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EBM > EBM Exam 2 > Flashcards

EBM Exam 2 Flashcards

1
Q

Nominal data

1 confounded

2 samples (independent, parallel design)

A

Mantel-Haenszel

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2
Q

Nominal data

Greater than 2 confounders

2 samples (independent, parallel design)

A

Logistic regression

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3
Q

Nominal data

No confounders

2 samples (related, crossover or pre-post design)

A

McNemar’s test

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4
Q

Nominal data

No confounders

3 or more samples (independent, parallel design)

A

Chi square (bonferroni)

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5
Q

Nominal data

1 confounder

3 or more samples (independent, parallel design)

A

Chi square (bonferroni)

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6
Q

Nominal data

2 or more confounders

3 or more samples (independent, parallel design)

A

Logistic regression

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7
Q

Nominal data

No confounders

3 or more samples (related, cross-over design)

A

Cochran’s Q (bonferroni)

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8
Q

Ordinal data

No confounders

2 samples (independent, parallel)

A

Wilcoxon rank sum or Mann Whitney U

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9
Q

Ordinal data

1 confounder

2 samples (independent, parallel)

A

2 way ANOVA ranks

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10
Q

Ordinal data

2 or more confounders

2 samples (independent, parallel)

A

ANOVA ranks

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11
Q

Ordinal data

No confounders

2 samples (related, cross-over or pre-post design)

A

Wilcoxon signed rank test

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12
Q

Ordinal data

1 confounder

2 samples (related, cross-over or pre-post design)

A

2-way repeated ANOVA ranks

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13
Q

Ordinal data

2 or more confounders

2 samples (related, cross-over or pre-post)

A

Repeated measures regression

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14
Q

Ordinal data

No confounder

3 or more samples (independent, parallel)

A

Kruskal-Wallis ANOVA (MCP or bonferroni)

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15
Q

Ordinal data

1 confounder

3 or more samples (independent, parallel)

A

2 way ANOVA

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16
Q

Ordinal

2 or more confounders

3 or more samples (independent, parallel)

A

ANCOVA ranks

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17
Q

Ordinal

No confounders

3 or more samples (related, cross-over)

A

Friedman ANOVA

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18
Q

Ordinal

1 confounder

3 or more samples (related, cross-over)

A

2 way repeated ANOVA ranks

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19
Q

Ordinal

2 or more confounders

3 or more samples (related, cross-over)

A

Repeated measures regression

20
Q

Continuous

No confounders

2 samples (independent, parallel)

A

Student’s t test

21
Q

Continuous

1 confounder

2 samples (independent, parallel)

A

2 way ANOVA

22
Q

Continuous

2 or more confounders

2 samples (independent, parallel)

A

ANCOVA

23
Q

Continuous

No confounders

2 samples (related, cross-over or pre-post)

A

Paired student’s t test

24
Q

Continuous

1 confounder

2 samples (related, cross-over or pre-post)

A

2 way repeated ANOVA

25
Q

Continuous

2 or more confounders

2 samples (related, cross-over or pre-post)

A

Repeated measures regression

26
Q

Continuous

No confounders

3 samples (independent, parallel)

A

1 way ANOVA (MCP)

27
Q

Continuous

1 confounder

3 samples (independent, parallel)

A

2 way ANOVA

28
Q

Continuous

2 or more confounders

3 samples (independent, parallel)

A

ANCOVA

29
Q

Continuous

No confounders

3 samples (related, cross-over)

A

Repeated measures ANOVA (MCP)

30
Q

Continuous

1 confounder

3 samples (related, cross-over)

A

2 way repeated ANOVA

31
Q

Continuous

2 or more confounders

3 samples (related, cross-over)

A

Repeated measures regression

32
Q

Nominal data

No confounders

2 samples (independent, parallel design)

A

Chi square or fisher’s exact

33
Q

Mean

A

Continuous data

Sensitive to outliers

34
Q

Median

A

Ordinal and continuous data

Insensitive to outliers

35
Q

Standard error of the mean

A

Statistical trick
Makes the variability in the data appear smaller than it is
An estimation of the error in the study’s mean
NOT a measure of variability of the data

36
Q

Type I error

A

Claiming that there is a difference when one does not exist

37
Q

P value

A

The chance of a type I error occurring

38
Q

Type II error

A

Accept null hypothesis when it is false

39
Q

Power

A

1-beta

Determine the number of pts needed per group to detect a difference

Smaller differences require larger study pops

40
Q

P value trumps power or power trumps p value?

A

P value trumps power – results are still significant if achieve p value but do not achieve power

41
Q

If confidence interval includes 0 (for absolute values) or 1 (for ratios),

A

Not statistically significant

42
Q

Wider CI means

A

More uncertainty – suspicious of type II error

43
Q

Problem with relative risk

A

Artificially inflates effectiveness and adverse events

44
Q

NNT and NNH can only be calculated for…

A

NOMINAL data

Statistically significant results

45
Q

Intention to treat

A

All data is included in the analysis, regardless of whether the person completed the study
(Real world, more conservative)

46
Q

Per protocol

A

Only pts that followed protocol perfectly included in analysis

EBM (2 decks)

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