Let us consider the important decision on selecting controls [1] [2] [3] [4] [5]

Two broad types of control groups can be considered, unmatched and matched.

In unmatched or population controls, the aim is to obtain a random sample of the population that gives rise to cases. One suitable method is to seek controls from a population register. A random sample of the source population should be achievable if the register has a high level of completeness, contains the cases (it should be possible to check that all the cases are identified in the register), can identify the parameters specified in the control definition (e.g. area of residence, age), and is accessible to the investigator

If a register is not available or is not suitable, other methods of population sampling can be considered. A commonly used method is random digit dialling [6]. This involves phoning random numbers (cold calling), a system that has the advantage of speed and convenience but has important limitations. The source population is limited to those who have a phone and to those who are available to answer. It may be difficult to be sure that the relevant geographical area is covered, or alternatively one may find that such a large area is covered by the phone listings that it is difficult to find controls from the (smaller) source area. This is more of a problem if phone numbers are used that do not have an area code e.g. mobile phone numbers.   Co-operation from those receiving such calls may be low.

Hospital controls are a type of population control that can be used if the cases have all been admitted to hospital. Controls are easily identified and available at low cost from the same dataset that contains the cases e.g. hospital episode statistics. Disadvantages may be that there are different catchment populations for different diseases so that the controls are not representative of the source population for the cases. More particularly the same causative factors can be responsible for the disease under study and other diseases that result in hospital admission. This will reduce the chances of showing a true association with the causative factor (bring the OR towards 1). In the study of any disease caused by smoking, selection of hospital controls would have a high chance of selecting people who were admitted with other conditions caused by smoking [7].

The above examples of controls are all attempts to draw a random sample of the source population. The following  examples are not attempts to draw a random sample of the population. Controls are selected because they have one or more characteristic in common with the cases.  This method of selection is called matching

Neighbourhood controls. This involves selecting controls from the same neighbourhood as the cases i.e. they are matched for neighbourhood [8]. One advantage is that there is no need for a population register. Also, controls are likely to be similar to cases in respect of socio-economic factors. This may be helpful if we wish to control for such complex factors and if we cannot measure them sufficiently. 

Disadvantages are low co-operation (selection bias), it may be time consuming and expensive (low efficiency), and if we wish to measure the risk associated with socio-economic factors, we may not be able to do so.  In case control study of a disease that has a socio-economic gradient, e.g. invasive meningococcal disease, picking neighbourhood controls may not show any association between illness and level of income. People living in the same neighbourhood are likely to have the same or similar socio-economic characteristics.

Friend controls are another way of selecting matched controls.  Where speed of investigation is of the essence, eg. in a suspected outbreak of E.coli O157, friends  offer a rapid and convenient means of finding controls. Similarity of socio- economic characteristics and social behaviours have the same advantages and disadvantages as neighbourhood controls. In investigations of outbreaks of food borne infection, our aim is to identify a common source. Although friends may be more likely to share similar food habits as their corresponding case leading to an underestimate of the strength of association, the relative risk estimates can still be very high [9]. More of a problem may be a reluctance on the part of the case to give the names of friends to be interviewed [10].

Family controls (relatives) are rarely used in field epidemiology as exposures in family controls are often so similar to those of the cases that the association of interest may not be shown at all.

Cases as their own control. Cases act as their own controls in the case cross over method. Selecting controls in this manner is useful for diseases that have short incubation times and has the advantage of being efficient, achieving perfect matching and controlling for confounding on subject characteristics that are stable over time. A disadvantage, particularly in outbreaks caused by transient contamination of food or water, is that cases that are exposed to the contaminated product during their time as a case will also be more exposed to the product during their time as a control when that exposure was not associated with any increased risk. This will bias the OR towards 1.

Controls with the same disease as cases. In case-case studies, controls have the same disease as cases but are infected with a different subtype of the organism [11]. For example cases of Campylobacter coli have been compared to Campylobacter jejuni cases to investigate potential risk factors [12]. Controls can be selected randomly or through systematic sampling. Both selection bias and recall bias that occurs when cases are compared to healthy controls are removed through this method. Disadvantages are that general risk factors cannot be explored because their distribution will be similar in the two groups and cases and controls will differ for the exposure that led to them being infected. 

The above are given as examples of different types of controls. Other questions on selection of controls may arise when considering different study designs, deciding numbers of controls, and what might happen when asymptomatic cases and immune subjects are included in the control group.  


1. Rothmann KJ. Epidemiology: an introduction. Oxford University Press 2002.

2. Hennekens CH, Epidemiology in Medicine. Lippincott-Williams and Wilkins 1987.

3. Gregg MB. Field epidemiology. Oxford University Press 1996.

4. Wacholder S, McLaughlin JK, Silverman DT, Mandel JS. Selection of controls in case control studies I-III. Am J Epidemiol 1992; 135: 1019-50.

5. Grimes DA and Schulz KF. Compared to what? Finding controls for case-control studies. Lancet.2005;365:1429-33

6. Hartge P, Brinton LA, Rosenthal JF, Cahill JI, Hoover RN, Waksberg J. Random digit dialing in selecting a population-based control group. Am J Epidemiol 1984; 120:825–33.  

7. Cartwright RA, Adib R, Appleyard I, Glashan RW, Gray B, et al. Cigarette smoking and bladder cancer: an epidemiological inquiry in West Yorkshire. J Epidemiol Community Health. 1983; 37:256-63.

8. Werber D, Lausević D, Mugosa B, Vratnica Z, Ivanović-Nikolić L, et al. Massive outbreak of viral gastroenteritis associated with consumption of municipal drinking water in a European capital city. Epidemiol Infect. 2009; 137:1713-20.

9. Killalea D, Ward LR, Roberts D, de Louvois J, Sufi F et al. International epidemiological and microbiological study of outbreak of Salmonella agona infection from a ready to eat savoury snack - I: England and Wales and the United States.  BMJ 1996; 313:1105-7.

10. Boccia D, Charlett A, Bennett S, Orr H, Sarangi J, Stuart JM. Outbreak of a new Salmonella phage type in South West England: alternative epidemiological investigations are needed. Commun Dis Public Health 2004; 7:339-43.

11. McCarthy and Giesecke. Case-case comparisons to study causation of common infectious diseases. Int J Epidemiol 1999; 28:764-8.

12. Sopwith WBirtles AMatthews MFox AGee Set al. Investigation of food and environmental exposures relating to the epidemiology of Campylobacter coli in humans in Northwest England. Appl Environ Microbiol. 2010; 76:129-35.