Difference between case control cohort and cross sectional studies pdf
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- Lesson 1: Introduction to Epidemiology
- Case-control and Cohort studies: A brief overview
- Lesson 1: Introduction to Epidemiology
The prodominant study designs can be categorised into observational and interventional studies. Observational studies, such as cross-sectional, case control and cohort studies, do not actively allocate participants to receive a particular exposure, whilt interventional studies do. Each of the above study designs are described here in turn. In a cross-sectional study, data are collected on the whole study population at a single point in time to examine the relationship between disease or other health-related outcomes and other variables of interest exposures.
Lesson 1: Introduction to Epidemiology
The prodominant study designs can be categorised into observational and interventional studies. Observational studies, such as cross-sectional, case control and cohort studies, do not actively allocate participants to receive a particular exposure, whilt interventional studies do.
Each of the above study designs are described here in turn. In a cross-sectional study, data are collected on the whole study population at a single point in time to examine the relationship between disease or other health-related outcomes and other variables of interest exposures. Cross-sectional studies therefore provide a snapshot of the frequency of a disease or other health-related characteristics in a population at a given point in time.
This methodology can be used to assess the burden of disease or health needs of a population, for example, and is therefore particularly useful in informing the planning and allocation of health resources. Non-response is a particular problem affecting cross-sectional studies and can result in bias of the measures of outcome.
This is a particular problem when the characteristics of non-responders differ from responders. Recall bias can occur if the study asks participants about past exposures. In a cross-sectional study all factors exposure, outcome, and confounders are measured simultaneously. The main outcome measure obtained from a cross-sectional study is prevalence:.
For continuous variables such as blood pressure or weight, values will fall along a continuum within a given range. Prevalence may therefore only be calculated when the variable is divided into those values that fall below or above a particular pre-determined level. Alternatively, mean or median levels may be calculated.
In analytical cross-sectional studies the odds ratio can be used to assess the strength of an association between a risk factor and health outcome of interest, provided that the current exposure accurately reflects the past exposure. In a case-control study the study group is defined by the outcome e.
The study starts with the identification of a group of cases individuals with a particular health outcome in a given population and a group of controls individuals without the health outcome from the same population. The prevalence of exposure to a potential risk factor is then compared between cases and controls. If the prevalence of exposure is more common among cases than controls, the exposure may be a risk factor for the outcome under investigation.
One of the advantages of case-control studies is that they can be used to study outcomes or diseases that are rare.
However, a major characteristic is that data on potential risk factors are collected retrospectively and as a result may give rise to bias. This is a particular problem associated with case-control studies and therefore needs to be carefully considered during the design and conduct of the study. Incident cases comprise cases newly diagnosed during a defined time period. The use of incident cases is considered to be preferable because the recall of past exposure s may be more accurate amongst those who have been recently diagnosed with a condition.
In addition, the temporal sequence of exposure and disease is easier to assess among incident cases. Prevalent cases comprise individuals who have had the outcome under investigation for some time. Inclusion of prevalent cases may mean the results of the study are more generalisable to the wider population. However, it may also give rise to recall bias as prevalent cases may be less likely to accurately remember past exposures. As a result, the interpretation of results based on prevalent cases may prove more problematic as it may be more difficult to ensure that reported events exposure relate to a time before the development of disease rather than being a consequence of the disease process itself.
For example, individuals may modify their exposure following the onset of disease. Another disadvantage of sampling prevalent cases is the risk of preferentially including the milder cases which have a better survival; these may not be representative of all disease cases, and may have different levels of the exposure s of interest. The goal is to select individuals in whom the distribution of exposure status would be the same as that of the cases in the absence of an exposure-disease association.
That is, if there is no true association between exposure and disease, the cases and controls should have the same distribution of exposure. To put it another way, controls should meet all the criteria for cases, apart from having the disease itself, so if the cases are women aged years with breast cancer, the controls should be selected from a similar group who do not have breast cancer.
The source of controls is dependent on the source of cases. In order to minimise bias, controls should be selected to be a representative sample of the population which produced the cases. For example, if cases are selected from a defined population such as a GP register then controls should comprise a sample from the same GP register. In case-control studies where cases are hospital based, it is common to recruit controls from the hospital population.
However, the choice of controls from a hospital setting should not include individuals with an outcome related to the exposure being studied. For example in a case-control study of the association between smoking and lung cancer, the inclusion of controls being treated for a condition related to smoking e.
Recruiting more than one control per case may improve the statistical power of the study, especially where the number of cases is limited. However, there is little additional statistical power to be gained by recruiting more than 4 controls per case.
