Design and analysis of clinical study 10 cohort study
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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”.


Design and analysis of clinical study 10 cohort study

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


Design and analysis of clinical study 10 cohort study

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 study

Prospective Cohort Study


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


  • Prospective cohort study2

    Prospective Cohort Study


    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-years18582769

    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-years18582769

    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


    Incidence

    Incidence


    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-years18582769

    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-years18582769

    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


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