© 2010 Jones and Bartlett Publishers, LLC

Design Strategies and Statistical Methods in Analytic Epidemiology
Chapter 7 Design Strategies and Statistical Methods in Analytic Epidemiology © 2010 Jones and Bartlett Publishers, LLC

Objectives Define analytic epidemiology. Distinguish between observational and experimental analytic epidemiologic studies. Define case-control and cohort studies, and identify their distinctive features, strengths, and weakness. Identify appropriate measures of association in case-control and cohort studies. Identify common measures used in epidemiology for describing cohort data. Identify potential biases in case-control and cohort studies. Identify ways to control for biases in case-control and cohort studies at the design and analysis levels. Distinguish between effect modification and confounding. © 2010 Jones and Bartlett Publishers, LLC

Analytic epidemiology
An analytic study attempts to answer why and how a health-related state or event occurred Tests specific a priori hypotheses Comparison group © 2010 Jones and Bartlett Publishers, LLC

Analytic observational versus experimental studies
In analytic observational studies researchers observe relationships between variables In analytic experimental studies a portion of the participants are assigned the intervention Experimental studies will be the focus of the next chapter A variable is any characteristics that can be measured or categorized The observed variables are beyond the control or influence of the researchers © 2010 Jones and Bartlett Publishers, LLC

Analytic observation studies
Case-control studies Cohort (prospective and retrospective) studies These studies may be exploratory or analytic (specific a prior hypothesis) © 2010 Jones and Bartlett Publishers, LLC

Case-control study The outcome is always identified prior to the exposure Identify cases (persons experiencing a health-related state or event) Identify controls (similar except not cases) Investigate whether the cases are more or less likely than controls to have had past experiences, lifestyle behaviors, or exposures © 2010 Jones and Bartlett Publishers, LLC

Selection of cases May consist of new cases (incidence) that show selected characteristics during a specific time period in a specified population and a particular area. Cases may also consist of existing cases at a point in time (prevalence). With prevalence data, it may be more difficult to link a specific cause with a disease outcome because it is influenced by both the development and duration of disease. For example, suppose researchers were interested in assessing whether an association existed between exercise and the prevalence of arthritis. It may be that exercise patterns before the development of arthritis are much different than after the onset of symptoms; thus, the timing of when the exposure was evaluated could have a large impact on the association. For this reason, whenever possible, incident cases are preferred to prevalent cases in case-control studies. © 2010 Jones and Bartlett Publishers, LLC

Sources of cases Cases come from Records from public health clinics Physician offices Health maintenance organizations Hospitals Industrial and government sources Cases should be representative of all persons with the disease © 2010 Jones and Bartlett Publishers, LLC

Selection of cases Sampling Representation requires random selection with a sufficiently large sample size Restriction May improve validity May limit generalization © 2010 Jones and Bartlett Publishers, LLC

Selection of controls Control subjects should look like the case subjects with the exception of not having the disease An epidemiologic assumption is that controls are representative of the general population in terms of probability of exposure and that controls have the same possibility of being selected or exposed as the cases Controls drawn from a population of the same area or populace of the cases should reflect the same gender, age, and other significant factors. Controls from a general population are assumed to be normal and healthy and to reflect the well population from the area. © 2010 Jones and Bartlett Publishers, LLC

Controls Advantages Disadvantages Hospital Easily identified, sufficient number, low cost Subjects more likely aware of antecedent events or exposures Selection factors that influence decision to come to a particular hospital similar to those for cases More likely to cooperate, thereby minimizing potential bias from non-response Differ from healthy people such that they do not accurately represent the exposure distribution in the population where cases were obtained © 2010 Jones and Bartlett Publishers, LLC

Controls Advantages Disadvantages General population Represent the population from which cases were selected More costly and time consuming than hospital controls Population lists might not be available May be difficult to contact healthy people with busy work and leisure schedules May have poorer recall than hospital controls Less motivated to participate than controls from the hospital or special groups © 2010 Jones and Bartlett Publishers, LLC