Note that in case-control studies the measurement of exposure is established after the development of disease and as a result is prone to both recall and observer bias. Various methods can be used to ascertain exposure status.
The procedures used for the collection of exposure data should be the same for cases and controls. Due to the retrospective nature of case-control studies they are particularly susceptible to the effects of bias which may be introduced as a result of a poor study design or during the collection of exposure and outcome data. Because the disease and exposure have already occurred at the outset of a case-control study there may be differential reporting of exposure information between cases and controls based on their disease status.
Cases and controls may recall past exposure differently, because knowledge of being a case may affect whether the individual remembers a certain exposure, for example recall bias. Selection bias can occur in case-control studies when the selected control group are not representative of the population from which the cases arose, thus comparisons of exposure distributions between cases and controls may give misleading results. Temporal bias also known as reverse causality may also occur in case-control studies.
When trying to establish a link between exposure and outcome, it must be clear that the exposure occurred well before the diagnosis of the disease of interest. Therefore, the design and conduct of the study must be carefully considered as there are limited options for the control of bias during the analysis.
A confounder is a factor associated independently with both the exposure and outcome, and can be a problem where cases and controls differ with respect to a potential confounder. It can be dealt with at two stages:. The odds ratio OR is used in case-control studies to estimate the strength of the association between exposure and outcome. It is not possible to estimate the incidence or risk of disease from a case-control study, unless the study is population-based and all cases in a defined population are obtained.
It is calculated as follows:. Example : Calculate the odds ratio from a hypothetical case-control study of smoking and pancreatic cancer among cases and controls, the results of which are shown below. The OR calculated from the hypothetical data suggests that individuals with cancer of the pancreas cases are more likely to have smoked than those without the disease.
Specifically, participants with pancreatic cancer have 4. NB: The odds ratio above has been calculated without adjusting for potential confounders. Further analysis of the data would involve stratifying by levels of potential confounders such as age. The 2x2 table can then be extended to allow stratum-specific rates of the confounding variables to be calculated and, where appropriate, an overall summary measure adjusted for the effects of confounding and a statistical test of significance.
In addition, confidence intervals for the odds ratio would also be presented. A nested case-control study is one where the cases and controls are selected from individuals within an established cohort study. Cases of a disease that arise within the defined cohort during the follow up period are identified, then a specified number of matched controls who have not developed the disease are selected from the same cohort.
The main advantage of nested case-control studies is that certain exposure data will already have been collected for both cases and controls which limits the potential for recall bias.
Analysis is carried out in the same way as for normal case-control studies, with the calculation of odds ratios. Case-control studies have been used in a variety of situations to evaluate possible causes of rare conditions. Classic examples include the investigation of cases of childhood leukaemia near the nuclear procession plant at Sellafield in Cumbria UK , as well as cases of vaginal adenocarcinoma, which is normally rare, but were seen in higher numbers than usual in the USA in the s.
The difference between the young women with vaginal adenocarcinoma and their comparison group was that the mothers of cases had taken stilboestrol during the pregnancy to prevent miscarriage , but the mothers of the controls had not. Cohort studies evaluate a possible association between exposure and outcome by following a group of exposed individuals over a period of time often years to see whether they develop the disease or outcome of interest.
The incidence of disease in the exposed individuals of the cohort is then compared to the incidence of disease in unexposed, or lowest risk, individuals, and a relative risk incidence risk or incidence rate is calculated to assess whether the exposure and disease are associated.
Cohort studies may be prospective or retrospective, but both types define the cohort on the basis of exposure, not outcome. Prospective cohort studies — participants are identified and followed up over time until the outcome of interest has occurred, or the time limit for the study has been reached. A temporal relationship between exposure and outcome can thus be established.
Retrospective cohort studies — exposure and outcome have already occurred at the start of the study. Pre-existing data, such as medical notes, can be used to assess any causal links, so lengthy follow-up is not required.
This type of cohort study is therefore less time consuming and costly, but it is also more susceptible to the effects of bias. For example, the exposure may have occurred some years previously and adequate, reliable data on exposures may be differentially recorded in eventual cases compared to controls. In addition, information on confounding variables may be unavailable, inadequate or difficult to collect.
If the exposure is common, a defined study population can be selected for longitudinal assessment before classifying individuals as exposed or unexposed population-based cohort study. This could involve, for example, a selection of the general population e. If the exposure is rare, individuals may be chosen on the basis of exposure, to ensure sufficient exposed persons are enrolled.
For example, this may be workers at a particular factory who regularly handle a chemical of interest. The comparison group might be workers at the same factory whose roles do not bring them into contact with the chemical. A particular problem within cohort studies is determining whether individuals in the control group are truly unexposed.
