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Research Eval > Biostats 2 > Flashcards

Biostats 2 Flashcards

1
Q

Internal validity

A

extent to which a piece of evidence supports a claim about cause and effect, within the context of a particular study

That is, cause precedes the effect and they happen together, with little possibility for confounders – within the population

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2
Q

External validity

A

applies well to a population outside of the study

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3
Q

Least important of mean, median, mode?

A

Mode

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4
Q

quantifies the amount of variability, or spread, around the mean of the measurements.

A

Variance (σ2 )

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5
Q

a measure of variation of scores about the mean

more commonly used

A

standard deviation

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6
Q

The statement that establishes a relationship between variables being assessed

A

Alternative hypothesis (Ha or H1)

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7
Q

The statement of no difference or no relationship between the variables

A

Null hypothesis (Ho)

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8
Q

A ___ error is made if we reject the null hypothesis when null hypothesis is true.

A

type I

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9
Q

A ___ error is made if we fail to reject null hypothesis

A

Type 2

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10
Q

More important than p value – a better determination of significance
Any statistic is simply an estimate of the true value of that statistic

A

Confidence interval (CI)

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11
Q

95% CI states that we can be 95% certain that the “true” value is within the CI range

A

Narrower CI is better

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12
Q

Odds ratio of 1

A

no assocation

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13
Q

A screening test is used to separate from a large group of apparently well persons those who have a high probability of having the disease, so that they may be given a diagnostic work up, and if diseased can be treated.

What are the conditions for a screening?

A

The target disease is an important cause of mortality and morbidity.

A proven and acceptable test exists to detect individuals at an early stage of disease.

There is a treatment available to prevent mortality and morbidity once positives have been identified.

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14
Q

Sensitivity

A

ability of test to correctly identify those who have the disease

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15
Q

Specificity

A

ability of the test correctly identify those who DO NOT have the disease

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16
Q

Tends to rule OUT the disease

High Sensitivity means low probability of false positive

A

Sensitivity

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17
Q

Screening test’s ability to identify presence of disease

A test with high sensitivity will not miss many patients
who have the disease

A highly useful test when NEGATIVE

A

Sensitivity

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18
Q

Tends to rule IN the disease

High Specificity means low probability of false negative

A

Specificity

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19
Q

Screening test’s ability to truly identify absence of disease

That is, how likely is a negative test actually reporting the right answer?

A highly useful test when it is POSITIVE

A

Specificity

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20
Q

A highly ___ test is most useful to the clinician when it is NEGATIVE

A

sensitive

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21
Q

A highly ___ test is most useful to the clinician when it is POSITIVE

A

specific

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22
Q

Allows up to calculate the net sensitivity and net specificity of using both tests in sequence. After completing both tests there is a loss in net sensitivity and net gain in specificity.

A

Sequential (Two-Stage) Testing

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23
Q

proportion of patients who HAVE the disease and a positive test

POPULATION related

(e.g., HIV prevalence in suburban city in US vs. HIV prevalence in sub-Saharan Africa)

A

Positive Predictive Value (PPV)

With low prevalence (% of population) of disease:
Lower PPV
False positives increase
Less reliable positive test result

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24
Q

proportion of patients who DO NOT HAVE the disease, and have a negative test

A

Negative Predictive Value (NPV)

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25
Q

the occurrence, rate, or frequency of a disease

A

Incidence

Obtained from cohort studies
Must follow a cohort through time

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26
Q

the number of occurrences at one particular time

A

Prevalence- Obtained from cross-sectional studies

No time line, only a snap shot

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27
Q

Relationship between incidence and prevalence

A

slide 33

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28
Q

Allows the researcher to explore the relationship between two continuous variables

A

Regression analysis

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29
Q

A method of predicting change in the dependent variable by changing one or more independent variables

What % of variation in the dependent variable can be explained by a change in the independent variable

A

Regression analysis

30
Q

Two broad data types

A

Categorical

Continuous

31
Q

Categorical

A

Nominal

Ordinal

32
Q

Continuous

A

Interval

Ration

33
Q

named categories with no implied order

A

Nominal

34
Q

sequenced or ranked data

A

Ordinal

E.g., smallest to largest, lightest to heaviest, easiest to most difficult

35
Q

intervals along the scale are equal to one another (i.e. integers)

A

Interval

Continuous Data

36
Q

characterized by the presence of absolute zero on the scale

Most precise

A

Ratio

Continuous Data

37
Q

In ____ screening, a less ex­pensive, less invasive, or less uncomfortable test is generally performed first, and those who screen positive are recalled for further testing with a more expensive, more invasive, or more uncomfortable test, which may have greater sensitivity and specificity. It is hoped that bringing back for further testing only those who screen positive will reduce the problem of false positives.

A

sequential or two-stage

38
Q

Summarizes the same kind of information sensitivity and specificity and can be used to calculate the probability of disease in a low prevalence setting

A

Likelihood ratio (LR)

39
Q

provides indication of the test’s discriminatory power

Predictive values are lower with a low prevalence

can be defined for the entire range of test result values

A

Likelihood ratio (LR)

40
Q

is the proportion of diseased people with a negative test result (1-sensitivity) divided by the proportion of non-diseased people with negative test results (specificity)

A

negative LR (LR-)

How good the test is at “Ruling out” disease

The smaller the better (Desirable: 0.2 or less)

41
Q

Loss in net specificity, and gain in net specificity

A

Sequential (Two-stage) Testing

42
Q

Net gain in sensitivity, and net loss in specificity.

