 STR1 6298.799 6995.153 53
0 STR2 9892.051 11378.500 38
1 STR3 24264.470 35450.673 14
0 STR4 10946.446 15472.971 81
0 STR5 17095.066 20558.138 50
2 STR5 44130.380 NA 1
0 STR6 15979.599 17771.120 41
where disease level 0 indicates control.
I am interested in the estimation of the coefficients for the
difference disease levels. Since cost is usually very skewed to the
right, gamma or log-normal is usually preferred to normal
distribution. There is also known heteroscedasticity (higher mean =
higher variance) and heterogeneity between strata. I was thinking of
applying some of the approaches for meta-analysis, and perhaps a
random effects model, addressing those issues. I was referred to lme
but I am not sure if it is appropriate (I have no experience with it),
or if other methods (e.g. Bayesian hierarchical models with WinBUGS)
would be preferred.
Any lead or suggestion would be highly appreciated.
Thanks,
Nicholas
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
