Biostats/Epi Flashcards
Three ways to characterize the center of normal distribution
Mean: average of all numbers
Median: middle number of data set when all lined up in order
Mode: most commonly found number
Skewness
positive or negative based on location of tail
if tail is pointing toward lower/negative #s then it is negative; if pointing toward larger/positive #s then is positive
least likely to be affected by outlier in central tendency
mode
adding one outlier changes mean and median; it will only change the mode if it changes most common number and one outlier is unlikely to change the most common number
Central Tendency key points
if distribution is equal: mean=mode=median
mode is ALWAYS at the peak
In skewed data: mean is always furthest away from the mode toward the tail
Mode is the least likely to be affected by outliers
Z score
describes a single data point; how far a data point is from the mean
z score of 0 is the mean
z score of +1 is 1SD above mean
z score of -1 is 1SD below mean
Standard of the mean
how far is the dataset mean from the true population mean
SEM = SD/number of population squared
Confidence intervals
range of 95% of repeated measurements would be expected to fall; 95% chance true population falls within this range
CI95% = mean +/- 1.96*(SEM)
Null hypothesis
H0: there is no difference
type 1 (alpha) error
there is no difference in reality but our study finds a difference
type 2 (beta) error
there is a difference in reality but our study misses it
Power
chance of detecting difference
power = 1 - beta
P-value
represents chance that the null hypothesis is correct; used to accept or reject the null hypothesis
if p<0.05 we usually reject the null hypothesis; difference in means is “statistically significant”
The 3 ways the power of a study increased
Increased sample size (the one thing you can control)
large difference means
less scatter of data
Power calculation
1 - Beta (type II error); if want to increase then need to increase the number of subjects for a high power; common power goal is 80%
new drugs that improve survival on incidence and prevalence
incidence is unchanged (not preventing new people from getting the disease)
prevalence changes (people are living longer with the disease)
vaccines on incidence and prevalence
both incidence and prevalence will fall
test that is good at ruling OUT disease
high sensitivity (TP/TP+FN)
test that is good at ruling IN disease
high specificity (TN/TN+FP)
a test is negative in 80% of people who do not have the disease is telling you what?
the true negative of the test; specificity
a test is positive in 50% of the people who do have the disease is telling you what?
the true positive of the test; sensitivity
Positive predicative value (PPV)
of all the people that test positive on a test, what percentage of those are true positives?
PPV=TP/TP+FP
Negative predicative value (NPV)
of all the people that test negative on a test, what percentage of those are true negatives?
NPV=TN/TN+FN
Accuracy
quantified by the area under the ROC curve (AUC); the more accurate the test is, the closer the AUC value is to 1.0
Selection Bias
errors in selection or retention of study groups
subtypes: sampling bias, attrition bias, berkson’s bias, nonresponse bias, prevalence bias
