Date:         Tue, 15 Sep 2009 22:02:39 -0400
Reply-To:     Wensui Liu <liuwensui@GMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Wensui Liu <liuwensui@GMAIL.COM>
Subject:      Re: rate in PersonYear ==> Linear Model or Poisson Model?
Comments: To: Helen <helen.guiyun.li@gmail.com>
In-Reply-To:  <e4188344-2f7c-4892-a08a-eda3b332c0e2@m3g2000pri.googlegroups.com>
Content-Type: text/plain; charset=ISO-8859-1

Helen,
If I were you, I will model sick_day as poisson response with person year as
offset term.
for the amt $, my hunch is gamma. but i am 100% sure.

On Thu, Sep 10, 2009 at 7:29 PM, Helen <helen.guiyun.li@gmail.com> wrote:

> Dear all,
>
> Many thanks in advance for any suggestions you can provided in the
> following 3 questions.
>
>
> 1. Should I treat the following outcomes as continuous variables
> using
> linear regression modelling, or treat it as count variables using
> Poisson model, please?
>
>
> outcome1 = sick-days/PersonYear
> outcome2 = Amount-of-dollars/PersonYear
>
>
> 2. How to handle the sample size calculation for a Poisson
> distributed
> outcome, please? such as: the CONTROL group rate is 10 events/
> personyear, if I think TREATMENT group with 8 events/personyear
> [(10-8)/10=20% decrease] will be good enough to concluded that the
> TREATMENT is significant better than CONTROL, how many 'personyear'
> in
> each group at 0.05 confidential level with 80% power should I have,
> please? any suggestion (software, paper) is very appreciated!
>
>
> 3. To compare whether or not the rate_2005 (10/PersonYear) vs.
> rate_2006 (20/PersonYear) vs. rate_2007 (40/PersonYear) in whoe BC
> province, which options do you perfer, please?
>
>
> (1) No P value avaialble because no "sampling", the rate_2005, 2006,
> 2007 were calculated based on the all events and all people in whole
> province. We can simply concluded:
> - compared to 2006, rate_2007 increased 50% [(40%-20%)/40% = 50%]
> -compared to 2005, the rate-2007 increased 75% [(40%-10%)/40% = 75%].
>
>
> (2) use Poisson model to get a P value. GEE in the Poisson model to
> handle clustering within subject (one person may have injury in 2005,
> and then in 2006, and then in 2007-multipel innury) .
>
>
> Sincerely
> Helen
>



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WenSui Liu
Blog   : statcompute.spaces.live.com
Tough Times Never Last. But Tough People Do.  - Robert Schuller
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