5 Bias and confounding

Transcription

1 Bias and confounding 37 5 Bias and confounding Learning objectives In this chapter students learn about Most important factors that can systematically impair the results of a population based study, like selection and information bias Definitions and common examples of biases and confounders and Methods to avoid bias and confounding in the study design and analysis Study results drawn from samples can be influenced by random errors. This effect is estimated via statistical test procedures. In addition, there are a number of systematic biases which might lead to a misleading study result. Errors are created by chance whereas biases are systematic falsifications of data. Several factors can lower the quality of study results and some reduce the possibility to find a real risk factor - which lowers the power of a study. Other factors can skew the results. Sometimes the direction in which the data are skewed can be estimated - but this is not always the case. The design of the study or behavior of participants can impair and skew the results. Most interesting are systematic biases, which are usually divided into three broad categories: Selection bias occurs during the choice and selection of participants Information bias occurs when information, e.g. from a questionnaire or interview, is wrongly collected Confounders are additional factors which can distort the relationship between exposure and outcome 5.1 Selection bias As indicated above, this type of bias might occur during the sampling and selection of participants for the study. In the following example the researcher is interested in knowing how many women attended a mammography screening for breast cancer prevention during the last years. He is planning a cross-sectional study and sending invitation letters to a representative group of 1000 women between 40 and 60 years. From these 1000 women who were invited, only 300 participate in the study. This corresponds to a 30% response rate and it also means that 700 women did not answer. What happens if only those 300 questionnaires are analyzed? To answer the question it is important to find out how many women participate normally in a mammography screening. According to a large-scale Danish study about 70% of women participated in a mammography screening during the years in Copenhagen (Euler-Chelpin et al. 2006). What, in comparison, is the result of the new study? The percentage of participants actually looks quite dif-

2 38 Introduction in Epidemiology ferent. 95 to 97% of the 300 women report that they regularly attend a mammography screening. Due to the low response rate we can assume that mostly women who are familiar with and interested in the topic will answer the questionnaire. Or on the other hand, women that are not familiar with the screening might not be interested in answering a questionnaire about the topic. We expect an invalid result due to this selfselection process among the participants. This is a typical and mostly inevitable problem in epidemiological studies. Another problem might be the choice of a wrong participant group, which is mostly recognized just after finishing a study. Take for example a case control study on the relationship of alcohol consumption and lung cancer. A recruitment of lung cancer cases is done from a surgical department of a clinic. An easy identifiable control group would be non-lung cancer patients from an emergency department of the same hospital. There are enough patients and recruiting is easy because information can be gathered directly in the hospital. After analyzing the descriptive data we might discover that our control group is not suitable for comparison. We might have selected a group which consumes more alcohol than the population average since accidents regularly occur in connection with alcohol consumption. In this case the control group is not suitable to answer our study question because this group does not represent the alcohol consumption in the underlying population. Further examples for selection bias: In cross-sectional studies we want to show the whole profile of the population. Usually this is not feasible since not all parts of a population can be easily reached. Severely ill people for example are hard to reach when they are in hospital. In a cohort study, selection problems occur during followup - the longitudinal observation of a population. Some might leave the region or country where the study is conducted and cannot be traced and others refuse further participation. Another well-known type of selection bias is the "healthy-worker bias". In an occupational cohort there are usually more healthy people than in the general population, because ill, disabled and older persons mostly do not work anymore. 5.2 Information bias This kind of bias occurs when information is assessed wrongly by e.g. questionnaires or interviewers. What sounds like faulty planning (i.e. badly formulated or incomprehensible questions or questions that do not measure what you want to know) might rather be due to the fact that participants do not answer correctly. How can you use a questionnaire to find out if someone has diabetes? Are you injecting insulin? Are you taking pills? Did the doctor tell you not to eat sugar anymore? Which question is the right one? Even if the diagnosis is based on data from a general practitioner, this data must be available in the first place. In the beginning the researcher believes it is easy to collect relevant data, but during the planning process of a study he or she realizes that problems occur even when collecting "simple" information. Assessing the disease status usually is a simple question. But exposure status such as the dietary patterns like in the EPIC study or mobile phone use like in the INTER- PHONE study are quite difficult to objectively assess Therefore complex catalogues with questions are used but mistakes can be made here as well. It is important to think about that in advance. An important measure to assess the quality of a measurement instrument such as a questionnaire is its validity. It indicates whether the instrument really measures what it is supposed to measure or what the researcher is interested in.

