WesVar Analysis Example Replication C7
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- Claribel Jacobs
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1 WesVar Analysis Example Replication C7 WesVar 5.1 is primarily a point and click application and though a text file of commands can be used in the WesVar (V5.1) batch processing environment, all examples presented here use the GUI method. For more information on the batch processing approach, see the WesVar documentation addendum for V5.1. Due to use of GUI method, no syntax is presented prior to results. Typically, WesVar results and setups are stored in WesVar workbooks. The analysis example replication documents include selected parts of the workbook output to highlight key results. For more on additional outputs and program features, see the WesVar documentation. 1
2 Output WesVar Analysis Example Replication C7 Note Codes for Race, Age and Marital Status are reversed to match Stata output. Original codes are and reversed are: Race/Ethnicity 1=Mexican-Am 2=Other Hispanic 3=NH White 4=NH Black 5= Other Race Marital Status 1=Currently Married 2=Previously Married 3=Never Married Gender 1=Male 2=Female Reversed Codes are simply the reversed of original. Example 7.5 Bivariate Testing, Model Parameter Estimates and F Tests (Each model is run as a separate bivariate linear model) Regression Coefficients PARAMETER STANDARD ERROR TEST FOR H0: PARAMETER ESTIMATE OF ESTIMATE PARAMETER=0 PROB> T LOWER 95% UPPER 95% agec rev_race rev_race rev_race rev_race r_gender r_marcat r_marcat Significance Tests TEST F VALUE NUM. DF DENOM. DF PROB>F agec rev_race[5] r_gender[2] r_marcat[3]
3 Example 7.5 Naive Analysis Ignoring Sample Design Features, Not Easily Done in WesVar Example 7.5 Weighted Regression Analysis, Not Easily Done in WesVar Regression Models 3
4 Example 7.5 Appropriate Analysis using All Sample Design Features WESVAR VERSION NUMBER : v TIME THE JOB EXECUTED : 14:12:04 06/30/2017 INPUT DATASET NAME : P:\ASDA 2\Data sets\nhanes 2011_2012\c7_nhanes_r.var TIME THE INPUT DATASET CREATED : 13:13:17 06/30/2017 FULL SAMPLE WEIGHT : WTMEC2YR REPLICATE WEIGHTS : RPL01...RPL31 VARIANCE ESTIMATION METHOD : JKn JKn FACTOR(S) : TYPE OF ANALYSIS : LINEAR VALUE OF ALPHA (CONFIDENCE LEVEL %) : ( %) OPTION OUTPUT REPLICATE COEFFICIENTS : OFF OPTION OUTPUT ITERATION HISTORY : OFF MODEL(S): bpxdi1_1 = r_gender[2] agec rev_race[5] NUMBER OF REPLICATES : 31 NUMBER OF OBSERVATIONS READ : 9756 WEIGHTED NUMBER OF OBSERVATIONS READ : MODEL : bpxdi1_1 = r_gender[2] agec rev_race[5] Class Variable Index : r_gender.1 : 1 r_gender.2 : 2 rev_race.1 : 1 rev_race.2 : 2 rev_race.3 : 3 rev_race.4 : 4 rev_race.5 : 5 4
5 Model Fit MODEL : 4.852e+08 ERROR : 2.736e+10 TOTAL : 2.785e+10 R_SQUARE VALUE : Parameter Estimates (Note that Marital Status is removed from model.) PARAMETER STANDARD ERROR TEST FOR H0: PARAMETER ESTIMATE OF ESTIMATE PARAMETER=0 PROB> T LOWER 95% UPPER 95% INTERCEPT r_gender agec rev_race rev_race rev_race rev_race Note: Ouput residuals and predicted values are not easily produced in WesVar nor are plots based on specific models. As a result, diagnostic plots of residuals v. Predicted values are not shown here. 5
