Case-control studies are useful when epidemiologists investigate an outbreak of a disease because the study design is powerful enough to identify the cause of the outbreak especially when the sample size is small. Attributable risks may also be calculated.

Case-control study designs are used to estimate the risk for a disease from a specific risk factor. The estimate is the odds ratio, which is a good estimate of the relative risk especially when the disease is rare.

While a case-control study design offers less support for a causation hypothesis than the longer and more expensive cohort design, it does provide stronger evidence than a cross-sectional study.

Case-control studies are useful when:

• Exposure data is difficult or expensive to obtain
• The disease is rare
• The disease has a long induction and latent period
•  Little is known about the disease
• The underlying population is dynamic

The approach for a case-control study is straightforward. Case-control studies begin by enrolling persons based on their current disease status. Previous exposure status is subsequently determined for each case and control. However, because these studies collect data after the disease has already occurred, they are considered retrospective, which is a limitation.

A most critical and often controversial component of a case-control study is the selection of the controls. Controls must be comparable to cases in every way except that they do not have the disease. Preferably controls are drawn from the same population as the cases. Some studies, though, draw the controls from a different data source. For example, cases may be detected from a disease registry but the controls are selected randomly from another data source. Controls should be selected without regard to their exposure status (e.g., exposed/non-exposed), but may be sampled proportional to their time at risk (which is called density sampling).

There are two basic types of case-control studies, distinguished by the method used to select controls.

• Non-matched case-control study:  The first is a non-matched case-control study in which we enroll controls without regard to the number, or characteristics of the cases. In this study design, the number of controls does not necessarily equal the number of cases.
• Matched case-control study: In a matched study, we enroll controls based on some characteristic(s) of the case. For example, we might match the sex of the control to the sex of the case. The idea in matching is to match upon a potential confounding variable in order to remove the confounding effect. (We will look at how matching occurs in the example below.)

  There are two basic types of matched designs:

  • one-to-n matching (i.e., one case to one control, or one case to a specific number of controls) and
  • frequency-matching, where matching is based upon the distributions of the characteristics among the cases. For example, 40% of the cases are females so we choose the controls such that 40% of the controls are females.

This design is useful when the risk factor/exposure is transient. For example, cell phone use or sleep disturbances are transitory occurrences. Each case serves as its own control, i.e the study is self-matched. For each person, there is a 'case window', the period of time during which the person was a case, and a 'control window', a period of time associated with not being a case. Risk exposure during the case window is compared to risk exposure during the control window.

In a general sense, exposure can be defined as any of a subject's attributes (association) or any agent (effect) with which the subject may come into contact. These attributes or agents may be relevant to his or her health (Armstrong et al., 1998).

This definition would include smoking, drinking, exposure through an occupation (farmers, pesticide applicators, etc.), or age (e.g., menopause >> endogenous estrogen levels) as exposures.

For an environmental factor, exposure can be more precisely defined as contact with some agent at the boundary between humans and the environment, at a specific concentration, over an interval of time (Wallace, 1995). Exposures can be harmful or beneficial.

1. Exposure intensity: the agent/risk factor concentration in the medium that is in contact with the body
2. Exposure frequency: designates how often the exposure occurs
3. Exposure duration: the length of the time that the exposure occurs
4. Microenvironment: defined as any location or activity in which a distinct exposure occurs.

When investigating whether exposure is related to the risk of disease, the epidemiologist must consider many possibilities. Could the effect be related to the concentration? the frequency of exposure? duration? or is there something peculiar to a certain micro-environment?

Environmental tobacco smoke or second-hand smoke is another example. What questions might you ask?

(Consider: Are children exposed to environmental tobacco smoke in their home environment? How many hours are smokers smoking in the house? How many smokers? How frequently is the child in the house? How long have smokers lived in the house?)

Here's another example.

Other factors may result in differential effects of exposure. For example, serious adverse health outcomes may be limited to individuals in a specific setting, region, worksite, or community. Individual susceptibilities may differ. There may be differences in exposure due to the type of agent (chemical, biological or physical), the medium through which the exposure occurs (air, water, food), or the route of exposure (inhaled, ingested, or dermal). In considering a potential association of BaP in grilled burgers with a certain particular cancer, we might ask how much carcinogen is in the charred meat? how much is in the smoke arising from the meat drippings? etc.

