Question

Describe Observational study designs: cohort study and case-control study.

Choose an example for each study type and discuss the different statistical analysis used to interpret and analyze the results.

Suggest a case-control study that you prefer to conduct and explain the statistical tests that you would use in your study.

Answer 1

ANSWER::

Cohort studies are a type of medical research used to investigate the causes of disease and to establish links between risk factors and health outcomes. The word cohort means a group of people. These types of studies look at groups of people. They can be forward-looking (prospective) or backward-looking (retrospective).Prospective studies are planned in advance and carried out over a future period of time.Retrospective cohort studies look at data that already exist and try to identify risk factors for particular conditions. Interpretations are limited because the researchers cannot go back and gather missing data. These long-term studies are sometimes called longitudinal studies.

example-

conducting cross sectional research (a method that compares different age groups at the same point in time) to find out how basic mathematics ability improves with age. You give the same basic math standardized test to groups of students who are 7-years-old, 14-years-old, and 21-years-old. You get the following mean results:

• 7-years-old: 24% correct
• 14-years-old: 48% correct
• 21-years-old: 72% correct

You might conclude that every 7 years that pass makes a difference of 24% in scores. However, what you haven’t accounted for is the cohort effect. The students differ not only in age, but they belong to different cohorts (in this case, groups of people born around the same time), some of which may have grown up when basic mathematics was strongly emphasized in schools. If the 21-year-old cohort in the above study experienced strong emphasis on basic math, it’s a possibility that they could have achieved 72% when they were 14-years old or even 7-years-old.

The problems associated with the cohort effect can be lessened by testing the same cohort over a period of time, a method called longitudinal research. In the above example, you would test a group of 7-year-olds, then test the same group every 7 years. A disadvantage to longitudinal research is that it’s costly, and dropout rates can affect the results.

case control study-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). Researchers study the medical and lifestyle histories of the people in each group to learn what factors may be associated with the disease or condition. For example, one group may have been exposed to a particular substance that the other was not. Also called retrospective study.

example-

Within a short period of time 20 cases of hepatitis A were identified in the Marshfield area. The epidemic curve suggested a point source epidemic, and the spot map showed the cases to be spread across the entire South Shore of Massachusetts, although the pattern suggested a focus near Marshfield. Hypothesis-generating interviews resulted in five food establishments that were candidate sources. Moreover, the disease was rare, so that even if they interviewed a sample of patrons at each of the restaurants, it is most likely that few, if any would have had recent hepatitis, even from the responsible restaurant.

In a situation like this a case-control design is a much more efficient option. The investigators identified as many cases as possible (19 agreed to answer the questionnaire), and they selected a sample of 38 non-diseased people as a comparison group (the controls). In this case, the "controls" were non-diseased people who were matched to the cases with respect to age, gender, and neighborhood of residence. Investigators then ascertained the prior exposures of subjects in each group, focusing on food establishments and other possibly relevant exposures they had had during the past two months.

When using a case-control strategy for sampling, it is not possible to calculate the incidence (attack rate) in exposed and non-exposed subjects, because the denominators of the exposure groups are unknown. However, one can calculate the odds of disease in exposed and non-exposed subjects, and these can be expressed as an odds ratio, which is a good approximation of a risk ratio in a situation like this, i.e., when the outcome is rare. An odds ratio can be computed for each of the possible sources. Consider the following example:

	Cases	Controls
Ate at Papa Gino's	10	19
Did not eat at Papa Gino's	9	19
	19	38

Given these hypothetical results, the odds that someone who ate a Papa Gino's was a case were 10/19, while the odds that someone not exposed to Papa Gino's became a case were 9/19. These odds are quite similar, and the odds ratio is close to 1.0. The odds ratio can be interpreted the same way as a risk ratio.

Odds Ratio = (10/19) / (9/19) = 1.1

This certainly provides no compelling evidence to suggest an association with Papa Gino's, but, as we did with the risk ratio, we could compute a 95% confidence interval for the odds ratio, and we could also compute a p value. In this case the 95% confidence interval is 0.37 to 3.35, and p= 0.85.

In contrast, consider the findings for Ron's Grill:

	Cases	Controls
Ate at Ron's Grill	18	7
Did not eat at Ron's	1	29
	19	38

For Ron's Grill the odds ratio would be computed as follows:

Odds Ratio = (18/7) / (1/29) = 75

This suggests that patrons of Ron's Grill had 75 times the risk of being a case compared to those who did not eat at Ron's. The other three restaurants that had been suspects had odds ratios that were close to 1.0. This certainly provides strong evidence that a Ron's Grill was the source of the outbreak, and further investigation confirmed that one of the food handlers at Ron's had recently had a subclinical case of hepatitis A.

In case-control studies, one of the most difficult decisions is how to select the the controls. Ideally they should be non-diseased people who come from the same source population as the cases, and, aside from their outcome status, they should be comparable to the cases in order to avoid selection bias. Note that in the Marshfield case-control study the controls were selected in a way to ensure that they were comparable with respect to age and gender and lived in similar neighborhoods

statsitics- The statistical tests that can be used for ascertain an association depends upon the variable characteristics also. If the researcher wants to find the association between two categorical variables (e.g., a positive versus negative test result and disease state expressed as present or absent), Cochran-Armitage test, which is same as Pearson Chi-squared test can be used. When the objective is to find the association between two interval or ratio level (continuous) variables, correlation and regression analysis can be performed. In order to evaluate statistical significant difference between the means of cases and control, a test of group difference can be performed. If the researcher wants to find statically significant difference among means of more than two groups, analysis of variance can be performed. A detailed explanation and how to calculate various statistical tests will be published in later issues. The success of the research directly and indirectly depends on how the following biases or systematic errors, are controlled.

a case control study i would like to perform is incidence in postmenopausal women at risk of breast cancer.

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