clinical epidemiology concepts in order to guide clinical decision-making. This requires an evaluation of the quality and applicability of existing research studies to individual clinical scenarios. Appropriate interpretation of the results of a research study in the right context requires a basic understanding of the following foundational concepts (found in the “Epidemiology” article): (e.g., , ), common study designs (e.g., , , , ), , and other reasons for observed associations (e.g., , , ). This article focuses on an approach to , and epidemiological concepts often encountered in studies of clinical interventions, i.e., (e.g., , , , ), measures used to evaluate screening and diagnostic test (e.g., , , , ), , and .and involve the practical application of
The following concepts are discussed separately: population health, foundational statistical concepts (e.g., , , , ), and guidance on .(e.g., , ) commonly used in studies of
Evidence-based medicine 
- Definition: The practice of medicine in which the physician uses clinical decision-making methods based on the best available current research from peer-reviewed clinical and epidemiological studies with the aim of producing the most favorable outcome for the patient.
Application in clinical practice
- Define the patient's clinical problem (can be formulated as a ).
- Search for sources of information about the clinical problem.
- Perform a of relevant research studies.
- Apply the information
- Before discussing the research findings with the patient, consider how and to which extent the researched options can improve patient care.
- Present comprehensive, but synthesized evidence to the patient using clear and understandable language.
- Engaged in , considering individual patient's risk profile and preferences.
Levels of evidence 
- Definition: a method used in to determine the strength of the findings from a clinical and/or epidemiological study
- Methods: Several different systems exist for assigning levels of evidence.
|Levels of evidence |
|Level||Source of evidence|
Grades of clinical recommendation 
A system developed by the US Preventive Task Force (USPSTF) to rate clinical evidence and create guidelines for clinical practice based on medical evidence. 
|Grades of Recommendation |
|Grade||Net benefit||Level of certainty||Recommendation|
|A|| || || |
|B|| || || |
|C|| || || |
|D|| || || |
|I|| || || |
Levels of certainty
Critical appraisal of research studies
Clinical practice ( )
- Evaluation of the literature relevant to an individual patient's condition
- Review of updated guidelines on diagnosis and management of medical conditions
- Clinical decision-making
Research and academia
- Gathering background information for a research study
- Serving as a reviewer for a medical journal
- Participation in a journal club
Perform an overall assessment and an in-depth analysis of the different study sections. 
|Questions to ask when critically appraising a research paper |
|Relevant questions to address|
|Overall assessment|| |
The degree of association between exposure and disease is typically evaluated using a , which compares the presence/absence of disease with the history of exposure to a risk factor.
|Disease (outcome)|| |
No disease (no outcome)
|Exposure (risk factor)||a||b||a + b|
|No exposure (no risk factor)||c||d||c + d|
|Total||a + c||b + d||a + b + c+ d|
- Risk factor: a variable or attribute that increases the probability of developing a disease or injury 
Absolute risk: the likelihood of an event occurring under specific conditions 
- Commonly expressed as a percentage
- Equal to the , which can be calculated as follows: × the time of follow-up
- Aim: to measure the probability of an individual in a study population developing an outcome
- Used in: cohort studies
- Formula: (number of new cases)/(total individuals in a study group) = (a + c)/(a + b + c + d)
- : See “Estimates of association strength.”
- : See “Estimates of population impact.”
Formulas of common measures of association
- Measures that help quantify the strength of association
- Measures that help quantify the impact of an association on a population
Estimates of association strength
Relative risk (RR; risk ratio) 
- Description: : the likelihood of an outcome in one group exposed to a potential risk factor compared to the risk in another group that has not been exposed
- Used in: :
- Formula: (incidence of disease in exposed group)/(incidence of disease in unexposed group) = (a/(a + b))/(c/(c + d))
- RR = 1: Exposure neither increases nor decreases the risk of the defined outcome.
- RR > 1: Exposure increases the risk of the outcome.
- RR < 1: Exposure decreases the risk of the outcome.
