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Frontiers > Weeks 1 And 2 > Flashcards

Weeks 1 And 2 Flashcards

1
Q

Pearson correlation

A

Linear regression for Gaussian data

-null is that the population correlation is 0 (no linear relationship)

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2
Q

Spearman correlation

A

Linear ranking method used with extreme values and applied to regression for a more Gaussian look

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3
Q

Regression equation

A

Y hat= bx+a

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4
Q

When there are Equal SD for regression, blank = blank

A

B=r

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5
Q

Multivariate

A

Techniques using multiple factors to remove confounding (except analysis of variance)

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6
Q

Multiple linear regression

A

For predicting a numeric dependent variable with multiple variables

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7
Q

Logistic regression

A

Used for 2 levels of dependent variables (yes/no, success/failure, etc)

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8
Q

Proportional hazards

A

Dependent variable is the time until a certain event

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9
Q

Relative risk

A

How many times more likely it is for 1 outcome vs another

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10
Q

Odds ratio

A

An approx of relative risk

Used in logistic regression

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11
Q

Hazard ration

A

Approx of relative risk, used in proportional hazards

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12
Q

Mean duration of survival

A

Best if all subjects die; mean amount of time they live

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13
Q

Median duration of survival

A

How long pts live, works better with censored data

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14
Q

Case fatality rate

A

% of deaths from condition

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15
Q

5 yr mortality rate

A

Proportion of deaths in a 5 yr period

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16
Q

Mortality rate per person yrs of observation

A

of deaths/total pt years (alive and dead)

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17
Q

Survival curves

A

Kaplan Meier, life table

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18
Q

Censored events

A

If event of interest doesn’t occur by the end of the study or there is a competing cause of death, etc

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19
Q

True experiment

A

Randomized design

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20
Q

Crossover trial

A

Each subjects gets 2 or more tx (each subject is his own control)

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21
Q

Equivalence trial

A

Shows that 2 tx or equivalent or close enough

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22
Q

Non inferiority trial

A

1 tx is not worse than an existing tx

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23
Q

Quasi experimental design

A

Strong element of control BUT no random assignment of individuals to groups

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24
Q

Single subject multiple baseline

A

Quasi exp

-many observations, intervention, many observations

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25
Q

Group multiple baseline

A

Quasi exp

-multiple baseline, intervention to group, multiple observations

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26
Q

Community trial

A
  • quasi
  • unit is the community but unit of analysis is the individual
  • need verifiably comparable community
  • randomly assigned intervention
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27
Q

Observational design

A

No intervention, only observation

-researcher does NOT randomly assign or control for various conditions

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28
Q

Case report

A

Details of 1 interesting case

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29
Q

Case series

A

Collection of case reports to show a pattern

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30
Q

Ecological/correlational

A

Look at a group and try to draw causal relationship

Ex: try to draw assoc between rate of homicide/suicide in London and the mean mental illness score in the boroughs

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31
Q

Cross sectional design/prevalence study

A

Exposure and disease status measure at a GIVEN time
Ex: ask if pt uses condoms CURRENTLY and see if they have an STD
Con: don’t know if exposure (use of condom) was before contraction of disease or started using after contracting disease

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32
Q

Case control

A

Group of cases with disease compared to group without disease

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33
Q

Cohort study

A

Group of initially healthy pts are evaluated for exposure status and then followed to see what happens

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34
Q

Attrition bias

A

Loss of subjects –> distortion of experiment’s effects

-at least 80% of subjects should complete trial

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35
Q

Allocation concealment

A

People assessing elegibility should not be able to know what group the next entering patient will be assigned to

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36
Q

Rating bias

A

Inaccurate responses because of beliefs, expectations

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37
Q

File drawer bias

A

Non significan small studies end up being filed instead of published; can also be done on purpose by drug companies

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38
Q

Patient oriented outcomes

A

Disability, pain, additional surgeries all are things patients would care about

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39
Q

Surrogate marker

A

Easily measured indicator of disease status that might not be clinically important (disease oriented outcomes)

40
Q

Noncompliance bias

A

When subjects don’t take assigned meds

41
Q

Small effects

A

Hard to be confident about these

Want larger effects like higher relative risk with CI not including 1 (or not 0 for mean)

42
Q

Non-randomized studies

A

Main kind of study confounding is a problem in

43
Q

Necessary cause

A

Must be present for the disease to occur

44
Q

Sufficient cause

A

Will produce effect when present

45
Q

Biological plausibility

A

Belief in cause and effect relationship is higher if there is a known biological mechanism by which exposure can reasonably alter risk of disease development