Controls Advantages Disadvantages Special groups (e.g., family, relatives, friends) Healthier than hospital controls More likely to cooperate than people in the general population Provide more control over possible confounding factors If the exposure is similar to the one experienced by cases, an underestimation of the true association would result © 2010 Jones and Bartlett Publishers, LLC

Exposure status Information about exposure status may be obtained through medical records Interviews Questionnaires surrogates such as spouses, siblings, or employers Because bias can result in studies where the results are based on individual recall, exposure information from medical records is always preferable, when available. For example, researchers interested in assessing the association between chest radiographs during adolescence and female breast cancer should use medical records indicating whether chest radiographs were performed rather than relying on the recall of the study participants, assuming the records exist. It is possible that if the study is based on recall, women with breast cancer would have better recall of having had chest radiographs than women without breast cancer, thereby biasing the results. © 2010 Jones and Bartlett Publishers, LLC

Measuring the association between exposure and outcome variables
The appropriate measure of association to use depends on the nature of the data When exposure and outcome variables are dichotomous (two level nominal data) Odds ratio – use with case-control study Risk ratio – use with cohort study Rate ratio – use with cohort study © 2010 Jones and Bartlett Publishers, LLC

Bias Systematic error in the collection or interpretation of epidemiologic data Results in inaccurate (over or under) estimation of the association between exposure and disease Avoiding bias at the design stage of a study is paramount because of the difficulty to identify and account for it thereafter © 2010 Jones and Bartlett Publishers, LLC

Types of bias in case-control studies
Selection Observation Recall Interviewer © 2010 Jones and Bartlett Publishers, LLC

Selection bias Cases and controls being selected into the study based in some way on the exposure The relationship between exposure and disease among participants in the study differs from what the relationship would have been among individuals in the population of interest © 2010 Jones and Bartlett Publishers, LLC

Observation bias Recall bias – differential accuracy of recall between cases and controls Interviewer bias – interviewer probes cases differently than controls © 2010 Jones and Bartlett Publishers, LLC

Confounding Occurs when an extrinsic factor is associated with a disease outcome and, independent of that association is also associated with the exposure Exposure Outcome Confounder © 2010 Jones and Bartlett Publishers, LLC

Selected strengths and weaknesses of case-control studies Strengths Weaknesses Useful for studying rare outcomes Short duration Relatively inexpensive Relatively small Yields odds ratio (usually a good approximation of relative risk) Does not establish sequence of events Potential bias in measuring exposure variables Limited to a single outcome variable Does not yield prevalence, incidence or excess risk Prone to selection and observation bias © 2010 Jones and Bartlett Publishers, LLC

Case-crossover study design
Compares the exposure status of a case immediately before its occurrence with that of the same case at a prior time The case-crossover study design is especially appropriate where individual exposures are intermittent, wherein the disease occurs abruptly and the incubation period for detection and the induction period are short. © 2010 Jones and Bartlett Publishers, LLC

Nested Case-Control Study Design Also called a case-cohort study
A case control study nested” within a cohort study © 2010 Jones and Bartlett Publishers, LLC

Cohort studies In the context of epidemiology, it generally refers to a group of persons being studied who were born in the same year or time period As time passes, the group moves through different and successive time periods of life; as the group ages, changes can be seen in the health and vital statistics of the group © 2010 Jones and Bartlett Publishers, LLC

NEJ 1989;321: We examined the mortality from breast cancer in a cohort of 31,710 women who had been treated for tuberculosis at Canadian sanatoriums between 1930 and 1952. Women exposed to greater than or equal to 10 cGy of radiation had a relative risk of death from breast cancer of 1.36, as compared with those exposed to less than 10 cGy . The risk was greatest among women who had been exposed to radiation when they were between 10 and 14 years of age With increasing age at first exposure, there was substantially less excess risk, and the radiation effect appeared to peak approximately 25 to 34 years after the first exposure. We conclude that the risk of breast cancer associated with radiation decreases sharply with increasing age at exposure … © 2010 Jones and Bartlett Publishers, LLC

Prospective cohort study
The predictor variable is measured before the outcome has occurred © 2010 Jones and Bartlett Publishers, LLC

Retrospective cohort study
A historical cohort is reconstructed with data on the predictor variable (measured in the past) and data on the outcome collected (measured in the past after some follow-up period) © 2010 Jones and Bartlett Publishers, LLC

