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Statistics Flashcards

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1
Q

Hypothesis-generating study designs

A
  • observational
  • survey
  • case report/series
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2
Q

Hypothesis testing study designs

A 
Experimental
-randomized
Observational
-cross-sectional
-case-control
-cohort
-other
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3
Q

Meta-analysis

A

Pooled data of observational studies

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4
Q

Cross-sectional

A

Single point in time

Temporal trends

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5
Q

Cohort

A

Onset of observation with the exposure

Estimates incidence/rate of exposures and outcomes

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6
Q

Case-control

A

Compare the frequency of exposure between patients who have/have not experienced outcome of interest
Search for risk factors

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7
Q

Case-report

A

Highlight an unusual procedure or event

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8
Q

Continuous variable

A

Can take on any number of values within a specified range of possibilities
Ex: Age, length of stay

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9
Q

Categorical variables

A

Have discrete values
Ex: binary (sex), ordinal (ordered categorical variables such as cancer stage), nominal (unordered categorical variables such as race)

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10
Q

Time-to-event variables

A

Two variables: continuous variable that measures the time interval from an established start point (ex: date of diagnosis) to failure event (ex: death) and a binary variable which indicates whether the failure event occurred
Ex: long-term survival

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11
Q

Measurement of continuous variables

A

Mean (for normally distributed data)

Median (for skewed data)

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12
Q

Descriptive statistics for continuous variables

A

Unpaired t-test
Paired t-test
ANOVA

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13
Q

Multivariate regression model for continuous variables

A

Linear

Need 10-15 observations per variable

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14
Q

Measurement of categorical variables

A

Proportion

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15
Q

Descriptive statistics for categorical variables

A

Chi-squared test

Mantel-Haenszel odds

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16
Q

Multivariate regression model for categorical variables

A

Logistic

Need at least 10 events and equivalent number of non events per variable

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17
Q

Measurement for time-to-event variables

A

Kaplan-Meier

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18
Q

Descriptive statistics for time-to-event variables

A

Log-rank test

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19
Q

Multivariate regression model for time-to-event variables

20
Q

Unpaired t-test

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A

Compare 2 independent groups with continuous outcome variables

21
Q

Paired t-test

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A

Compare 2 dependent groups with continuous outcome variables

22
Q

ANOVA

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A

Compare more than 2 groups with continuous outcome variables

23
Q

Chi-squared

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A

Compare distributions of 2 or more groups with categorical outcome variables (sex, mortality)

24
Q

Fisher exact

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A

Compare distributions of 2 or more groups with categorical outcome variables with small sample size

25
Log-rank test
Compare 2 groups with time-to-event outcome variables
26
Alpha (type 1) error
Observe a difference when one does not exist | False-positive
27
Beta (type 2) error
No difference is observed when when one actually exists False-negative Insufficient power to detect true differences, directly related to sample size
28
Confidence interval (CI)
Difference between groups are provided as estimated ratio or absolute difference Odds ratio/relative risk ratio: if includes 1, no statistical difference Absolute difference/relative risk: if includes 0, no statistical difference
29
Wide confidence interval
Lack of precision
30
Tight confidence interval
Minimal uncertainty
31
PICOT framework to summarize research question
``` Population in the study Independent variables (intervention/exposure, covariates) Comparator group, if applicable Outcome, end point (dependent variable) Time frame of outcomes assessment ```
32
Confounder
Measured or unmeasured variable associated with the exposure of interest and associated with the outcome
33
Generalizability
Ability to take research findings and apply them to clinical practice -is this reproducible in a clinical setting? In my patient population?
34
Bradford Hill criteria for causality
Strength of association Consistency: do all or most studies indicate that A causes B? Specificity Temporality: if A causes B, then A must precede B. Just because A precedes B, A does not necessarily cause B. Biological gradient (dose-response): the more a person is exposed to A, the more likely they will get disease B Plausibility: there should be a reasonable biological mechanism to explain why A causes B Coherence: should make sense with what we already know about A and B Experiment Analogy
35
Wilcoxon test
Used to study the relationship between an ordinal variable such as satisfaction scores, in 2 samples (before and after treatment)
36
Kruskal-Wallis test
Used for ordinal data from 3 or more groups
37
Relative Risk Reduction formula
(Incidence in unexposed - incidence in exposed) / incidence in unexposed
38
Risk ratio
Used in cohort studies and RCT data is collected prospectively Calculate incidences and incidence rates and compare these as risk ratios
39
Odds ratios
Used in case-control studies Only prevalence rates can be calculated Also used to summarize data from cohort studies and RCTs
40
True or False: PPV and NPV vary depending on the prevalence of disease
True As disease prevalence increases, more people actually have the disease (increase in TP) and fewer people do not have the disease (decrease in TN) Increase in TP signifies a higher PPV. Decrease in TN signifies a lower NPV
41
True or False: sensitivity and specificity vary depending on prevalence of a disease
False
42
Student's t-test
Test for normally distributed continuous variables
43
Mann-whitney test
Used for non-normally distributed continuous variables
44
Absolute Risk Reduction
Difference in rates between the control group and the experimental group Incidence in unexposed - incidence in exposed
45
Relative Risk
Computes the possibility of disease when exposed to a certain agent relative to the risk of disease when not exposed to the same agent Incidence in exposed / incidence in unexposed
46
Odds ratio
Measure of association between an exposure and an outcome | Diseased in exposed / healthy in exposed) / (diseased in not exposed / healthy in not exposed
47
Hazard ratio
Hazard rate of one exposure variable relative to the hazard rate of another exposure variable

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