As you can see, exposure assessment requires considering many factors that can affect exposure. Let's look further at some of these factors.

Data can be collected by directly monitoring an individual or indirectly, each method with advantages and disadvantages.

Direct Method:

Indirect Method:

Suppose you are asked to estimate the population prevalence of attention deficit hypersensitivity disorder (ADHD) among U.S. school-age children. How will you identify the children who should be counted as cases having ADHD? What defines a ‘case’?

The definition of a ‘case’ is critical in planning an epidemiologic investigation. The case definition must be carefully formulated to meet the objectives of the investigation, while also permitting valid comparisons with results from other studies. In this example, it may be of interest to consider whether the proportion of school-age children with ADHD has changed over a period of time. If the case definition changes significantly from one time period to the next, comparisons with previous years are problematic.
Suppose instead of estimating prevalence, the task is to define cases for a case-control study that is examining the risk from exposure. If the case definition is broad, it will be easier to include prospective cases, hastening the enrollment of study participants. However, variability among the cases will be greater than if the case definition was more narrow. A narrow case definition can slow the identification of sufficient numbers of cases but has the potential to reduce false positives.

Just as a clinical diagnosis for an individual requires meeting specific clinical and laboratory criteria, measuring disease frequency in populations requires the prior stipulation of which clinical, laboratory, epidemiologic, or quantitative criteria indicate the presence of the disease. Case definitions can include a degree of certainty (e.g. probable or confirmed, etc.) or specify the method to be used in assessing whether or not criteria are met.

In the United States, disease surveillance is not a responsibility directly given to the federal government. This means each state in the US may establish its own requirements for reporting diseases. Can you imagine the confusion if all 50 states had used their own definitions for different diseases? To assist the states, the U.S. Centers for Disease Control and Prevention (CDC) and the Council of State and Territorial Epidemiologists have published a set of uniform criteria for reporting cases of  Surveillance Case Definitions for Current and Historical Conditions.

Cases of disease can be categorized as follows:

1. Clinically compatible case

   A clinical syndrome is generally compatible with the disease, as described in the clinical description. A general clinical impression is that this is a case of disease.
2. Confirmed case

   A case that is classified as confirmed for reporting purposes. The case meets established criteria.
3. Epidemiologically linked case

   A case in which…

   1. the patient has had contact with one or more persons who either have/had the disease or have been exposed to a point source of infection (i.e., a single source of infection, such as an event leading to a foodborne disease outbreak, to which all confirmed case-patients were exposed) and
   2. transmission of the agent by the usual modes of transmission is plausible. A case may be considered epidemiologically linked to a laboratory-confirmed case if at least one case in the chain of transmission is laboratory confirmed
4. Laboratory-confirmed case

   A case that is confirmed by one or more of the laboratory methods listed in the case definition under laboratory criteria for diagnosis. Although other laboratory methods can be used in clinical diagnosis, only those listed are accepted as laboratory confirmation for national reporting purposes
5. Probable case

   A case that is classified as probable for reporting purposes. Supportive or presumptive laboratory results: specified laboratory results that are consistent with the diagnosis, yet do not meet the criteria for laboratory confirmation
6. Suspected case

   A case that has a lower certainty; is classified as suspected for reporting purposes
7. Clinically compatible case

   A clinical syndrome is generally compatible with the disease, as described in the clinical description. A general clinical impression is that this is a case of disease.

Each type has utility in different settings. To investigate a highly infectious, transmissible, or serious and deadly disease, casting a broad net will capture all suspected and probable cases. On the other hand, if declaring an individual to be a 'case' is likely to result in imposing severe restrictions, such as closing schools or removing a product from the market, the case definition should be more stringent. For example, a series of suspected cases of disease would not be sufficient to support a product recall. The epidemiologist would prefer to have a confirmed case or a laboratory-confirmed case to justify such action.

With case-control studies, we essentially work down the columns of the 2 × 2 table. Cases are identified first, then controls. The investigator then determines whether cases and controls were exposed or not exposed to the risk factor.

