# C. Observational Studies

## C-1 Case-control studies

### C-1.1 Choice of control group in case-control studies

Consider a case-control study (see A-1.4) that is concerned with identifying the risks for cardiovascular disease. Smoking history is an obvious variable worthy of investigation. In case-control studies, one must first decide upon the population from which subjects will be selected (e.g. a hospital ward, clinic, general population, etc.). Where cases have been obtained from a hospital clinic, controls are often selected from another hospital population for ease of access. However, the latter scenario can introduce selection bias. For example, if cases are obtained from a cardiovascular ward and smoking history is one of the variables under investigation, it would be unsuitable to obtain controls from a ward containing patients with smoking related disease e.g. lung cancer. The choice of a suitable control group is fraught with problems both practical and statistical. These problems are discussed in detail in Breslow & Day 1980. In general the ideal is to select as controls a random sample from the general population that gave rise to the cases. However this assumes the existence of a list of subjects in the population (i.e. a sampling frame exists) which in many cases it does not.

### C-1.2 Matching in case-control studies

Sometimes in a case-control study (see A-1.4) cases and controls are matched. You can have 1-1 matching of one control for each case or 1-m matching of m controls per case. The latter is often used to increase statistical power for a given number of cases (see Breslow & Day 1980). For each case, 1 or more controls are found that have the same, or very similar, values in a set of matching variables. Matching variables typically include age and sex etc. Typically two or three matching variables are selected; anymore would make the selection of controls difficult. It is hoped that by matching, any differences between cases and controls are not a result of differences between groups in the matching variables. The extent to which this aim is achieved depends to some extent on the closeness of the matching. Here a balance has to be struck between matching as closely as possible and what can be achieved. Pilot work (see A-1.9) may be useful in making this judgement. When describing a matched case-control study in a grant application, it is not sufficient to say that a control will be matched to each case for age for example. The reviewer will want to know how closely - to within one year, to within 5 years etc? Also of interest to the reviewer is how the controls will be selected (e.g. at random from a list of all possible matches) and what happens when a control refuses. Will a second control be selected as a replacement and how many replacements will be permitted?

The main purpose of matching is to control for confounding (see A-1.6). However it should be appreciated that confounding factors can be controlled for in other ways (see E-5) and these other ways become increasingly appealing when we consider some of the problems associated with matching:

1) It is not possible to examine the effects of the matching variables upon the status of the disease/disorder (either present or absent). Thus although the disease or condition of interest will be related to the matching variables, the matching variables should not be of interest in themselves.

2) If we match we should take the matching into account in the statistical analysis. This makes the analysis quite complicated (see E-6.1, Breslow & Day 1980).

3) In a 1-1 matched case-control study matched pairs are analysed together and so missing information on a control means that it's case is also treated as missing in the statistical analysis. Similarly missing information on a case leads to the loss of information on its matched control(s).

4) Bias can arise if we match on a variable that turns out to form part of the causal pathway between the risk factor under study and disease. This bias is said to be due to overmatching.

See Bland & Altman (1994c) and Breslow & Day (1980), for further discussion on matching.

## C-2 Assessment bias

Consider a case-control study where for example the interest may be to investigate an association between diet and bowel cancer. Let us assume that diet is to be assessed by an interviewer administered food frequency questionnaire. If the interviewer is aware of the medical condition of the patients then this may lead to assessment bias, namely a difference between the information recorded by the interviewer (assessor) and the actual "truth". The interviewer may record poorer diets than actually consumed for those patients with cancer. Assessment bias can be overcome if the assessor is 'blind' to the medical condition, thus avoiding any manipulation of results either conscious or subconsciously (although 'blinding' is difficult to do in a case-control study of cancer where interviews are face to face). Assessment bias can even arise in a case-control study when data is being extracted from medical records as the process of extraction may be influenced by the knowledge of outcome (e.g. case or control). In this case 'blind' extraction is advocated.

## C-3 Recall bias

This is a particular problem in both case-control studies (see A-1.4) and cross-sectional studies (see A-1.5) when information is collected retrospectively, as the patients outcome e.g. disease status, is known, and they are being asked to recall past events. Patient data collected retrospectively may be of poor quality as it is based on the patient's ability to recollect the past. In addition, their ability to recall may be influenced by their known outcome and it is this difference in ability that may bias observed associations. If recall bias is likely to be a problem then the grant applicants should at least consider alternative methodologies. Could the data be collected from another source e.g. 'blind' extraction from historical records? Would it be possible to undertake a prospective study where for example exposure information is collected prior to and in the lack of knowledge of future disease?

