Chapter 11

Case-Control Studies

Overview

Individual participants in a case-control study are selected for inclusion in the study based on their disease status•Cases = participants with the disease of interest•Controls = participants without the disease

Both cases and controls are asked the same set of questions about past exposures

FIGURE 11- 2 Framework for a Case-Control Study

(The letters a, b, c, and d correspond to the equation shown in Figure 11-4.)

Use

A case-control study is often the best study approach when the disease is relatively uncommon and a study of the general population is unlikely to yield more than a few cases

Finding Cases

• A key initial step is identifying an appropriate and accessible source of individuals with the disease of interest

• Hospitals, specialty clinics, physicians’ offices, public health agencies, disease registries, and disease support groups may be able to assist researchers in identifying individuals who are likely to meet the study’s case definition

Case Definition

• All cases must have the same disease, disability, or other health-related condition

• A case definition should specify exactly what characteristics must be present or absent for a person to be deemed a case

Finding Controls

• Controls must be reasonably similar to cases except for their disease status

• The inclusion and exclusion criteria for cases that do not specifically relate to the disease should also apply to controls– For example, if cases must be males between 25

and 39 years of age, controls must also be men in this age group

Finding Controls

Controls may be recruited from, among other sources:•Friends and relatives of cases•Hospital or clinic patients without the disease of interest•The general population

Matching

There are three basic options for matching cases and controls: •no matching•frequency (group) matching•matched-pairs (individual) matching

Matching

• Many case-control studies use no matching. • They assume that similar inclusion and exclusion

criteria for cases and controls will result in case and control populations that have similar distributions according to sex, age group, socioeconomic status, and other characteristics.

Matching

• The goal of frequency (group) is to recruit a control population that is similar to the case population.

• Individual cases are not tied to individual controls during analysis.

• Example: For each hospital case a researcher identifies select one control from the hospital registration files who was admitted the same week as the case, who is the same sex as the case, and who is within ± 3 years of the age of the case.

Matching

• In matched-pairs (individual) matching, each case is personally linked to a particular individual control.

• This approach is fairly common in genetic studies, in which a case is linked to a genetic sibling or other close genetic relative for analysis.

• This kind of matched-pairs approach requires a special type of analysis.

Limitations of Matching

• The variables used as matching criteria cannot be considered as exposures during analysis. – Example: If cases and controls are matched based

age, then the cases and controls in the study will have the same mean age, even if in the general population cases tend to be older than non-cases.

• It can be difficult to find controls who meet all of the matching criteria when there are many matching characteristics.

Misclassification Bias

• All participants must be asked questions that confirm whether each is a case, a control, or neither

• Adhering to strict definitions for what constitutes a case and what constitutes a control minimizes the risk of misclassification bias

Recall Bias

• Recall bias occurs when cases and controls systematically have different memories of the past

• Cases may have more vivid memories than controls of participation or lack of participation in activities perceived to be risky or beneficial because they are searching for explanations for their illnesses

Analysis: Odds

• Odds are the most familiar from their connection with betting

• A horse with an equal chance of winning a race (50% likely to win) or of losing a race (50% likely to lose) is said to have “even odds,” or odds of 1 (50%/50%)

Analysis: Odds

• A case-control study compares the chance of having had a particular exposure to not having had it

• If 50% of the participants in a study report a history of exposure and 50% report no exposure history, then the odds of exposure are 50%/50%, or 1

• If 25% report having the exposure and 75% do not, then the odds are 25%/75%, or 0.33

• If 2% report being exposed in the past and 98% report no exposure, then the odds are 2%/98%, or 0.02

FIGURE 11- 3 Odds

2 × 2 Tables

• Two-by-two tables compare two dichotomous (yes/no) variables

• The columns are for disease status (case = yes and control = no)

• The rows are for exposure status (exposed = yes and not exposed = no)

• All of the participants in the study are assigned to one of the four resulting boxes

Analysis: Odds Ratios

Odds ratio (OR) = the ratio of the odds of exposure in cases to the odds of exposure in controls

Figure 11-4: Analysis:

Odds Ratios

Analysis: Odds Ratios

• OR = 1: the odds of exposure are the same for cases and controls

• OR > 1: cases have higher odds of exposure than controls, implying that the exposure was risky

• OR < 1: cases have lower odds of exposure than controls, implying that the exposure was protective

Analysis: OR & 95% CI

• If the entire 95% confidence interval is less than 1, then the OR is statistically significant, and the exposure is deemed protective– “Cases had greater odds of exposure than controls”

• If the entire 95% confidence interval is greater than 1, then the OR is statistically significant, and the exposure is deemed risky– “Cases had lesser odds of exposure than controls.”

Analysis: OR & 95% CI

• 95% confidence interval (95% CI) overlaps OR = 1– The lower end of the confidence interval is less

than 1, suggesting protection– The higher end of the confidence interval is greater

than 1, suggesting risk– Conclusion: The OR is not statistically significant,

and the exposure and disease are deemed to have no association

FIGURE 11-5 Interpretation of the Odds Ratio Based on Its 95% Confidence Interval

FIGURE 11-6 Examples of Odds Ratio Calculations

Matched Case-Control Studies

• Individually-matched case-control studies require the calculation of a matched-pairs odds ratio that uses a special kind of 2×2 table

• A ratio of the number of times the case in a pair was exposed and the control was not to the number of times the control in a pair was exposed and the case was not provides an estimate for a special type of odds ratio

FIGURE 11-7 Matched-Pairs Odds

Ratio

FIGURE 11- 1 Key Characteristics of Case-Control Studies