We calculate the odds of exposure among cases (A/C) and the odds of exposure among controls (B/D). The odds ratio is then (A/C)/(B/D), which simplifies after cross-multiplication to (A*D)/(B*C).

For case-control studies, since the ratio of cases to controls is not necessarily representative of the ratio in the population, the odds ratio must be used as the summary measure.  The relative risk is not an accurate measure in this type of study.

Analytic methods for non-matched case-control studies include:

• Chi-square 2 × 2 analysis;
• Mantel-Hanszel statistic (This test takes into account the possibility that there are different effects for the different strata (e.g., effect modification))
• Fisher’s Exact test (This test is used if the expected cell size is <5)
• Unconditional logistic regression (The method is used to simultaneously adjust for multiple confounders; a multivariable analysis).

To review, for a simple non-matched case-control study, you find a case, then determine whether the person is exposed or not. Find a control; determine their exposure status.

In an analysis of a matched study design, only discordant pairs are used. A discordant pair occurs when the exposure status of the case is different from the exposure status of the control. The most commonly used analytic method for matched case-control studies is conditional logistic regression, conditioned upon the matching.

The matched case-control study has linked a case to a control based on the matching of one or more variables. The summary table will differ for a matched case-control study

Case-control studies are one of the two main types of observational study designs in epidemiology. This design is desirable when exposure data are difficult to obtain, the disease is rare or has a long induction period, or when the underlying population is dynamic. In a case-control study, first patients are selected based on their disease (outcome) status and classified as cases or controls. Then investigators obtain and compare the exposure histories between groups. A case-crossover study is a specific type of case-control study where patients serve as their own controls. This is appropriate for transient exposures, where a patient contributes time as both a case and a control.

Cohort studies are useful for estimating disease risk, incidence rates, and/or relative risks. Non-cases may be enrolled from a well-defined population, their current exposure status (at t0) determined, and their disease onset observed over time. Disease status at t1 can be compared to exposure status at t0.

There are two main types of cohort studies: prospective and retrospective.  In general, the descriptor, 'prospective' or 'retrospective', indicates when the cohort is identified relative to the initiation of the study.
Sometimes investigators enter into an ongoing prospective cohort study before the cases are determined. An investigator may pose a new question during an intermediate time period of an ongoing study. The cohort is already determined.

Exposure and case status information is available from the beginning of the ongoing study; subjects can be followed forward to collect cases. For example, suppose genotyping is performed in a cohort study. Later, an investigator may decide to use these data and subsequent case status to consider the relationship of a genetic factor to a particular disease. The investigator may wish to follow the subjects for several more years to ascertain more cases. This would be a mixture of a prospective and retrospective cohort.

In a prospective cohort study, the investigators identify the study population at the beginning of the study and accompanies the subjects through time. When proposing a prospective cohort study, the investigator first identifies the characteristics of the group of people he/she wishes to study. The investigator then determines the present case status of individuals, selecting only non-cases to follow forward in time. Exposure status is determined at the beginning of the study. A member of the cohort reaches the endpoint either by dying, becoming a case, or reaching the end of the study period. A subject can also be lost to follow-up over the course of the study.

Challenges:

• loss to follow up;
• differential nonresponse;
• loss of funding support;
• continually improving methods for detecting exposure (leading to greater misclassification than would be expected in current practice)

An advantage of a well-run cohort study is the multiple outcomes that can be considered. A group well-characterized and followed over a long period of time provides much useful information. For example, the Framingham study has studied 3 generations and added to our understanding of the roles of obesity, HDL lipids, and hypertension in heart disease and stroke as well as contributing an algorithm for predicting CHD risk and identifying 8 genetic loci associated with hypertension. The use of sub-cohorts for specific purposes can minimize cost and the length of a study.

An investigator accesses a historical roster of all exposed and non-exposed persons and then determines their current case/non-case status. The investigator initiates the study when the disease is already established in the cohort of individuals, long after the original measurement of exposure. Doing a retrospective cohort study requires good data on exposure status for both cases and non-cases at a designated earlier time point.

