El PROFESSOR BIAS AND CONFOUNDIG BIAS & CONFOUNDING In every research and evaluation project, it is important to identify and address sources of error that may impact the accuracy of your findings and the relevance of your recommendations. Two common sources of error in evaluation are bias and confounding. Here, we will look at what bias and confounding are (and are not), the differences between them, and important considerations to take to prepare for and address both in your next evaluation project. BIAS Bias relates to systematic sources of error which need to be considered. Why? The internal validity of a study depends greatly on the extent to which biases have been accounted for and necessary steps taken to diminish their impact. In a poor-quality study, bias may be the primary reason the results are or are not “significant” statistically! Bias may preclude finding a true effect; it may lead to an inaccurate estimate (underestimate or overestimate) of the true association between exposure and an outcome. Significance testing in itself does not take into account factors which may bias study results. Bias can be divided into three general categories: (1) selection bias; (2) information bias; and (3) confounding Types of Bias • Information bias Information bias results from systematic differences in the way data on exposure or outcome are obtained from the various study groups.1 This may mean that individuals are assigned to the wrong outcome category, leading to na incorrect estimate of the association between exposure and outcome. Errors in measurement are also known as misclassifications, and the magnitude of the effect of bias depends on the type of misclassification that has occurred. There are two types of misclassification – differential and non-differential – and these are dealt with elsewhere (see “Sources of variation, its measurement and control”). Interviewer bias occurs where an interviewer asks leading questions that may systematically influence the responses given by interviewees. Minimising observer / interviewer bias: El PROFESSOR BIAS AND CONFOUNDIG • • • • • • Where possible, observers should be blinded to the exposure and disease status of the individual Blind observers to the hypothesis under investigation. In a randomised controlled trial blind investigators and participants to treatment and control group (doubleblinding). Development of a protocol for the collection, measurement and interpretation of information. Use of standardised questionnaires or calibrated instruments, such as sphygmomanometers. Training of interviewers. Recall (or response) bias – In a case-control study data on exposure is collected retrospectively. The quality of the data is therefore determined to a large extent on the patient’s ability to accurately recall past exposures. Recall bias may occur when the information provided on exposure differs between the cases and controls. For example na individual with the outcome under investigation (case) may report their exposure experience differently than na individual without the outcome (control) under investigation. Recall bias may result in either na underestimate or overestimate of the association between exposure and outcome. Methods to minimise recall bias include: • • Collecting exposure data from work or medical records. Blinding participants to the study hypothesis Detection bias occurs where the way in which outcome information is collected differs between groups. Instrument bias refers to where na inadequately calibrated measuring instrument systematically over/underestimates measurement. Blinding of outcome assessors and the use of standardised, calibrated instruments may reduce the risk of this. • Selection bias Selection bias occurs when there is a systematic difference between either: • • Those who participate in the study and those who do not (affecting generalisability) or Those in the treatment arm of a study and those in the control group (affecting comparability between groups). That is, there are differences in the characteristics between study groups, and those characteristics are related to either the exposure or outcome under investigation. Selection bias can occur for a number of reasons. Sampling bias describes the scenario in which some individuals within a target population are more likely to be selected for inclusion than others. For example, if El PROFESSOR BIAS AND CONFOUNDIG participants are asked to volunteer for a study, it is likely that those who volunteer will not be representative of the general population, threatening the generalisability of the study results. Volunteers tend to be more health conscious than the general population. Allocation bias occurs in controlled trials when there is a systematic difference between participants in study groups (other than the intervention being studied). This can be avoided by randomisation. Loss to follow-up is a particular problem associated with cohort studies. Bias may be introduced if the individuals lost to follow-up differ with respect to the exposure and outcome from those persons who remain in the study. The differential loss of participants from groups of a randomised control trial is known as attrition bias. Selection bias in case-control studies Selection bias is a particular problem inherent in case-control studies, where it gives rise to non-comparability between cases and controls. In case-control studies, controls should be drawn from the same population as the cases, so they are representative of the population which produced the cases. Controls are used to provide na estimate of the exposure rate in the population. Therefore, selection bias may occur when those individuals selected as controls are unrepresentative of the population that produced the cases. The potential for selection bias in case-control studies is a particular problem when cases and controls are recruited exclusively from hospital or clinics. Such controls may be preferable for logistic reasons. However, hospital patients tend to have different characteristics to the wider population, for example they may have higher levels of alcohol consumption or cigarette smoking. Their admission to hospital may even be related to their exposure status, so measurements of the exposure among controls may be different from that in the reference population. This may result in a biased estimate of the association between exposure and disease. For example, in a case-control study exploring the effects of smoking on lung cancer, the strength of the association would be underestimated if the controls were patients with other conditions on the respiratory ward, because admission to hospital for other lung diseases may also be related to smoking status. More subtly, the effect of alcohol on liver disease could potentially be underestimated if controls are taken from other wards: higher than average alcohol consumption may result in admission for a variety of other conditions, such as trauma. As the potential for selection bias is likely to be less of a problem in population-based case-control studies, neighbourhood controls