PUB 540 What are the two main types of analytic studies?
PUB 540 What are the two main types of analytic studies?
PUB 540 What are the two main types of analytic studies?
Analytic epidemiology compares using a control group of people that were not affected. This allows the epidemiologist to root out certain characteristics that are specifically associated with the disease as opposed to not being associated with the disease. Just like in the church outbreak, the people that belonged to the church, but had not had dinner on that night were excluded from the pool to be investigated. When characteristics can be identified that are associated with the disease, it gives public health the chance to start working on interventions. It can also lead to determining what a specific cause may be. This is done by studying the associated and non-associated groups by either an experimental study or observational study. Observational studies can then be either: cohort studies, case-control studies, and cross-sectional studies
Hypothesis testing is part of the analytical epidemiology but is involved more so with interventional study. That part that helps create a cure. As discussed by Friis and Sellers (2020) a statistical test is done to determine the validity of the results. Is that data from the interventional group significantly different from the results of the non-interventional group. In other words, those that got the vaccine and those that did not. We saw this play out in the phase 4 trials of the COVID-19 clinical trials. Can those results be arrived at under the same circumstances with similar study participants? If yes, then your study is significantly important. This is what Pfizer, Moderna, and Johnson & Johnson did. Of course, this is simplified, but this is the gist of hypothesis testing. This is a type is a cohort study.
References
Dicker et al. (2012). Principles of Epidemiology in Public Health Practice: An Introduction to Applied
Epidemiology and Biostatistics. Centers for Disease Control and
Prevention. https://www.cdc.gov/cels/dsepd/ss1978/lesson1/section7.html
Friis, R. H., & Sellers, T. (2020). Epidemiology for public health practice (6th ed.). Jones & Bartlett Learning.
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Analytical studies help identify and quantify associations. In order to test hypotheses and answer the how and why analytical studies are categorized by two types. Most importantly the key feature of analytical is the comparison of groups (Provost, 2011). The two types are known as observational and experimental. According to the CDC studies the experimental component is made up of random control trials while the observational utilizes cross-section, case-control, and cohort. Investigators began to determine through a controlled environment with a clinical or community trial exposure and record over time the effects of the effects of the exposure (CDC, 2019). The trial is conducted depending on the hypothesis that’s being tested which was established by the epidemiologist. Moreover, the observational studies utilize data that has been gathered from community, induvial, and populations as a whole thru questionnaire.
Previously learned through scientific projects that hypotheses are nothing more than educated guest. To specify hypothesis are tested against data that has been gathered for acceptance or decline. Within analytical studies hypotheses are utilized to evaluated relationship between given variables and samples. Hypothesis testing remains the common approach and is known to be beneficial through medical science. An example would be the following scenario: Research intends to identify relationship between height and gender. Furthermore, an assumption or hypothesis will be developed based on one’s knowledge about human physiology. Hypothesis could Men on average have taller height than women. Next, you’ll collect information and then execute statistical testing and determine if the data gather support or refute the hypothesis.
Reference:
CDC. (2019). Principle of epidemiology. Center for Disease Control and Prevention. https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section7.html
Provost L. P. (2011). Analytical studies: a framework for quality improvement design and analysis. BMJ quality & safety, 20 Suppl 1(Suppl_1), i92–i96. https://doi.org/10.1136/bmjqs.2011.051557
Analytical studies quantify the relationship between disease exposure/cause and disease outcome). These studies test hypotheses to identify the root cause of diseases. According to Ranganathan et al. (2019), the two main types of analytical studies include observational and experimental studies. Observational analytical studies help determine exposure naturally compared to experiment analytical studies, which involve exposure variance between a study population and a control group. Experiment analytical studies include randomized clinical trials. Therefore, experiment analytical studies minimize bias through randomization. According to Thakur & Shah (2021), experiment analytical studies apply the principles of sequence generation and allocation concealment. Participants have equal chances of allocation to exposure or non-exposure group. Additionally, participants remain unaware of the allocated group until intervention administration.
