Greetings, everyone.
I need some assistance with the selection of an analysis method for a health
economics data set.
I have a file which contains a random sample from the population. The dependent
variables are the costs of physician services over a 1 year period for various
disease diagnoses. Independent variables include age, sex, and use of tobacco
and
alcohol. I have coded the latter 2 as dichotomous. My difficulty is that, when
looking at heart disease as a diagnosis, the majority of the population, even at
the oldest ages, has a physician cost of $0. Among those for whom costs were
incurred, the costs are, more or less, lognormally distributed. I wish to
compute
the average incremental cost in the population attributable to smoking and saved
by alcohol consumption. (The good news is that for, heart disease and stroke,
the
odds ratio for having a physician's service is 1.5 for those consuming less than
1 drink per day compared with 1 or more drinks per day.)
The data thus consists of 2 subpopulations; the majority with a cost of $0 and
the remainder for whom the cost is lognormally distributed. I've considered a
variety of methods including linear regression (because the residuals will not
be
normally distributed inference will not be valid, but I assume that the means
will be correctly computed), tobit regression (assuming a censor point of
log($1), but again the resuduals will not be appropriately distributed, and an
accelerated failure time model with a lognormal link.
I'd be grateful for advice on how to analyze this dataset.
Best wishes,
Murray Finkelstein
murray.finkelstein@utoronto.ca
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