Cohort studies often aim to estimate disease occurrence by cumulative incidence or incidence rates.

An important component of calculating the incidence rate is the calculation of person-time. For each person in the study, the time they contribute is the time from study enrollment until becoming a case, or the time until study completion or dropping out of the study.  

The incidence rate is the number of persons who newly experience the outcome during a specified period of time divided by the sum of the time that each member of the population is at risk.

Since all people in the cohort study are non-cases at the start, if there is a specific exposure of interest, the relative risk of becoming a case can be calculated for the exposed versus non-exposed.  More details regarding analysis methods will be seen later in the course. 

This is a case-control study within a cohort study. At the beginning of the cohort study, (t0), members of the cohort are assessed for risk factors. Cases and controls are identified subsequently at time t1. The control group is selected from the risk set (cohort members who do not meet the case definition at t1.) Typically, the nested case-control study is less than 20% of the parent cohort.

Advantages of nested case-control

• Efficient – not all members of the parent cohort require diagnostic testing
• Flexible – allows testing of hypotheses not anticipated when the cohort was drawn (at t0)
• Reduces selection bias – cases and controls sampled from the same population
• Reduces information bias – risk factor exposure can be assessed with an investigator blind to case status

Disadvantages

• Reduces power (from parent cohort) because of reduced sample size by 1/(c+1), where c = number of controls per case

Nested case-control studies can be matched, not matched, or counter-matched. Matching cases to controls according to baseline measurements of one or several confounding variables is done to control for the effect of confounding variables.

A counter-matched study, in contrast, is when we matched cases to controls who have a different baseline risk factor exposure level. The counter-matched study design is used to specifically assess the impact of this risk factor; it is especially good for assessing the potential interaction (effect modification!) of the secondary risk factor and the primary risk factor. Counter-matched controls are randomly selected from different strata of risk factor exposure levels in order to maximize variation in risk exposures among the controls. For example, in a study of the risk for bladder cancer from alcohol consumption, you might match cases to controls who smoke different amounts to see if the effect of smoking is only evident at a minimum level of exposure.

A case-cohort study is similar to a nested case-control study in that the cases and non-cases are within a parent cohort; cases and non-cases are identified at time t1, after baseline. In a case-cohort study, the cohort members were assessed for risk factors at any time prior to t1. Non-cases are randomly selected from the parent cohort, forming a subcohort. No matching is performed.

Advantages of Case-Cohort Study:

Disadvantages of Case-Cohort Study:

Cohort studies are the second main type of observational study design in epidemiology.  This design is desirable when there are many potential outcomes you want to investigate, when the goal is a direct measure of incidence or risk, and for rare exposures.  Cohort studies can be prospective, where the investigators assemble the cohort and then follow them to observe outcomes, or retrospective when the investigators identify the cohort based on past exposures and evaluate outcomes that have already occurred. Prospective cohort studies are less vulnerable to bias and can evaluate the temporal relationship between exposure and outcome.  Retrospective studies are good for diseases with long induction and latent periods.  Some weaknesses of cohort studies are that they are inefficient for rare outcomes, can be very expensive and time-consuming, and retrospective cohort studies can be more prone to bias. There are many examples of large and small cohort studies, some of which have been going on for decades, and there are still many questions that can be answered from these studies! 

A cross-sectional study is a study with individual-level variables that measures exposure and disease at one point in time. In other words, cross-sectional studies take a snapshot of a population. These types of studies are often used for public health planning.

Advantages & Disadvantages of cross-sectional studies

An ecological study is an observational study in which at least one variable is measured at the group level. An ecological study is especially appropriate for the initial investigation of causal hypotheses.
 
So...why conduct an ecological study? Several reasons support using an ecological study design.

• The hypothesis is relatively new
• Adequate measurement of individual-level variables is not possible
• Adequate design of an individual-level study is not possible (i.e., not ethical)
• We are interested in the effect of ecological variables, for which there is no correlation at the individual level
• We have limited funds or limited time to do the study

Three types of ecological variables:

Analysis of Ecologic Studies

Analytic models in ecologic studies are of different forms:

Experimental studies are used to investigate the role of some agent or intervention in the causation, prevention, or treatment of a disease or outcome.  The investigators assign patients to either receive (or not receive) the intervention, which is the most distinguishing differentiation between an experimental study and an observational study.