Observational studies include cohort (prospective and retrospective) and case-control. Cohort studies involve a group of people with shared characteristics. In essence, epidemiologists select a group of people with varying exposure levels and monitor their progress to evaluate the onset of outcomes (Ranganathan et al., 2019). Participants are free of disease outcomes at baseline. Therefore, several exposures and one or more disease outcomes are studied simultaneously. According to Ranganathan et al. (2019), case-control studies involve two groups of participants exposed to disease causative-agent. That is a group presenting with disease outcomes and one without disease outcomes at baseline. These studies are retrospective, thus allowing epidemiologists to elicit a history of exposure. Most importantly, case-control studies enable epidemiologists to identify the relationship between an outcome and several levels of disease exposure.
Hypothesis testing helps verify the plausibility of the hypothesis by examining data samples from random populations. A hypothesis can involve preventive interventions or disease exposure. For instance, one hypothesis involving preventive interventions is maternal adherence to healthy lifestyle practices reduces the risk of obesity in offspring. The researchers conducted a prospective cohort study (Dhana et al., 2018). One hypothesis involving disease exposure is air pollution shortens lung cancer survival (Eckel et al., 2016). The researchers used a cohort study to evaluate the time of death of participants. A descriptive statistical method evaluated survival and exposure to air pollution.
References
Dhana, K., Haines, J., Liu, G., Zhang, C., Wang, X., Field, A. E., … & Sun, Q. (2018). Association between maternal adherence to healthy lifestyle practices and risk of obesity in offspring: Results from two prospective cohort studies of mother-child pairs in the United States. BMJ, 362.doi: https://doi.org/10.1136/bmj.k2486
Eckel, S. P., Cockburn, M., Shu, Y. H., Deng, H., Lurmann, F. W., Liu, L., & Gilliland, F. D. (2016). Air pollution affects lung cancer survival. Thorax, 71(10), 891- 898.http://dx.doi.org/10.1136/thoraxjnl-2015-207927
Ranganathan, P., & Aggarwal, R. (2019). Study designs: Part 3 – Analytical observational studies. Perspectives in Clinical Research, 10(2), 91–94. https://doi.org/10.4103/picr.PICR_35_19
Thakur N & Shah D. (2021 Dec 15). Interventional Study Designs. Indian Pediatr, 58(12):1171- 1181. Epub 2021 Sep 22. PMID: 34553689.
Analytic studies in epidemiology can be a key component of field investigations. Analytic studies typically should be used to test hypotheses, not generate them. However, in certain situations, collecting data quickly about patients and a comparison group can be a way to explore multiple hypotheses, (Jackson & Griffin, 2018). The two main analytic studies are experimental and observational studies. Jackson & Griffin, (2018) noted “As evident in public health and clinical guidelines, randomized controlled trials (e.g., trials of drugs, vaccines, and community-level interventions) are the reference standard for epidemiology, providing the highest level of evidence. Investigators must rely on observational studies, which can provide sufficient evidence for public health action. Because field analytic studies are used to quantify the association between exposure and disease, defining what is meant by exposure and disease is essential. Exposure is used broadly, meaning demographic characteristics, genetic or immunologic makeup, behaviors, and environmental to identify risk factors and their impact on the population, (Ranganathan & Aggarwal, 2019). A hypothesis in an epidemiologic research study on the relation between smoking and lung cancer and evaluating the occurrence of an outcome. Methods used can be cohort studies, case-control studies, and or case-case studies, (Ranganathan & Aggarwal, 2019).
References
Jackson, B. R., & Griffin, P, D., (2018). Designing and Conducting Analytic Studies in the Field | Epidemic Intelligence Service, The CDC Epidemiology Manuel. https://www.cdc.gov/eis/field-epi-manual/chapters/design-conduct-analyze-field-studies.html
Ranganathan, P., & Aggarwal, R. (2019). Study designs: Part 3 – Analytical observational studies. Perspectives in clinical research, 10(2), 91–94. https://doi.org/10.4103/picr.PICR_35_19
Cohort studies can be retrospective or prospective. Retrospective cohort studies are NOT the same as case-control studies.
