```Date: Wed, 22 Dec 2010 11:06:39 -0800 Reply-To: dfisher@csulb.edu Sender: "SAS(r) Discussion" From: "Dennis G. Fisher, Ph.D." Organization: California State University, Long Beach Subject: Re: Compare risk adjusted mortality rates Comments: To: Morley.Herbert@HCAHEALTHCARE.COM In-Reply-To: <8ECA5DB5E27D214E955AA0084AC9739B56C94F6A6A@FWDCWPMSGCMS06.hca.corpad.net> Content-Type: text/plain; charset="iso-8859-1" I will take a stab at this. I would assert that what you have is two percentages which is the same as two proportions. Assuming that we can treat the two proportions as independent, then you can do a test of the significance of the difference between two proportions as either a chi-square or as a binomial which are mathematically equivalent. The easiest way in SAS is as a chi-square using PROC FREQ. HTH DGF Dennis G. Fisher, Ph.D. Professor and Director Center for Behavioral Research and Services 1090 Atlantic Avenue Long Beach, CA 90813 tel: 562-495-2330 x121 Fax: 562-983-1421 http://www.csulb.edu/centers/cbrs/ -----Original Message----- From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Morley Herbert Sent: Wednesday, December 22, 2010 10:36 AM To: SAS-L@LISTSERV.UGA.EDU Subject: Compare risk adjusted mortality rates I had asked this question previously, but in the interests of brevity, I probably did not make myself clear (or 'perfectly clear - as noted in A Few Good Men). In risk adjusting cardiac patient deaths, we use two factors. The raw mortality rate (opmort) which counts the number of patients dying in a fixed period of time expressed as a percent of the number of surgical cases . Thus we might have 20 deaths in 1000 cases so opmort = 2.00%. Surgical patients run the gamut from those relatively healthy and needing possibly a single artery operation to those needing multiple bypasses, while also having co-morbid diseases, eg: diabetes, obesity, increased age etc. There is an algorithm that produces a predicted risk of mortality (prom) for each patient based on a series of factors. Values range from 0 to 100% theoretically (the bulk of the patients range 5-25%). To compare performance between years, between centers etc, an observed/expected (O/E) ratio is calculated. Numerator is the opmort rate while the denominator is the prom value. A value of 1 indicates the mortality rate is exactly as predicted based on patient disease severity, while < 1 shows performance better than expected. Question - how do I test between two O/E ratios? Say in year 2008 I have 1000 patients with a prom of 2.07 ± 2.11 and opmort rate of 22 deaths in the 1000 cases so the rate is 2.2%. Now the O/E ratio is 1.06. Then in year 2009, there are 1500 patients, prom is 1.99 ± 1.86 while opmort is 19/1500 or 1.27%. Now the O/E ratio is 0.64. I want to know how to test if 2008 is statistically different from 2009. I have an individual prom for each patient, but obviously the opmort is a yes/no count variable. Thanks in advance for any assistance Wishing all list members the best of the season. Morley Herbert ```

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