## C-4 Sample survey: selecting a representative sample

We may be interested in demonstrating an association between unemployment and current poor health. We might decide to undertake a cross-sectional study and obtain a sample from a London Borough. The aim of the research would be to extrapolate our findings from this sample to the population of the borough and then possibly nationally. Therefore, our sample should be at least representative of the London borough population from where it was obtained. In practice, we could only obtain a truly representative sample through random sampling of the whole borough. Nonetheless, the sample would still only be representative to a particular time period. It may even be difficult to extrapolate the results to the same borough during another time period, and therefore possibly nationally.

Sometimes by chance a random sample is not as representative as we would like. For example in our cross-sectional survey to investigate associations between unemployment and current health it may be particularly important to ensure that we have an adequate representation of all postal areas in the borough, thereby reflecting the socioeconomic deprivation that exists. One way of doing this is to undertake stratified random sampling. Stratified random sampling is a means of using our knowledge of the population to ensure the representative nature of the sample and increase the precision of population estimates. Post-code area would be known as the stratification factor. Usually we undertake proportional stratified sampling. The total sample size is allocated between the strata proportionally, with the proportion determined by the strata total size as a proportion of the total population size. For example if 10% of the borough live in one postal code area then we randomly select 10% of the sample from this strata.

Stratification does not depart from the principle of random sampling. All it means is that before any selection takes place, the population is divided into strata and we randomly sample in each strata. It is possible to have more than one stratification factor. For example in addition to stratifying by post-code area, we may stratify by age group within the post code area. Nonetheless, we have to be careful not to stratify by too many factors. Stratified random sampling requires that we have a large population, for which all of the members and their stratification factors are listed. Obviously as the number of stratification factors increase then so also does the time and expense involved. Nonetheless we can be more confident of the representative nature of the sample and thereby the generalisability of the results.

## C-5 Generalisability and extrapolation of results

All medical research is undertaken on a group of selected individuals. However, the usefulness of any medical research is centred in the generalisation of the findings rather than in the information gained about a group of particular individuals. Nonetheless, most studies often use very restrictive inclusion criteria making it very difficult to generalize results. For example, if the study subjects in a cross-sectional study concerned with investigating associations between bowel cancer and diet were selected from an area that was predominately social class IV or V, can the results be extrapolated to individuals in a different social class? Such extrapolation of results is not obvious and the researchers of such a study should have considered incorporating other geographical areas with a wider range of social classes. Even if the study had such a sample, the reader of the journal article must pay careful attention to the ethnicity of the study subjects before extrapolating the results of a study conducted in the UK to say Asia. Observational studies are conducted to investigate associations between risk factors and a disease or disorder, rather than to find out anything about the individual patients in the study. See Altman & Bland (1998) for further discussion on generalisation and extrapolation.

## C-6 Maximising response rates to questionnaire surveys

The response rate to a questionnaire survey is the proportion of subjects who respond to the questionnaire. Questionnaire surveys, particularly postal surveys, tend to have low response rates (anything from 30%-50% is not unusual). The subjects that respond to questionnaires differ from those that don't and so the results of a study with a low response rate will not be seen as representative of the population of interest. Thus, if a grant proposal includes a questionnaire survey the reviewers will be looking for ways in which the applicants plan to maximise response. Response rates can be enhanced by including self-addressed stamped envelopes, informing respondents of the importance of the study and ensuring anonymity. If anonymity is not given, then response rates can also be increased by following up the first posting with another copy of the questionnaire or telephone call. Alternatively if anonymity is given then a second posting of the questionnaire may result in duplication from some respondents. See Edwards et al. (2002) for a discussion on improving response rates to questionnaires.

### References for this chapter

Altman DG. & Bland JM. (1998) Generalisation and extrapolation. British Medical Journal 317 409-410.

Bland JM & Altman DG. (1994c). Matching. British Medical Journal 309 1128.

Breslow NE and Day NE. (1980) Statistical Methods in Cancer Research: Volume 1 - The analysis of case-control studies. IARC Scientific Publications No. 32, Lyon.

Edwards P., Roberts I., Clarke M., DiGuiseppi C, Pratap S., Wentz R., Kwan I. (2002). Increasing response rates to postal questionnaires: systematic review. British Medical Journal 324 1183-1185.