6 Example 7.5 Add Age Squared Term to Model with All Design Features Included WESVAR VERSION NUMBER : v TIME THE JOB EXECUTED : 14:28:54 06/30/2017 INPUT DATASET NAME : P:\ASDA 2\Data sets\nhanes 2011_2012\c7_nhanes_r.var TIME THE INPUT DATASET CREATED : 13:13:17 06/30/2017 FULL SAMPLE WEIGHT : WTMEC2YR REPLICATE WEIGHTS : RPL01...RPL31 VARIANCE ESTIMATION METHOD : JKn JKn FACTOR(S) : TYPE OF ANALYSIS : LINEAR VALUE OF ALPHA (CONFIDENCE LEVEL %) : ( %) OPTION OUTPUT REPLICATE COEFFICIENTS : OFF OPTION OUTPUT ITERATION HISTORY : OFF MODEL(S): bpxdi1_1 = agec agecsq r_gender[2] rev_race[5] NUMBER OF REPLICATES : 31 NUMBER OF OBSERVATIONS READ : 9756 WEIGHTED NUMBER OF OBSERVATIONS READ : MODEL : bpxdi1_1 = agec agecsq r_gender[2] rev_race[5] Class Variable Index : r_gender.1 : 1 r_gender.2 : 2 rev_race.1 : 1 rev_race.2 : 2 rev_race.3 : 3 rev_race.4 : 4 rev_race.5 : 5 6
7 Model Output MODEL : 3.176e+09 ERROR : 2.467e+10 TOTAL : 2.785e+10 R_SQUARE VALUE : PARAMETER STANDARD ERROR TEST FOR H0: PARAMETER ESTIMATE OF ESTIMATE PARAMETER=0 PROB> T LOWER 95% UPPER 95% INTERCEPT agec agecsq r_gender rev_race rev_race rev_race rev_race TEST F VALUE NUM. DF DENOM. DF PROB>F OVERALL FIT agec agecsq r_gender[2] rev_race[5]
8 Example 7.5 Test of Age X Race Interactions and Age X Gender Interactions (Race Entered First and Retained, then Gender Added in Next Model) WESVAR VERSION NUMBER : v TIME THE JOB EXECUTED : 14:52:06 06/30/2017 INPUT DATASET NAME : P:\ASDA 2\Data sets\nhanes 2011_2012\c7_nhanes_r.var TIME THE INPUT DATASET CREATED : 13:13:17 06/30/2017 FULL SAMPLE WEIGHT : WTMEC2YR REPLICATE WEIGHTS : RPL01...RPL31 VARIANCE ESTIMATION METHOD : JKn JKn FACTOR(S) : TYPE OF ANALYSIS : LINEAR VALUE OF ALPHA (CONFIDENCE LEVEL %) : ( %) OPTION OUTPUT REPLICATE COEFFICIENTS : OFF OPTION OUTPUT ITERATION HISTORY : OFF MODEL(S): bpxdi1_1 = agec agecsq r_gender[2] rev_race[5] agec * rev_race[5] agecsq * rev_race[5] NUMBER OF REPLICATES : 31 NUMBER OF OBSERVATIONS READ : 9756 WEIGHTED NUMBER OF OBSERVATIONS READ : MODEL : bpxdi1_1 = agec agecsq r_gender[2] rev_race[5] agec * rev_race[5] agecsq * rev_race[5] Class Variable Index : r_gender.1 : 1 r_gender.2 : 2 rev_race.1 : 1 rev_race.2 : 2 rev_race.3 : 3 rev_race.4 : 4 rev_race.5 : 5 8
9 Model Output TEST F VALUE NUM. DF DENOM. DF PROB>F OVERALL FIT agec agecsq r_gender[2] rev_race[5] agec*rev_race[5] agecsq*rev_race[5] Test of Age*Race
10 Example 7.5 Final Model with Main Effects and Age X Race and Gender Interactions WESVAR VERSION NUMBER : v TIME THE JOB EXECUTED : 08:25:12 07/01/2017 INPUT DATASET NAME : P:\ASDA 2\Data sets\nhanes 2011_2012\c7_nhanes_r.var TIME THE INPUT DATASET CREATED : 13:13:17 06/30/2017 FULL SAMPLE WEIGHT : WTMEC2YR REPLICATE WEIGHTS : RPL01...RPL31 VARIANCE ESTIMATION METHOD : JKn JKn FACTOR(S) : TYPE OF ANALYSIS : LINEAR VALUE OF ALPHA (CONFIDENCE LEVEL %) : ( %) OPTION OUTPUT REPLICATE COEFFICIENTS : OFF OPTION OUTPUT ITERATION HISTORY : OFF MODEL(S): bpxdi1_1 = bpxdi1_1 = agec agecsq r_gender[2] rev_race[5] agec* r_gender[2] agecsq * r_gender[2] agec * rev_race[5] agecsq * rev_race[5] NUMBER OF REPLICATES : 31 NUMBER OF OBSERVATIONS READ : 9756 WEIGHTED NUMBER OF OBSERVATIONS READ : MODEL : bpxdi1_1 = agec agecsq r_gender[2] rev_race[5] agec* r_gender[2] agecsq * r_gender[2] agec * rev_race[5] agecsq * rev_race[5] Class Variable Index : r_gender.1 : 1 r_gender.2 : 2 rev_race.1 : 1 rev_race.2 : 2 rev_race.3 : 3 rev_race.4 : 4 rev_race.5 : 5 10
