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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

Design and Analysis of Clinical Study 10. Cohort Study

Dr. Tuan V. Nguyen

Garvan Institute of Medical Research

Sydney, Australia

uses of cohort study
Uses of Cohort Study
  • Identification of risk factors (or prognostic factors)?
  • Uses of risk factors (or prognostic factors)
  • Relation of risk measures to risk factors
  • “It is estimated that smoking is responsible for 100,000 deaths from lung cancer annually”.
slide3
Risk
  • Risk =
    • Prospective chance (probability)
    • Rate of occurrence (incidence)

of a health- related event

  • Measures of risk
    • Incidence density (including mortality rate)
    • Cumulative incidence(including "attack rate")
    • Case-fatality rate
    • Survival rate
criteria to be fulfilled in cohort studies
Criteria to be Fulfilled in Cohort Studies
  • Observation must take place over a meaningful period of time
  • All members of the cohort must be observed. Drop-outs distort the study.
types of cohort
Types of Cohort

Present

Future

Past

Concurrent Cohort

Followed into future

Assembled now

Assembled

from past records

Historical Cohort

Followed till now

Exposure and outcome

Retrospective Cohort

analysis of cohort studies
Analysis of Cohort Studies

Exposed

Time

Diseased (n=39)

Healthy

n = 30 000

Not exposed

Diseased (n=6)

Healthy

n = 60 000

slide8
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

prospective cohort study1
Prospective Cohort Study
  • Basal cohort(s)
      • Sampling from defined population, or
      • Stratified assembly, or
      • Matched assembly
  • Observation for defined period under specified observational protocol
  • Time of data collection: prospective vs. Retrospective cohort studies
factors in prospective cohort study
Factors in Prospective cohort study
  • Event (e.g. disease)
  • Person at risk, population at risk
  • Person-years
population at risk n 200
Population at risk (N=200)

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week 1
Week 1

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O

O

week 2
Week 2

O

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O

O

O

O

week 3
Week 3

O

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O

O

O

O

O

O

person time
Person-time
  • Person-time = # persons x duration

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

estimation of incidence rates
Estimation of Incidence Rates
  • Consider a study where P patient-years have been followed and N cases (eg deaths, survivors, diseased, etc.) were recorded.
  • Assumption: Poisson distribution.
  • The estimate of incidence rate is: I = N / P
  • Standard error of I is:
  • 95% confidence interval of “true” incidence rate: I+ 1.96 x SD(I)
relative risk
Relative Risk
  • Relative risk (RR):

Incidence rate of ischemic heart disease (IHD)

<2750 kcal >2750 kcal

______________________________________________________________

Person-years 1858 2769

New cases 28 17

______________________________________________________________

Estimate rate 15.1 6.1

SD of est. rate 2.8 1.5

  • L = log(RR) = 0.908
  • Standard error of log(RR)
  • 95% of L: L ±1.96xSE

= 0.908 ± 1.96x0.3075

= 0.3055, 1.51

  • 95% of RR:

= exp(0.3055), exp(1.51)

= 1.36, 4.53

analysis of difference in incidence rates
Analysis of Difference in Incidence Rates
  • Difference:

D = 15.1 – 6.1 = 8.93

Incidence rate of ischemic heart disease (IHD)

<2750 kcal >2750 kcal

______________________________________________________________

Person-years 1858 2769

New cases 28 17

______________________________________________________________

Estimate rate 15.1 6.1

SD of est. rate 2.8 1.5

  • Standard error (SE) of D
  • 95% of D

= D ±1.96xSE

= 8.93 ± 1.96x0.032

= 3.65, 14.2

logistic regression analysis using r
Logistic Regression Analysis using R

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

incidence density
Incidence Density

Number of new casesID = ––––––––––––––––––– Population time

Number of new cases 7ID = ––––––––––––––––––– = –––––– Population time ?

incidence rate
Incidence Rate
  • Population time at risk:
    • 200 people for 3 weeks = 600 person-wks
    • But 2 people became cases in 1st week
      • 3 people became cases in 2nd week
      • 2 people became cases in 3rd week
  • Only 193 people at risk for 3 weeks
incidence rate1
Incidence Rate
  • Population-time:
    • 2 people who became cases in 1st week were at risk for 0.5 weeks each = 2 @ 0.5 = 1.0
    • 3 people who became cases in 2nd week were at risk for 1.5 weeks each = 3 @ 1.5 = 4.5
    • 2 people who became cases in 3rd week were at risk for 2.5 weeks each = 2 @ 2.5 = 5.0
    • Non-cases = 193 @ 3 = 579
    • TOTAL POPULATION – TIME = 589.5 Person-weeks
  • Incidence rate:

7 ID = –––––– = 0.0119 cases / person-wk 589.5

average over 3 weeks

incidence proportion
Incidence Proportion

Number of new casesCI = ––––––––––––––––––– Population at risk

73-week CI = –––– = 0.035 200

summary of cohort study s results
Summary of Cohort Study’s Results

Relative risk (RR) = I1 / I2

person time1
Person-time
  • Person-time = # persons x duration

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

estimation of incidence rates1
Estimation of Incidence Rates
  • Consider a study where P patient-years have been followed and N cases (eg deaths, survivors, diseased, etc.) were recorded.
  • Assumption: Poisson distribution.
  • The estimate of incidence rate is: I = N / P
  • Standard error of I is:
  • 95% confidence interval of “true” incidence rate: I+ 1.96 x SD(I)
relative risk1
Relative Risk
  • Relative risk (RR):

Incidence rate of ischemic heart disease (IHD)

<2750 kcal >2750 kcal

______________________________________________________________

Person-years 1858 2769

New cases 28 17

______________________________________________________________

Estimate rate 15.1 6.1

SD of est. rate 2.8 1.5

  • L = log(RR) = 0.908
  • Standard error of log(RR)
  • 95% of L: L ±1.96xSE

= 0.908 ± 1.96x0.3075

= 0.3055, 1.51

  • 95% of RR:

= exp(0.3055), exp(1.51)

= 1.36, 4.53

analysis of difference in incidence rates1
Analysis of Difference in Incidence Rates
  • Difference:

D = 15.1 – 6.1 = 8.93

Incidence rate of ischemic heart disease (IHD)

<2750 kcal >2750 kcal

______________________________________________________________

Person-years 1858 2769

New cases 28 17

______________________________________________________________

Estimate rate 15.1 6.1

SD of est. rate 2.8 1.5

  • Standard error (SE) of D
  • 95% of D

= D ±1.96xSE

= 8.93 ± 1.96x0.032

= 3.65, 14.2

logistic regression analysis using r1
Logistic Regression Analysis using R

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

dubbo osteoporosis epidemiology study
Dubbo Osteoporosis Epidemiology Study
  • 1989 – 1993:
    • Recruit 3000 individuals
    • Measure bone mineral density (BMD)
    • Classified BMD into normal and osteoporosis
  • 1989 – 2005:
    • Record the number of fractures
    • Analysis of association between BMD and fracture
dubbo osteoporosis epidemiology study1
Dubbo Osteoporosis Epidemiology Study

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%

advantages and disadvantages of cohort studies
Direct calculation of incidence

Time sequence can be established

Different outcomes for one agent can be determined

Advantages and Disadvantages of Cohort Studies

Disadvantages

Advantages

  • Large numbers to be measured over a long time
  • Subclinical disease may escape diagnosis
ad