Post on 08-Jul-2015
description
Background
Methods
Result & Discussion
Conclusion
Reommendation
Background
Background
Risk Factor of Stroke
Image Source: http://www.easierliving.com/blog/2013/05/14/know-how-to-prevent-recurrent-stroke/
STROKE
Risk Factor of Stroke
Image Source: http://www.easierliving.com/blog/2013/05/14/know-how-to-prevent-recurrent-stroke/
STROKE
Risk Factor of Stroke
Image Source: http://www.easierliving.com/blog/2013/05/14/know-how-to-prevent-recurrent-stroke/
STROKE
Controlled
Research Question
How do the prevalence of diabetes
influence the prevalence of stroke
in Indonesia’s province, given each
province has constant prevalence
of hypertension ?
Background
Methods
Result & Discussion
Conclusion
Recommendation
What is the study design?
Where is the study population?
Image Source: http://www.pksbalikpapantengah.org/2013/05/wujudkan-indonesia-sebagai-sepenggal.html
How is the data collected?
Data Resource:
Data Resource: http://labmandat.litbang.depkes.go.id/
How is the data collected?
Data Resource: http://labmandat.litbang.depkes.go.id/
How is the data collected?
Data Resource: http://labmandat.litbang.depkes.go.id/
How is the data collected?
Data Resource: http://labmandat.litbang.depkes.go.id/
How is the data collected?
Data Resource: http://labmandat.litbang.depkes.go.id/
How is the data collected?
Data Resource: http://labmandat.litbang.depkes.go.id/
Prevalence of
Stroke
How is the data collected?
Data Resource: http://labmandat.litbang.depkes.go.id/
Prevalence of
Diabetes
Prevalence of
Stroke
How is the data collected?
Data Resource: http://labmandat.litbang.depkes.go.id/
Prevalence of
Diabetes
Prevalence of
Stroke
Prevalence of
Hypertension
How is the data analyzed?
Statistical Tool & Model
How is the data analyzed?
Y : prevalence of stroke
X : prevalence of diabetes
X : prevalence of hypertension
i
1i
2i
Statistical Tool & Model
How is the data analyzed?
Y : prevalence of stroke
X : prevalence of diabetes
X : prevalence of hypertension
i
1i
2i
Statistical Tool & Model
E(Y |X , X ) = βX + βX + α (constant) + ɛ (residual error) 1i 2i 1i 2i i
How is the data analyzed?
Y : prevalence of stroke
X : prevalence of diabetes
X : prevalence of hypertension
i
1i
2i
Statistical Tool & Model
STATA ®12
E(Y |X , X ) = βX + βX + α (constant) + ɛ (residual error) 1i 2i 1i 2i i
Background
Methods
Result & Discussion
Conclusion
Recommendation
Regression Assumption
Regression Assumption
1) Independent observation
Regression Assumption
1) Independent observation
2) Linear relationship
Regression Assumption
1) Independent observation
2) Linear relationship
3) Homoscedasticity (constant variance)
Regression Assumption
1) Independent observation
2) Linear relationship
3) Homoscedasticity (constant variance)
4) Y | X, is normaly distributed
510
15
20
.5 1 1.5 2 2.5Prevalence of Diabetes
Regression fit Prevalence of Stroke
Pre
va
len
ce
of
str
ok
e
Prevalence of diabetes
r = 0.61 (p < 0.001)
Peason’s correlation analysis
510
15
20
.5 1 1.5 2 2.5Prevalence of Diabetes
Regression fit Prevalence of Stroke
Pre
va
len
ce
of
str
ok
e
Prevalence of diabetes
r = 0.61 (p < 0.001)
Multivariate linear regression
E(Y | X ,X ) = 6.11 + 4.45 X - 0.083X + ɛ ~N (0, 2.5 ) 1i 2i 1i 2i i
Prevalence of stroke increased significantly with prevalence of diabetes
(t(30) = 4.25 ,p=0.000).