Odds ratio (OR) 
- Purpose: to measure the strength of an association between a risk factor and an outcome
- Used in: :
Odds ratio of exposure: compares the odds of exposure among individuals with an outcome (e.g., disease) against the odds of exposure among individuals without an outcome
- Odds of exposure in individuals with disease (i.e., case group): (exposure in individuals with disease)/(no exposure in individuals with disease) = a/c
- Odds of exposure in individuals without disease (i.e., control group): (exposure in individuals without disease)/(no exposure in individuals without disease) = b/d
- Odds ratio: (odds of exposure in individuals with disease)/(odds of exposure in individuals without disease) = (a/c)/(b/d) = ad/bc = (a/b)/(c/d)
- Odds ratio of exposure: compares the odds of exposure among individuals with an outcome (e.g., disease) against the odds of exposure among individuals without an outcome
- OR = 1: The outcome is equally likely in exposed and unexposed individuals.
- OR > 1: The outcome is more likely to occur in exposed individuals.
- OR < 1: The outcome is less likely to occur in exposed individuals.
- Rare disease assumption
Hazard ratio (HR)
- Description: : a measure of the effect of an intervention on an outcome at any given point in time during the study period 
- Purpose: to help determine how long it takes for an event to occur in individuals in the case group, compared to individuals in the control group
- Used in:
- Formula: (observed number of events in exposed group / expected number of deaths in exposed group) at time (t) / (observed number of events in unexposed group/expected number of deaths in unexposed group) at time (t) 
- HR = 1: no relationship
- HR > 1: The outcome of interest is more likely to occur in exposed individuals.
- HR < 1: The outcome of interest is less likely to occur in exposed individuals.
The RR is the risk of an event occurring by the end of the study period (i.e., cumulative risk), while the HR is the risk of an event occurring at any point in time during the study period (i.e., instantaneous risk). 
Estimates of population impact
Attributable risk (AR) 
- Description: the absolute difference between the risk of an outcome occurring in exposed individuals and unexposed individuals
- Purpose: to measure the excess risk of an outcome that can be attributed to the exposure
- Used in: cohort studies
Attributable risk percent (ARP) 
- Description: the proportion of disease incidence among exposed individuals that can be attributed to the risk factor
- Purpose: to determine the proportion of cases in the exposed population that can be attributed to the risk factor
- Used in: cohort studies and case-control studies
Formulas: (incidence risk among exposed) - (incidence risk among unexposed)/(incidence risk among unexposed) x 100
- ARP = (RR - 1)/RR x 100
- The RR cannot be calculated for case-control studies, so the OR (an estimate of the RR) can be used to calculate the attributable risk: ARP = (OR–1)/OR x 100.
- Alternatively, ARP = AR/(incidence of disease in the exposed group) x 100 = (a/(a + b) – c/(c + d)) / (a/(a + b)) x 100
Relative risk reduction (RRR)
- Description: : the proportion of risk in the exposure group after an intervention compared to the risk in the nonexposure group
- Purpose: to determine how much the treatment reduces the risk of negative outcomes
- Used in: cohort studies and
- Example: RRR can be used to demonstrate vaccine effectiveness = (risk among unvaccinated – risk among vaccinated)/(risk among unvaccinated) × 100. 
Absolute risk reduction (ARR; risk difference)
- Description: : the difference between the risk in the exposure group after an intervention and the risk in the nonexposure group (e.g., risk of death)
- Purpose: to show the risk without treatment as well as the risk reduction associated with treatment
- Used in: cohort studies, cross-sectional studies, and clinical trials
- Formula: : (absolute risk in the unexposed group) - (absolute risk in the exposed group) = c/(c + d) – a/(a + b)
Number needed to treat (NNT)
- The number of individuals that must be treated, in a particular time period, for one person to benefit from treatment (i.e., to not develop the disease)
- Inversely related to the effectiveness of a treatment
- Purpose: to compare the effectiveness of different treatments
- Used in: clinical trials
- Formula: : 1/ARR
Number needed to harm (NNH)
- The number of individuals who need to be exposed to a certain risk factor before one person develops an outcome
- Directly correlates to the safety of the exposure
- Purpose: to determine the potential harms of an intervention
- Used in: clinical trials
- Formula: : 1/AR
Number needed to screen (NNS)
- Before a diagnostic modality (e.g., laboratory study, imaging study, diagnostic criteria) can be used in clinical practice, it needs to be determined how well the modality can distinguish between individuals with the disease and individuals without the disease.
