Research and Stats Flashcards
Define positive predictive value
probability a patient with a positive test actually has a disease
PPV = TP / (TP + FP)
Define variance
an estimate of the variability of each individual data point from the mean
define effect size
magnitude of difference in means between two groups
case control studies (retrospective) typically generate what statistical measure?
odds ratio
Define Type I error (alpha)
null hypothesis is rejected even though it is true
negative likelihood ratio: define and formula
how the likelihood of a disease is changed by a negative test result
(1-sensitivity)/specificty
what is a type II error?
beta.
false negative
detecting no difference when there is one
accepting a null hypothesis wrongly
typically set at 0.8
Describe Post-test odds of disease
post-test probability = (pretest probabililty) X (likelihood ratio)
- likelihood ratio = sensitivity / (1 - specificity)
- pre-test odds = pre-test probability / (1 - pre-test probability)
post-test probability = post-test odds / (post-test odds + 1)
What does positive predictive value depend on?
prevalence of a disease
Define effect size
magnitude of the difference in the means of the control and experimental groups in a study with respect to the pooled standard deviation
Effect sizes are normally used for continuous variables in contrast to relative risk reduction which is used for dichotomous variables
NNT formula and definition
NNT=1/ARR
number needed to treat to get one additional favourable outcome
alternatively, ARR=1/NNT
Define negative predictive value
probability a patient with a negative test actually has no disease
= TN / (FN + TN)
a highly ____ test with a (neg/pos) result can rule (in/out) the outcome of interest
SENSITIVE tests with NEGATIVE results rule OUT the outcome
“SNNOUT”
SPECIFIC tests with POSITIVE results rule IN the outcome
“SPPIN”
define incidence
number of newly reported cases of a disease during a given time period
What test do you use to compare 2 independent means from numeric data?
t-test
numeric data = t-test
How do you determine absolute risk reduction?
From NNT
NNT = 1 / ARR
ARR = 1 / NNT
or
ARR = (risk in control group - risk in experimental group)
*this was on a previous exam
test for comparing means of 3 or more continuous dependent variables
(each with categorical independent variables)
ANOVA
this is essentially a t-test for 3 or more groups
ANOVA on 2 groups will give the same result as a t-test
Define Type II error
Beta error
a false negative difference that can occur by:
- detecting no difference when there is a difference
- accepting a null hypothesis when it is false and should be rejected
sensitivity formula
TP/(TP+FN)
Student t-test
used to compare means of continuous data that is normally distributed
What does a relative risk > 1 mean?
incidence of the outcome is greater in the exposed/treated group
test to see if two continuous variables are related or not
linear regression
e.g. comparing age to BP
Kaplan-Meier equation (in laymans terms)
The number of failures / total number still being followed
statistical tool in meta-analyses to detect publication bias
funnel plot
describe negative likelihood ratio
describe how the likelihood of a disease is changed by a negative test result
negative likelihood ratio = (1 - sensitivity) / specificity
define relative risk and give formula
- risk of developing disease with exposure compaerd to risk of developing disease without exposure
- risk of disease with exposure=(have exposure and disease)/(all with exposure)
- risk of disease without exposure=(no exposure but have disease)/(all without exposure)
- RR=(risk of disease with exposure)/(risk of disease without exposure)
Mann-Whitney or Wilcoxon rank sum tests
comparing means of non-continuous data
Describe Relative risk
risk of developing disease for people with known exposure
compared to
risk of developing disease for people without known exposure
incidence definition
number of new cases in a given time period
How do you compare categorical data?
chi-square test
What is the fisher exact test used for?
Comparing proportional or categorical data when sample sizes are small or number of occurences in a group is low