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1

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

type 1 error

2

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

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

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

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

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

cohort study

47

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

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

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

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

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

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

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

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

GAP protection technique

gaps in care (shift change, etc) can lead to loss of information and error. prevention - structured handoffs and checklists (face to face if possible; standardizing orders; reading back orders if verbal