Biostats ABC Flashcards
Power analysis explained
4 components
- 3 are known and your solving for one that is not
1) effect size
2) significance level =type 1 error= alpha=probability of finding an effect that is not there = typical 0.05 (5%) (most similar to p-value)
3) power= beta= type 2 error= probability of finding no effect that actually is there = failing to reject the null = typically 0.2 (80% chance of identifing)
d) sample size (n) - what you are solving for
how do you calculate effect size
estimated from literature
clinical significance?
why use case control
rare outcomes
retrospective
why use cohort
start with exposure
can be prospective or retrospective
accurate
free of error or bias
Precise
minimal effects from chance
types of bias
recall
reporting - subjects in one group more likely to report prior events
selection (food diary)
inter/intra observer
confounding variable
when a characteristic or variable is not distributed the same in the study vs the control (chance or bias)
sensitivity
of everyone with the disease this % will test positive
True positive/all with disease
A/A+C
Disease on top
exposure/test on sides
Does not change with prevelance
specificity
of everyone without the disease this % will test negative
true neg/all negative
D/B+D
of all the patient’s without a disease x% had a negative test
does not change with prevelance
PPV
if the test is positive the chance the patient actually has the disease
- increases with prevalence
A/A+B
NPV
the probability that if the test is negative the subject actually does not have the disease
D/D+C
- decreases with prevalence
ROC curve
x axis- rate of false positive (1-specificity(true negative))
y- axis rate of true positive (sensitivity)
two types of experimental studies
randomized/non-randomized
two type of observational studies
analytical vs descriptive
cohort
case control
cross section
cross-sectional study
looks a prevelance and not incidence
temporal relationship can be unclear
stats for cohort study
true incidence rate
attributable risk
relative risk
case control studies
careful of control group and recall bias
can only calculate odds ratio - when outcome is rare it is very similar to rr
consider more stringent inclusions to ensure less confounding (preeclampsia with severe features requiring delivery vs preelcampsia)
major problem with non-randomized experimental studies
selection bias
radomized controlled trials plus and minus
avoid confounding and selection bias
external validity can be a concern -volunteers can be different from the population
ratio
numerator is not included in the denomator
MMR
proportion
numerator is included in the denomator
-prevalance (proportion)
dimensionless
Rate
numerator is included in the denomator and takes into consideration time
- incidence rate
relative risk
Frequency of the outcome in an exposed group / frequency of he outcome in the unexposed
