Gao Liu <[email protected]>
The outcome is the number of diabetic patients prescribed metformin,
and the total number of diabetic patients treated by the provider
should be included as an explanatory variable. If you can think of the
log ratio prescribed as affected by treatment, you want a poisson-type
model:
g lntot=ln(totalpatients)
xtgee prescribed lntot treatment, link(log) r
On Wed, Sep 17, 2008 at 1:04 PM, Gao Liu <[email protected]> wrote:
> Dear Statalist:
>
> We are estimating the impact of an intervention on the practice of
> health providers. One outcome measure is the ratio of metformin
> prescribed to diabetic patients. This ratio is defined as the ratio
> between the number of diabetic patients prescribed with metformin and
> the total number of diabetic patients treated by the provider. We try
> to examine whether metformin ratios are different for treatment group
> and unexposed group before and after the intervention date. Xtgee is
> used to estimate the impact of the intervention.
>
> But there is a problem: the number of diabetic patients varies a lot
> from one provider to another. Some had only one or two diabetic
> patients in a quarter, while others had more than 50. If a provider
> has only 2 diabetic patients, one of whom was prescribed with
> Metformin, then one patient's drop from being prescribed with
> metformin would lead to a change of 50% in the metformin ratio. In
> contrast, a health provider with 100 diabetic patients will have very
> stable metformin ratio. Thus, it is inappropriate to treat all
> providers the same in the regression.
>
> Should we use the pweight option in the regression? Or is there any
> other better approach that works for this study? Thank you
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