- A test is compared to the two-by-two table. using a
- A two-by-two table can be used to calculate a test's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
|Features of a two-by-two table summarizing screening or diagnostic test results|
|Positive test result|| || |
|Negative test result|| || |
Example 2 x 2 table of a diagnostic test 
|Diagnostic test for tuberculosis (TB)|
|Patients with TB||Patients without TB||Total|
|Positive test result||800 (TP)||400 (FP)||1200 (TP + FP)|
|Negative test result||200 (FN)||3600 (TN)||3800 (FN + TN)|
|Total||1000 (TP + FN)||4000 (FP + TN)||5000 (TP + FP + FN + TN)|
- Sensitivity: TP/(TP + FN) = 800/(800 + 200) = 80%
- Specificity: TN/(FP + TN) = 3600/(400 + 3600) = 90%
- False positive rate: FP/(FP + TN) = 400/(400 + 3600) = 10%
- False negative rate: FN/(TP + FN) = 200/(800 + 200) = 20%
- PPV: TP/(TP + FP) = 800/(800 + 400) = 66.6 %
- NPV: TN/(FN + TN) = 3600/(200 + 3600) = 94.7%
Sensitivity and specificity 
- Every diagnostic test generally involves a tradeoff between sensitivity and specificity.
- Sensitivity and specificity are inversely proportional, meaning that as the sensitivity increases, the specificity decreases, and vice versa.
|Overview of sensitivity and specificity of clinical tests|
|Sensitivity (epidemiology) (true positive rate)||Specificity (epidemiology) (true negative rate)|
|Description|| || |
A highly sensitive test can rule out a disease if negative, and a highly specific test can rule in a disease if positive
Predictive values 
- Description: the probability that a patient with a particular manifestation has a specific disease before the result of the diagnostic test is known
- The pretest probability of a disease is reflected by its prevalence in a particular region.
- NPVs and PPVs depend on the test subject's pretest probability of disease (unlike sensitivity and specificity).
- Description: the probability that a patient has a particular disease after a diagnostic test is carried out
Positive predictive value (PPV)
- Description: the proportion of individuals who test positive for a disease that actually have that disease
- See the above.
- The probability that an individual who tested positive actually does not have the disease: 1 - PPV
Negative predictive value (NPV)
- Description: : the proportion of individuals who test negative for a disease that are actually disease-free
Unlike sensitivity and specificity, which are determined solely by the diagnostic test itself, predictive values are also influenced by disease prevalence.
- Interpretation: reflects how much more likely a disease is in a person with a given test result compared to their pretest probability
- Positive likelihood ratio
- Negative likelihood ratio
Cutoff values 
Definition: dividing points on measuring scales where the test results are divided into different categories
- Positive: has the condition of interest
- Negative: does not have the condition of interest
- Features: Sensitivity, specificity, PPVs, and NPVs vary according to the criterion and/or the cutoff values of the data.
Interpretation: What happens when a cutoff value is raised or lowered depends on whether the test in question requires a high value (e.g., tumor marker for cancer, lipase for pancreatitis) or a low value (e.g., hyponatremia, agranulocytosis).
- Lowering or raising a cutoff value for a high value test:
Lowering or raising a cutoff value for a low value test:
- Decreased cutoff value (i.e., narrowed inclusion criteria): higher specificity, lower sensitivity, higher PPV (decrease in false positives > decrease in true positives), lower NPV (increase in false negatives > increase in true negatives)
- Increased cutoff value (i.e., broadened inclusion criteria): lower specificity, higher sensitivity, lower PPV (increase in true positives > increase in false positives), higher NPV (decrease in false negatives > decrease in true negatives)
Receiver operating characteristic curve (ROC curve) 
- Description: a graph that compares the sensitivity and specificity of a diagnostic test
- Shows the tradeoff between clinical sensitivity and specificity for every possible , to evaluate the ability of the test to correctly diagnose subjects
The y-axis represents the sensitivity (i.e., true positive rate) and the x-axis corresponds to 1 - specificity (i.e., the false positive rate).
- A test is considered more accurate the more closely the curve follows the y-axis.
- A test is considered less accurate if the curve is closer to the diagonal.
- The area under the ROC curve (AUROC) also allows the usefulness of tests to be compared: The larger the AUROC, the higher the accuracy of the test. 
- Used to identify disease in asymptomatic individuals (e.g., mammogram for breast cancer, for cervical cancer)
- Should have a high sensitivity
|Potential sources of bias in screening tests|
|Lead-time bias||Length-time bias|
|Description|| || |
|Example|| || |