1.2 Managing confounding
In the previous topic 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.
1.2.1 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).
1.2.2 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.
1.2.3 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 is often one of the few practical means available.
Measure, observe, assess or record 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 1.2 (Confounding) An observational study of 2599 kiwifruit orchards (Froud, Beresford, and Cogger 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.1.2.4 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 1.1). As a result, researchers often summarise the groups being compared on various potential confounding variables.
Example 1.3 (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 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 1.1. 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.| 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 percentage 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:
In other words, the difference between hospitals and the staff may have been a confounding variable.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