Cohort Study
- Start with exposed and non-exposed individuals
- Compare rates of disease in exposed and non-exposed
- Calculate Relative Risk or Odds Ratio
- Ideal when exposure of interest is rare
Case Control Study
- Start with individuals with and without the disease
- Compare proportions of individuals with the exposure of interest with those without the exposure of interest
- Calculate the Odds Ratio only (cannot calculate RR)
- Ideal when the disease is rare
Alpha (α) Maximum P value to be considered statistically significant; the risk of committing a type I error
Alpha error Type I error
Alternative hypothesis Hypothesis considered an
alternative to the null hypothesis; usually the alternate hypothesis is
that there is an effect of the studied treatment on the measured
variable of interest; also called test hypothesis
Beta (β) Risk of committing a type II error
Beta error Type II error
Null hypothesis Hypothesis that there is no effect of
the studied treatment on the measured variable of interest
Power Probability of detecting a treatment effect the
size of the treatment effect sought (i.e., obtaining a P value <α),
given alpha, and the sample size of the clinical trial; power = 1 −β
P value Probability of obtaining results similar to those actually obtained, if the null hypothesis were true
Type I error Obtaining a statistically significant P
value when, in fact, there is no effect of the studied treatment on the
measured variable of interest; also called false positive
Type II error Not obtaining a statistically
significant P value when, in fact, there is an effect of the treatment
on the measured variable of interest that is as large as or larger than
the effect the trial was designed to detect; also called false negative
Student's t-test Used to test whether the means of
measurements from two groups are equal, assuming that the data are
normally distributed and that the data from both groups have equal
variance
Wilcoxon rank sum test (Mann-Whitney test) Used to
test whether two sets of observations have the same distribution;
similar in use to the t-test, but does not assume the data are normally
distributed
Chi-square test Used with categoric variables (two or
more discrete treatments with two or more discrete outcomes) to test
the null hypothesis that there is no effect of treatment on outcome;
assumes at least five expected observations of each combination of
treatment and outcome under the null hypothesis
Fisher's exact test Used similar to chi-square test;
may be used even when fewer than five observations are expected in one
or more categories of treatment and outcome
One-way analysis of variance (ANOVA) Used to test the
null hypothesis that three or more sets of continuous data have equal
means, assuming the data are normally distributed and that the data
from all groups have identical variances; may be regarded as a t-test
for three or more groups
Kruskal-Wallis test Nonparametric test analogous to
one-way ANOVA; no assumption is made regarding normality of the data;
may be regarded as a Wilcoxon rank sum test for three or more groups
Bias Tendency to yield a result that lies to one side
or the other of the true value; an error that is not centered around
zero
Critically appraised topic (CAT) Summary of the
results of an analysis of the evidence relevant to a particular
clinical question
Likelihood ratio, negative (LR−) Probability that a
negative test result would occur in a patient with the disease in
question divided by the probability that a negative test result would
occur in a patient without the disease in question (generally <1)
Likelihood ratio, positive (LR+) Probability that a
positive test result would occur in a patient with the disease in
question divided by the probability that a positive test result would
occur in a patient without the disease in question (generally >1)
Negative predictive value Fraction of patients with a
negative test result who, in fact, do not have the disease in question, depends on disease prevalence
Number needed to treat (NNT) Number of patients who
must be treated with the better of two treatments so that one
additional “good” outcome is obtained
Odds Probability of an outcome divided by the probability of the outcome not occurring
Odds ratio Odds of the outcome of interest occurring
in one group of patients divided by the odds of the outcome of interest
occurring in another group (case-control or cohort study)
Positive predictive value Fraction of patients with a
positive test result who, in fact, do have the disease in question, depends on disease prevalence
Post-test odds Odds of the outcome of interest (e.g.,
particular disease being present), once a particular test result
(positive or negative) is known to have occurred; equal to the pretest
odds times the likelihood ratio for the test result obtained
Post-test probability Probability of the outcome of
interest once a particular test result is known to have occurred
Pretest odds Odds of the outcome of interest before the test result is known
Pretest probability Probability of the outcome of interest before the test result is known
Relative risk Probability of an outcome in one group
divided by the probability of the same outcome in another group (cohort study, a case-control cannot have a RR)
Reliability Degree with which a clinical trial or
diagnostic test is likely to yield consistent results when repeated
Sensitivity Fraction of patients with the disease in question who have a positive test result
Specificity Fraction of patients without the disease in question who have a negative test result
Validity, external Degree to which the results from a
study accurately reflect what would happen to similar patients who were
not enrolled, including patients at other locations
Validity, internal Degree to which a study accurately measures the outcomes for the enrolled study population