11 OPTIONS : Intercept, No Standardized Coefficient, Degrees of Freedom = 17 t VALUE : TEST(S) : TEST1 : agec*r_gender.1=0, agecsq*r_gender.1=0, agec*rev_race.1=0,agec*rev_race.2=0, agec*rev_race.3=0, agec*rev_race.4=0, agecsq*rev_race.1=0, agecsq*rev_race.2=0, agecsq*rev_race.3=0, agecsq*rev_race.4=0 Age X Gender : agec*r_gender.1=0, agecsq*r_gender.1=0 BY : age18p = 0 MISSING : 2328 (UNWEIGHTED) (WEIGHTED) NONMISSING : 1564 (UNWEIGHTED) (WEIGHTED) BY : age18p = 1 MISSING : 752 (UNWEIGHTED) (WEIGHTED) NONMISSING : 5112 (UNWEIGHTED) (WEIGHTED) 11
12 Model Output MODEL : 3.351e+09 ERROR : 2.450e+10 TOTAL : 2.785e+10 R_SQUARE VALUE : PARAMETER STANDARD ERROR TEST FOR H0: PARAMETER ESTIMATE OF ESTIMATE PARAMETER=0 PROB> T LOWER 95% UPPER 95% INTERCEPT agec agecsq r_gender rev_race rev_race rev_race rev_race agec*r_gender agecsq*r_gender agec*rev_race agec*rev_race agec*rev_race agec*rev_race agecsq*rev_race agecsq*rev_race agecsq*rev_race agecsq*rev_race TEST F VALUE NUM. DF DENOM. DF PROB>F OVERALL FIT agec agecsq r_gender[2] rev_race[5] agec*r_gender[2] agecsq*r_gender[2] agec*rev_race[5] agecsq*rev_race[5] Age X Race Age X Gender NOTE: No additional regression diagnostics available from WesVar, could be done using other software such as Stata or SAS. 12
13 Example 7.5 Q Weighted Model Using Pfefferman Method (Data set prepared using SAS) WESVAR VERSION NUMBER : v TIME THE JOB EXECUTED : 11:05:15 07/01/2017 INPUT DATASET NAME : P:\ASDA 2\Data sets\nhanes 2011_2012\c7_nhanes_q_r.var TIME THE INPUT DATASET CREATED : 11:01:27 07/01/2017 FULL SAMPLE WEIGHT : q_wtmec2yr REPLICATE WEIGHTS : RPL01...RPL31 VARIANCE ESTIMATION METHOD : JKn JKn FACTOR(S) : TYPE OF ANALYSIS : LINEAR VALUE OF ALPHA (CONFIDENCE LEVEL %) : ( %) OPTION OUTPUT REPLICATE COEFFICIENTS : OFF OPTION OUTPUT ITERATION HISTORY : OFF MODEL(S): bpxdi1_1 = agecsq agec r_gender[2] r_ridreth1[5] agec*r_gender[2] agecsq*r_gender[2] agec*r_ridreth1[5] agecsq*r_ridreth1[5] NUMBER OF REPLICATES : 31 NUMBER OF OBSERVATIONS READ : 9756 WEIGHTED NUMBER OF OBSERVATIONS READ : MODEL : bpxdi1_1 = agecsq agec r_gender[2] r_ridreth1[5] agec*r_gender[2] agecsq*r_gender[2] agec*r_ridreth1[5] agecsq*r_ridreth1[5] Class Variable Index : r_gender.1 : 1 r_gender.2 : 2 r_ridreth1.1 : 1 r_ridreth1.2 : 2 r_ridreth1.3 : 3 r_ridreth1.4 : 4 r_ridreth1.5 : 5 13
14 Model Output MODEL : ERROR : TOTAL : R_SQUARE VALUE : PARAMETER STANDARD ERROR TEST FOR H0: PARAMETER ESTIMATE OF ESTIMATE PARAMETER=0 PROB> T LOWER 95% UPPER 95% INTERCEPT agecsq agec r_gender r_ridreth r_ridreth r_ridreth r_ridreth agec*r_gender agecsq*r_gender agec*r_ridreth agec*r_ridreth agec*r_ridreth agec*r_ridreth agecsq*r_ridreth agecsq*r_ridreth agecsq*r_ridreth agecsq*r_ridreth TEST F VALUE NUM. DF DENOM. DF PROB>F OVERALL FIT agecsq agec r_gender[2] r_ridreth1[5] agec*r_gender[2] agecsq*r_gender[2] agec*r_ridreth1[5] agecsq*r_ridreth1[5]
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