In medical research, it is important for a researcher to know about different analytical studies. The objectives of different analytical studies are different, and each study aims to determine different aspects of a disease(s) such as prevalence, incidence, cause, prognosis, or effect of treatment. Therefore, it is essential to identify the appropriate analytical study associated with certain objectives. Analytical studies are classified as experimental and observational studies. While in an experimental study, the investigator examines the effect of presence or absence of certain intervention(s), he does not need to intervene in a observational study, rather he observes and assesses the relation between exposure and disease variable. Interventional studies or clinical trials fall under the category of experimental study where investigator assigns the exposure status. Observational studies are of four types: cohort studies, case-control studies, cross-sectional studies, and longitudinal studies
While experimental studies are sometimes non indicative or not ethical to conduct or very expensive, observational studies probably are the next best approach to answer certain investigative questions. Well-designed observational studies may also produce similar results as controlled trials; therefore, probably, the observational studies may not be considered as second best options. In order to design an appropriate observational study, one should able to distinguish between four different observational studies and their appropriate application depending on the investigative questions. Following is a brief discussion on four different observational studies (each will be discussed in detail individually in my upcoming blogs):
Observational Analytical Study Designs
Cohort studies
Cohort methodology is one of the main tools of analytical epidemiological research. The word “cohort” is derived from the Latin word “cohors” meaning unit. The word was adopted in epidemiology to refer a set of people monitored for a period of time. In modern epidemiology, the word is now defined as “group of people with defined characteristics who are followed up to determine incidence of, or mortality from, some specific disease, all causes of death, or some other outcome” (Morabia, 2004). In cohort studies, individuals are identified who initially do not have the outcome of interest and followed for a period of time. The group can be classified in sub sets on the basis of the exposure. For example, a group of people can be identified consisting of both smoker and non-smoker and followed them for the incidence of lung cancer. At the beginning of the study none of the individuals have lung-cancer and the individuals are grouped into two sub sets as smoker and non-smoker and then followed for a period of time for different characteristics of exposure such as smoking, BMI, eating habits, exercise habits, family history of lung cancer or cardiovascular diseases, etc. Over the time, some individuals develop the outcome of interest. From the data collected over time, it is convenient to evaluate the hypothesis whether smoking is related with the incidence of lung cancer. The following schematic shows the basic design of a cohort study. There are two types of cohort studies: prospective and retrospective. A prospective study is conducted at present but followed up to future i.e., waiting for the disease to develop. On the other hand, a retrospective study is carried out at present on the data collected in the past. This is also called as historic cohort study. In the next blog, I will discuss these in detail.
Case-control studies
In terms of objective, case-control studies and cohort studies are same. Both are observational analytical studies, which aim to investigate the association between exposure and outcome. The difference lies in the sampling strategy. While cohort studies identify the subjects based on the exposure status, case-control studies identify the subjects based on the outcome status. Once the outcome status is identified the subjects are divided into two sets: case and control (who do not develop the outcome). For example, a study design which determines the relation between endrometrial cancer with use of conjugated estrogen. For this study, subjects are chosen based on the outcome status (endrometrium cancer) i.e., with disease present (case) and absent (control), and then these two subsets are compared with respect to the exposure (use of conjugated estrogen). Therefore, case-control study is retrospective in nature and cannot be used for calculating relative risk. However, odd ratio can be measured, which in turn, is approximate to relative risk. In cases of rare outcomes, case control study is probably the only feasible analytical study approach.
Cross-sectional studies
Cross-sectional study is a type of observational analytical study which is used primarily to determine the prevalence without manipulating the study environment. For example, a study can be designed to determine the cholesterol level in walker and non-walker without exerting any exercise regime or activity on non-walkers or modifying the activity of the walkers. Apart from cholesterol other characteristics of interest, such as age, gender, food habits, educational level, occupation, income, etc., can also be measured. The data collected at one time in present with no further follow up. In cross-sectional design, one can study a single population (only walkers) or more than one population (both walker and non-walker) at one point of time to see the association between cholesterol level and walking. However, the design of this study does not allow to examine the causal of a certain condition since the subjects are never been followed either in past or present.
Longitudinal studies
Longitudinal studies, similar to cross-sectional studies, are also a type of observational analytical studies. However, the difference of this study design with the cross-sectional study is the following up the subjects for a longer time; hence, can contribute more to the association of causative to a condition. For example, the design that aims to determine the cholesterol level of a single population, say the walkers over a period of time along with some other characteristics of interest such as age, gender, food habits, educational level, occupation, income, etc. One may choose to examine the pattern of cholesterol level in men aged 35 years walking daily for 10 years. The cholesterol level is measured at the onset of the activity (here, walking) and followed up throughout the defined time period, which enables to detect any change or development in the characteristics of the population.
Following two tables summarize different observational analytical studies with regard to the objectives and time-frame.
I will define several terms, such as risk factor, odd ratio, probability, confounding factors, etc., related to study designs along with the detail discussion on individual analytical study design and tips to choose correct design depending on the research question in my upcoming blogs. Visit the blog section of the website (www.manuscriptedit.com) for more such informative and educative topics.
References
[1] Morabia, A (2004). A History of Epidemiologic Methods and Concepts. Birkhaeuser Verlag; Basel: p. 1-405.
[2] Hulley, S.B., Cummings, S.R., Browner, W.S., et al (2001). Designing Clinical Research: An Epidemiologic Approach. 2nd Ed. Lippincott Williams & Wilkins; Philadelphia: p. 1-336.
[3] Merril, R.M., Timmreck, T.C (2006). Introduction to Epidemiology. 4th Ed. Jones and Bartlett Publishers; Mississauga, Ontario: p. 1-342.
[4] Lilienfeld, A.M., and Lilienfeld, D.E. (1980): Foundations of Epidemiology. Oxford University Press, London.
