7.4 Logistic regression in domain analysis

If we want to investigate the experiencing dry mouth indicated by the variable “\(\texttt{ORH_EXP_DRM_MCQ}\)” for the group of people who responses in English, we may apply the following codes.

R

LogisticReg3_EN<-svyglm(ORH_EXP_DRM_MCQ ~ SEX_ASK_TRM + Age_group_5 +
                  Education + WGHTS_PROV_TRM, family =quasibinomial, 
                  design = subset(CLSA.design.anly, startlanguage == "en"))
summary(LogisticReg3_EN)

SAS

PROC SURVEYLOGISTIC data = CLSAData ;            
CLASS ORH_EXP_DRM_MCQ  SEX_ASK_TRM Age_group_5 Education 
      WGHTS_PROV_TRM /param = ref ;
MODEL ORH_EXP_DRM_MCQ(event = 'Yes') = SEX_ASK_TRM Age_group_5 Education 
      WGHTS_PROV_TRM /clodds ; 
DOMAIN  startlanguage;    
STRATA GEOSTRAT_TRM ;             
WEIGHT WGHTS_ANALYTIC_TRM;                                         
ODS output ParameterEstimates = Est;
RUN; 

PROC PRINT data = EST;
WHERE  startlanguage ="en";
FORMAT _numeric_ 15.9; RUN; 

SPSS

Analyze \(\rightarrow\) Complex Samples \(\rightarrow\) Logistic Regression… -\(\rightarrow\) Select the file “CLSADesignAnyl.csaplan” in the Plan panel \(\rightarrow\)select [Target Variables] to the “Dependent Variable”, “Factor” and “Covariate” panels \(\rightarrow\) Select \(\texttt{startlanguage}\) to “Subpopulation” and enter the target category \(\rightarrow\) Click “Reference Category” and select “Lowest value” \(\rightarrow\) Click “Statistics…” \(\rightarrow\) select “Estimate” and “Standard error” \(\rightarrow\) Click “Continue” \(\rightarrow\) Click “OK”.

Stata

svyset entity_id, strata(StraVar) weight(WGHTS_ANALYTIC_TRM) 
                    vce(linearized) singleunit(certainty) 
svy linearized,  bpop(if startlanguage == "en"):logit ORH_EXP_DRM_MCQ 
                i.SEX_ASK_TRM i.Age_group_5 ib3.Education i.WGHTS_PROV_TRM

Result comparison

	R		SAS		SPSS		Stata
Population Estimates	Coeff.	SE	Coeff.	SE	Coeff.	SE	Coeff.	SE
(Intercept)	-0.9380	0.7591	-0.9380	0.7673	-0.9380	0.7591	-0.9380	0.7591
SEX_ASK_TRM=“M”	0.0224	0.3212	0.0224	0.3246	0.0224	0.3212	0.0224	0.3212
Age Groups: relative to Age_Gpr0: Age 45-48
Age_Gpr1:Age 49-54	-0.1932	0.6036	-0.1932	0.6101	-0.1932	0.6036	-0.1932	0.6036
Age_Gpr2:Age 55-64	-0.0805	0.5351	-0.0805	0.5409	-0.0805	0.5351	-0.0805	0.5351
Age_Gpr3:Age 65-74	-0.3359	0.6068	-0.3359	0.6133	-0.3359	0.6068	-0.3359	0.6068
Age_Gpr4:Age 75+	-0.1661	0.5741	-0.1661	0.5803	-0.1661	0.5741	-0.1661	0.5741
Education Levels: relative to Lower Education
Medium Education	-0.4169	0.4932	-0.4169	0.4985	-0.4169	0.4932	-0.4169	0.4932
Higher Education lower	-0.3929	0.5497	-0.3929	0.5556	-0.3929	0.5497	-0.3929	0.5497
Higher Education upper	-0.2881	0.4922	-0.2881	0.4975	-0.2881	0.4922	-0.2881	0.4922
Provinces: relative to Alberta
British Columbia	-0.1843	0.5509	-0.1843	0.5569	-0.1843	0.5509	-0.1843	0.5509
Manitoba	0.8442	0.6592	0.8442	0.6664	0.8442	0.6592	0.8442	0.6592
New Brunswick	-0.6450	0.8076	-0.6450	0.8163	-0.6450	0.8076	-0.6450	0.8076
Newfoundland & Labrador	-1.5032	0.8618	-1.5032	0.8711	-1.5032	0.8618	-1.5032	0.8618
Nova Scotia	1.1995	0.6817	1.1995	0.6890	1.1995	0.6817	1.1995	0.6817
Ontario	-0.2185	0.5167	-0.2185	0.5222	-0.2185	0.5167	-0.2185	0.5167
Prince Edward Island	0.2387	0.8349	0.2387	0.8440	0.2387	0.8349	0.2387	0.8349
Quebec	0.0644	0.5627	0.0644	0.5688	0.0644	0.5627	0.0644	0.5627
Saskatchewan	0.4553	0.6699	0.4553	0.6772	0.4553	0.6699	0.4553	0.6699