Cohort Study

Cohort study The exposure(s) of interest is/are determined for each member of the cohort at baseline or time of study; then the group is followed though time to document and compare the incidence of an outcome among the exposed and nonexposed members to examine the relationship between the exposure(s) and the outcome.

Cohort ：A population group with the same characteristic or exposing to a certain factor. 1. Fixed cohort ： 2. Dynamic population:

Characteristics ： 1.Observational study. 2.Have control group. 3. From ‘ cause ’ to ‘ outcome ’ . 4.Could examine the causal association between the exposure and the outcome.

Types of study 1. Prospective cohort study The basic type of cohort study, outcome is not exist when the study begin. 2. Historical cohort study Or retrospective cohort study,exposure was determine according to the past time and outcome exists when the study begin. 3. Ambispective cohort study

Study design and practice Exposure determining is based on descriptive study and case-control study. Characteristics of exposure should be defined, such as character,time, frequency, intensity. Exposure and non-exposure, different levels of exposure should be defined clearly. Collecting data on the demography characteristics of the objects and possible confounding factors.

Outcome Ultimate outcome（ morbidity/ mortality ） or intermedial outcome. （ blood antibody, blood glucose and blood fat reaching a certain level) Quantity or quality. Outcome judgment bases on clear and unified standard. Collect many outcomes possible related to the exposure besides the major outcome.

Study field and population Population is relatively fixed and easy to be followed up. Have better medical condition. Have not apparent enviromental pollution. Have higher incidence rate or exposure rate.

2. Study population Selection: Occupation group Special exposure group Common group Organized group

2. Population Selection of control: Inner control ： Parallel control ： Whole population control ： Multiple controls ：

Cohort size 1. Factors influencing sample size ： （ 1 ） Incidence rate of non- exposed population of common population( P0) ； （ 2 ） Incidence rate of exposed population（ P1 ） or relative( RR) ； （ 3 ） significance level（α）； （ 4 ） power. （ 1 －β）

2.Methods of sample size estimating Cohort size 2.Methods of sample size estimating （ 1 ） According to table （ 2 ） Equation:

Following up and data collecting 1. Baseline data ： Exposure status Quantity , time, style, et al. Disease status ； Those individuals who got the disease at the time of study should be excepted from the study populations. others: Demography characteristics and confounding factor.

Following up and data collecting 1. Methods of baseline data collecting ： Look up records or file ； Visit the objects ； Physical examine and laboratory examine ； Enviromental survey and detection.

2. Follow- up ： Methods ： Contents ： Same with baseline survey. The same methods with two groups. Contents ： Same with baseline data. Outcome is the most important.

2. Follow- up ： End- point ： The time that object has the anticipate outcome. End point of observation ：time when the whole study could reach to a conclusion. Times and time interval of follow-up.

Data sorting and analyses Steps ： Correctness and integrality of the data: Descriptive analyses: Concluding analyses ： The association and intensity of the association between the exposure and the outcome.

Rate calculation 1. Cumulative incidence rate, (CI) Used in fixed cohort with large number.

2. Incidence density, ID Incidence rate per person per year.used in dynamic population. Person years= persons followed × years followed

3.Standardized mortality ratio （ 1 ） SMR SMR =actual deaths (incidences)/anticipated deaths (incidences) SMR > 1 risk factors; SMR < 1 protective factors; SMR = 1 having no association.

3.Standardized ratio （ 2 ） SPMR SPMR =actual deaths (incidences)/anticipated deaths (incidences)

Statistical test of rates differences When the sample size is large, P and （ 1- P ） are not small ， the frequency distribution of sample rates is similar to normal distribution, μ test and x2 test could be used.

Statistical test of rates differences When the sample size is small, the frequency distribution of sample rates is not similar to normal distribution, exact propability , binomial distribution could be used. X2 could be used to test SMR and SPMR.

intensity of association （ effect ） 1 ． Relative risk ， RR Also was called risk ratio or rate ratio, the ratio of the disease or death among the exposed to the risk among the unexposed.

Point estimated value of RR; 95% confidential interval of; Sense of RR ： ① RR>1 risk factor; ② RR<1 protective factor; ③ RR=1 no association. Point estimated value of RR; 95% confidential interval of;

2. Attributable risk , AR AR was also called special risk, and rate difference( RD ） , refers to the difference of disease or deaths between the exposed and unexposed. or

AR is different from RR ： RR the ratio of the disease or death among the exposed to the risk among the unexposed. and has etiology sense. AR is commonly used in a population, refers to ， refers to the extra disease or deaths of exposed compared with unexposed ， could be used in disease prevention and public health.

3. AR% AR% is also called etiologic fraction, (EF), refers to the percentage of disease and deaths attributed to the exposure to the whole disease and deaths among exposed. AR% is associate with RR.

4. Population attributable risk, PAR PAR = It - I0 5. PAR% PAR%=(It - I0) / It × 100% or (* Pe is the exposure rate among the population)

RR ， AR ， AR% refers to the biological effect of the exposure, the effect that causes disease. PAR ， PAR% refers to the social effect of the exposure, the harm of the exposure to the population.

Analyses of dose response relation Calculate incidence rate or death rate, RR and RD by the different levels of exposure. If necessary, should test the tendency of rate by different levels of exposure.

Bias and control Types of bias 1. Selection bias: Cause ：the sample can ’ t represent the population. Is the most common, especially bias of loss to follow-up.

2. Information bias: There is systematic error when collect information on exposure, outcome and others. Is also called misclassification bias. Causes ：instrument is not accurate, diagnosis criteria is not clear, et al.

3. Confounding bias ： The association between exposure and outcome was confounded by extra factors. Confounding factor ： Is a risk factor of the disease, and is associated with the exposure under study. If not equally exist in the exposed and unexposed, it will cause confounding bias. Age, gender and resident location are confounding factors most common to see.

Prevention of bias 1. Prevention of selective bias ： It is difficult to control once exist. Methods ： During the study design ， proper sampling method should be taken, and selective criteria should be clear. Avoid loss to follow-up during the investigate. Fully estimate the influence of loss to follow-up on the result when drew a conclusion.

2. Prevention of information bias ： Methods ： Correcting the instrument ； Unifying and making the criteria clear ； Training the investigator.

3. Prevention of confounding bias ： Methods ： Design ：restriction, matching and randomization. Analyses ： Stratified analyses. Multivariable analyses. Standardized rate calculation.

Advantages and disadvantages 1.Avoiding recalling bias; 2.Calculating incidence/mortality rate and RR AR, getting the causal relationship between the exposure and disease. 3.The time sequence of exposure and disease is clear, and has less bias, can be used to test hypothesis of causal relationship

4.Can study the relationship between one exposure and many outcomes; Advantages: 4.Can study the relationship between one exposure and many outcomes; 5.Sample size is large and the result is stable.

Disadvantages 1. Need lager sample, longer study time, more human resource and financial resource. 2. Can ’ t be used to study disease with low incidence rate. 3. Can ’ t avoid loss to follow up. 4. During the follow up, emerging of unknown variables and the change of known variables will make the analysis complicated.