Chapter 14: Biostatistics Flashcards
Continuous Data
Can be ratio data or interval data
Continuous data has a logical order with values that continuously increase or decrease by the same amount
Ratio Data
Equal distance between values with a true, meaningful 0
(0=none)
E.G. age, height, weight, time, blood pressure
Interval Data
Equal difference between values but without a meaningful 0
(0=/ none)
E.G. Celsius and Fahrenheit temperature scales
Discrete or Categorical Data
Can be nominal data or ordinal data
Discrete data fits into a limited number of categories
Nominal Data
Categories are in an arbitrary order. Order of categories does not matter
E.G. gender, ethnicity, marital status
Ordinal Data
Categories are ranked in a logical order, but the difference between categories is not equal. Order of the categories matters.
E.G. NYHA functional class I-IV, 0-10 pain scale
Independent Variables
Are variables changed by the researcher
Include: drugs, drug doses, placebos, patients included (gender, age, comorbid conditions)
Dependent Variables
Can be affected by the independent variables
Include: HF progression, A1C, blood pressure, cholesterol values, mortality
Null Hypothesis
States that there is no statistical difference between groups
Alternative Hypothesis
States that there is a statistical difference between the groups. This is what the researcher is trying prove or accept.
Alpha
Is the maximum permissible error margin.
Alpha is the threshold for rejecting the null hypothesis and is commonly set at 5% or 0.05
P-values
P-value is compared to alpha. If alpha is set to 0.05 and the p-value is less than 0.05 then the null hypothesis is rejected and the result is termed statistically significant
Confidence Interval (CI)
CI provides the same information about significance as the p-value, plus the precision of the result. Alpha and CI will correlate with each other.
For example if alpha is 0.05 the study reports a 95% CI; and alpha of 0.01 corresponds to a CI of 99%
Confidence Interval (CI) Significance
The result is statistically significant if the CI range does not include 0 when comparing difference data (means)
The result is statistically significant if the CI range does not include 1 when comparing ratio data (relative risk, odds ratio, hazard ratio)
Type I Errors
Null hypothesis is rejected in error
“False Positive”
Type II Errors
Null hypothesis is accepted when it should have been rejected.
“False Negative”
Study Power
Power is the probability that a test will reject the null hypothesis correctly (i.e. the power to avoid a type II error)
Risk Formula
Risk refers to the probability of an event, when an intervention, such as a drug, is given
Risk= # of subjects is a group with an unfavorable event/ total # of subjects in group
Relative Risk
Is the ratio of risk in the exposed group (tx group) divided by risk in the control group
RR= risk in tx group/risk in control group
RR Interpretation
RR = 1 implies no difference in risk of the outcome between groups
RR > 1 implies greater risk of the outcome in tx group
RR < 1 implies lower risk (reduced risk) of the outcome in tx group
Relative Risk Reduction
Indicates how much risk is reduced in the tx group compared to the control group
RRR = 1 - RR
or
RRR = % risk in control - % risk in tx / % risk on control
RR vs RRR
RR = AS likely (vs. the control) RRR = Less likely (vs. control)
Absolute Risk Reduction
Is more useful because it includes the reduction in risk and the incidence rate of the outcome
ARR = % risk on control - % risk in tx
ARR interpretation
For example is ARR is 12%; 12 out of every 100 patients will benefit from tx. Said another way, for every 100 patients treated, 12 patient will benefit.
