SUGI 28 Statistics and Data Analysis Analysis Univariate Analyses Using PROC

Sugi 28 statistics and data analysis analysis

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SUGI 28 Statistics and Data Analysis
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Analysis Univariate Analyses Using PROC FREQ and PROC UNIVARIATE, an initial univariate analysis of the demographic, exposure, and deployment variables crossed with hospitalization experience was carried out to determine possible significant explanatory variables to be included in the model runs. An exploratory model analysis was then performed to explore the relations between the variables while simultaneously adjusting for all other variables that had influences on the outcome of interest. After investigation of confounding, all variables with p-values of 0.15 or less were considered possible confounders and were retained for the model analysis. Additionally, the distributions of attrition were checked to see if attrition rates differed for the categories of exposure over the study period. Multivariable Cox Modeling Approach Dummy variables were created for reference cell coding of the categorical variables. These were necessary for the output of measures of association using the reference category of choice. Starting with a saturated model, PROC PHREG was run using a manual backward stepwise model building approach. This created a final model with statistically significant effects of explanatory variables on survival times while controlling for possible confounding of exposure effects. SAS Programming PROC PHREG data=analydat; model inhosp*censor(0)=expose1-expose6 pwhsp status1 sex1 age1-age3 ms1 paygr1-paygr2 oc_cat1-oc_cat9 ccep / rl ties=efron ; title1 'Cox regression with exposure status in the model'; run; The options used in this survival analysis procedure are described below: DATA=ANALYDAT names the input data set for the survival analysis. RL requests for each explanatory variable, the 95% (the default alpha level because the ALPHA= option is not invoked) confidence limits for the hazard ratios. TIES=EFRON gives the researcher the approximations to the EXACT method without using the tremendous CPU it takes to run the EXACT method. Both the EFRON and the BRESLOW methods do reasonably well at approximating the EXACT when there are not a lot of ties. If there are a lot of ties, then the BRESLOW approximation of the EXACT will be very poor. If the time scale is not continuous and is therefore discrete, the option TIES=DISCRETE should be used. Stratification By Exposure Status These data were then stratified by exposure and the models were run with the exposure flag covariate withdrawn from the model. This allowed for inspection of confounding between exposure status and covariates. Running these separate models also allowed for the computation of survival function estimates using the BASELINE function in PROC PHREG. The survival curves (which are really step functions, however there are such numerous events that they appear continuous) were now available to compute the cumulative distribution function for the separate exposure categories.
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  • Fall '15
  • kelvin
  • Probability distribution, Probability theory, probability density function, Cumulative distribution function, Survival analysis

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