New research suggests that the occurrence of celiac disease in a subject depends on the subject’s gender and whether the subject already has another autoimmune disorder (such as Hashimoto disease and/or Type I Diabetes). Researchers collected information from 860 patients complaining of gastrointestinal discomfort. data celiac; input gender $ hashimoto $ t1diabetes $ positive total; cards; female yes yes 27 124 female yes no 21 133 female no yes 22 117 female no no 16 109 male yes yes 14 95 male yes no 14 87 male no yes 9 97 male no no 11 99 ; run; title "Logistic model with gender"; proc logistic data=celiac; class gender (ref='male') / param=ref; model positive/total = gender; run; title "Logistic model with gender adjusted for presence of autoimune (t1diabetes and hashimoto) diseases"; proc logistic data=celiac; class gender (ref='male') / param=ref; class hashimoto (ref='no') / param=ref; class t1diabetes (ref='no') / param=ref; model positive/total = gender hashimoto t1diabetes; run; a) Conduct the appropriate hypothesis test at a 5% significance level to test whether adding information about Hashimoto disease and Type I Diabetes as predictors together significantly improves the initial model for occurrence of Celiac disease based on gender. Provide null and alternative hypotheses, test statistic, df, test result and your interpretation. b) Based on the data input into SAS, compute the observed odds of having Celiac disease for a male who does not have Hashimoto disease nor Type I Diabetes. No need for interpretation. c) Based on the second model, compute the estimated odds of having Celiac disease for a male who does not have Hashimoto disease nor Type I Diabetes. No need for interpretation. d) Based on the crude model, compute the Wald chi-squared test statistic for testing whether gender is associated with having Celiac disease. No need to perform the actual hypothesis test. e) Compute the 95% confidence interval for the crude OR of Celiac disease for females compared to males. No need for interpretation. (z0.975 = 1.96)
New research suggests that the occurrence of celiac disease in a subject depends on the subject’s gender and whether the subject already has another autoimmune disorder (such as Hashimoto disease and/or Type I Diabetes). Researchers collected information from 860 patients complaining of gastrointestinal discomfort. data celiac; input gender $ hashimoto $ t1diabetes $ positive total; cards; female yes yes 27 124 female yes no 21 133 female no yes 22 117 female no no 16 109 male yes yes 14 95 male yes no 14 87 male no yes 9 97 male no no 11 99 ; run; title "Logistic model with gender"; proc logistic data=celiac; class gender (ref='male') / param=ref; model positive/total = gender; run; title "Logistic model with gender adjusted for presence of autoimune (t1diabetes and hashimoto) diseases"; proc logistic data=celiac; class gender (ref='male') / param=ref; class hashimoto (ref='no') / param=ref; class t1diabetes (ref='no') / param=ref; model positive/total = gender hashimoto t1diabetes; run; a) Conduct the appropriate hypothesis test at a 5% significance level to test whether adding information about Hashimoto disease and Type I Diabetes as predictors together significantly improves the initial model for occurrence of Celiac disease based on gender. Provide null and alternative hypotheses, test statistic, df, test result and your interpretation. b) Based on the data input into SAS, compute the observed odds of having Celiac disease for a male who does not have Hashimoto disease nor Type I Diabetes. No need for interpretation. c) Based on the second model, compute the estimated odds of having Celiac disease for a male who does not have Hashimoto disease nor Type I Diabetes. No need for interpretation. d) Based on the crude model, compute the Wald chi-squared test statistic for testing whether gender is associated with having Celiac disease. No need to perform the actual hypothesis test. e) Compute the 95% confidence interval for the crude OR of Celiac disease for females compared to males. No need for interpretation. (z0.975 = 1.96)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Problem 1P
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New research suggests that the occurrence of celiac disease in a subject depends on the subject’s gender and whether the subject already has another autoimmune disorder (such as Hashimoto disease and/or Type I Diabetes). Researchers collected information from 860 patients complaining of gastrointestinal discomfort.
