Statistics Flashcards
Categorical variable
- Subject is placed into categories
- Two possible outcomes (yes/no, male/female)–> dichotomous
Ordinal
- Inherent order
- Ranked
- May be summarized by a median value
Continuous
- Age, weight, height, lab values
- Means and medians used to summarize
Unpaired
- Independent means the values of one group cannot be predicted from the other
Paired
- Paired the values of one group may be predicted from the other ( patient measured before and after therapy)
1, 2, and 3 standard deviations
68.3, 95.8, and 99.7
Standard error of the mean
- Because samples drawn from an underlying population do not each produce the same mean (but tend to cluster around the same value), one much calculate the range of where the true (unknown) population mean lies
- The greater the sample size the smaller the SEM
- Always less than SD
Null Hypothesis
There is no relationship between two phenomena
Chi Squared
- Also called Pearsons
- Suitable for unpaired categorical date from large samples
T test
- Used for continuous data
- Used for comparing two sample means from either independent (non paired T test) or matched (paired T test) samples
ANOVA
Used to compare the means of several groups, instead of just two
- Looks at the difference within and between groups
Confidence interval
- A range of values that there is a specificed probability that value of a parameter lies within it
- 95 percent CI can be estimated if the mean and the SEm are known
- If it crosses zero–> no good
Type I error
- Failing to reject the null hypothesis
- Saying a relationship does except when it doesn’t
- Alpha error set at 0.05
Type II error
- Failing to reject the null
- Saying there is no difference when there is a difference
- Beta error (0.2)
Which error is better? Type I or Type II
- Type II
Absolute risk
- Chance of developing a disease over a time period
Absolute risk reduction
- The absolute risks of different exposures can be compared to each other
- Risk of drug A is 15%; risk of drug B is 5%–> 10% risk reduction
NNT
The inverse of the absolute risk reduction 1/0.10 = 10
Relative risk (risk ratio)
- used to compare risk in two different groups with different risk factors exposures
- Calculated by dividing the absolute risk in the group exposed to the risk factor by the absolute risk in the unexposed group
- RR>1 exposure associated with increased risk of disease
- RR=0 no association
- RR <1 exposure is protective
Relative risk reduction
1-RR
- Tells you by how much the exposure reduced or increased the risk
Odds ratio
- Used in case control ( no incidence rates).
- Calculated by dividing the odds of exposure among cases by odds of exposure among controls
Hazard ratio
- Risk ratio gives you the cumulative risk of a time span, while HR gives you the instantaneous risk at a particular point in time
Regression analyses
- One way model in which predictor independent (x) variables are thought to affect the dependent outcome (y) variable but not vice versus
- Linear regression (continuous)
- Logistic regression (categorical)
Sensitivity
- Percentage of people with disease who test positive
- TP/TP+FN
- Negative test rules out disease
- SNOUT