For example, study participants may start smoking after enrolment, or they may fail to correctly recall past exposure. Similarly, those in the exposed group may change their behaviour in relation to the exposure such as diet, smoking or alcohol consumption. The ability to repeatedly measure exposures over time, and to account for these, helps mitigate against such changes in behavior.
Exposure data may be obtained from a number of sources, including medical or employment records, standardised questionnaires, interviews and by physical examination. A major source of potential bias in cohort studies is losses to follow-up. Cohort members may die, refuse to continue participation in the study or fail to maintain contact.
Such events may be related to the exposure, outcome or both, resulting in loss to follow-up bias. For example, individuals who develop a precursor to the outcome, such as symptoms of angina where the outcome of interest is a heart attack, may be less likely to continue to participate in the study. The degree to which losses to follow-up are correlated with either exposure or outcome can lead to significant bias in the measurement of the relationship between the exposure and outcome.
Another source of potential bias in cohort studies arises from the degree of accuracy with which subjects have been classified with respect to their exposure or disease status. Differential misclassification — when one group of participants is more likely to have been misclassified than the other — can lead to an over- or underestimation of the relationship between the exposure and outcome.
The analysis of a cohort study uses the ratio of either the risk or rate of disease in the exposed cohort, compared with the risk or rate in the unexposed cohort.
Case-control and Cohort studies: A brief overview
Metrics details. We propose a conceptualization of cohort studies in systematic reviews of comparative studies. The main aim of this conceptualization is to clarify the distinction between cohort studies and case series. We discuss the potential impact of the proposed conceptualization on the body of evidence and workload. All studies with exposure-based sampling gather multiple exposures with at least two different exposures or levels of exposure and enable calculation of relative risks that should be considered cohort studies in systematic reviews, including non-randomized studies. Instead, all studies for which sufficient data are available for reanalysis to compare different exposures e. There are possibly large numbers of studies without a comparison for the exposure of interest but that do provide the necessary data to calculate effect measures for a comparison.
Cohort and case-control methodologies are the main tools for analytical epidemiological research. Other important types of epidemiological studies mainly for generating hypotheses include cross-sectional and ecological, or correlation studies. The conclusions that can be drawn from findings of these types of studies are, however, much weaker compared to those of cohort and case-control studies. This is not to say that findings from cohort and case-control studies always reflect true associations which can be universally generalized. Epidemiological research is, to a large extent, of an observational character as opposed to experimental research. Experimental research provides data from which firmer conclusions can be made as compared with epidemiological studies.
are used to determine prevalence. They are relatively quick and easy but do not permit.
Lesson 1: Introduction to Epidemiology
Although the relative risk from a prospective cohort study is numerically approximate to the odds ratio from a case-control study for a low-probability event, a definite relationship between case-control and cohort studies cannot be confirmed. In this study, we established a different model to determine the relationship between case-control and cohort studies. Two analysis models the cross-sectional model and multiple pathogenic factor model were established. Incidences in both the exposure group and the nonexposure group in a cohort study were compared with the frequency of the observed factor in each group diseased and nondiseased in a case-control study.
As noted earlier, descriptive epidemiology can identify patterns among cases and in populations by time, place and person.
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This short article gives a brief guide to the different study types and a comparison of the advantages and disadvantages. Figure 1 shows the tree of possible designs, branching into subgroups of study designs by whether the studies are descriptive or analytic and by whether the analytic studies are experimental or observational. The list is not completely exhaustive but covers most basics designs. Figure: Tree of different types of studies Q1, 2, and 3 refer to the three questions below. Our first distinction is whether the study is analytic or non-analytic. Descriptive studies include case reports, case-series, qualitative studies and surveys cross-sectional studies, which measure the frequency of several factors, and hence the size of the problem. To quantify the effect we will need to know the rate of outcomes in a comparison C group as well as the intervention or exposed group.
Posted on 6th December by Saul Crandon. Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence. These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies 1. Although these studies are not ranked as highly as randomised controlled trials, they can provide strong evidence if designed appropriately. Case-control studies are retrospective. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups. See Figure 1 for a pictorial representation of a case-control study design.
Cross-sectional study designs are used when studying one or more variables within a given population at one point in time. Such studies are useful for establishing associations rather than causality and for determining prevalence, rather than incidence. In cohort studies, a group of people within a population is followed over a specified period of time to track who experiences or develops the same significant life event or treatment. This type of design can be used "to study incidence, causes, and prognosis. Because they measure events in chronological order they can be used to distinguish between cause and effect. As well, two groups may be followed: one containing the agent of interest and the other acting as a control group.