3 Bias and confounding 39 This is a difficult task and one starts with assessing the instrument's repeatability - if it leads to the same results when used in the same persons repeatedly. A common example of information bias usually occurs in case-control studies. It is called recall bias. Cases and controls might not remember the exact information about the exposure if asked retrospectively. Another aspect of this bias is when cases and controls are concerned about the exposure and remember information about it to a different extent. 5.3 Confounding Study results can also be distorted by a third factor that occurs in a population and is connected to the disease under study. A good example: You might know the story about storks bringing babies. This relationship can even be shown statistically. Storks appear more frequently in regions with many children (Mathews 2000, Höfer et al. 2004). Should this statistical relationship be accepted and the biological knowledge be reconsidered? Instead we might consider a possible third factor which creates this association and allows consistency with current knowledge. We know that storks build their nests often in rural areas. Families who like to have more children more often live in rural areas, as well. There is a third factor (confounder) which is associated with the exposure (number of storks in the area) and the outcome (number of children born in the area). This factor can be accounted for in the analysis and we do not need to think about new biological theories to explain the increase in number of babies. In the following figure 5.1 this relationship is shown graphically: Exposure Outcome Confounder Figure 5.1: Relationship between exposure, outcome and confounder Requirements to identify a third factor as a confounder: 1. A confounder is associated with the exposure. 2. A confounder is associated with the outcome. 3. The real relationship between exposure and outcome will be skewed if the confounder is not considered. 4. The confounder is not an intermediate step or necessary requirement for the relationship between exposure and outcome. The risk for most diseases increases with older age. If you analyze the effect of an exposure which occurs more frequent in certain age groups then age can be a confounder. Another very common confounder is sex because some exposures as well as outcomes are differently distributed among men and women. Other common confounders are smoking, socio-economic status and nationality. Depending on the study question there might be other confounders as well.

4 40 Introduction in Epidemiology If you want to analyze the relationship between physical activity and high blood pressure you could divide the participants into two groups according to the amount of physical activity: one group with a lot and the other one with little activity. You will probably find higher blood pressure among the participants in the group with little activity. Does this prove a relationship? Furthermore it is well known that the blood pressure increases with higher age and that older people practice less physical activity than younger ones. Our division of the participants into groups leads also to a division of older and younger participants. Among the older people there will be higher blood pressure independent of their amount of physical activity. Age is a confounder here for the relationship between physical activity and blood pressure. In a study on the relationship between alcohol and lung cancer, smoking would have to be considered as a confounder since its effect on the outcome is well known. Among persons who drink alcohol, the proportion of smokers is higher than in the general population. Smoking and lung cancer are associated as well. 5.4 Avoiding bias and confounding To avoid all possible errors, bias and confounding is the primary goal of every epidemiological study. It should be considered as much as possible during planning of the study. Selection and information bias can only be considered in the study design and just to a very limited extent in the analysis. In contrast to selection and information bias, confounding can be considered in the study design but also in statistical analyses. Confounders must always be considered in population based studies to get a result which shows the real relationship between exposure and outcome. Information about a confounder must be known beforehand and sampled before. Even though you can consider confounders in statistical analyses, you must also consider them in the study design. If for example the smoking status and age of the participants were not assessed they cannot be considered as confounders in the analysis Selection and information bias in planning a study To avoid selection bias it is important to attain a response rate as high as possible. In the follow-up of a cohort study all information should be collected in detail. Data base management is a very important task in the study plan. The more accurately the data is documented, the easier it is to follow-up participants, evaluate study processes and detect errors. It is important to have comprehensive knowledge on how to develop a questionnaire to avoid information bias. If possible, it is recommended to use validated and widely used instruments. The process of data collection should be standardized and interviewers should be trained to conduct interviews in a standardized way. A comprehensive data base management is also important to avoid information bias. Data collection procedure must be assessed to allow finding and solving problems; when collecting data, the time of data collection and even identification numbers of used devices needs to be documented. If a device is not functioning properly the relevant data can be retrieved easily.