Cohort effect Also called generation effect
Is the change and variation in the disease or health status of a study population as the study group moves through time Cohort effects include any exposure or influence from environmental effects to societal changes © 2010 Jones and Bartlett Publishers, LLC

Common measures used in epidemiology for describing cohort data
Cumulative incidence rate – attack rate Incidence density rate – person-time rate © 2010 Jones and Bartlett Publishers, LLC

Measures of association in cohort studies
Ratio of attack rates Risk ratio Ratio of person-time rates Rate ratio © 2010 Jones and Bartlett Publishers, LLC

Cohort with person-time data
Cases Controls Total Exposed a --- PT1 Not Exposed c PT0 a + c PT1 + PT0 Time may be measured in hours, days, weeks, months, or years. © 2010 Jones and Bartlett Publishers, LLC

Equations based on 2x2 table
© 2010 Jones and Bartlett Publishers, LLC

Expressing RR’s as percentages
We can also express these RR’s as percent change. RR > % Increase Change = (RR – 1)x100 RR < % Decrease Change = (1 - RR)x100 © 2010 Jones and Bartlett Publishers, LLC

When does the odds ratio approximate the risk ratio?
For health-related states or events that are rare (i.e., affecting less than 10% of the population), a + b can be approximated by b, and c + d can be approximated by d. Then © 2010 Jones and Bartlett Publishers, LLC

Some common measures used in epidemiology for describing cohort data Cumulative incidence rate in the exposed group: Cumulative incidence rate in the unexposed group: Cumulative incidence in the total group: Risk ratio (or relative risk): Attributable risk: Attributable risk percent: Population attributable risk: Population attributable-risk percent: © 2010 Jones and Bartlett Publishers, LLC

Total cardiovascular disease according to smoking status among 41,782 men aged living in 45 communities across Japan from to the end of 1999 Cases Controls Person-years Current smoker 882 220,965 Non-smoker 673 189,254 Total 1,555 410,219 The incidence rate per 100,000 persons-years of cardiovascular disease among current smokers is 399, among non-current smokers is 356, and overall is 379. The rate ratio (RR) is 1.122, meaning male current smokers are times (or 12.2%) more likely than non-smokers to develop cardiovascular disease. © 2010 Jones and Bartlett Publishers, LLC

Attributable Risk The AR is 43 ( ) per 100,000 Interpretation The excess occurrence of cardiovascular disease among male smokers attributable to their smoking is 43 per 100,000 © 2010 Jones and Bartlett Publishers, LLC

Attributable Risk % The AR% equals 10.9% [( )/1.122 x 100] Interpretation If smoking causes cardiovascular disease, nearly 10.9% of cardiovascular disease in males who currently smoke is attributed to their smoking © 2010 Jones and Bartlett Publishers, LLC

Population Attributable Risk
The PAR for our example is 23 ( ) per 100,000 Interpretation If current smoking were eliminated from the population, we would expect cardiovascular disease incidence rate to drop by 23 per 100,000 © 2010 Jones and Bartlett Publishers, LLC

Population Attributable Risk %
The PAR% is 6.2% [( )/379 x 100] Interpretation If smoking were eliminated from the population, we would expect a 6.2% decrease in the incidence rate of cardiovascular disease © 2010 Jones and Bartlett Publishers, LLC

Double cohort Distinct from conventional cohort studies in that two distinct populations are involved with different levels of an exposure of interest Double-cohorts are employed when the exposure is rare and a relatively small number of people are affected © 2010 Jones and Bartlett Publishers, LLC

Constructing a double cohort study
Outcome Cohort 1 Outcome Samples are taken from each of the two populations, unless the populations are small enough such that the entire populations are considered The two cohorts are followed up and the outcome of interest measured Cohort 2 © 2010 Jones and Bartlett Publishers, LLC

Selecting the study cohort
From population choose those at risk of becoming a case Exclude Individuals who already have a disease outcome of interest (prevalent cases) Those not at risk (e.g., they have had an organ removed such that they cannot become a case) © 2010 Jones and Bartlett Publishers, LLC

