CLINICAL EPIDEMIOLOGIC DEFINATIONS
1. STUDY DESIGN
Case-series:
Report of a number of cases of disease.
Cross-sectional study:
Study design, concurrently measure outcome (disease) and risk factor. Compare proportion of diseased group with risk factor, with proportion of non-diseased group with risk factor.
Case-control study:
Retrospective comparison of exposures of persons with disease (cases) with those of persons without the disease (controls) (see Retrospective study).
Retrospective study:
Study design in which cases where individuals who had an outcome event in question are collected and analyzed after the outcomes have occurred (see also Case-control study).
Cohort study:
Follow-up of exposed and non-exposed defined groups, with a comparison of disease rates during the time covered.
Prospective study:
Study design where one or more groups (cohorts) of individuals who have not yet had the outcome event in question are monitored for the number of such events, which occur over time.
Randomized controlled trial:
Study design where treatments, interventions, or enrollment into different study groups are assigned by random allocation rather than by conscious decisions of clinicians or patients. If the sample size is large enough, this study design avoids problems of bias and confounding variables by assuring that both known and unknown determinants of outcome are evenly distributed between treatment and control groups.
Bias (Syn: systematic error):
Deviation of results or inferences from the truth, or processes leading to such deviation. See also Referral Bias, Selection Bias.
Recall bias: Systematic error due to the differences in accuracy or completeness of recall to memory of past events or experiences.
Referral filter bias: The sequence of referrals that may lead patients from primary to tertiary centres raises the proportion of more severe or unusual cases, thus increasing the likelihood of adverse or unfavorable outcomes.
1 Selection Bias: A bias in assignment or a confounding variable that arises from study design rather than by chance. These can occur when the study and control groups are chosen so that they differ from each other by one or more factors that may affect the outcome of the study.
Interviewer bias: Systematic error due to interviewer's subconscious or conscious gathering of selective data.
Lead-time bias: If prognosis study patients are not all enrolled at similar, well- defined points in the course of their disease, differences in outcome over time may merely reflect differences in duration of illness.
Blind(ed) study (Syn: masked study):
A study in which observer(s) and/or subjects are kept ignorant of the group to which the subjects are assigned, as in an experimental study, or of the population from which the subjects come, as in a non-experimental or observational study. Where both observer and subjects are kept ignorant, the study is termed a double-blind study. If the statistical analysis is also done in ignorance of the group to which subjects belong, the study is sometimes described as triple blind. The purpose of "blinding" is to eliminate sources of bias.
Confounding variable, Confounder:
A variable that can cause or prevent the outcome of interest, is not an intermediate variable, and is associated with the factor under investigation. A confounding variable may be due chance or bias. Unless it is possible to adjust for confounding variables, their effects cannot be distinguished from those of factor(s) being studied
Intention to treat analysis:
A method for data analysis in a randomized clinical trial in which individual outcomes are analyzed according to the group to which they have been randomized, even if they never received the treatment they were assigned. By simulating practical experience it provides a better measure of effectiveness. (Versus efficacy)
MEASUREMENTS IN CLINICAL STUDIES
Disease or outcome Exposure or risk factor or treatment Positive Negative Positive A B Negative C D
Prevalence
Proportion of study population who have a disease (at one point or period in time).
2 Number of old cases and new cases divided by total population.
In cross-sectional studies: (A + B)/(A + B + C + D).
Incidence:
Number of people in study population who newly develop an outcome (disease) per total study population per given time period.
Number of new cases divided by the total population over a given time period.
For cohort studies and clinical trials: (A + B)/(A + B + C + D).
Absolute risk:
The observed or calculated probability of an event in the population under study.
Absolute risk difference:
The difference in the risk for disease or death between an exposed population and an unexposed population.
Absolute risk reduction (ARR):
The difference in the rates of adverse events between study and control populations (ie: the difference in risk between the control group and the treated group: ARR=CER-EER)
Relative risk (RR):
Ratio of incidence of disease among people with risk factor to incidence of disease among people without risk factor.
For cohort studies or clinical trials: [A/(A + C)]/[B/ (B + D)].
RR = 1 means no effect of exposure (or treatment) on outcome (or disease). RR <1 indicates exposure or treatment protective against disease. RR >1 indicates exposure/treatment increases probability of outcome/disease.
Relative risk reduction (RRR):
The extent to which a treatment reduces a risk, in comparison with patients not receiving the treatment of interest (ie., the percent reduction in events in treated compared to controls: RRR=[(CER-EER)/CER]).
Odds ratio (OR):
For case-control studies, ratio of odds of having risk factor in people with disease (A/B) to odds of having risk factor in people without disease (C/D), or (A/B)/(C/D) = (A × D)/(B × C).
Good estimate of RR if disease is rare. OR = 1 means no risk factor-disease association. OR >1 suggests risk factor associated with increased disease, and OR <1 suggests risk factor protective against disease.
Alpha (significance level of statistical test):
Probability of finding a statistical association by chance alone when there truly is no association (type I error).
Often set at 0.05; low alpha especially important when interpreting a finding of an association.