5 Bias and confounding Considering confounding in the study design In a study where it is known that smoking can be a confounder, it might be useful to just recruit non-smokers. By doing this, the confounding effect is eliminated because none of the participants smoke. This is also done when sex is a possible confounder and only male or only female participants are recruited. Another method to avoid confounding that can be applied is using a matched design. This method is frequently used in case-control studies and describes the process of aligning cases and controls according to certain factors. An example is individual matching according to age and sex of the participants. If a case is female and 30 years of age, then the control should be also a woman with a similar age. This way the confounders age and sex are equally distributed in the case and control groups and cannot impair the association between exposure and outcome in each of the groups Considering confounders when analyzing the data A variable should only be considered as confounder if it is differently distributed among the groups that are compared. An unequal distribution of these factors might impair the real association between the exposure and outcome of interest. An analysis with confounders always lowers the power of a study and therefore its significance and should be avoided if possible. However, if typical confounders like smoking status, age and others are differently distributed among cases and controls they must be included in the analysis. Otherwise the presented results are not valid. There are three methods to consider confounding: Sub-group analysis / stratification The study population is divided into sub-groups in which the analysis is done separately. This method is often called stratification and is a basic idea for simple cases of confounding. Standardization Mostly used as age-standardization in health reports. The two relevant methods of standardization are described in chapter 5. Multiple regression Regression analyses are an extension of sub-group analyses that can be used for more complex data and several confounders. To conduct a sub-group or stratified analysis, the study population has to be divided into groups according to categories in the confounding variable (e.g. smokers and non-smokers, older and younger participants; also more than two groups are possible like 10-year age groups). The analysis - for example of an OR - is done for the whole population and for each of the sub-groups (also called strata). By comparing those results it can be seen whether or not the third variable is a confounder and how strong the actual relationship between exposure and outcome is. The following tables show a stratified analysis of a fictive case-control study on malaria infection. In total, 150 cases (persons with malaria infection) and 150 controls (without malaria infection) were analyzed. The first step was to investigate the influence of the participants' sex on the risk of infection (see table 5.1).

6 42 Introduction in Epidemiology Table 5.1: Crude results of a case-control study on the effect of sex on malaria infection Cases Controls Total Exposure Men Women Total The OR is the measure of association in a case-control study. Using the numbers from table 5.1 the following OR can be calculated: OR= (88*82)/(62*68)=1.71. It seems that men have a higher chance of malaria infection than women. But might there be a confounder? A confounder would have to be associated with the exposure (sex) and the outcome (malaria infection). The work place of the participants might be such a confounding factor, since malaria infections occur more often among persons who work outside and in areas where the anopheles mosquito can be found. It is also known that in some regions men are usually working on the fields. A stratified analysis is shown in table 5.2. Table 5.2: Stratified results of a case-control study on the effect of sex on malaria infection Persons, who did not work on the field Persons, who worked on the field Cases Controls Total Cases Controls Total Men Men Women Women Total Total OR=1.00 OR=1.06 If the whole study sample (n=300) is divided into persons who did not work on the field (n=219) and persons who did (n=81), we have a different result. In the stratified analysis the association between sex and malaria infection disappears. How can we interpret these results? The crude OR for the whole study sample (n=300) shows the combined effect of sex and field work on malaria infection. When stratified according to field work there is no effect of sex on malaria infection. In this case stratified ORs show the isolated effect of sex on malaria infection. We can conclude that there is no difference in malaria infections between men and women but rather between persons who work on the field and persons who do not work on the field. The crude (or not stratified) analysis is wrong, because it is impaired by the variable working on the field. A confounding effect can be seen if the real effect is increased, decreased or hidden. One characteristic of a confounder is that the effects have basically the same size in the different strata. There are exceptions or specific situational circumstances, such as when you look at the case of asbestos exposure and risk of lung cancer. In this case smoking is a confounder but in the stratified analysis it can be seen that smokers have a much higher risk of developing lung cancer from asbestos than non-smokers. The OR in both strata are very different. This phenomenon is called interaction or effect modification.

7 Bias and confounding 43 Remember In every population based study there is a potential for systematic bias due to characteristics of the study population or methods of data collection. The three forms are selection bias, information bias and confounding. Careful planning and conducting of the study help to avoid or reduce selection bias, information bias and confounding. Confounding factors can be accounted for by sub-group or stratified analyses, regression analyses or standardization. For further reading: Aschengrau A, Seage GR. Essentials of Epidemiology in Public Health. 2 nd edition. Jones and Bartlett Publishers 2008 relevant chapters: 10, 11 Bhopal R: Concepts of Epidemiology. 2 nd edition, Oxford University Press 2009 relevant chapter: 4 Gordis L: Epidemiology. 4 th edition. Saunders 2009 relevant chapter: 15 Scientific publication: Euler-Chelpin M, Olsen AH, Njor S, Vejborg I, Schwartz W, Lynge E. Women s patterns of participation in mammography screening in Denmark. Eur J Epidemiol 21(203-9, Hoefer T, Przyrembel H, Verleger S: For the Classroom: New evidence for the Theory of the Stork. Pediatr Perinat Epidemiol. 18:88-92, Further reading and online resources

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