Effects model development methods for statistical analysis
Explore the main effects model development through techniques such as throwing everything in, backward stepwise selection, forward stepwise selection, and all subsets selection. Understand the process of including and dropping variables based on statistical significance for a more refined model.
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1. Throw everything in. 2. Backward Stepwise. 3. Forward Stepwise. 4. All subsets.
1. Throw everything in. Include all candidates and drop if p value too large.
%let target=chd; %let continuous=age pulse chol hematocrit fvcht sbp bmi; %let categorical=diab male mi_chol mi_hem currsmok; /*throw everything in*/ ods select parameterestimates globaltests; proc proc logistic logistic data=a.chd2018_a descending; /*since all categorical variables are 0,1 I don't need a class statement*/ model chd=&continuous &categorical; run run;
Probably, Id also drop mi_hem, since the major reason for the missing indicators is scorability. I might also drop pulse for a couple of reasons (more later)
/*drop hematocrit mi_chol mi_hem and re- run*/ %let target=chd; %let continuous=age pulse chol fvcht sbp bmi; %let categorical=diab male currsmok; /*throw everything in*/ ods select parameterestimates globaltests; proc proc logistic logistic data=a.chd2018_a descending; /*since all categorical variables are 0,1 I don't need a class statement*/ model chd=&continuous &categorical; run run;
First Candidate Main Effects Model, based on throw everything in.
%let target=chd; %let continuous=age pulse chol hematocrit fvcht sbp bmi; %let categorical=diab male mi_chol mi_hem currsmok; ods select parameterestimates; proc proc logistic logistic data=a.chd2018_a descending; model chd=&continuous &categorical/selection=backward; run run;
%let target=chd; %let continuous=age pulse chol hematocrit fvcht sbp bmi; %let categorical=diab male mi_chol mi_hem currsmok; ods select parameterestimates; proc proc logistic logistic data=a.chd2018_a descending; model chd=&continuous &categorical/selection=forward; run run;
Compare forward and backward Backward Forward
All subsets %let target=chd; %let continuous=age pulse chol hematocrit fvcht sbp bmi; %let categorical=diab male mi_chol mi_hem currsmok; proc proc logistic logistic data=a.chd2018_a descending; model chd=&continuous &categorical/selection=score; run run;
Final candidate main effects model, first try %let target=chd; %let continuous_1=age pulse chol fvcht sbp bmi; %let categorical_1=diab male currsmok; proc proc logistic logistic data=a.chd2018_a descending; ods select parameterestimates; model chd=&continuous_1 &categorical_1; run run;
A closer look. Negative Coefficient?
A logit plot %PlotLogits PlotLogits(indata=a.chd2018_a,numgrp=10 indepvar=pulse,depvar=chd); 10,
Final candidate main effects model %let target=chd; %let continuous_1=age chol fvcht sbp bmi; %let categorical_1=diab male currsmok; proc proc logistic logistic data=a.chd2018_a descending; ods select parameterestimates; model chd=&continuous_1 &categorical_1; run run;
