proc phreg sas example


In our previous article we have seen Longitudinal Data Analysis Procedures, today we will discuss what is SAS mixed model. The ESTIMATE statement results show that the effect of increasing x4 by one unit with x3 at its mean is 61.8. Both linear and quadratic effects of AGE are included in the model and the BaseDeficit spline is allowed to interact with both AGE effects. Effect HGB is entered. For binary response models, the ODDSRATIO statement is available in the LOGISTIC procedure. These statements produce the coefficients needed to assess the effect of increasing model year by 1 and 5 years on domestic cars at a horsepower rating of 100. When the variable of interest is categorical, and therefore is specified in the CLASS statement, this is most easily done using the Based on the Wald statistics, neither LogBUN nor HGB is removed from the model. By using the PLOTS= option in the PROC PHREG statement, you can use ODS Graphics to display the predicted survival curves. To make use of it, fit the desired model in PROC PHREG and include one or more HAZARDRATIO statements for the variable(s) to be assessed. For example: ods graphics on; proc phreg plots(cl)=survival; model Time*Status(0)=X1-X5; baseline covariates=One; run; For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS. Best Subset Selection. Consider the following data from Kalbfleisch and Prentice (1980). Examples: PHREG Procedure. Investigators follow subjects until they reach a prespecified endpoint (for example… Output 64.1.1 displays the chi-square statistics and the corresponding p-values. In these SAS Mixed Model, we will focus on 6 different types of procedures: PROC MIXED, PROC NLMIXED, PROC PHREG, PROC GLIMMIX, PROC VARCOMP, and ROC HPMIXED with examples & syntax. The EFFECT and MODEL statements below specify this model. Those coefficients are then used in the ORTHOREG procedure to fit the model and produce the estimates. Following are the coefficients produced by the HAZARDRATIO statement. Output 64.1.2 displays the results of the first model. Example 87.13 and Example 87.14 illustrate Bayesian methodology, and the other examples use the classical method of maximum likelihood. The following statements model the response, Y, as a function of two variables, X3 and X4, and their interaction. The former adds variables to the model, while the latter removes variables from the model. In this model, the predictors are the prestige of the scientists' PhD program (PHD) and the number of young children they have (KID5). The data are available in the SAS/ETS® Sample Library in example programs for the COUNTREG procedure. The PHREG Procedure Example 64.1 Stepwise Regression Krall, Uthoff, and Harley ( 1975 ) analyzed data from a study on multiple myeloma in which researchers treated 65 patients with alkylating agents. This example fits a Poisson model to data from Long (1997) that models the numbers of articles published by scientists (ART) as a function of various predictors. When the interacting variable is categorical rather than continuous, it is the effect of changing the continuous variable at each level of the categorical variable that is of interest. Similarly, the HAZARDRATIO statement is available in the PHREG procedure. Then fit the same model in your intended modeling procedure and add ESTIMATE or CONTRAST statements using those coefficients. The EFFECTPLOT statement below produces a plot of the predicted response against x4 with x3 fixed at its mean. The variable LogBUN is thus entered into the model. The results from PROC ORTHOREG include tables (from the E option, not shown) that verify that the coefficients from PHREG were properly used and tables of estimates. PROC PHREG enables you to plot the cumulative incidence function for each disease category, but first you must save these three Disease values in a SAS data set, as in the following DATA step: data Risk; Disease=1; output; Disease=2; output; Disease=3; output; format Disease DiseaseGroup. Note that if the response contains any negative values, those observations are omitted by PROC PHREG. You can elect to output the predicted survival curves in a SAS data set by optionally specifying the OUT= option in the BASELINE statement. Further, the difference between the estimated response values at the two points is the same as the above estimate. Stepwise selection is requested by specifying the SELECTION=STEPWISE option in the MODEL statement. Stepwise Regression. The next step consists of selecting another variable to add to the model. Lecture 8 (Feb 6, 2007): SAS Proc MI and Proc MiAnalyze XH Andrew Zhou Professor, Department of Biostatistics, University of Washington Measurement, Design, and Analytic Techniques in Mental Health and Behavioral Sciences – p. 1/28 The variables thought to be related to survival are LogBUN (log(BUN) at diagnosis), HGB (hemoglobin at diagnosis), Platelet (platelets at diagnosis: 0=abnormal, 1=normal), Age (age at diagnosis, in years), LogWBC (log(WBC) at diagnosis), Frac (fractures at diagnosis: 0=none, 1=present), LogPBM (log percentage of plasma cells in bone marrow), Protein (proteinuria at diagnosis), and SCalc (serum calcium at diagnosis). PROC PHREG syntax is similar to that of the other regression procedures in the SAS System. It is a good idea to include the E option in the ESTIMATE statement to verify that the coefficients are the same as provided by PROC PHREG. The coefficients can then be used in ESTIMATE statements when fitting the model in PROC GENMOD. It is easiest to simply generate a variable of random values for any nonmissing values in the original response. The contrast coefficients appear in the Hazard Ratios table. These statements use the HAZARDRATIO statement to produce the contrast coefficients to estimate the effects of changing the program prestige by 2 and 3 units when the scientist has no or two young children. Other predictors in the model are the horsepower rating and number of cylinders. We also state All of the procedures mentioned above produce estimates similar to the following from PROC ORTHOREG. The model contains the following effects: Step 2. The model can now be fit using PROC ORTHOREG and the effect estimated using the coefficients provided by the HAZARDRATIO statement. This note discusses and illustrates the use of all five statements in varying models and describes the process involved in determining contrast coefficients. The PLM procedure can use the saved model to produce plots and predicted values. 