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March 26, 2016
Table of Contents

1 Introduction
case-control

Wikipedia

 

Case-control is a type of epidemiological, clinical study design. It is typically used for retrospective studies, but can also be applied to prospective studies as well. In a case-control study, people with a disease (often, a specific diagnosis, perhaps lung cancer) are matched with people who do not have the disease (the 'controls'). Further data are then collected on those individuals and the groups are compared to find out if other characteristics (perhaps a history of smoking) are also different between the two groups.

They have pointed the way to a number of important discoveries and advances, but their retrospective, non-randomized nature limits the strength of their conclusions. However, cohort studies and randomized controlled trials, which provide stronger evidence, usually take much more time and money. Case-control studies are a relatively inexpensive and frequently-used type of epidemiological study that can be carried out by small teams or individual researchers in single facilities over a relatively short period of time. Ethical approval is more easily obtained for an observational rather than an experimental study.

One of the most significant triumphs of the case-control study was the demonstration of the link between tobacco smoking and lung cancer, by Sir Richard Doll and others after him. Doll was able to show a statistically significant association between the two in a large case control study. Opponents argued (correctly) for many years that this type of study cannot prove causation, but the eventual results of cohort studies confirmed the causal link which the case-control studies suggested, and it is now accepted that tobacco smoking is the cause of about 87% of all lung cancer mortality in the US.

Comparison with randomized controlled trials

For establishing cause-and-effect relationships, e.g., between the use of thrombolysis and the risk of dying after myocardial infarction, no study design is more highly regarded than the double blind randomized controlled trial, a specific type of experiment. While such trials may be ideal for testing the efficacy of (what are hoped to be) beneficial interventions, such as surgeries or drug treatments, there are many instances in which trials would be unethical. For example, it would generally be seen as unethical to assign research subjects randomly to be exposed to toxic substances in order to evaluate the substances' effects. In this case, only observational studies would be likely to receive ethical approval.

Studying infrequent events such as death from cancer using randomized clinical trials or other controlled prospective studies requires that large populations be tracked for lengthy periods to observe disease development. In the case of lung cancer this could involve 20 to 40 years, potentially longer than the careers of many epidemiologists. Case-control studies use patients who already have a disease or other condition and look back to see if there are characteristics of these patients that differ from those who don???t have the disease. This can usually be done with relatively moderate costs in time and money.

All observational studies have more and more likely ways of being wrong than experimental studies. The main problem is confounding, where factors that are not fully described in the data are not accounted for in the analysis. In case-control studies it is difficult, often impossible, to separate the chooser from the choice. For example, studies of road accident victims found that those wearing seat belts were 80% less likely to suffer serious injury or death in a collision, but data comparing rates for those collisions involving two front-seat occupants of a vehicle, one belted and one unbelted, show a measured efficacy only around half that. A further problem is that case-control studies depend on correct and honest reporting of the risk factor, which may be many years in the past or may be seen as socially (un)desirable. Case-control studies can be biased if the risk factor inquired about is incorrectly reported. Recent research has shown that a substantial majority of highly cited case-control studies are subsequently contradicted or found to be substantially over-ambitious when more rigorous investigations are conducted. Thus, case-control studies are rated as low quality, grade 3, on a standard scale of medical evidence .

A number of case-control studies identified a link between combined hormone replacement therapy (HRT) and reductions in incidence of coronary heart disease (CHD) in women. Credible mechanisms were advanced as to why this link might be causal, and a consensus arose that HRT was protective against CHD. The evidence was sufficiently compelling that a full clinical trial was initiated - and this indicated that the effect was both far smaller and in the opposite direction - combined HRT showed a small but significant increase in risk of CHD in the study population. Subsequent analysis has shown that the group of women opting for HRT were predominantly from higher socio-economic groups and therefore had, on average, better diet and exercise habits. The studies had falsely attributed the benefits of these confounding factors to the intervention itself. There have been similar controversies regarding links between vitamins and cancer; MMR vaccine and autism; antibiotics and asthma; cannabis and psychosis. All these have been identified through small-scale case-control studies but fail to show any effect in whole population time series or other investigations.

A comparison with the tobacco/cancer link is instructive. Here the case-control studies pointed the way, but further confirmation was available in the form of cohort studies tracking levels of smoking in large groups in relation to eventual cause of death, and in the form of laboratory experiments on animals.

