Statistics Flashcards
Sensitivity
Ability of a test to correctly identify patients with the disease
True positive rate =
True positives / (True positive + False negative)
snNout : in highly sensitive test, negative will rule out disease
Specificity
Ability of test to identify patients without a disease
True negative rate =
True negative / True negative + False positive
spPin: highly specific will rule in disorder
Positive predictive value
Probability a person having the disease will test positive
PPV= True positives / True positives + False positives
Negative predictive value
Probability a person not having the disease if the test is negative
NPV = True negative / (True negative + False negative)
Likelihood Ratio
If the test is positive the odds of patient having the disease
LR= Sensitivity ( 1-Specificity)
Accuracy
(True positive + True negative) / Population
Null hypothesis
Any difference between study groups is by chance. i.e no true difference
Alternate Hypothesis
Two study groups have a true difference
Type 1 error (alpha)
False positive
Type 2 error (Beta)
False negative
Probability of missing an effect that is really there
Power
Ability to detect a true difference in outcome between two arms
Probability a type II error will not occur
Power = 1 - Beta
*B usually arbitrarily set as 0.2, from postulation that type 1 error 4 x as serious as type 2 error: alpha x 4 = beta (0.05 x 4 =0.2)
Effect size
The quantitative measure of the magnitude of the difference between groups
P-value
Probability of results given a true null hypothesis
<0.05 is statistically significance: result due to chance is less than 1 in 20
How to calculate sample size?
- acceptable level of significance
- power of the study
- expected effect size
- underlying event rate in population (prevalence)
- standard deviation in population
Prevalence
Proportion of population with disease at a given time point
= number of existing disease/ population
Incidence
Rate of occurrence of new cases over a period of time
= number of new cases (in a given period of time) / population
Absolute risk
Incidence rate of outcome in a group
number of event during FU / number of persons event free at the start
Relative risk
Exposed group absolute risk/ Control group absolute risk
Absolute risk reduction
Change in risk of outcome after intervention
= AR of control group - AR of experimental group
Relative Risk Reduction
Absolute risk reduction / Absolute risk fo control group
Number needed to treat
Number of subjects must be treated for one extra person to experience benefit
= 1 / Absolute risk reduction
Odds ratio
Probability of event / Probability of non-event
used in cohort or case control study
When does Odds ratio = Relative Risk?
When incidence of disease if very small
What is a ROC curve?
Receiver operating characteristics curve; a graphical plot used to show the diagnostic ability of binary classifiers