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, Microsoft Windows Server 2012 R2 Datacenter. By default, the E option in the HAZARDRATIO statement adds to this table the contrast coefficients that estimate the effect of a one-unit increase in x4 at the mean of the interacting continuous variable, x3. The score chi-square for a given variable is the value of the likelihood score test for testing the significance of the variable in the presence of LogBUN. PHREG can also make it. The first observation has survival time 0 and survivor function estimate 1.0. Effect LogBUN is entered. After fitting the model, it is of interest to estimate the effect of increasing BaseDeficit by one unit, from -10 to -9, when AGE is fixed at 10. The DETAILS option requests detailed results for the variable selection process. These statements estimate the change in odds or hazards for fixed amount(s) of change in the specified continuous predictor variable, optionally at specific values of the interacting variable(s). The variable SCalc is then removed from the model in a step-down phase in Step 4 (Output 64.1.6). To determine the coefficients needed in an ESTIMATE statement, fit the model in PROC PHREG and include the HAZARDRATIO statement. PS: The confidence intervals of "Parameter Estimate" and "Hazard Ratio" were both missing. The spline is a very flexible function that can accommodate complex relationships between predictor and response. The following statements define the model and include a HAZARDRATIO statement to produce the coefficients needed to estimate this effect. The advantage of the LSMEANS, SLICE, and LSMESTIMATE statements is that these coefficients are determined for you, removing the considerable chance of error present when using the ESTIMATE or CONTRAST statement. hazardratio x4 / units=1.5 2 at (x3=50 75 100) e; For software releases that are not yet generally available, the Fixed Note that the PARAM=GLM option is specified in the CLASS statement to use the conventional 0/1 coding of dummy variables, which will also be used when fitting the Poisson model in PROC GENMOD. Thousand Oaks, CA: Sage Publications. The variable Time represents the survival time in months from diagnosis. Again, the amount(s) of change in the continuous variable can be specified using the UNITS= option. You can fit the PWP total time model with common effects by using the following SAS statements. Tom In the particular cases of binary response models, such as logistic or probit models, and the Cox survival model, there are statements that again provide an alternative to the more complex ESTIMATE and CONTRAST statements. If the value of VStatus is 0, the corresponding value of Time is censored. The whas100, actg320, gbcs, uis and whas500 data sets are used in this chapter. © 2009 by SAS Institute Inc., Cary, NC, USA. The names of the graphs that PROC PHREG generates are listed separately in Table 66.11 for the maximum likelihood analysis and in Table 66.12 for the Bayesian analysis. The HAZARDRATIO statement produces the following table. Ignore all PHREG procedure output except the values labeled "Coefficient" in the "Hazard Ratios" table. The default is the value of the ALPHA= option in the PROC PHREG statement, or 0.05 if that option is not specified. This section contains 16 examples of using PROC PHREG. The variable LogBUN has the largest chi-square value (8.5164), and it is significant (p=0.0035) at the SLENTRY=0.25 level. Sashelp Data Sets Tree level 1. The variable VStatus consists of two values, 0 and 1, indicating whether the patient was alive or dead, respectively, at the end of the study. Notice in the following statements that model year is involved in one two-way interaction with a categorical variable, in another two-way interaction with a continuous variable, and finally in a three-way interaction with both. The common statistics that you output from PROC LIFETEST are Median, 95% Confidence Intervals, 25th-75th percentiles, Minimum and Maximum, and p-values for Log-Rank and Wilcoxon. Based on the theory behind Cox proportional hazard model, I need the 95% CI. We present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. INTRODUCTION We begin by defining a time-dependent variable and use Stanford heart transplant study as example. The table of coefficients verifies that the coefficients were the same as shown earlier by PHREG. Since the Wald chi-square statistic is significant () at the SLSTAY=0.15 level, LogBUN stays in the model. The following DATA step creates the data set Myeloma. Consider the surgery data modeled with PROC GENMOD in the "Getting Started" section of that procedure's documentation. data simulation; do covariate1=0 to 1; do i=1 to 2000; baseline=0.1; rateratio1=1.5; rateratio2=2; t=rand('exponential',1/(baseline*rateratio1**(covariate1=1)));; if t>5 then t=5+rand('exponential',1/(baseline*rateratio2**(covariate1=1)));; entry=0; event=1; output; end; end; drop i; run; proc phreg data=simulation nosummary; class covariate1/param=glm ; model (entry t)*event(0)=covariate1; run; proc phreg … The EFFECTPLOT statement below is included to visualize the effect of interest. The