Design and Analysis of Clinical Study 10. Cohort Study. Dr. Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia. Uses of Cohort Study. Identification of risk factors (or prognostic factors)? Uses of risk factors (or prognostic factors)
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Design and Analysis of Clinical Study 10. Cohort Study
Dr. Tuan V. Nguyen
Garvan Institute of Medical Research
Sydney, Australia
of a health- related event
Present
Future
Past
Concurrent Cohort
Followed into future
Assembled now
Assembled
from past records
Historical Cohort
Followed till now
Exposure and outcome
Retrospective Cohort
Exposed
Time
Diseased (n=39)
Healthy
n = 30 000
Not exposed
Diseased (n=6)
Healthy
n = 60 000
Modifiable risk factors
Non-modifiable risk factors
-Medication:
Corticosteroids
- Bone-related factors: BMD,
bone strength indice…
- Fall and fall-related factors
- Prior fracture
- Lifestyle: smoking, alcohol
- Advancing age
- Family history
- Genetics
Fracture
Intervention strategies
Identify high-risk group
O
O
O
O
O
O
O
O
O
O
O
O
O
O
1
o
2
x
2
4
3
4
4
8
5
2
0 2 4 6 8
Time (week)
Incidence rate (IR). During (2+4+4+8+2)=20 person-years,
there were 2 incident cases: IR = 2/20 = 0.1
Incidence rate of ischemic heart disease (IHD)
<2750 kcal >2750 kcal
______________________________________________________________
Person-years18582769
New cases 28 17
______________________________________________________________
Estimate rate 15.1 6.1
SD of est. rate 2.8 1.5
= 0.908 ± 1.96x0.3075
= 0.3055, 1.51
= exp(0.3055), exp(1.51)
= 1.36, 4.53
D = 15.1 – 6.1 = 8.93
Incidence rate of ischemic heart disease (IHD)
<2750 kcal >2750 kcal
______________________________________________________________
Person-years18582769
New cases 28 17
______________________________________________________________
Estimate rate 15.1 6.1
SD of est. rate 2.8 1.5
= D ±1.96xSE
= 8.93 ± 1.96x0.032
= 3.65, 14.2
fracture <- read.table(“fracture.txt”, header=TRUE, na.string=”.”)
attach(fulldata)
results <- glm(fx ~ bmd, family=”binomial”)
summary(results)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0287 -0.8242 -0.7020 1.3780 2.0709
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.063 1.342 0.792 0.428
bmd -2.270 1.455 -1.560 0.119
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 157.81 on 136 degrees of freedom
Residual deviance: 155.27 on 135 degrees of freedom
AIC: 159.27
Number of new casesID = ––––––––––––––––––– Population time
Number of new cases 7ID = ––––––––––––––––––– = –––––– Population time ?
7 ID = –––––– = 0.0119 cases / person-wk 589.5
average over 3 weeks
Number of new casesCI = ––––––––––––––––––– Population at risk
73-week CI = –––– = 0.035 200
Relative risk (RR) = I1 / I2
1
2
2
4
3
4
4
8
5
2
0 2 4 6 8
Time
Incidence rate (IR). During (2+4+4+8+2)=20 person-years,
there were 2 incident cases: IR = 2/20 = 0.1
Incidence rate of ischemic heart disease (IHD)
<2750 kcal >2750 kcal
______________________________________________________________
Person-years18582769
New cases 28 17
______________________________________________________________
Estimate rate 15.1 6.1
SD of est. rate 2.8 1.5
= 0.908 ± 1.96x0.3075
= 0.3055, 1.51
= exp(0.3055), exp(1.51)
= 1.36, 4.53
D = 15.1 – 6.1 = 8.93
Incidence rate of ischemic heart disease (IHD)
<2750 kcal >2750 kcal
______________________________________________________________
Person-years18582769
New cases 28 17
______________________________________________________________
Estimate rate 15.1 6.1
SD of est. rate 2.8 1.5
= D ±1.96xSE
= 8.93 ± 1.96x0.032
= 3.65, 14.2
fracture <- read.table(“fracture.txt”, header=TRUE, na.string=”.”)
attach(fulldata)
results <- glm(fx ~ bmd, family=”binomial”)
summary(results)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0287 -0.8242 -0.7020 1.3780 2.0709
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.063 1.342 0.792 0.428
bmd -2.270 1.455 -1.560 0.119
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 157.81 on 136 degrees of freedom
Residual deviance: 155.27 on 135 degrees of freedom
AIC: 159.27
1287women
Low BMD 345 (27%)
Not Low BMD 942 (73%)
Fx = 137 (40%)
No Fx = 208 (60%)
Fx = 191 (20%)
No Fx = 751 (80%)
42%
Direct calculation of incidence
Time sequence can be established
Different outcomes for one agent can be determined
Disadvantages
Advantages