ITE CA-2 stats stuff

Question 1

nominal data

Answer 1

nominal data (also known as nominal scale) is a type of data that is used to label variables without providing any quantitative value. … One of the most notable features of ordinal data is that, nominal data cannot be ordered and cannot be measured

Question 2

compare 2 different groups of nominal data

Answer 2

chi-squared or fisher’s exact test

Question 3

parametric data

Answer 3

When we assume that the distribution of some variable (like course grades) follows a well-known distribution (like the Normal distribution), that can be boiled down to knowledge of just a couple of parameters (like mu and sigma), and then we use that assumption in the performance of some statistical test, we are said to be using a parametric test.

Question 4

nonparametric data

Answer 4

When you can’t make such an assumption about the underlying distribution of a variable, before looking at the data, and must instead use more robust (but frequently less powerful) methods as a result, to answer the same kinds of questions, then you are using a nonparametric test.

Question 5

compare 2 different parametric interval groups

Answer 5

un-paired t-test (2-sample)

The unpaired t-test allows for comparison of two populations with respect to a single variable with continuous data. In our example, one population is the group of patients receiving remifentanil and the other population is the group receiving sevoflurane. Our single variable is the mean arterial pressure.

Question 6

compare 2 different ordinal or nonparametric interval groups

Answer 6

Wilcoxon-Mann-Whitney test

Wilcoxon-Mann-Whitney is a nonparametric test designed for studies for ordinal numbers (ranking: 1st, 2nd, 3rd, etc.).

Question 7

ordinal data

Answer 7

nominal variables are used to “name,” or label a series of values. Ordinal scales provide good information about the order of choices, such as in a customer satisfaction survey. Interval scales give us the order of values + the ability to quantify the difference between each one.

Question 8

compare more than 2 different nominal groups

Answer 8

chi-square or fisher’s exact test
Chi-square testing is for comparison of two populations with respect to a single variable with discrete (not continuous) data.

Chi-square test is used to compare categorical data and not means.

Question 9

compare more than 2 parametric interval groups

Answer 9

one-way ANOVA
Analysis of the variance (ANOVA) is similar to a t-test except that it is designed to analyze >1 variable.

Analysis of variance (ANOVA) is a statistical test used to compare means between more than two groups or test differences in repeated measurements within the same group.

Question 10

compare more than 2 different ordinal or non-parametric groups

Answer 10

kruskall-wallis

Question 11

compare 2 paired nominal groups

Answer 11

McNemar

Question 12

compare 2 paired parametric groups

Answer 12

paired t-test

Question 13

compare 2 paired ordinal or non-parametric groups

Answer 13

Wilcoxon-signed-rank test

Question 14

compare more than 2 matched nominal groups

Answer 14

Repeated measures logistic regression

Question 15

compare more than 2 matched parametric interval groups

Answer 15

Repeated measures ANOVA

Question 16

compare more than 2 matched ordinal or non-parametric groups

Answer 16

Friedman test

Question 17

Case control cannot measure

Answer 17

In a case-control study, you cannot measure incidence, because you start with diseased people and non-diseased people, so you cannot calculate relative risk

Question 18

cohort study

Answer 18

The cohort study design identifies a people exposed to a particular factor and a comparison group that was not exposed to that factor and measures and compares the incidence of disease in the two groups

Question 19

case control

Answer 19

The case-control design uses a different sampling strategy in which the investigators identify a group of individuals who had developed the disease (the cases) and a comparison of individuals who did not have the disease of interest. The cases and controls are then compared with respect to the frequency of one or more past exposures. If the cases have a substantially higher odds of exposure to a particular factor compared to the control subjects, it suggests an association.

Question 20

odds ratio

Answer 20

AD/CB

Question 21

Number needed to treat

Answer 21

1/absolute risk reduction

Question 22

relative risk

Answer 22

(A/[A+B])/(C/[C+D})

Question 23

2 std dev contain

Answer 23

95%

Question 24

risk

Answer 24

frequency at which a specific event occurs

Question 25

What is AIMS

Answer 25

The use of computerized anesthesia information management systems (AIMS) is increasing in modern anesthesia practice. AIMS are a form of the electronic health record which allows automatic collection, storage, and presentation of patient data during the perioperative period.