More can also be learned about experimental studies in both STAT 503 and STAT 509

Equipoise

Type of intervention (note: this is not an exhaustive list!)

Treatment assignment and randomization

Masking

Additional study designs included in this lesson were cross-sectional, ecological, and experimental. Along with case-control and cohort study designs, these give a good overview of typical study designs used for epidemiologic research. There are also other study designs as well that are not covered in this class, including specific types of experimental study designs. The most important thing to keep in mind is the research question. Only once the investigators are clear about the research question, can the team decide the best study design to use. The study design must fit and be able to answer the research question and not the other way around.  

Cross-sectional studies take a snapshot of a population and are often used for public health planning. The National Health Interview Survey is an example of a cross-sectional survey that has been done over the years to monitor the health of the US population. Although it is done yearly, the same individuals are not included, so it is different from a cohort study. Ecological studies include at least one study variable measured at the group level. These can be very useful for the initial investigation of causal hypotheses, but it is important to be aware and cautious of the possibility of an ecological fallacy. Finally, experimental studies are in contrast to observational studies in that the investigators assign participants to certain study conditions. Both observational and experimental studies are important for the advancement of public health, and it is important to consider the pros and cons of each when deciding how best to study the research/policy/health question of interest. 

Bias is a systematic error in the design, recruitment, data collection, or analysis that results in a mistaken estimation of the true effect of the exposure and the outcome.

Bias occurs when the method used to select subjects or collect data results in an incorrect association.  Bias is something to consider while designing the study - it usually cannot simply be “corrected” in the analysis stage of the study.  

Two main types of bias are selection and information bias.

Measurement error

Sampling variability

P-values and confidence intervals

Confounding is a situation in which the effect or association between an exposure and outcome is distorted by the presence of another variable. Positive confounding (when the observed association is biased away from the null) and negative confounding (when the observed association is biased toward the null) both occur.

If an observed association is not correct because a different (lurking) variable is associated with both the potential risk factor and the outcome, but it is not a causal factor itself, confounding has occurred. This variable is referred to as a confounder. A confounder is an extraneous variable that wholly or partially accounts for the observed effect of a risk factor on disease status. The presence of a confounder can lead to inaccurate conclusions.

Confounders

Controlling potential confounding starts with a good study design including anticipating potential confounders.

Effect modification is not a problem that investigators need to protect against, instead, it is a natural phenomenon that the investigators wish to describe and understand. Different groups may have different risk estimates when effect modification is present.  

Effect modification occurs when the effect of a factor is different for different groups. We see evidence of this when the crude estimate of the association (odds ratio, rate ratio, risk ratio) is very close to a weighted average of group-specific estimates of the association. Effect modification is similar to statistical interaction, but in epidemiology, effect modification is related to the biology of disease, not just a data observation.

In the hypertension example, we saw both stratum-specific estimates of the odds ratio went to one side of the crude odds ratio. With effect modification, we expect the crude odds ratio to be between the estimates of the odds ratio for the stratum-specific estimates.
 
  Why study effect modification? Why do we care?

Planning for effect modification investigation

In addition, to studying design considerations, investigators should also plan to maximize the validity of the study results by minimizing bias and random error.  Bias is a systematic error in the design, recruitment, data collection, or analysis that results in a mistaken estimation of the true effect of the exposure and the outcome, and it usually cannot be “corrected” during analysis.  Random error is the false association between exposure and disease that arises from chance and can arise from two sources - measurement error and sampling variability.  Ways to minimize random error include using large sample sizes, repeating measurements, and selecting random samples to hopefully select a sample that is representative of the population.  

Potential confounding and/or effect modification by variables other than the main exposure/outcome of interest are also very important to consider.  A confounder is a third variable that masks the true relationship between the exposure and outcome, and an effect modifier is a third variable that alters the relationship between the exposure and outcome.  It is important to consider these additional variables before data collection starts to make sure that all important variables are being measured.  Once the data have been collected, variables can be evaluated as potential confounders and/or effect modifiers.