may be a preferable choice when using cases from a hospital or clinic setting. Alternatively, the potential for selection bias may be minimised by selecting controls from more than one source. For example, the use of both hospital and neighbourhood controls. El PROFESSOR BIAS AND CONFOUNDIG Selection bias in cohort studies Selection bias can be less of problem in cohort studies compared with case-control studies, because exposed and unexposed individuals are enrolled before they develop the outcome of interest. However, selection bias may be introduced when the completeness of follow-up or case ascertainment differs between exposure categories. For example, it may be easier to follow up exposed individuals who all work in the same factory, than unexposed controls selected from the community (loss to follow-up bias). This can be minimised by ensuring that a high level of follow-up is maintained among all study groups. The healthy worker effect is a potential form of selection bias specific to occupational cohort studies. For example, na occupational cohort study might seek to compare disease rates amongst individuals from a particular occupational group with individuals in na external standard population. There is a risk of bias here because individuals who are employed generally have to be healthy in order to work. In contrast, the general population will also include those who are unfit to work. Therefore, mortality or morbidity rates in the occupation group cohort may be lower than in the population as a whole. In order to minimise the potential for this form of bias, a comparison group should be selected from a group of workers with different jobs performed at different locations within a single facility1; for example, a group of non-exposed office workers. Alternatively, the comparison group may be selected from na external population of employed individuals. Selection bias in randomised trials Randomised trials are theoretically less likely to be affected by selection bias, because individuals are randomly allocated to the groups being compared, and steps should be taken to minimise the ability of investigators or participants to influence this allocation process. However, refusals to participate in a study, or subsequent withdrawals, may affect the results if the reasons are related to both exposure and outcome. • Confounding Confounding provides na alternative explanation for na association between an exposure (X) and an outcome. It occurs when na observed association is in fact distorted because the exposure is also correlated with another risk factor (Y). This risk factor Y is also associated with the outcome, but independently of the exposure under investigation, X. As a consequence, the estimated association is not that same as the true effect of exposure X on the outcome. El PROFESSOR BIAS AND CONFOUNDIG Na unequal distribution of the additional risk factor, Y, between the study groups will result in confounding. The observed association may be due totally, or in part, to the effects of differences between the study groups rather than the exposure under investigation. A potential confounder is any factor that might have na effect on the risk of disease under study. This may include factors with a direct causal link to the disease, as well as factors that are proxy measures for other unknown causes, such as age and socioeconomic status. In order for a variable to be considered as a confounder: 1. The variable must be independently associated with the outcome (i.e. be a risk factor). 2. The variable must also be associated with the exposure under study in the source population. 3. The variable should not lie on the causal pathway between exposure and disease. Examples of confounding A study found alcohol consumption to be associated with the risk of coronary heart disease (CHD). However, smoking may have confounded the association between alcohol and CHD. Smoking is a risk factor in its own right for CHD, so is independently associated with the outcome, and smoking is also associated with alcohol consumption because smokers tend to drink more than nonsmokers. El PROFESSOR BIAS AND CONFOUNDIG Controlling for the potential confounding effect of smoking may in fact show no association between alcohol consumption and CHD. Effects of confounding Confounding factors, if not controlled for, cause bias in the estimate of the impact of the exposure being studied. The effects of confounding may result in: • • • • An No An An observed association when no real association exists. observed association when a true association does exist. underestimate of the association (negative confounding). overestimate of the association (positive confounding). Controlling for confounding Confounding can be addressed either at the study design stage, or adjusted for at the analysis stage providing sufficient relevant data 1 have been collected. A number of methods can be applied to control for potential confounding factors and the aim of all of them is to make the groups as similar as possible with respect to the confounder(s). Controlling for confounding at the design stage Potential confounding factors may be identified at the design stage based on previous studies or because a link between the factor and outcome may be considered as biologically plausible. Methods to limit confounding at the design stage include randomisation, restriction and matching. • 1 Randomisation Bias and Confounding – El PROFESSOR El PROFESSOR BIAS AND CONFOUNDIG This is the ideal method of controlling for confounding because all potential confounding variables, both known and unknown, should be equally distributed between the study groups. It involves the random allocation (e.g. using a table of random numbers) of individuals to study groups. However, this method can only be used in experimental clinical trials. • Restriction Restriction limits participation in the study to individuals who are similar in relation to the confounder. For example, if participation in a study is restricted to non-smokers only, any potential confounding effect of smoking will be eliminated. However, a disadvantage of restriction is that it may be difficult to generalise the results of the study to the wider population if the study group is homogenous. 1 • Matching Matching involves selecting controls so that the distribution of potential confounders (e.g. age or smoking status) is as similar as possible to that amongst the cases. In practice this is only utilised in case-control studies, but it can be done in two ways: 1. Pair matching - selecting for each case one or more controls with similar characteristics (e.g. same age and smoking habits) 2. Frequency matching - ensuring that as a group the cases have similar characteristics to the controls Bibliographys 1. Weiss N S. New York: Oxford University Press; 2006. Clinical Epidemiology: The Study of the Outcome of Illness. 2nd ed. [Google Scholar] 2. Sackett, D. L. (1979). Bias in analytic research. Journal of Chronic Disease. 3. Gordis 5th edition, chapiter 15. 4. Basic Epidemiology, BONITA 2nd edition pages 51-60.