46
Q

Biological gradient

A

Greater degrees of exposure result in greater risk of death

47
Q

Temporal sequence

A

Exposure was present consistently before the disease

48
Q

Reversibility

A

Removal of exposure reduces risk of disease in exposed population

49
Q

Nuremberg Code

A

Ethical code developed after Nazi experiments

50
Q

Tuskegee study

A

Study of syphilis in AA which continued even after a tx was found

51
Q

Common Rule

A

Second code of ethics that formally regulates hunam subjects research

52
Q

Vulnerable populations defined by NIH

A

Children, preg women, neonates, prisoners, pts without decision making capacity, suicidal persons

53
Q

Vulnerable population not listed by NIH

A

Impoverished, unemployed, AA/minorities, immigrants, elderly, LGBTQIA individ, etc

54
Q

Therapeutic misconception

A

Congnitive error that participation in research will provide direct benefits to the subject (ex: when physician recomments pt to clinical study, suggesting pt will benefit)

55
Q

Meta analysis

A

Math. Method of combining study results to get a combined conclusion

  • provides a p value and/or CI
  • can increase sample size/serve as guide for N
  • deal with conflicted findings
  • limitation such as difficult with assignment of different weightage
56
Q

Qualitative research

A
  • in depth interviews, focus groups, participant observation
  • semi structured and structures
  • open ended questions
57
Q

Type 1 error (alpha)

A

Rejecting the null hypotheses when there is, in fact, no population difference (rejecting null when null is actually true)

58
Q

Type II error (beta; inverse to power)

A

You fail to reject the null when there is, in fact, a difference in the population

59
Q

Prevalence rate

A

Proportion of the population @ risk with disease at a particular point in time

60
Q

Period prevalence

A

Proportion of population with disease @some point during a time interval

61
Q

Incidence rate

A

Proportion of initially healthy population at risk that develops disease during pd of interest (number who develop it/#(who develop it+healthy). EXCLUDE IN DENOM (and num) PEOPLE WHO ARE SICK ALREADY

62
Q

Attack rate

A

Prop of specified population that develops a disease from a specific cause like an endemic

63
Q

Odds

A

Number of times event occurs/number of times it does not

64
Q

Probability to odds

A

P/(1-P)

65
Q

Odds to prob

A

Odds/(1+odds)

66
Q

Standard error (SE(M))

A

Indicates the accuracy with which a sample mean estimates the population mean (NOT individuals)

67
Q

Z score

A

Number of standard deviations from the mean

68
Q

When are t tests employed?

A

Simple comparison of means

69
Q

Unpaired t tests

A

2 independent samples

70
Q

SEDoM

A

SE difference of means

Sqrt[(S1^2+S22^2)/n]

71
Q

T calculation

A

(Mean1-mean2)/SEDoM

72
Q

Degrees of freedom in unpaired t

A

2n-2

73
Q

Relative risk

A

Incidence of a disease in exposed population/incidence in an unexposed population

74
Q

Odds ratio

A

Odds of disease in exposed group/odds of disease in controls

75
Q

Attributable risk percent

A

Percent of people with a disease and risk factor, who got it BECAUSE of their exposure to a risk factor

76
Q

Population attributable risk percent

A

Percentage of all cases of a disease due to a particular risk factor

77
Q

Absolute risk reduction

A

Absolute difference between rate of bad outcome in treated and untreated groups

78
Q

Relative risk reduction

A

Absolute risk reduction as a percentage of risk in untreated group (ARR/Rate in untreated group)

79
Q

Number to treat

A

How many you need to treat to see a benefit in 1 person

1/ARR

80
Q

Lead time bias

A

Apparent but false increase in survival (b/c of early detection, not actual changes in survival)

81
Q

Sensitivity

A

If a person has the disease, the probability that the test will give a positive result

82
Q

Specificity

A

If a person doesn’t have the disease, the probability of a negative result

83
Q

Positive predictive value

A

If a person gets a positive result, the probability he has the disease

84
Q

Negative predictive value

A

If the pt gets a negative result, the probability he doesn’t have the disease

85
Q

Gold standard

A

Test/info you regard as true and correct

86
Q

Likelihood ratio

A

Sensitivity/(100-specificity)

87
Q

Power

A

Probability that the researcher will reject H0 when it should be rejected

88
Q

Child mortality rate

A

Under 5 mortality per 1000

89
Q

Infant mortality rate

A

Number of deaths of infants under the age of 1 per 1000 LIVE births

90
Q

Neonatal mortality rate

A

Number of deaths of infants under 28 days of age per 1000 births

91
Q

Maternal mortality rate

A

Number of women who die while preg. Or within 42 days of pregnancy termination per 100,000 live births

92
Q

YLL

A

Years lives lost (when person died subtracted from ave life expectancy)

93
Q

YLD

A

Years lived with disability (years of healthy life lost because of disability)

94
Q

DALY- disability adjusted life year

A

YLL+ YLD

95
Q

QUALY= Quality adjusted life year

A

1 QUALY= 1 yr of life in perfect health

96
Q

Demographic transition

A

High fertility/high mortality as country develops
-population shift with economic development –> high fertility but low mortality
Developed nations have low fertility and aging populations

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