In retrospective cohort studies, the exposure and outcomes have already happened. They are usually conducted on data that already exists (from prospective studies) and the exposures are defined before looking at the existing outcome data to see whether exposure to a risk factor is associated with a statistically significant difference in the outcome development rate.
Prospective cohort studies are more common. People are recruited into cohort studies regardless of their exposure or outcome status. This is one of their important strengths. People are often recruited because of their geographical area or occupation, for example, and researchers can then measure and analyze a range of exposures and outcomes.
The study then follows these participants for a defined period to assess the proportion that develop the outcome/disease of interest. Therefore, cohort studies are good for assessing prognosis, risk factors and harm. The outcome measure in cohort studies is usually a risk ratio / relative risk (RR).
Descriptive epidemiology refers to the area of epidemiology that focuses on describing disease distribution by characteristics relating to time, place, and people, while analytical epidemiology refers to the area of epidemiology, which measures the association between a particular exposure and a disease, using information collected from individuals, rather than from the aggregate population. (Kobayashi, John) The main difference between descriptive and analytical epidemiology is that descriptive epidemiology generates hypotheses on risk factors and causes of disease, whereas analytical epidemiology tests hypotheses by assessing the determinants of diseases, focusing on risk factors and causes as well as, analyzing the distribution of exposures and diseases. Furthermore, descriptive epidemiology is comparatively a small and less complex study area, while analytical epidemiology is a larger and more complex study area.
An example of descriptive epidemiology examines case series using person, place, and time of first 100 patients with SARS, while analytical epidemiology measures risk factors for SARS such as contact with animals and infected people.
Reference:
Kobayashi, John. “Study Types in Epidemiology.” Nwcphp.org, Northwest Center for Public Health Practice
The two main types of analytic studies are experimental and observational. In experimental studies the researcher introduces an intervention and study effect from that. While observational study observes relationships between variables. In Hypothesis testing for risk factors and disease the primary purpose to determine if there is substantial statistical evidence in favor of a convinced belief or hypothesis about a parameter (CDC,2018). One example from Lahey, D’Onofrio & Waldman, (2009) suggest “Epidemiologic methods are increasingly being used to move developmental psychopathology from studies that catalogue correlates of child and adolescent mental health to designs that can test rival hypotheses regarding causal genetic and environmental influences. A two-part strategy is proposed for the next phase of epidemiologic research. First, to facilitate the most informed tests of causal hypotheses, it is necessary to develop and test models of the structure of hypothesized genetic and environmental influences on mental health phenotypes. This will involve testing the related hypotheses that there are both (a) dimensions of psychopathology that are distinct in the sense of having at least some unique genetic and/or environmental influences, and (b) higher-order domains of correlated dimensions that are all apparently influenced in part by the same genetic and/or environmental factors.”
References
Centers for Disease Control and Prevention (2018). Foodborne Outbreaks. Step 4: Test Hypotheses. Retrieved from https://www.cdc.gov/foodsafety/outbreaks/investigating-outbreaks/investigations/hypotheses.html
Lahey, B. B., D’Onofrio, B. M., & Waldman, I. D. (2009). Using epidemiologic methods to test hypotheses regarding causal influences on child and adolescent mental disorders. Journal of child psychology and psychiatry, and allied disciplines, 50(1-2), 53–62. https://doi.org/10.1111/j.1469-7610.2008.01980.x
According to CDC.gov (2019) analytic study is a comparison group, can also be identify patterns among cases and population by time, place and person. From these observations, epidemiologists develop hypotheses about the causes of these patterns and about the factors that increase risk of disease. According to CDC, the epidemiologist simply observes the exposure and disease status of each study participant. The two most common types of observation studies are cohort studies and case-control studies; a third is cross-sectional studies.
