Choosing the most appropriate study design is a fundamental step in an epidemiological study (Aschengrau and Seage, 2008). According to Aschengrau and Seage (2008), epidemiologists use different epidemiological study designs to answer research questions. This paper highlights epidemiological study designs most commonly used during epidemiological research.
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According to Aschengrau and Seage (2008), investigators often use experimental studies when investigating the role of a given agent in the prevention and cure of diseases.
The main characteristic of experimental study is that it involves the study of prevention and treatment of diseases (Aschengrau and Seage, 2008).
Strength of Experimental
Experimental studies often yield more accurate results than other study designs.
Weakness of Experimental
Experimental studies are extremely costly and involve thorny ethical issues.
This study design involves the observation of the natural experience of the group of people with similar characteristics.
Observational studies examine causes, prevention and possible treatments of specified diseases. In observational studies, investigators take part in passive observation of a given group of people or events. During the study, investigators do not interfere with the group or events (Friss, 2010).
Observational study can be used on a wider range of exposure, such as occurrences, prevention measures and treatments of diseases (Friss, 2010).
According to Aschengrau and Seage (2008), the main weakness of this study is that researchers do not have absolute control over unsettling influences or inappropriate factors.
Cohort study can also be referred to as incidence or specific studies. It involves the study of group of people who are free of diseases (Friss, 2010). The selected people are classified into groups according to their level of exposure to a potential cause of disease or outcome (Aschengrau and Seage, 2008).
Cohort study involves examination of multiple health effects of an exposure (Aschengrau and Seage, 2008).
According to Friss (2010), Cohort Study is extremely beneficial for studying the national progression of diseases or risk factors for diseases.
In the cohort study, cases such as dropouts or non-response can result in bias.
The most appropriate measure of associations for the three study designs
The most appropriate measure of association in an epidemiologic study depends on the plan used to collect the needed information (Till and Grohan, 2012).
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For instance, the most appropriate measure of association for experimental study is the comparison of disease level between two groups: a group that has experienced the exposure of interest and one that has not. Experiential studies are the most effective way of examining disease level during exposure (Aschengrau and Seage, 2008).
Secondly, the appropriate measure of association for observational study is rating new cases in population at given point. This measure is appropriate because it is easy to observe natural occurrences within new populations (Aschengrau and Seage, 2008).
On the other hand, the most appropriate measure of association for cohort is rating of new cases in population at any given period (Mill, 2011). Cohort studies are effective when handling new cases in a population.
This section describes the potential biases that are most likely to be present in the study descriptions and how they affect the measure of association.
The possible bias that can result in this scenario would be a selection bias. Investigators could have been biased when selecting children that are sick against those that are not sick. Selection bias would lead to underestimation of the outcome of the study (Filed, 2010).
The possible bias that can come up from this situation is differential recall bias. Recall bias could result from this study survey because some individuals in the groups might decide to give false information during study survey. Recall bias, in this situation, would lead to overestimation of the results (Aschengrau and Seage, 2008).
The type of selection bias, known as healthy worker effect, could result from this situation. Investigators, in this scenario, categorize the participants with respect to exposure status after which a follow up is done to record the disease (Aschengrau and Seage, 2008). However, ten years is an unusually long period and can lead to underestimation of measures of association.
Investigators could possibly introduce misclassification bias in this case. For instance, error could have occurred during classification of disease or exposure. The different groups used in the study survey could have also used drugs that are different from the ones prescribed by the doctors (Filed, 2010).
This bias can lead to overestimation of the measures of associations because the outcome might not reflect the effect of placebo (Till and Grohan, 2012).
Conclusively, while carrying out investigations, investigators should choose the best study designs that can help minimize biases to a considerable level. Investigators should also chose the most appropriate measure of association when carrying out epidemiological research (Aschengrau and Seage, 2008).
Aschengrau, A., and Seage, G. (2008). Essentials of Epidemiology in Public Health (second edition). New York, NY: Jones & Bartlett Publishers, LLC.
Filed, D. (2001). Researching Palliative Care: Facing Death. New York, NY: Open University Press.
Friss, R. (2010). Epidemiology 101. New York, NY: Jones & Bartlett Learning.
Mill, R. (2011). Principles of Epidemiology Workbook: Exercises and Activities. New York, NY: Jones & Bartlett Learning.
Till, A and Grohan, A. (2008). Radiological Risk Assessment and Environmental Analysis. New York, NY: Oxford University Press.