2
510
15
20
.5 1 1.5 2 2.5Prevalence of Diabetes
Regression fit Prevalence of Stroke
Pre
va
len
ce
of
str
ok
e
Prevalence of diabetes
r = 0.61 (p < 0.001)
Multivariate linear regression
E(Y | X ,X ) = 6.11 + 4.45 X - 0.083X + ɛ ~N (0, 2.5 ) 1i 2i 1i 2i i
Prevalence of stroke increased significantly with prevalence of diabetes
(t(30) = 4.25 ,p=0.000).
2
Adj-R = 0.35 2
510
15
20
.5 1 1.5 2 2.5Prevalence of Diabetes
Regression fit Prevalence of Stroke
Pre
va
len
ce
of
str
ok
e
Prevalence of diabetes
r = 0.61 (p < 0.001)
Multivariate linear regression
E(Y | X ,X ) = 6.11 + 4.45 X - 0.083X + ɛ ~N (0, 2.5 ) 1i 2i 1i 2i i
Prevalence of stroke increased significantly with prevalence of diabetes
(t(30) = 4.25 ,p=0.000).
Y : prevalence of stroke i
2
Adj-R = 0.35 2
510
15
20
.5 1 1.5 2 2.5Prevalence of Diabetes
Regression fit Prevalence of Stroke
Pre
va
len
ce
of
str
ok
e
Prevalence of diabetes
r = 0.61 (p < 0.001)
Multivariate linear regression
E(Y | X ,X ) = 6.11 + 4.45 X - 0.083X + ɛ ~N (0, 2.5 ) 1i 2i 1i 2i i
Prevalence of stroke increased significantly with prevalence of diabetes
(t(30) = 4.25 ,p=0.000).
Y : prevalence of stroke i
X : prevalence of diabetes 1i
2
Adj-R = 0.35 2
510
15
20
.5 1 1.5 2 2.5Prevalence of Diabetes
Regression fit Prevalence of Stroke
Pre
va
len
ce
of
str
ok
e
Prevalence of diabetes
r = 0.61 (p < 0.001)
Multivariate linear regression
E(Y | X ,X ) = 6.11 + 4.45 X - 0.083X + ɛ ~N (0, 2.5 ) 1i 2i 1i 2i i
Prevalence of stroke increased significantly with prevalence of diabetes
(t(30) = 4.25 ,p=0.000).
Y : prevalence of stroke i
X : prevalence of diabetes 1i
X : prevalence of hypertension 2i
2
Adj-R = 0.35 2
510
15
20
.5 1 1.5 2 2.5Prevalence of Diabetes
95% CI Regression fit
Prevalence of Stroke
Pre
va
len
ce
of
str
ok
e
Prevalence of diabetes
r = 0.61 (p < 0.001)
Multivariate linear regression
E(Y | X ,X ) = 6.11 + 4.45 X - 0.083X + ɛ ~N (0, 2.5 ) 1i 2i 1i 2i i
Prevalence of stroke increased significantly with prevalence of diabetes
(t(30) = 4.25 ,p=0.000). The increase was estimated to be 4.45% (95% CI from 2.31% -
6.59%) per 1 % increased of prevalence of diabetes,
2
Adj-R = 0.35 2
510
15
20
.5 1 1.5 2 2.5Prevalence of Diabetes
95% CI Regression fit
Prevalence of Stroke
Pre
va
len
ce
of
str
ok
e
Prevalence of diabetes
r = 0.61 (p < 0.001)
Multivariate linear regression
E(Y | X ,X ) = 6.11 + 4.45 X - 0.083X + ɛ ~N (0, 2.5 ) 1i 2i 1i 2i i
Prevalence of stroke increased significantly with prevalence of diabetes
(t(30) = 4.25 ,p=0.000). The increase was estimated to be 4.45% (95% CI from 2.31% -
6.59%) per 1 % increased of prevalence of diabetes, given controlling prevalence of
hypertension (within province has constant prevalence of hypertension).
2
Adj-R = 0.35 2
Background
Methods
Result & Discussion
Conclusion
Recommendation
Thank you