Restriction Used to improve validity of study
Restriction involves selecting cohorts with limited exposure, narrow behaviors or activities, or from a limited work environment with restricted exposures or health problems This limits generalization but often improves feasibility and focus © 2010 Jones and Bartlett Publishers, LLC

Healthy worker effect Occurs in cohort studies when workers represent the exposed group and a sample from the general population represents the unexposed group This is because workers tend to be healthier, on average, than the general population © 2010 Jones and Bartlett Publishers, LLC

Loss to follow-up A circumstance where researchers lose contact with study participants, resulting in unavailable outcome data on those people A common problem in cohort studies, increasingly so in cohorts with longer follow-up times Reasons Refusal to participate Unable to locate Unable to be interviewed Death © 2010 Jones and Bartlett Publishers, LLC

Misclassification Differential misclassification arises if exposure classification influences differential accuracy in ascertaining outcome information Non-differential misclassification may arise by inaccuracies in classifying exposure status of individuals, but these misclassifications occur similarly between exposed and unexposed groups © 2010 Jones and Bartlett Publishers, LLC

Controlling for bias in cohort studies
Healthy worker bias may be avoided by selecting a comparison group made up of workers, only unexposed © 2010 Jones and Bartlett Publishers, LLC

Loss to follow-up bias may be minimized by Restricting the study participants to those likely to remain in the study (e.g., excluding those with a highly fatal disease or who are likely to move out of the area) Collecting personal identifying information (e.g., the participants telephone number and address, as well as that of their employer and a family member) Making periodic contact, and providing incentives (e.g., cash or free medical exam) © 2010 Jones and Bartlett Publishers, LLC

Misclassification may be minimized by refining the definition of the exposed and unexposed groups and avoiding exposure classifications that result in differential outcome ascertainment © 2010 Jones and Bartlett Publishers, LLC

Controlling for confounding in cohort studies
At the study design level Restriction to avoid bias due to confounding. In double-cohort studies, confounding reduced by choosing comparison groups as alike as possible to the exposed population. At the analysis level Collecting data on potential confounders at the beginning of the study makes it possible to adjust for these potential confounders at the analysis level through stratification and multiple regression techniques © 2010 Jones and Bartlett Publishers, LLC

Study Design Description Strengths Weaknesses Prospective cohort The investigator identifies participants, measures exposure status, and follows the cohort over time to monitor outcome events. More control over selection of participants and exposure and outcome measures than the retrospective cohort More expensive Longer duration than the retrospective cohort Limited to one exposure variable © 2010 Jones and Bartlett Publishers, LLC

Study Design Description Strengths Weaknesses Retrospective cohort The investigator identifies a cohort with already available exposure and outcome data. Shorter duration Less expensive Fewer numbers required than the prospective cohort More than one exposure can be identified and studied in the same data set Less control over selection of participants and exposure and outcome measures than the prospective cohort © 2010 Jones and Bartlett Publishers, LLC

Study Design Description Strengths Weaknesses Double cohort Two distinct populations with different levels of the exposure are followed. Useful when distinct cohorts have different or rare exposures Potential confounding bias from sampling two populations © 2010 Jones and Bartlett Publishers, LLC

Effect modification When an association between an exposure and disease outcome is modified by the level of an extrinsic factor, beyond random variation, the extrinsic variable is called an effect modifier. Effect modification may occur in either cohort or case-control data © 2010 Jones and Bartlett Publishers, LLC

Examples of confounding and effect modification in case-control studies using stratified data Crude OR Stratum-specific OR Effect (ORc) (OR1) (OR2) Confounding Modification None Absent Positive Absent Negative Absent None Present Positive Present Negative Present © 2010 Jones and Bartlett Publishers, LLC

Confounding and effect modification shown using OR’s
If ORc > OR1 = OR2, positive confounding If ORc < OR1 = OR2, negative confounding (small differences in OR1 and OR2 assumed explained by random error) OR1 ≠ OR2, effect modification present © 2010 Jones and Bartlett Publishers, LLC

Confounding and effect modification treated differently
Control for confounding Present results from effect modification © 2010 Jones and Bartlett Publishers, LLC

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