According to CDC cohort study is similar in concept to the experimental study. In a cohort study the epidemiologist records whether each study participant is exposed or not, as well as with case-control study, investigators start by enrolling a group of people with disease(at CDC such persons are called case-patients rather than cases, because case refers to occurrence of disease, not a person).
Reference
CDC. (2019). Principle of epidemiology. Center for Disease Control and Prevention.
Friis, R. H., & Sellers, T. (2020). Epidemiology for Public Health Practice (6th ed). Jones & Bartlett Learning.
Analytic study is the study in which action is take on a cause system that focuses on prediction. The two types of analytic studies according to CDC.gov (2013) are experimental which comprises of randomized control trial and observational which includes cohort, case-control and cross sectional.
Friis & Sellers (2021) explain that researchers apply a statistical test which is the t-test to their data. Statistical tests are performed t determine whether results for a test group (interventional group) are significantly different from those of the control group (non-interventional group) (pg. 477). With respect to a null hypothesis and alternative hypothesis a decision is made.
A great example of how hypothesis is used in epidemiological research is as follows.
“To determine whether a new medication lowers patients’ blood pressure. The null hypothesis alleges that the drug did not make a difference in the mean blood pressures obtained in the test and control groups. The alternative hypothesis (H1) is that the drug made a difference between the mean blood pressures of the test and control groups” (Friis & Sellers, 2021).
References
CDC.gov (2013). Descriptive and Analytic studies. Retrieved from https://www.cdc.gov/globalhealth/healthprotection/fetp/training_modules/19/desc-and-analytic-studies_ppt_final_09252013.pdf
Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.)
Analytical epidemiological studies test ideas and answer why and how the relationship between exposure and a health outcome is concerned (CDC, 2012; CDC, 2013, slides 18-19). Usually, they fall into two categories, namely experimental and observational studies (CDC, 2012). Experimental studies involve establishing a cause and effect relationship between two variables. Here, epidemiologists attempt to quantify through a controlled process the relationship between the effect of an intervention or exposure for each individual or community and then track the particular groups over time to assess the impact of exposure. In contrast, observational studies involve establishing a relationship between exposure and the disease outcome of each individual done in a cohort study or a case-control study. This means that epidemiologists only observe the effects of the disease or treatment without trying to change who is or is not exposed to it.
Hypothesis testing is critical in epidemiologic research because it measures the reliability and validity of outcomes and whether research data is statistically significant. It assesses the plausibility of a hypothesis. It provides evidence to establish whether the observed differences were simply due to random error so that they can be refined to have a narrower (CDC, 2013, slide19). This is critical because a hypothesis is an idea that can be scientifically tested.
An example of a hypothesis in epidemiological research could be that children exposed to pets are more likely to develop childhood asthma due to animal allergens. The exposure would be exposure to pets, and the outcome would be childhood asthma. The hypotheses could be tested by measuring and examining a random sample of children exposed to pets. Analysis of variance (ANOVA) could examine the amount of variation across the means within each sample group assigned to low, medium and high levels of exposure (Fancello et al., 2020, p. 997).
References
CDC. (2012, May). Lesson 1: Introduction to Epidemiology – Section 7: Analytic Epidemiology. Retrieved from CDC: https://www.cdc.gov/csels/dsepd/ss1978/lesson1/section7.html.
CDC. (2013). Descriptive and Analytic Studies. Retrieved from CDC: https://www.cdc.gov/globalhealth/healthprotection/fetp/training_modules/19/desc-and-analytic-studies_ppt_final_09252013.pdf.
Fancello, G., Adamu, M., Serra, P., & Fadda, P. (2020). Comparative analysis of the effects of mobile phone use on driving performance using ANOVA and ANCOVA. IET Intelligent Transport Systems, 14(9), 993-1003.