As a result the following guidelines have been proposed when assessing case-control evidence :

  • Do not turn a blind eye to contradiction. Do not ignore contradictory evidence but try to understand the reasons behind the contradictions.

  • Do not be seduced by mechanism. Even where a plausible mechanism exists, do not assume that we know everything about that mechanism and how it might interact with other factors.

  • Suspend belief. Of the researchers defending observational studies, Pettiti says this: "belief caused them to be unstrenuous in considering confounding as an explanation for the studies". Do not be seduced by your desire to prove your case.

  • Maintain scepticism. Question whether the factor under investigation can really be that important; consider what other differences might characterise the case and control groups. Do not extrapolate results beyond the limits of reasonable certainty (e.g. with grandiose forecasts of "lives saved"). Specifically, ask whether the rare disease assumption was used to overreach.

The case-control study provides a cheaper and quicker study of risk factors than any experimental study; if the evidence found from a case-control study is convincing enough, then resources can be allocated to stronger and more comprehensive work.

Comparison with cross-sectional studies

Cross-sectional studies also involve data collected at a defined time, often using survey research methods. However, they include data on the entire population under study, whereas case-control studies typically include only individuals with a specific characteristic, and a sample, often a tiny minority, of the rest of the population.

One major disadvantage of case-control studies is that they do not give any indication of the absolute risk of the factor in question. For instance, a case-control study may tell you that a certain behaviour is associated with a tenfold increased risk of death as compared with the control group. Although this sounds alarming, it would not tell you that the actual risk of death would change from one in ten million to one in one million, which is much less alarming. For that information, data from outside the case-control study must be consulted.

Aggregated data and the "ecological fallacy"

Cross-sectional studies can contain individual-level data (one record per individual, for example, in national health surveys). However, in modern epidemiology it is seldom possible to survey the entire population of interest, so cross-sectional studies often involve secondary analysis of data collected for another purpose. Many cross-sectional studies only convey group-level information; that is, no individual records are available to the researcher. Major sources of such data are often large institutions like the Census Bureau or the Centers for Disease Control in the United States. Recent census data is not provided on individuals - in the UK individual census data is released only after a century. Instead data are aggregated at the group level. For example, by zip code, urban zone, or even by states/provinces or country. Inferences about individuals based on aggregate data are weakened by the ecological fallacy. For example, it might be true that there is no correlation between infant mortality and family income at the city level, while still being true that there is a strong relationship between infant mortality and family income at the individual level. All aggregate statistics are subject to compositional effects, so that what matters is not only the individual-level relationship between income and infant mortality, but also the proportions of low, middle, and high income individuals in each city. Because case-control studies are usually based on individual-level data, they do not have this problem.

Targeted questions or use of routine data

 

For example, although cross-sectional studies confirm that people who consume large amounts of alcohol also show high rates of many other diseases, routinely-collected data does not normally describe whether the drinking or the depression came first. Most case-control studies collect specifically-designed data on all participants, and these would normally include questions designed to test the hypothesis of interest. However, in issues where strong personal feelings may be involved, specific questions may be a source of bias. For example, past alcohol consumption may be incorrectly reported by an individual wishing to reduce their personal feelings of guilt. Such bias may be less in routinely-collected statistics, or effectively eliminated if the observations are made by third parties, for example taxation records of alcohol by area.

Cross-sectional studies using data originally collected for other purposes are often unable to include data on confounding factors, other variables that affect the relationship between the putative cause and effect.

The use of routine data allows large cross-sectional studies to be made at little or no expense. This is a major advantage over all other forms of study. A natural progression has been suggested from cheap cross-sectional studies which suggest hypotheses, to case-control studies testing them more specifically, then to cohort studies and randomized trials which cost much more and take much longer, but give stronger evidence.




  • Nested case-control study

  • Epi Info software program

  • OpenEpi software program





  • (Still a very useful book, and a great place to start, but now a bit out of date.)




  • A list of computer programs for genetic analysis including case-control genetic association analysis

  • Study Design Tutorial Cornell University College of Veterinary Medicine

  • Wellcome Trust Case Control Consortium



This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "case-control".


Last Modified:   2010-11-25


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