# 5.1 - Rationale & Designs

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

## Case-Control Study Design Section

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.

### Stop and Think!

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Why can't we determine the incidence rate from a case-control study?

We have selected cases and controls from a population, often an unknown population. For example, we might enroll patients in a hospital, but we don't really know the size of the general population that would have come to the hospital. Also, we have not followed persons at risk to monitor the development of disease. Furthermore, the investigator selects the number of cases relative to the number of controls.

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.

## Case-Crossover Study Design Section

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.

• Efficient – self-matching
• Efficient – select only cases
• Can use multiple control windows for one case window

• Information bias – the inaccurate recall of exposure during the control window (can be overcome by choosing the control window to occur after the case window)
• Requires careful selection of the time period during which the control window occurs (circumstance associated with the control window should be similar to circumstances associated with the case window; e.g., traffic volume)
• Requires careful selection of the length and timing of the windows (e.g., in an investigation of the risk of cell phone usage in auto accidents, cell phone usage that ceases 30 minutes before an accident is unlikely to be relevant to the accident)

## Example & Guidance Material Section

#### Abstract

The first decade of experience with case-crossover studies has shown that the design applies best if the exposure is intermittent, the effect on risk is immediate and transient, and the outcome is abrupt. However, this design has been used to study single changes in exposure level, gradual effects on risk, and outcomes with insidious onsets. To estimate relative risk, the exposure frequency during a window just before outcome onset is compared with exposure frequencies during control times rather than in control persons. One or more control times are supplied by each of the cases themselves, to control for confounding by constant characteristics and self-confounding between the trigger's acute and chronic effects. This review of published case-crossover studies is designed to help the reader prepare a better research proposal by understanding triggers and deterrents, target person times, alternative study bases, crossover cohorts, induction times, effect and hazard periods, exposure windows, the exposure opportunity fallacy, a general likelihood formula, and control crossover analysis.

#### Background

Because of the belief that the use of cellular telephones while driving may cause collisions, several countries have restricted their use in motor vehicles, and others are considering such regulations. We used an epidemiologic method, the case-crossover design, to study whether using a cellular telephone while driving increases the risk of a motor vehicle collision.

#### Methods

We studied 699 drivers who had cellular telephones and who were involved in motor vehicle collisions resulting in substantial property damage but no personal injury. Each person's cellular-telephone calls on the day of the collision and during the previous week were analyzed through the use of detailed billing records.

#### Results

A total of 26,798 cellular telephone calls were made during the 14-month study period. The risk of a collision when using a cellular telephone was four times higher than the risk when a cellular telephone was not being used (relative risk, 4.3; 95 percent confidence interval, 3.0 to 6.5). The relative risk was similar for drivers who differed in personal characteristics such as age and driving experience; calls close to the time of the collision were particularly hazardous (relative risk, 4.8 for calls placed within 5 minutes of the collision, as compared with 1.3 for calls placed more than 15 minutes before the collision; P<0.001); and units that allowed the hands to be free (relative risk, 5.9) offered no safety advantage over hand-held units (relative risk, 3.9; P not significant). Thirty-nine percent of the drivers called emergency services after the collision, suggesting that having a cellular telephone may have had advantages in the aftermath of an event.

#### Conclusions

The use of cellular telephones in motor vehicles is associated with quadrupling the risk of a collision during the brief period of a call. Decisions about the regulation of such telephones, however, need to take into account the benefits of the technology and the role of individual responsibility.