Statistics Flashcards

Question

When is it appropriate to use a Wilcoxon test?

Answer 1

Paired 2 samples, non-parametric data

Answer 2

Distribution Parametric = Chi squared (for large samples with n>20 [at least]; Fisher exact test for smaller samples) Non-parametric = Kolmogorov-Smirnov (can also use Fisher exact test for non-parametric data or small parametric samples...which again, these are hard to assume parametricitiy given often not perfectly "normal" data in small sample size)

Answer 3

All used with distribution Chi squared best with PARAMETRIC data, bigger samples (n>20) Fisher exact test can be used with smaller sample (n<20) parametric data, vs with non-parametric data Kolmogorov-Smirnov can be used to look at distribution in non-parametric data

Answer 4

>2 samples Parametric = ANOVA; with several dependent variables, use a MANOVA Non-Parametric = Kruskal Wallis

Answer 5

>2 samples, non-parametric data

Answer 6

Association = is there a RELATIONSHIP between 2 or more variables The type of test, of course, varies based on if data are parametric vs non-parametric

Answer 7

I want to look at the association between two groups. Yay! Parametric data = Pearson Product Non-parametric data = Kendall Tau >2 groups? Use the correlation matrix version of each of the above tests

Answer 8

I want to look at the association between groups, with one dependent variable (and multiple independent variables). Parametric data = linear regression Non-parametric data = logistic regression

Answer 9

When would it be appropriate to use a linear regression? - ONE dependent variable, MULTIPLE independent variables - PARAMETRIC data

Answer 10

When would it be appropriate to use a LOGISTIC regression? - ONE dependent variable, MULTIPLE independent variables - NON-PARAMETRIC data

Answer 11

I want to look at the association between groups, with multiple dependent variables. Parametric data = Ologit regression (categories are ORDERED) Non-parametric data = Discriminate Analysis (categories are NOT ORDERED) or use Multinominal regression

Answer 12

When would it be appropriate to use an OLOGIT regression? - MULTIPLE dependent variables - PARAMETRIC data - Categories that are ORDERED

Answer 13

When would it be appropriate to use a DISCRIMINATE ANALYSIS or MULTINOMIAL regression? - MULTIPLE dependent variables - NON-PARAMETRIC data - Categories that are NOT ORDERED

Answer 14

Proportion of TRUE POSITIVES (proportion by percentage of patients who DO have the disease of interest who register a POSITIVE test finding) SnNOut (therefore, with high sensitivity, a NEGATIVE tests helps you more confidently rule OUT for the dx)

Answer 15

Proportion of TRUE NEGATIVES (proportion by percentage of patients who do NOT have the disease of interest who register a NEGATIVE test finding) SpPIN (therefore, with high specificity, a POSITIVE test helps you more confidently rule IN for the dx)

Answer 16

Probability that subjects with a POSITIVE TEST truly DO have the disease

Answer 17

Probability that subjects with a NEGATIVE TEST truly DO NOT have the disease

Answer 18

LR+ Commonly used to rule in a condition (probability of a true positive) / (probability of a false positive) Positive LR = sensitivity / (100 – specificity). ``` AKA: (Probability of a patient WITH the disease and a POSITIVE test) divided by the (probability of a patient without the disease and a positive test). ```

Answer 19

LR- Commonly used to rule out a condition (probability of a false negative) / (probability of a true negative) Negative LR = (100 – sensitivity) / specificity. ``` AKA: (Probability of a person who has the disease testing negative) divided by the (probability of a person who does not have the disease testing negative) ```

Answer 20

Measures the strength of treatment effect (magnitude of the intervention) INDEPENDENT of sample size - very useful when evaluating data from under- or over-powered studies' In randomized trials (comparative studies), effect sizes are often reported as "trivial, small, moderate, or large"

Answer 21

Odds that an outcome will occur given a particular exposure vs to the odds of the outcome occurring in the ABSENCE of that exposure OR >1 = finding is more likely OR <1 = finding is less likely

Answer 22

Positive LR = sensitivity / (100 – specificity). Negative LR = (100 – sensitivity) / specificity. Recall: - Sensitivity = TRUE POSITIVE rate - Specificity = TRUE NEGATIVE rate

Answer 23

Tell you likelihood of a disease/condition/result. 0-1: Decreased likelihood of disease. +LR 1/2 (0.5) = 15% less likely +LR 1/5 (0.2) = 30% less likely +LR 1/10 (0.1) = 45% less likely 1: Null, no diagnostic value. > 1: increased evidence for disease. High +LR helps you rule IN for a disease. +LR 2 = 15% more likely +LR 5 = 30% more likely +LR 10 = 45% more likely An LR over 10 is very strong evidence to rule in a disease.

Answer 24

"How much of this journal being cited during the most recent X (often 2 or 5) years?" Reflects the number of citations made in the current year to articles in the previous two years, divided by the total number of citable articles from the previous two or five years

Answer 25

Phase I: assess the SAFETY of an intervention. Phase II: test EFFICACY of the intervention in a tightly controlled environment. Phase III: EFFECTIVENESS (randomized and blinded testing in a REAL WORLD environment) Phase IV: tests the impact of the intervention for costs, overall long-term care, etc.