Case-Control Study Design
According to the Babylon dictionary (1) case-control design is defined as “A study that compares two groups of people: those with the disease or condition under study (cases) and a very similar group of people who do not have the disease or condition (controls)”. Lewallen (57) describes the case-control design as an observational design. According to the above case, the patient population subjected to the study design is a selection of an outcome variable. This epidemiological study focuses on effect and cause. It is an analytical study that focuses on outcomes then proceeds to study the causes. Patients who have developed specific problems with past exposures to aetiological factors are identified and compared with a selected group of cases or controls that do not have or have not developed a similar problem or medical condition. These are referred to as “controls” as argued by Schlesselman and Stolley (14). Lewallen (57) affirms that in a case-control study “patients who have developed a disease are identified and their past exposure to suspected aetiological factors is compared with that of controls or referents who do not have the disease”. Lewallen (57) describes the design as a retrospective, implying that outcomes about a case are already known by the investigator. A case must have been proved on acceptable clinical diagnosis and sufficiently identifiable on a clinical criterion. Controls should be similar in many aspects such as sex and age. According to Lewallen (57), when cases and controls are marched, they form a case controls design.
Advantages of Case-Controlled Studies
According to the Journal of Epidemiology and Community Health (2) case-controlled studies require shorter lengths of studies to give adequate statistical strength which is a comparative advantage to other study designs. Lewallen (58) indicates that case-controlled studies are quicker, easier, and inexpensive to conduct as compared to other study designs. Lewallen (58) asserts that the design is suitable for conducting investigations on outbreaks and for studying rare diseases. Such studies may include endophthalmitis which is due to ocular surgery and the study of risk factors associated with corneal cancer as argued by Lewallen (58). Lewallen (58) has determined that it is possible to enroll a large number of patients with a rare disease which is an advantage of the design. Lewallen concludes that “The practical value of producing rapid results or investigating rare outcomes may outweigh the limitations of case-control studies. Because of their efficiency, they may also be ideal for preliminary investigation of a suspected risk factor for a common condition;..”, in addition to the design requiring a few study subjects.
Disadvantages of Case Controlled Studies
Lewallen (58) argues that case-controlled studies have the disadvantage of business. Bias arises if a report on certain risk factors is incorrect after a rigorous study is conducted to verify the same report. As another disadvantage, case-controlled studies can not be relied upon if records are inadequately kept. This implies that some data may be missing or may not be precisely recorded. Results could be susceptible to flaws. Another disadvantage is that it is difficult to generate incident data on a case. According to the Journal of Epidemiology and Community Health (2) this may be due to the interference from confounding variables. Confounding variables lurk within a scientific study thus influencing the resulting outcome of an experiment. Lewallen (58) noted that selecting case controls can be very difficult. This disadvantage medically grades this design study on a lower quality scale.
Odds Ratio
“An odds ratio is the ratio of the odds of an exposure in the case group to the odds of an exposure in the control group”, a definition coined by Lewallen (58) and describes an odds ratio “As a measure of the strength of the association between an exposure and the outcome, case-control studies yield the odds ratio”. This measure determines the strength between a pair of binary values and determines the risks associated with a particular outcome. This definition may be based on the odds that are group-wise or on conditional probabilities on a pair of random binary values.
Confidence Interval
According to the free dictionary (1), confidence interval (n), usually expressed as a percentage, is defined as “an interval of values bounded by confidence limits within which the true value of a population parameter is stated to lie with a specified probability”. Confidence intervals are valuable statistical tools for measuring the validity and reliability of research results. According to the Journal of Clinical Nursing (2), a 95% confidence interval provides a high degree of a statistical parameter being true. It is a statistical interval estimate population parameter that is used to determine the degree of confidence or the extent to which an estimate can be relied upon.
Interpretation about Oral Cancer and Unemployment
The significant probability of oral cancer being incident in unemployed people was high since the OR (odds ratio) is 2.27. When the OR is less than one, the odds are less likely, but when OR>1, the odds are highly likely to be predominated by the unemployment factors. Note that this OR is greater than one (OR>1), i.e. (2.27) at a 95% confidence interval which is greater than one, i.e.1.21. Thus dividing the number of those who experience oral cancer and are unemployed against those who do not, results in the value of 4.26. This study illustrates the fact that unemployment positively impacts oral cancer as a socioeconomic factor.
About Oral Cancer and educational attainment an oral cancer
The odds ratio is less than one (OR<1) i.e. OR=0.17. This implies that the odds of the number of those with oral cancer and with educational attainment (ED) divided by those without oral cancer and with a similar educational attainment (n-ED) is less than one, i.e. (ED/n-ED) =0.17 i.e. (95%CI 0.05, 0.58). This implies that educational attainments positively influence will be affected. The 95% confidence interval illustrates that.
Additional information for drawing conclusions
Samples should be repeatedly taken from the same population. If the statistical results are the same or do not vary widely, then they are reliable else not. Random sampling should be used to avoid systematic bias. Observations on the sample should be independent. In addition, two types of variables should be used, i.e. dependent and independent variables.
References
Babylon Dictionary. NCI dictionary of cancer terms [Online]. 1997-2009. Web.
Journal of Clinical Nursing: Information Point. Odds ratio. [Online]. 2001 257ą269. Web.
Lewallen S. Journal of Community Eye Health, International Centre for Eye Health, London. Epidemiology in Practice: Case-Control Studies [Online]. 1998; 11(28); 57-58. Web.
Schlesselman JJ, Stolley PD. Case-Control Studies: design, conduct, analysis. Oxford University Press, Inc, 1982.
The free dictionary. Confidence interval [Online]. 2010. Web.