“Positive predictive value refers to the percentage of patients with a positive test for a disease who actually have the disease. The negative predictive value of a test is the proportion of patients with negative test results who do not have the disorder.
The percentage of patients with a disorder who have a positive test for that disorder is a test’s sensitivity. The percentage of patients without a disorder who have a negative test for that disorder is a test’s specificity.”
The AFP EBM Glossary.
The ideal confirmation test will have both high sensitivity and high specificity.
Having a positive or negative test isn’t final. A positive or negative predictive value predicts the likelihood that your test result is actually true.
Comparison of data
- “Chi-squared test: Used to compare percentages or proportions (nonnumerical or nominal data)
- T-test: Used to compare two means
- Analysis of variance (ANOVA): Used to compare three or more means.” From Crush Step 3
Incidence: (New cases of a disease) ÷ ( population at risk) in a given period of time
Prevalence: (Total cases of disease) ÷ ( population). Can be at single time point (“ point prevalence”) or over a period of time (“ period prevalence”)
Number needed to treat/ harm: # of pts that must be treated to prevent/ cause 1 pt to have the measured outcome; 1/( risk difference), e.g., 1/( 0.1) → NNT of 10
TYPES OF STUDIES IN EPIDEMIOLOGY
“A case-control study is designed to help determine if an exposure is associated with an outcome (i.e., disease or condition of interest). In theory, the case-control study can be described simply. First, identify the cases (a group known to have the outcome) and the controls (a group known to be free of the outcome). Then, look back in time to learn which subjects in each group had the exposure(s), comparing the frequency of the exposure in the case group to the control group.
By definition, a case-control study is always retrospective because it starts with an outcome then traces back to investigate exposures. When the subjects are enrolled in their respective groups, the outcome of each subject is already known by the investigator. This, and not the fact that the investigator usually makes use of previously collected data, is what makes case-control studies ‘retrospective’.” http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1706071/
A case-control study is also observational.
In summary, a case-control study is an observational, retrospective study that starts with a known outcome — disease or cases for the case group and no disease for the control group–and looks back in time to find differences in exposure or risk factors between the case and control group. A good example is comparing a group of patients with lung cancer (case group) with a control group without lung cancer to find out if smoking exposure is associated with lung cancer. Association in a case-control is measured with an odds ratio (not RR).
Like the case-control, a cohort is also an observational study. Unlike a case-control, cohort studies are usually forward-looking – that is, they are “prospective” studies, or planned in advance and carried out over a future period of time. The goal is to identify differences in outcome between groups characterized by exposure/ risk factor; e.g., patients who smoke & those who don’t smoke are followed over time to determine if lung cancer incidence is different between groups.
Assess simultaneously for outcome & exposure at a single point in time. For example, How many people in telephone survey are smokers? How many have lung cancer? May use RR or OR.
Non-randomised controlled studies
Randomized control trial
– Allows for inferred causality (exposure leads to outcome) rather than just association (exposure & outcome are somehow linked)
Considerations in Study Review
Bias/ study design:
- Leon Gordis, Epidemiology, 5th Edition.
- Pocket Primary Care
- Adam Brochert, Crush Step 3
- http://www.aafp.org/fpm/2004/0500/p47.html (A Simple Method for Evaluating the Clinical Literature)
- https://onlinecourses.science.psu.edu/stat507/node/71 (Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value)
- Goldman L, Schafer AI (eds): Goldman’s Cecil Medicine, ed 25. Elsevier Saunders, 2016, pp 37-41.