model assesses the association of subject age (AGE) and a measure of acidity (BaseDeficit) on the log of the serum C-peptide level (logCP). The following statements use PROC PHREG to produce a stepwise regression analyis. The results (not shown) indicate that the interaction is significant. proc lifetest data=example plots=(CIF(test)) conftype=loglog notable ; time time*disease(0)/eventcode=1; strata exposure; run; proc phreg data=example covs(aggregate) plots(overlay=stratum)=cif; model time*disease(0)=exposure/eventcode=1 ties=efron rl; baseline covariates=exposure; run; It is quite powerful, as it allows for truncation, time-varying covariates and provides us with a few model selection algorithms and model diagnostics. Since the determination of contrast coefficients does not depend on the actual response values, you can use any positive values. Lovedeep Gondara Cancer Surveillance & Outcomes (CSO) Population Oncology BC Cancer Agency Competing Risk Survival Analysis Using PHREG in SAS 9.4 Also, to estimate the effect of the change at specific values of the interacting variable(s), specify the AT option. Stepwise Regression Tree level 3. PROC PHREG is a semi-parametric procedure that fits the Cox proportional hazards model (SAS Institute, Inc. (2007c)). One should be carefull in practice, since the survival function can be difficult to estimate in the tail. The removal of SCalc brings the stepwise selection process to a stop in order to avoid repeatedly entering and removing the same variable. A natural cubic spline is applied to BaseDeficit to allow for a complex association of that variable with the response. The contrast coefficients are shown in the Hazard Ratios table. Node 126 of 127. Moreover, we are going to explore procedures used in Mixed modeling in SAS/STAT. Since the response is a count, it contains no negative values and can be used as is in PROC PHREG. Step 1. The data are available in the SAS/STAT® Sample Library in example programs for the GENMOD procedure. Each of the remaining 31 observations represents a distinct event time in the input data set Myeloma. At last, we also learn SAS mixe… The ODS SELECT statement limits the displayed results to this one table. Effect SCalc is removed. It is such that the integrated survival function gives the expected lifetime. For example, to estimate the effect of changing x4 by 1.5 and 2 units at several settings of x3 (50, 75, and 100), the following HAZARDRATIO statement provides the coefficients for use in subsequent ESTIMATE or CONTRAST statements. Output 64.1.3 displays the chi-square statistics and p-values of individual score tests (adjusted for LogBUN) for the remaining eight variables. Since MPG is a nonnegative variable, the variable can be used directly in PROC PHREG. The ICPHREG procedure is specifically designed to handle interval-censored data and offers different … Note that the syntax, x3|x4, is equivalent to specifying all main effects and interactions among variables X3 and X4. In the DATA step that follows, a variable, RAND, is created, which contains a random value between 0 and 1 for any nonmissing value of Y. The model contains the following effects: Step 3. Suppose that the model involves four variables and all possible interactions among three of them. CPREFIX=n specifies that, at most, the first n characters of a CLASS variable name be used in creating names for the corresponding design variables. Node 1 of 16 . When only plots=survival is specified on the proc phreg statement, SAS will produce one graph, a “reference curve” of the survival function at the reference level of all categorical predictors and at the mean of all continuous predictors. The data are available in the SAS/STAT® Sample Library in example programs for PROC ADAPTIVEREG. PROC MEANS displays the estimates at the two points and computes their difference. The variable HGB is selected because it has the highest chi-square value (4.3468), and it is significant () at the SLENTRY=0.25 level. fixed. Firth’s Correction for Monotone Likelihood. Fortunately, it turns out that the HAZARDRATIO statement in PROC PHREG can still be useful because it can tell you what the needed contrast coefficients are when the E option is added. Of particular interest is to estimate the effect of advancing model years on the mileage of domestic cars (ORIGIN=1). The parameterization of CLASS variables used in PROC PHREG should match the parameterization used when fitting the model and estimating effects. The stepwise selection process consists of a series of alternating forward selection and backward elimination steps. title2 'PWP Total Time Model with Common Effects'; proc phreg data=Bladder2; model (tstart,tstop) * status(0) = Trt Number Size; strata Visit; run; proc print data=Pred1(where=(logBUN=1 and HGB=10));run; As shown in Output 89.8.2, 32 observations represent the survivor function for the realization LogBUN=1.00 and HGB=10.0. When the ODS Graphics are in effect in a Bayesian analysis, each of the ESTIMATE, LSMEANS, LSMESTIMATE, and SLICE statements can produce plots associated with their analyses. particular example use Progression Free Survival data points. Individual score tests are used to determine which of the nine explanatory variables is first selected into the model. Unfortunately, when the variable of interest is a continuous variable, rather than a categorical variable in the CLASS statement, the LSMEANS, SLICE, and LSMESTIMATE statements cannot be used. To verify the estimate above, a data set, CHK, is created that contains two settings of x4 that are one unit apart and at the mean of x3. Note that the same CLASS parameterization and model are specified. data test; set dat; array pm25 {15} pm25_1999 - pm25_2013 ; do i = 1 to 15; if (age1999+i-1)

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