data celiac;
input gender $ hashimoto $ t1diabetes $ positive total;
cards;
female yes yes 27 124
female yes no 21 133
female no yes 22 117
female no no 16 109
male yes yes 14 95
male yes no 14 87
male no yes 9 97
male no no 11 99
;
run;
title "Logistic model with gender";
proc logistic data=celiac;
class gender (ref='male') / param=ref;
model positive/total = gender;
run;
title "Logistic model with gender adjusted for presence of autoimune (t1diabetes and
hashimoto) diseases";
proc logistic data=celiac;
class gender (ref='male') / param=ref;
class hashimoto (ref='no') / param=ref;
class t1diabetes (ref='no') / param=ref;
model positive/total = gender hashimoto t1diabetes;
run;
a) Conduct the appropriate hypothesis test at a 5% significance level to test whether adding information about Hashimoto disease and Type I Diabetes as predictors together significantly improves the initial model for occurrence of Celiac disease based on gender. Provide null and alternative hypotheses, test statistic, df, test result and your interpretation.
b) Based on the data input into SAS, compute the observed odds of having Celiac disease for a male who does not have Hashimoto disease nor Type I Diabetes. No need for interpretation.
c) Based on the second model, compute the estimated odds of having Celiac disease for a male who does not have Hashimoto disease nor Type I Diabetes. No need for interpretation.
d) Based on the crude model, compute the Wald chi-squared test statistic for testing whether gender is associated with having Celiac disease. No need to perform the actual hypothesis test.
e) Compute the 95% confidence interval for the crude OR of Celiac disease for females compared to males. No need for interpretation. (z0.975 = 1.96)
Transcribed Image Text:Logistic model with gender adjusted for presence of
autoimune (t1 diabetes andhashimoto) diseases
Criterion Intercept Only
AIC 746.519
SC 751.277
-2 Log L 744.519
Model Fit Statistics
Effect DF
Type 3 Analysis of Effects
Wald
Chi-Square
Intercept and Covariates
Log Likelihood Full Log Likelihood
745.611
45.924
764.644
64.957
737.611
37.924
gender 1 3.9519
hashimoto 1 1.8354
t1 diabetes 1 0.8168
Pr>ChiSq
0.0468
0.1755
0.3661
Analysis of Maximum Likelihood Estimates
Standard Wald
Parameter
DF Estimate
Error Chi-Square
Intercept
1
-2.1479 0.2118 102.8150 <.0001
gender female 1
0.3885 0.1954 3.9519
0.0468
hashimoto yes 1
0.2577
0.1902 1.8354
0.1755
t1diabetes yes 1 0.1710
0.1892
0.8168
0.3661
Odds Ratio Estimates
Point Estimate
Effect
gender female vs male 1.475
hashimoto yes vs no 1.294
t1 diabetes yes vs no 1.186
Pr>ChiSq
95% Wald
Confidence Limits
1.005 2.163
0.891
1.878
0.819 1.719
Transcribed Image Text:Criterion Intercept Only
Logistic model with gender
Model Fit Statistics
AIC 746.519
SC 751.277
-2 Log L 744.519
Effect
gender
Effect
Intercept and Covariates
Log Likelihood Full Log Likelihood
744.247
44.560
753.763
54.076
740.247
40.560
Type 3 Analysis of Effects
Wald
DF
Chi-Square
4.1718
Parameter
Intercept
1
gender female 1
Analysis of Maximum Likelihood Estimates
Standard Wald
DF Estimate
Error Chi-Square
-1.9278
0.3982
Pr>ChiSq
0.0411
gender female vs male 1.489
0.1545
0.1950 4.1718
Odds Ratio Estimates
Point Estimate
Pr>ChiSq
155.7443 <.0001
0.0411
95% Wald
Confidence Limits
1.016
2.182
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