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This difference is significantly nonzero (p<0.0001). So, you could easily compute the point estimate of the margin by creating a copy of your data set with your predictor set to one level and then compute all the predicted values and average them. In PROC GENMOD, using the DIFF option in the LSMEANS statement, or specifying the equivalent linear combination of model parameters in the ESTIMATE statement can provide estimates of rates and rate ratios. The relative merits of the odds ratio, risk ratio, and risk difference and procedures for estimating them have been discussed by many epidemiologists and statisticians (1–3). The MODEL statement indicates that the response count, c, is to be modeled as a Poisson variable with mean lambda. While the difference in rates cannot be obtained from the LSMEANS or ESTIMATE statements as noted above, it can be estimated using the NLMeans macro. %���� Year 2011 UTD Variable. stream Standard (or standardized) Incidence Ratio (SIR) is a measure showing how much the incidence rate in a cohort of interest is different from “standard” population-based incidence rate. If you’re ready for career advancement or to showcase your in-demand skills, SAS certification can get you there. Note that the estimated rate difference is exactly the difference between the rates shown in the PredRates data set produced by PROC PLM: 0.0840 - 0.2525 = -0.1685. By exponentiating, you can estimate the rate. The ESTIMATE and LSMEANS statements provide the rate ratio estimate, 0.3327, comparing the two ages. The usage and relative merits of the incidence rate ratio (IRR) and the incidence rate difference (IRD) have received much less attention. 1 0 obj Hi, it maybe helpful to post the Stata code as well? Note that the estimated log rate for age=1 is Intercept+age1 and the estimated log rate for age=2 is just Intercept, where Intercept, age1, and age2 are the parameter estimates of the fitted model shown above. I've been trying to calculate incidence rates for mortality with no success. Calculate incidence rate difference (a kind of attributable risk / excess risk) and its confidence intervals based on approximation, followed by null hypothesis (incidence rate difference equals to 0) testing. <> 2 0 obj An analogous problem for a logistic model is to compute differences of probabilities rather than odds ratios. The rate estimate is provided when the NOOFFSET and ILINK options are used. Re: How to compute incidence rate Posted 12-18-2017 11:08 AM (7023 views) | In reply to Ximena_O You would need to fit exactly the same model in both SAS and Stata since any difference in the fitted model can cause a difference, large or small, in estimated values. The SAS code looks correct and follows what is discussed in this note. A predictive margin for a level of your predictor is the average predicted value when all observations in your data have the predictor set to that level. 4 0 obj Description. 37.5% were up to date. Again, the results match those from the macros above. I moved this to the SAS/STAT community, so that the experts for that may be notified of your question. The same result occurs for medium and large cars. The fitted model is saved for later use with the STORE statement. I used proc genmod with lsmeans to estimate the IR using Poisson, but the results are very very different from those obtained by my colleague using STATA.This is the code I've been using: proc genmod data=muj;class muerto diabetes(ref=first) educacion(ref=first) sexo(ref=last);model muerto=edad educacion  diabetes/dist=poisson                        link=log                        offset=logpyears;lsmeans diabetes/ilink exp cl diff OM;run; The stata code used for this is the following: poisson muerto i.diabetes i.cat_edad i.educacion, exposure(pyears) irrmargins diabetes, predict(ir). Confidence intervals for the rates and the rate ratio are given. This coefficients data set is made available by the E option in the LSMEANS statement and is saved by the ODS OUTPUT statement shown above. You would need to fit exactly the same model in both SAS and Stata since any difference in the fitted model can cause a difference, large or small, in estimated values. endobj %PDF-1.5 fixed. Please Incidence rate can be measured in the format of a fraction like cumulative incidence (CI) or in the format of a rate like incidence density (ID). Specify this function of the model parameters in the f= parameter of the NLEstimate macro. The rate estimates for the two age levels are provided by the EXP option in the LSMEANS statement. Additionally, note that the predictive margin computed in Stata is not the same as an LS-mean. All three are illustrated in this note. Learn how to run multiple linear regression models with and without interactions, presented by SAS user Alex Chaplin. <> The difference in rates is then exp(Intercept+age1) - exp(Intercept). 0 50. endobj The results match those from the NLMeans macro above. To estimate the difference in rates, you need to estimate a nonlinear function of the model parameters. Tune into our on-demand webinar to learn what's new with the program. View source: R/fmsb.R. Finally, you specify that the link function used in the model is the log link. 0 30. Find more tutorials on the SAS Users YouTube channel. Note that the estimated log rate for age 1 is b0+b1 and the estimated log rate for age 2 is just b0. 3 0 obj A confidence interval for the rate difference, (-0.2239, -0.1131), is also given. INCIDENCE RATE AND PREVALENCE RATE Table 1 shows definitions and formulas for different types of IR and PR. What would be the equivalent in SAS fro the margin computed in STATA? In fmsb: Functions for Medical Statistics Book with some Demographic Data. 1 75 However, the difference in rates cannot be obtained with these statements. Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. See the description of the NLEstimate macro for details such as obtaining parameter names. Since the model is saturated in this example, the predicted rates (in the Exponentiated column) are identical to the observed rates — 0.0840 for age 1 and 0.2525 for age 2. For software releases that are not yet generally available, the Fixed <>>> 1 80. Using a subset of the car insurance data that appears in the "Getting Started" section of the GENMOD documentation, the following statements begin by computing estimates of the rates for the two levels of age in the data and the rate ratio comparing the ages as presented in this note. For details about using PROC NLMIXED, see the NLMIXED documentation. endobj The LAMBDA= assignment statement expresses the Poisson mean parameter, lambda, as a function of age, the offset (ln), and the model parameters (b0 and b1). Note that the observed rate is created as variable ObsRate. But your Stata code looks like the model only has one predictor while your SAS code has three, so that will clearly cause a difference. The ESTIMATE statement is used to estimate the rate difference. Since the log of the rate is the response function, such a model enables you to estimate the log rate for a setting of the predictors. Need further help from the community? To use the macro, you need to supply the saved model from the STORE statement and a data set of coefficients that define the individual LS-means. Next, the NLEstimate macro is used to estimate the rate difference. sign in and ask a new question. Thanks for the tip, just included the stata code in the question. Microsoft® Windows® for 64-Bit Itanium-based Systems, Microsoft Windows Server 2003 Datacenter 64-bit Edition, Microsoft Windows Server 2003 Enterprise 64-bit Edition, Microsoft Windows Server 2003 Datacenter Edition, Microsoft Windows Server 2003 Enterprise Edition, Microsoft Windows Server 2003 Standard Edition, Analytics ==> Categorical Data Analysis.

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