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Kelly > Biostatistics > Flashcards

Biostatistics Flashcards

1
Q

random variables

A

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

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2
Q

discrete variables

A

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))

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3
Q

Continuous variables

A

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)

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4
Q

Descriptive statistics

A

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

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5
Q

visual methods of desribing data

A

frequency of distribution, histogram, scatterplot

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6
Q

Measures of central tendency

A

mean, median, mode

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7
Q

Measures of data spread or variability

A

standard deviation, range, percentiles

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8
Q

Inferential stats

A

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

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9
Q

Normal (Gaussian) distribution

A

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

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10
Q

Kolmogorov-Smirnov test

A

formal test for a visual check of a Gaussian distribution

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11
Q

Parametric tests

A

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

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12
Q

nonparametric tests

A

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

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13
Q

One-sample Student t-test

A

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

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14
Q

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

A

parametric test that compares the means of two independent samples.

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15
Q

F test

A

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

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16
Q

Paired Student t-test

A

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

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17
Q

Analysis of variance

A

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

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18
Q

One-way ANOVA, aka single-factor ANOVA

A

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

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19
Q

Two-way ANOVA

A

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

20
Q

Repeated-measures ANOVA

A

Analysis of variance; related samples test

21
Q

Analysis of covariance

A

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)

22
Q

Wilcoxon rank sum and Mann-Whitney U test

A

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

23
Q

Kruskal-Wallis one way Anova by ranks

A

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

24
Q

Sign test & Wilcoxon signed rank test

A

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

25
Q

Friedman ANOVA by ranks

A

nonparametric test that compares 3 or more matched/paired groups

26
Q

Chi-square test

A

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

27
Q

Fisher exact test

A

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

28
Q

McNemar

A

nominal data; paired samples

29
Q

Mantel-Haenszel

A

nominal data; controls for the influence of confounders

30
Q

Type I decision error

A

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

31
Q

p-value

A

the calculated chance that a type I error has occurred

32
Q

Type II decision error

A

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

33
Q

Power

A

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

34
Q

Correlation

A

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

35
Q

Regression

A

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

36
Q

Pearson correlation

A

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

37
Q

Spearman rank correlation

A

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

38
Q

Kaplan-Meier method

A

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

39
Q

Log-rank test

A

compares the survival distributions b/t 2 or more groups

40
Q

Cox proportional hazards model

A

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

41
Q

ARR

A

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%.

42
Q

bias

A

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.

43
Q

Relative risk reduction

A

1 - RR

44
Q

Relative risk

A

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.

45
Q

Odds Ratio

A

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.

46
Q

NNT/NNH

A

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.

Kelly (12 decks)

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