Flashcards in chapter 44: statistics and patient safety Deck (71):

1

## rejects null hypothesis incorrectly -> falsely assumed there was a difference when no difference exists

### type 1 error

2

### type 1 error

3

## accepts null hypothesis incorrectly because of small sample size -> the treatments are interpreted as equal when there is actually a difference

### type 2 error

4

## hypothesis that no difference exists between groups

### null hypothesis

5

## p value that rejects the null hypothesis

### p

6

## p value: > 95% likelihood that the difference between the populations is true

### p

7

## likelihood that the difference is not true and occurred by chance alone with p

8

## spread of data around a mean

### variance

9

## population

### parameter

10

## most frequently occurring value

### mode

11

## average

### mean

12

## middle value of a set of data

### median

13

## prospective study with random assignment to treatment and non treatment groups

### randomized controlled trial (avoids treatment biases)

14

## prospective study in which patient and doctor are blind to the treatment

###
double-blind controlled trial

- avoids observational bias

15

## prospective study -> compares disease rate between exposed and unexposed groups (nonrandom assignment)

### cohort study

16

## retrospective study in which those who have the disease are compared with a similar population who do not have the disease; the frequency of the suspected risk factor is then compared between the 2 groups

### case-control study

17

## combining data from different studies

### meta-analysis

18

## 2 independent groups and variable is quantitative -> compares means (mean weight between 2 groups)

### student's t test

19

## variable is quantitative; before and after studies (e.g. weight before and after, drug versus placebo)

### paired t tests

20

## compares quantitative variables (means) for more than 2 groups

### ANOVA

21

## compare categorical (qualitative) variables (race, sex, medical problems and diseases, medications)

### nonparametric statistics

22

## compares 2 groups with categorical (qualitative) variables (number of obese patients with and without diabetes versus number of non obese patients with and without diabetes)

### chi-squared test

23

## small groups -> estimates survival

### Kaplan-Meyer

24

## incidence in exposed / incidence in unexposed

### relative risk

25

##
probability of making the correct conclusion = 1 - probability of type 2 error

- likelihood that the conclusion of the test is true

- larger sample size increases power of a test

### power of test

26

##
number of people with disease in a population (Eg number of patents in US with colon CA)

- long-standing disease increases prevalence

### prevalence

27

## number of new cases diagnosed over a certain time frame in a population (e.g. number of patients in the US newly diagnosed with colon CA in 2003)

### incidence

28

##
ability to detect disease = true-positives/(true-positives+false-negatives)

- indicates the number of people who have the disease who test positive

###
sensitivity

with high sensitivity, a negative test result means patient is very unlikely to have disease

29

##
ability to state no disease is present = true-negatives/(true-negatives + false-positives)

- indicates the number of people who do not have the disease who test negative

###
specificity

with high specificity, a positive test result means patient is very likely to have disease

30

##
true-positives / (true-positive + false-positive)

- likelihood that with a positive result, the patient actually has the disease

### positive predictive value

31

##
true-negatives / (true-negatives + false-negatives)

- likelihood that with a negative result, the patient does not have the disease

### negative predictive value

32

## depends on disease prevalence

### predictive value

33

## depends on disease prevalence

### predictive value

34

### type 2 error

35

## hypothesis that no difference exists between groups

### null hypothesis

36

## p value that rejects the null hypothesis

### p

37

## p value: > 95% likelihood that the difference between the populations is true

### p

38

## likelihood that the difference is not true and occurred by chance alone with p

### less than 5%

39

## spread of data around a mean

### variance

40

## population

### parameter

41

## most frequently occurring value

### mode

42

## average

### mean

43

## middle value of a set of data

### median

44

## prospective study with random assignment to treatment and non treatment groups

### randomized controlled trial (avoids treatment biases)

45

## prospective study in which patient and doctor are blind to the treatment

###
double-blind controlled trial

- avoids observational bias

46

### cohort study

47

### case-control study

48

## combining data from different studies

### meta-analysis

49

## 2 independent groups and variable is quantitative -> compares means (mean weight between 2 groups)

### student's t test

50

### paired t tests

51

## compares quantitative variables (means) for more than 2 groups

### ANOVA

52

## compare categorical (qualitative) variables (race, sex, medical problems and diseases, medications)

### nonparametric statistics

53

### chi-squared test

54

## small groups -> estimates survival

### Kaplan-Meyer

55

## incidence in exposed / incidence in unexposed

### relative risk

56

##
probability of making the correct conclusion = 1 - probability of type 2 error

- likelihood that the conclusion of the test is true

- larger sample size increases power of a test

### power of test

57

##
number of people with disease in a population (Eg number of patents in US with colon CA)

- long-standing disease increases prevalence

### prevalence

58

### incidence

59

##
ability to detect disease = true-positives/(true-positives+false-negatives)

- indicates the number of people who have the disease who test positive

###
sensitivity

with high sensitivity, a negative test result means patient is very unlikely to have disease

60

##
ability to state no disease is present = true-negatives/(true-negatives + false-positives)

- indicates the number of people who do not have the disease who test negative

###
specificity

with high specificity, a positive test result means patient is very likely to have disease

61

##
true-positives / (true-positive + false-positive)

- likelihood that with a positive result, the patient actually has the disease

### positive predictive value

62

##
true-negatives / (true-negatives + false-negatives)

- likelihood that with a negative result, the patient does not have the disease

### negative predictive value

63

## true-positives + true-negatives / true-positives+true-neg+false-pos+false-neg

### accuracy

64

## depends on disease prevalence

### predictive value

65

## are independent of prevalence

### sensitivity and specificity

66

## seeks to collect outcome date to measure and improve surgical quality in the US. outcomes are reported as observed vs expected ratios

### National Surgery Quality Improvement Program (NQSIP)

67

## JCAHO prevention of wrong site/procedure/patient protocol

###
- preop verification of patient and procedure

- operative site and side

- time out before incision is made (verifying patient, procedure, position site + side, and availability of implants or special requirements)

68

## promoting culture of safety

###
- confidential system of reporting errors

- emphasis on learning over accountability

- flexibility in adapting to new situations or problems

69

## risk factors for retained object after surgery (MC sponge)

### emergency procedure, unplanned change in procedure, obesity, towel used for closure

70

## unexpected occurrence involving death or serious injury, or the risk thereof; hospital undergoes root cause analysis to prevent and minimize future occurrences (Eg wrong site surgery)

### sentinel event (JCAHO)

71