8.2 Managing confounding

In Sect. 7.2 different methods were listed for managing confounding in experimental studies. Some, but not all, of these are still possible in observational studies:

  • Restricting the study to a certain group (for example, only people under 30).
  • Blocking. Analyse the data separately for different groups (for example, analyse the data separately for people under 30, and 30 and over).
  • Analysing using special methods (after measuring the age of each subject).
  • Randomly allocating people to groups: Not possible in observational studies.

8.2.1 Restrictions

As with experimental studies, observational studies can be restricted to certain parts of the population. For example, in the smoking study of Doll and Hill (1954), participants were restricted to males aged under 35 years since, at the time of the study:

… lung cancer is relatively uncommon in women and rare in men under 35 [and] useful figures are unlikely to be obtained in these groups for some years to come.

Doll and Hill (1954), p. 1452.

The reason for the restriction should be justified if possible (as shown in the quote above).

8.2.2 Blocking

Blocking can be used with observational studies; for example, those aged under 30 and those aged 30 or over could be analysed separately.

This, of course, requires the age of the participants to be available to the researchers.

8.2.3 Analysis

The best advice for observational studies is to measure, observe, assess or record all the information that is likely to be important for understanding the data. While this strategy is also useful for experimental studies, it is particularly important for observational studies, as managing confounding through analysis (Sect. 7.2.3) is often one of the few practical means available.

Measure, observe, assess or record all all the information that is likely to be important for understanding the data. This may include information about

  • the individuals in the study; and
  • the circumstances of the study.
Example 8.1 (Analysis) In a different smoking study, Doll and Hill (1950) recorded the the social class and place of residence of each participant, as potential confounding variables.

Example 8.2 (Confounding) An observational study of 2599 kiwifruit orchards (Froud et al. 2018) explored the relationship between the time since a bacterial canker was first detected (in weeks) and the orchard productivity (in tray equivalents per hectare).

The researchers also recorded information such as ‘whether or not the farm was organic,’ ‘elevation of the orchard’ and ‘whether or not general fungicides were used.’

They used these variables in their analysis to manage the potential effects of confounding. Their analysis showed that ‘elevation of the orchard’ and ‘whether or not general fungicides were used’ were important confounding variables, but ‘whether or not the farm was organic’ was not.

8.2.4 Random allocation

In observational studies, the study conditions are not allocated by the researchers (at random or otherwise), so random allocation of treatments is not possible

For this reason, confounding is often a major threat to internal validity in observational studies, as individuals who are in one group may be different to those who are in another group (Table 8.1). As a result, researchers often summarise the groups being compared on various potential confounding variables.

Example 8.3 (Comparing study groups) In an observational study comparing the iron levels of active and sedentary women aged 18 to 35 (Woolf et al. 2009), the authors compared the active women (\(n=28\)) to the sedentary women (\(n=28\)) on a variety of characteristics.

However, maybe the intrinsic physical differences between the women in the the two groups might explain any differences found between iron levels in two two groups.

To determine this, the researchers examined many characteristics of the women; some are shown in Table 8.1. d They conclude that active women in their sample tended to be (in general) slightly younger, slightly heavier and taller, and slightly more likely to use hormonal contraceptives. Hence, any difference in iron levels between the two groups may be because of the active/sedentary nature of the groups, or because the active group was (in general) younger, for example.
TABLE 8.1: The demographic information for those in the study of iron levels in women
Characteristic Active women Sedentary women
Average age (in years) 20 24
Average height (in cm) 169 166
Average weight (in kg) 68 62
Percentage using hormonal contraceptives 13 11

In the smoking study of Doll and Hill (1954), doctors who chose to smoke may be inclined to undertake other risky behaviours, whereas those doctors who choose not to smoke may also be inclined to not undertake other risky behaviours. It may be those other risky behaviours that lead to lung cancer, and not the smoking itself.

In a different smoking study, Doll and Hill (1950) used a control group. The control group was chosen to be very similar to those in the lung-cancer group, in terms of age and sex. (That is, the numbers of females and males within each age group was very similar for those with lung cancer, and those without lung cancer.)

Observational studies can (and often do) have control groups. Indeed, one specific type of observational study is called a case-control study.

However, individuals are not allocated to the control group by the researchers in observational studies.

A study (Gunnarsson et al. 2017) examined the difference between two types of helicopter transfer (physician-staffed; non-physician-staffed) of patients with a specific type of myocardial infarction (STEMI). The purpose of the study was:

…to evaluate the characteristics and outcomes of physician-staffed HEMS (Physician-HEMS) versus non-physician-staffed (Standard-HEMS) in patients with STEMI.

Gunnarsson et al. (2017), p. 1

The researchers

…studied 398 STEMI patients transferred by either Physician-HEMS (\(n=327\)) or Standard-HEMS (\(n=71\)) for […] intervention at 2 hospitals between 2006 and 2014.

Gunnarsson et al. (2017), p. 1

Since the study is an observational study (patients were not allocated by the researchers to the type of helicopter transport), the researchers recorded information about the patients being transported. They compared the patients in both groups, and found (for example) that both groups had similar average ages, a similar percentages of females, a similar percentage of smokers, and so on. They also compared information about the transportation, and found (for example) that both groups had similar average flight times and flight distances.

One conclusion from the study was that ‘Patients with STEMI transported by Standard-HEMS had longer transport times’ (p. 1), but one limitation of the study was that:

The patient cohorts received treatment by 2 different care teams at two hospitals, which is a potential confounder despite similar baseline characteristics

Gunnarsson et al. (2017), p. 5

In other words, the difference between hospitals and the staff may have been a confounding variable.


Doll R, Hill AB. Smoking and carcinoma of the lung. British Medical Journal. 1950;221(ii):739–48.
Doll R, Hill AB. The mortality of doctors in relation to their smoking habits. British Medical Journal. 1954;1(4877):1451.
Froud KJ, Beresford RM, Cogger NC. Impact of kiwifruit bacterial canker on productivity of cv. Hayward kiwifruit using observational data and multivariable analysis. Plant Pathology. Wiley Online Library; 2018;67(3):671–81.
Gunnarsson S, Mitchell J, Busch MS, Larson B, Gharacholou SM, Li Z, et al. Outcomes of physician-staffed versus non-physician-staffed helicopter transport for ST-elevation myocardial infarction. Journal of the American Heart Association. 2017;
Woolf K, St. Thomas MM, Hahn N, Vaughan LA, Carlson AG, Hinton P. Iron status in highly active and sedentary young women. International Journal of Sport Nutrition and Exercise Metabolism. 2009;19:519–35.