Biostatistics

Question 1

random variables

Answer 1

a variable whose observed values may be considered outcomes of an experiment and whose values cannot be anticipated with certainty before the experiment is conducted

Question 2

discrete variables

Answer 2

random variable that can only take a limited number of values within a given range (e.g. nominal (unordered, no relative severity, e.g. gender); ordinal (ranked in specific order with no consistent level of magnitude between ranks e.g. NYHA classification))

Question 3

Continuous variables

Answer 3

random variables that can take on any value within a given range.
Interval: data ranked in a specific order with consistent change in magnitude b/t units but the zero point is arbitrary (e.g. degrees Fahrenheit)
Ratio: like interval but w/absolute zero (e.g. degrees Kelvin, HR, time)

Question 4

Descriptive statistics

Answer 4

used to summarize and describe data that are collected or generated in research
i.e. visual methods of describing data, measures of central tendency, measures of data spread or variability

Question 5

visual methods of desribing data

Answer 5

frequency of distribution, histogram, scatterplot

Question 6

Measures of central tendency

Answer 6

mean, median, mode

Question 7

Measures of data spread or variability

Answer 7

standard deviation, range, percentiles

Question 8

Inferential stats

Answer 8

conclusions or generalizations made about a population from the study sample.

Question 9

Normal (Gaussian) distribution

Answer 9

most common model for population distributions; symmetrico r “bell-shaped” frequency distribution

Question 10

Kolmogorov-Smirnov test

Answer 10

formal test for a visual check of a Gaussian distribution

Question 11

Parametric tests

Answer 11

assume that data (i.e. parent population) have an underlying distribution that is normal or close to normal and that variances are homogeneous between the groups investigated

Question 12

nonparametric tests

Answer 12

used when data are not normally distributed or do not meet other criteria for parametric tests

Question 13

One-sample Student t-test

Answer 13

parametric test that compares the mean of the study sample with the population mean

Question 14

Two-sample, independent samples, or unpaired Student t-test

Answer 14

parametric test that compares the means of two independent samples.

Question 15

F test

Answer 15

formal test for differences in variances with two-sample, independent samples, or unpaired Student t-test

Question 16

Paired Student t-test

Answer 16

parametric test; compares the mean difference of paired or matched samples. A related samples test.

Question 17

Analysis of variance

Answer 17

Parametric test; more generalized version of the t-test that can apply to more than two groups

Question 18

One-way ANOVA, aka single-factor ANOVA

Answer 18

Analysis of variance; compares the means of 3 or more groups in a study. An independent samples test.

Question 19

Two-way ANOVA

Answer 19

Analysis of variance; additional factors added to one-way ANOVA

Question 20

Repeated-measures ANOVA

Answer 20

Analysis of variance; related samples test

Question 21

Analysis of covariance

Answer 21

provides a method to explain the influence of a categorical variable (independent variable) on a continuous variable (dependent variable) while statistically controlling for other variables (confounding)

Question 22

Wilcoxon rank sum and Mann-Whitney U test

Answer 22

nonparametric tests that compare two independent samples (related to a t-test)

Question 23

Kruskal-Wallis one way Anova by ranks

Answer 23

nonparametrics test that compares 3 or more independent groups (related to one-way ANOVA); post hoc testing

Question 24

Sign test & Wilcoxon signed rank test

Answer 24

nonparametric tests that compare 2 matched or paired samples (related to a paired t-test)

Question 25

Friedman ANOVA by ranks

Answer 25

nonparametric test that compares 3 or more matched/paired groups

Question 26

Chi-square test

Answer 26

nominal data; compares expected and observed proportions between 2 or more groups. Test of independence and goodness of fit.

Question 27

Fisher exact test

Answer 27

nominal data; specialized version of the chi-square test for small groups (cells) containing less than 5 predicted observations

Question 28

McNemar

Answer 28

nominal data; paired samples

Question 29

Mantel-Haenszel

Answer 29

nominal data; controls for the influence of confounders

Question 30

Type I decision error

Answer 30

probability of making this error is defined as a (alpha). alpha usually set to 0.05, meaning that 5% of the time a researcher will conclude a statistical difference when one does not actually exist

Question 31

p-value

Answer 31

the calculated chance that a type I error has occurred

Question 32

Type II decision error

Answer 32

probability of making this error is termed B (beta). Concluding that no difference exists when one truly does. Beta usually set to between 0.20 and 0.10

Question 33

Power

Answer 33

The probability of making a correct decision when null hypothesis is false; the ability to detect differences between groups if one actually exists

Question 34

Correlation

Answer 34

examines the strenth of the association between two variables; does not assume one variable predicts the other

Question 35

Regression

Answer 35

examines the ability of one or more variables to predict another variable

Question 36

Pearson correlation

Answer 36

the strength of the relationship b/t 2 variables that are normally distributed, ratio or interval scaled, and linearly related is measured with a correlation coefficient; the degree of association b/t 2 variables

Question 37

Spearman rank correlation

Answer 37

nonparametric test that quantifies the strength of an association b/t 2 variables but does not assume a normal distribution of continuous data; can be used for ordinal data or nonnormally distributed continuous data

Question 38

Kaplan-Meier method

Answer 38

uses survival times to estimate the proportion of people who would survive a given length of time under the same circumstances

Question 39

Log-rank test

Answer 39

compares the survival distributions b/t 2 or more groups

Question 40

Cox proportional hazards model

Answer 40

survival analysis; most popular method to evaluate the impact of covariates; reported (graphically) like Kaplan-Meier

Question 41

ARR

Answer 41

The absolute difference in rates of an outcome between treatment and control groups in a clinical trial. Example: A hypothetical clinical trial compares the effect of a new statin and placebo on the incidence of stroke. Over the course of the study, the incidence of stroke is 4% with the statin and 6% with placebo. The absolute risk reduction with the statin is 2%.

Question 42

bias

Answer 42

Bias: Flaws in the design or operation of a study that lead to overestimation of the efficacy of treatment. Bias can more easily be introduced into studies that are not blinded. There are many different ways in which bias can be introduced into a study. Publication bias: Investigators tend not to publish studies with negative outcomes. This can lead to overestimation of efficacy in meta-analysis when studies with positive outcomes are overly represented. Recall bias: People may remember things differently than how they occurred. Selection bias: Differences between treatment and control groups that result from the way patients were selected. Randomization and blinding should help prevent selection bias.

Question 43

Relative risk reduction

Answer 43

1 - RR

Question 44

Relative risk

Answer 44

compares the risk of an event in individuals with a particular characteristic to the risk of that event in individuals without that characteristic. In a clinical trial, this would be the outcome in the treatment group divided by the outcome in the control group.

Question 45

Odds Ratio

Answer 45

the odds of exposure in cases divided by the odds of exposure in controls.9 It is analogous to relative risk.7 Unlike relative risk, it can be used in case-control studies.

Question 46

NNT/NNH

Answer 46

NNT is the reciprocal of the absolute risk reduction with drug treatment (1 divided by absolute risk reduction). they have less potential to be misleading because they are based on absolute risk.