```Date: Tue, 15 Sep 2009 22:02:39 -0400 Reply-To: Wensui Liu Sender: "SAS(r) Discussion" From: Wensui Liu Subject: Re: rate in PersonYear ==> Linear Model or Poisson Model? Comments: To: Helen In-Reply-To: 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 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 > -- ============================== WenSui Liu Blog : statcompute.spaces.live.com Tough Times Never Last. But Tough People Do. - Robert Schuller ============================== ```

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