Patient is considered positive if they test positive on any test or both.

Patient is considered negative if they test negative on all the tests performed

A

Simultaneous Testing

43
Q

one of the most common ways to examine relationships between two or more categorical variables

A

Chi-square

44
Q

tests the null hypothesis that the variable are independent of each other, that there is no relationship between the two variable

A

chi-square of independence

45
Q

does not give any information about the strength of the relationship.

A

Chi-square statistic

46
Q

Computed the same way as the chi-square test for independence, but instead tests the hypothesis that the distribution of some variable is the same in all populations

A

Chi-square test for equality of proportion

47
Q

Is used to test they hypothesis that the distribution of a categorical variable within a population followed a specific pattern of proportion

A

Chi-square test of goodness of fit

48
Q

Fisher’s exact test

A

A non-parametric test similar, similar to the chi-square tests, but can be used with small or sparsely distributed data sets

49
Q

a type of chi-square test used when the data comes from paired samples.

A

McNemar’s Test for Matched Pairs

50
Q

Measures the strength of association between an exposure and disease

the effect of one intervention v another

= (AD)/(BC)

A

Odds ratio (OR)

51
Q

If exposure does not affect (either cause or protect from) disease, the OR is ____

If the exposure is ____ to the disease, the OR > 1

If the exposure is ___ against the disease, the OR < 1

A

~ 1

related

protective

52
Q

to be able to estimate the probability of an outcome associated with a dichotomous response for a single or multiple variables

A

Logistic Regression

53
Q

a single outcome (or set of outcomes) from an experiment

A

Event

54
Q

The proportion of subjects in a study group in whom the event is observed. Usually seen as a %.

A

Rate

55
Q

A measure of how often a particular event (such as response to a drug, adverse event or death) occurs within the scientific control group of an experiment

A

Control Event Rate (CER) %

56
Q

A measure of how often a particular event (such as response to a drug, adverse event or death) occurs within the experimental group of an experiment

A

Experimental Event Rate (EER) %

57
Q

Basic risk statements express the likelihood that a particular event will occur within a particular population

Identifies what in our environment can lead to beneficial or adverse medical outcomes

A

Relative risk

SAME AS RISK RATIO

58
Q

measures the magnitude of an association between an exposed and non-exposed (control) group.

calculated using cumulative incidence data to measure the probability of developing disease

A

Relative risk (same as Risk Ratio)

Must have incidence information to calculate

Cohort or clinical trials are conducted over time

59
Q

The percentage difference in outcome between control (C) and experimental (E) groups

A

Relative risk reduction

RRR= (CER-EER)/CER

60
Q

Not a good way to compare outcomes

Does not report the baseline risk of outcome

Measures such as percent reduction in mortality, is selected because it gives a more optimistic view of the effectiveness of a preventive measure.

Makes insignificant findings appear significant

A

Relative risk reduction

61
Q

The actual reduction in events in the treated group (EER)

The arithmetic difference in outcomes between treatment and control groups

The “true difference” between the experimental and control intervention

A

Absolute risk reduction

ARR = CER - EER

62
Q

Odds ratio are an ___ estimate of risk,

A

indirect

not a direct measure of risk

63
Q

In a case-control study, only the odds ratio can be calculated as a measure of association ,whereas in a ___ study, either the relative risk or odds ratio can be calculated.

A

cohort

64
Q

Odds ratios calculated in a case-control study are a good approximation of relative risk in the population when the following conditions are met:

A

When cases studied are representative, with regard to history of exposure, of all people with the disease in the population from which the cases were drawn.

When the controls studied are representative, with regard to history of exposure, of all people without the disease in the population from which the controls were drawn.

When the disease being studied does not occur frequently.

65
Q

The number of patients who need to receive the new intervention instead of the standard alternative in order for one additional patient to benefit

A

Number needed to treat

66
Q

Expresses the likelihood of the treatment to benefit an individual patient

There is NO absolute value for NNT that defines whether something is effective or not.

NNTs for treatments are usually low because we expect large effects in small numbers of people

A

Number needed to treat

67
Q

NNTs for very effective treatments are usually in the range of 2 to 4

Rule of thumb:
NNT 10 or less for therapy
NNT 20 or less for prevention

A

Larger NNTs can be found useful where few patients are affected in large populations

Use for prophylactic measures

Example: Aspirin prevents one death at five weeks after a myocardial infarction, NNT of 40

68
Q

When an experimental treatment is detrimental, the term number needed to harm (NNH) is often used.

The equations and approach are similar to those described above, except that NNH will have a negative absolute risk reduction

A

Number needed to harm

69
Q

Generally is used to analyze continuous data

Compares the means and standard deviations of two populations

Data be must be normally distributed

Computes a p-value to test the null hypothesis

A

T-test

70
Q

Assesses whether a difference between two groups’ averages is unlikely to have occurred because of random chance in sample selection. A difference is more likely to be meaningful and “real” if-

The difference between the averages is large.

The sample size is large.

Responses are consistently close to the average values and not widely spread out (the standard deviation is low).

A

T-test

71
Q

Independent variable

A

the variable you’re interested in

72
Q

Prevalence

A

cross-sectional

prevalance = disease burden

Research Eval (9 decks)

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