Question 26

Standard error of the mean

Answer 26

SE = SD / square root n

Question 27

1 std dev contain

Answer 27

68%

Question 28

Specificity

Answer 28

Specificity = TN / (TN+FP). Specificity is the percentage of time a test is truly negative. By definition, it takes into account the cases which yield a false positive (FP) test.

Question 29

Sensitivity

Answer 29

Sensitivity = TP / (TP+FN), the chance (%) to correctly detect the disease or problem. Sensitivity “rules out” the disease

Question 30

Positive predictive value

Answer 30

Positive predictive value = TP / (TP+FP), the chance (%) that a positive test result means that the subject actually has the disease or problem

Question 31

Negative predictive value

Answer 31

Negative predictive value = TN / (TN+FN), the chance (%) that a negative test result means that the subject does not actually have the disease or problem

Question 32

Multivariate logistic regression produces what measure for the outcome

Answer 32

Multivariate logistic regression produces “adjusted” odds ratio, where “adjusted” is short for “adjusted for known confounding variables”. Typically the adjusted odds ratio is lower than the unadjusted odds ratio, and some exposure factors may no longer be significantly associated with the outcome after adjusting.

Question 33

difference between odds and risk

Answer 33

The odds of an outcome are calculated as number of people with the outcome / number of people without the outcome. For example patients with perioperative MI / patients without perioperative MI. This is not calculated as people with outcome / all people, which would be probability (i.e. risk).

Question 34

unadjusted odds ratio

Answer 34

The “unadjusted” odds ratio (OR) is calculated as the odds of the exposed / odds of non exposed. For example, odds of perioperative MI in COPD patients / odds of perioperative MI in non-COPD patients.

Question 35

Multivariate logistic regression

Answer 35

Multivariate logistic regression is a technique to address confounding, and produces “adjusted” odds ratios. For example, the unadjusted OR for COPD patients developing perioperative MI might be 2.4 (95% CI 1.5-3.3), and the corresponding “adjusted” OR might be 1.2 (95% CI 0.6-1.8).

Question 36

power

Answer 36

power = 1-β
Practically speaking, the power tells us the chance that the null hypothesis (e.g. no treatment effect from a particular drug) will be rejected when an alternative hypothesis is actually true (e.g. a drug does have a treatment effect). The larger the sample population (usually denoted as “n = some number”), the greater the power.

Question 37

3 std dev contain

Answer 37

99%

Question 38

1 std dev contains

Answer 38

68%

Question 39

Which of the following statistical tests should be used to compare the effect of sevoflurane versus the effect of remifentanil on mean arterial blood pressure in patients undergoing craniotomy?

Answer 39

unpaired t test

Question 40

What test is used to compare multiple variables each with discrete values.

Answer 40

Logistics regression is used to compare multiple variables each with discrete values.

Logistics Regression
(“Chi-Squared on steroids”)

Question 41

test to analyze single variable survival (events)

Answer 41

kaplan-meier analysis

Question 42

test to analyze multivariable survival (events)

Answer 42

Cox Proportional Hazard Analysis

Question 43

Three surgeons wish to compare their mean operative durations for identical procedures. Each has done ten of these operations and the data for each are normally distributed. Which test is MOST appropriate?

Answer 43

Analysis of variance (ANOVA)

Question 44

compare means of two groups

Answer 44

Student T-test is used to compare means of two groups

Question 45

main advantage of registry studies

Answer 45

describe rare events (but cannot calculate incidence)

Question 46

what test use for comparing mean length of stay between patients having open or endovascular aortic aneurysm repair

Answer 46

t-test

Question 47

what test to compare rates of infection between 2 groups of patients

Answer 47

chi square

Question 48

graph to evaluate the agreement between two measurement techniques

Answer 48

A Bland-Altman plot is used evaluate the agreement between two measurement techniques.

Question 49

best measure of central tendency for ordinal data

Answer 49

Ordinal data sets refer to data sets that have a natural numerical ordering, e.g. the verbal numeric pain rating scale. With ordinal data, however, the intervals between the numbers may not be the same. For example, the difference between a 5 and a 6 on the verbal numerical pain rating scale may not be the same as the difference between a 9 and a 10 on the same scale. When using an ordinal scale, the central tendency of a group of items can be described by using the group’s mode.

Question 50

Factors that increase statistical power include:

Answer 50

Factors that increase statistical power include: increasing sample size, increasing the effect size, increasing alpha (e.g. p-value), and reducing population variability (e.g. standard deviation).