These studies are generally (although not always) used to test one or more specific hypotheses, typically whether an exposure is a risk factor for a disease or an intervention is effective in preventing or curing disease (or any other occurrence or condition of interest). Of course, data obtained in an analytic study can also be explored in a descriptive mode, and data obtained in a descriptive study can be analyzed to test hypotheses, making it analytical. In short, these studies are designed to examine etiology and causal associations. Types of analytical studies are cross sectional, case-control, cohort (retrospective and prospective) and ecological.
Cross-sectional study is also known as a prevalence study. It measures the cause and effect at the same time, but does not tell us the relationship, i.e. which one is the cause and which one is the effect. This is the commonest study design used in general practice and research, in general. These studies are relatively easy to do, inexpensive and can be carried out in a short time frame.
In studies using this particular design, patients who already have a certain condition (cases) are compared (e.g. diabetic patients with hospitalization) with people who do not have that condition (controls) (e.g. diabetic patients without hospitalization). The researcher goes through the past records of these subjects (both cases and controls) to find out whether the development of the condition only in one group of patients is due to presence of some causative factor (exposure). Thus, in a typical case-control study, the data collection is mainly retrospective (backward in time) .
These studies are less reliable than either randomized controlled trials or cohort studies. A major drawback to case-control studies is that one cannot measure the risk of developing a particular outcome because of an exposure. Additionally, in these studies one has to mainly rely on the memory of patients to identify what in the past might have caused their current disease, which is most often of long latency. This might induce a bias while analyzing the results, which is known as "recall bias". Because human memory is frequently imprecise, recall bias is commonly believed to be "pervasive in case-control studies."[3
The presence of disease affects both the patient's perception of the causes and his search for possible exposure to a hypothesized risk factor. Therefore, the recall of remote exposures in case-control studies is commonly presumed to be differential among study subjects depending on their disease status.[4
] Data, even about irrelevant exposures, are often remembered better by cases or/and underreported by controls.[5
] This trend in exposure recall tends to inflate the risk estimate in case-control studies. Also, recalling the exact timing of exposure, which is often important in determining temporality of an association and in estimating induction period of a disease, can be differential among exposed cases and exposed controls.
Despite the fact that recall bias is a major limitation of case-control studies, a number of methodological strategies documented in the literature can minimize the recall bias.[6
The advantages of case-control studies are that they can be done quickly and are very efficient for conditions/diseases with rare outcomes.[7
Cohort (Longitudinal studies)
A cohort study begins with a group of subjects with some causative factor (e.g. daily intake of Virrudha ahar) but free of the condition of interest (e.g. skin diseases). All the subjects are followed up and observed for the occurrence of the condition of interest.
In contrast to the case-control study, a cohort study is usually prospective (forward in time). It provides the best information about the cause of disease plus the most direct measurement of the risk of developing a particular outcome due to exposure .
Cohort (Longitudinal studies) design
These studies, however, require a large number of subjects and a long period of follow up to assess whether the event of interest has occurred, due to which these studies are very expensive to conduct. The main drawback of these studies due to long follow up is that there are high chances of subjects getting lost to follow up.[7
] If in the two groups, the degree of such losses is substantially different, it can lead to bias and false positive results.
These studies (sometimes called ecologic studies) explore the statistical connection between disease in different population groups and estimated exposures in groups rather than individuals. For example, they may correlate death rates by country with estimates of exposure, such as factory emissions in a given geographic area, proximity to waste sites, or air or water pollution levels. The geographical information system (GIS) is a very useful new tool that improves the ability of ecologic studies to be able to determine a link between health data and a source of environmental exposure.