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Power (of a statistical test):
Beta = Probability of finding no statistical association when there truly is one (type II error).
Power = 1 - beta = Probability of finding a statistical association when there truly is one.
Power often set at 0.80; high power especially important when interpreting a finding of no association.
Sample size:
Number of subjects required in a clinical study to achieve a sufficiently high power and sufficiently low alpha to obtain a clinically relevant result.
p value:
Probability of a finding by chance alone.
If p value < preset alpha level (often 0.05), then finding is interpreted as sufficiently unlikely to be due to chance as to be accepted as real.
Confidence interval (CI):
The range of numerical values in which we can be confident (to a computed probability, such as 90 or 95%) that the population value being estimated will be found. Confidence intervals indicate the strength of evidence; where confidence intervals are wide, they indicate less precise estimates of effect. (e.g. 95%CI = 95% probability that the reported interval contains the true value.)
Precision:
The range in which the best estimates of a true value approximates the true value. See Confidence interval.
MEASUREMENTS FOR EVALUATING A CLINICAL TEST
Condition (Diagnostic Interview) Present Absent True Positive False Positive Predictive Value TP ‘ TEST Positive TP FP (Positive) TP + FP (Screening False Negative True Negative Predictive Value TN ` Survey) Negative FN TN (Negative) TN + FN Sensitivity = Specificity = TP ` TN ` TP + FN TN + FN
4 Sensitivity (Sens):
Proportion of all diseased who have positive test: TP/TP + FN.
Use highly sensitive test to help exclude a disease. (Low false-negative rate. High likelihood ratio [LR] negative. This is good for screening.)
Specificity (Spec):
Proportion of all non-diseased who have a negative test: TN/TN + FN.
Use highly specific test to help confirm a disease. (Low false-positive rate. High LR positive.)
Predictive value:
In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., does have the disease), or that a person with a negative test truly does not have the disease. The predictive value of a screening test is determined by the sensitivity and specificity of the test, and by the prevalence of the condition for which the test is used.
Positive predictive value (PPV):
Proportion of all those with positive tests who truly have disease: TP/TP+FP
Increased PPV with higher disease prevalence and higher specificity (and, to a lesser degree, higher sensitivity).
Negative predictive value (NPV):
Proportion of all those with negative tests who truly do not have disease: TN/TN+FN
Increased NPV with lower prevalence (rarer disease) and higher sensitivity.
Number Needed to Treat (NNT):
The number of patients who must be exposed to an intervention before the clinical outcome of interest occurred; for example, the number of patients needed to treat to prevent one adverse outcome. Equal to the inverse of the absolute risk reduction: NNT=1/ARR = 1/CER-EER.Likelihood ratio (LR)
Likelihood ratio:
Ratio of the probability that a given diagnostic test result will be expected for a patient with the target disorder rather than for a patient without the disorder.
LR positive: Ability of positive test result to confirm diseased status: LR positive = (Sens)/(1 - Spec).
LR negative: Ability of negative test result to confirm non-diseased status: LR negative = (Spec)/(1 - Sens). [Alternative LR negative = (1 - Sens)/Spec.]
Good tests have LR 10. (Good tests 0.1 if using alternative LR negative formula.) Physical examination findings often have LR of about 2.
LR is not affected by disease prevalence. LR can be used to calculate increase in probability of disease from baseline prevalence with positive test (LR positive) and decrease in probability of disease from baseline prevalence with negative test (using alternative LR negative) for any level of disease prevalence
5 STUDY DESIGN
STUDY DESIGN COMPARISON Design type Definition Advantages Disadvantages Case- control Define diseased subjects Good for rare Highest potential for (often called (cases) and non-diseased diseases biases (recall, selection retrospective) subjects (controls); Smaller sample and others) compare proportion of size Weak evidence for cases with exposure (risk Faster (not followed causality factor) with proportion of over time) No prevalence, PPV, NPV controls with exposure Less expensive (risk factor). Cohort (usually In study population, define Defines incidence Expensive prospective; exposed group (with risk Stronger evidence Long study times occasionally factor) and non-exposed for causality May not be feasible for retrospective) group (without risk factor). Decreases biases rare diseases/outcomes Over time, compare (sampling, Factors related to proportion of exposed measurement, exposure and outcome group with outcome reporting) may falsely alter effect of (disease), with proportion exposure on outcome of non-exposed group with (confounding) outcome (disease). Cross-sectional In study population, Defines prevalence Selection bias concurrently measure Short time to Weak evidence for outcome (disease) and complete causality risk factor. Compare proportion of diseased group with risk factor, with proportion of non-diseased group with risk factor. Clinical trial In study population, assign Randomized Expensive (experiment) (randomly) subjects to blinded trial is gold Risks of experimental receive treatment or standard treatments in humans receive no treatment. Randomization Longer time Compare rate of outcome reduces Bad for rare (e.g., disease cure) confounding. outcomes/diseases between treatment and Best evidence for non-treatment groups. causality NPV, negative predictive value; PPV, positive predictive value.
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