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Step 1 - Public Health Sciences > Epidemiology and biostatistics > Flashcards

Epidemiology and biostatistics Flashcards

1
Q

Types of observational studies

A
  1. Cross-sectional
  2. Case-control
  3. Cohort
  4. Twin concordance
  5. Adoption
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2
Q

OR is used in

A

Case-control studies

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3
Q

RR is used in

A

Cohort studies

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4
Q

-Frequency of disease
-Frequency of risk related factors
Assessed in the present

A

Cross-sectional study: What is happening now?

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5
Q

Patients with COPD had higher odds of a history of smoking than those without COPD. What observational study did we use?

A

Case-control study

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6
Q

Smokers had a higher risk of developing COPD than nonsmokers. What observational study did we use?

A

Cohort study

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7
Q

Compares frequency with wich both monozygotic twins vs both disygotic twins develop the same disease

A

Twin concordance study

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8
Q

Compares siblings raised by biological vs adoptive parents

A

Adoption study

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9
Q

Blinding of the researchers analyzing the data

A

Triple-blind

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10
Q

Phases of a clinical trial

A

Phase I: is it safe?
Phase II: Does it Work?
Phase III: Improvements?
Phase IV: Market. Can it stay?

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11
Q

Can a cross-sectional study show association between a risk factor and a disease?

A

It can show association but it does not establish causality

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12
Q

Measures heritability and influence of environmental factors

A

Twin concordance study

Adoption study

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13
Q

Study sample used in phase I

A

Small number of healthy volunteers

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14
Q

Study sample used in phase II

A

Small number of patients with disease of interest

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15
Q

Study sample used in phase III

A

Large number of patients randomly assigned either to the treatment under investigation or the best available treatment or placebo
ITS THE ONLY ONE USING LARGE NUMBERS

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16
Q

Study sample used in phase IV

A

Postmarketing surveillance of patients after treatment is approved

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17
Q

Phase I of a clinical trial assesses…

A

Assesses safety, toxicity, pharmacokinetics and pharmacodynamics

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18
Q

Phase II of a clinical trial assesses…

A

Treatment efficacy
Optimal dosing
Adverse effects

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19
Q

Phase III of a clinical trial assesses…

A

Compares the new treatment to the current standard of care

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20
Q

Phase IV of a clinical trial assesses…

A

Rare or long term adverse effects

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21
Q

Formula of sensitivity

A

TP/(TP+FN)

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22
Q

If sensitivity test is negative, it…

A

rules OUT a disease

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23
Q

Proportion of all the people with disease among all those who test positive

A

Sensitivity

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24
Q

True positive rate

A

Sensitivity

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25
Q

True negative rate

A

Specificity

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26
Q

If specificity test is positive, we…

A

Rule IN disease

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27
Q

If sensitivity is 100%, what is the % of false negatives?

A

0% of false negatives. So all negatives are True Negatives

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28
Q

If specificity is 100%, what is the % of false positives?

A

0% of false positives. So all positives are True positives

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29
Q

The probability that when the disease is absent the test is negative

A

Specificity

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30
Q

The probability that when the disease is present the test is positive

A

Sensitivity

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31
Q

Test used for screening in diseases with low prevalence

A

Test with high Sensitivity

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32
Q

Test used for confirmation after a positive screening test

A

Test with high specificity

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33
Q

Fixed properties of a test

A

Sensitivity

Specificity

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34
Q

Variables that change depending on disease prevalence in population being tested

A

Positive and negative predictive values (PPV and NPV)

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35
Q

Define PPV

A

Proportion of all positive tests that are true positive.

If my results are positive, what are my chances of truly having the disease?

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36
Q

Define NPV

A

Proportion of all negative tests that are true negative.

If my results are negative, what are my chances of truly not having the disease?

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37
Q

Varies directly with pretest probability

A

PPV

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38
Q

Varies inversely with prevalence or pretest probability

A

NPV

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39
Q

Prevalence in low NPV

A

High

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40
Q

Prevalence in high PPV

A

High

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41
Q

Lowering the cutoff point of a test

A

Increases sensibility
Decreases specificity
Decreases PPV
Increases NPV

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42
Q

Rising the cutoff point of a test

A

Increases specificity
Decreases sensibility
Rises PPV
Decreases NPV

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43
Q

1 - false negative rate

A

Sensibility

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44
Q

1 - false positive rate

A

Specificity

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45
Q

LR+

A

= True positive rate / False positive rate

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46
Q

LR -

A

= False negative rate / True negative rate

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47
Q

LR+ and LR- indicators of a very useful diagnostic test

A

LR+ >10

LR- <0.1

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48
Q

RR>1

A

Exposure associated with more disease occurrence

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49
Q

RR<1

A

Exposure associated with less disease occurrence

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50
Q

RR=1

A

No association between exposure and disease

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51
Q

Risk of developing disease in the exposed group divided by the risk of developing disease in the non exposed group

A

Relative Risk

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52
Q

What happens with RR and OR when disease prevalence is low?

A

OR approximates RR

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53
Q

Attributable risk, Definition

A

The difference in risk between exposed and unexposed groups:

the % of disease occurrences that are attributable to the exposure

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54
Q

Relative Risk Reduction (RRR). Formula

A

RRR= 1 - RR

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55
Q

RRR definition

A

The proportion of risk reduction attributable to the intervention as compared to a control

56
Q

Calculate relative risk reduction:

3% of pacients who recieve a flu shot develop the flu, while 10% of unvaccinated patients develop the flue

A 
3/10= 0.3
1-0.3= 0.7= 70% of relative risk reduction
57
Q

Absolute risk reduction definition

A

The difference in risk (in absolute terms, not in proportion) attributable to the intervention as compared to a control).

58
Q

Calculate absolute risk reduction:

3% of pacients who recieve a flu shot develop the flu, while 10% of unvaccinated patients develop the flue

A

10%-3%= 7%=0.07

59
Q

Number needed to treat, definition

A

Number of patients who need to be treated for 1 single patient to benefit

60
Q

Number needed to treat, formula

A

NNT=1/ARR

61
Q

Number needed to harm, definition

A

Number of patients who need to be exposed to a risk factor for 1 patient to be hARmed

62
Q

Number needed to hARm, formula

A

NNH=1/AR

63
Q

NNT for a good treatment

A

Low

64
Q

NNH for a good treatment

A

High

65
Q

Incidence rate

A

Number of new cases / Total people at risk

During a specific period

66
Q

Prevalence

A

Number of existing cases / Total population

During a point in time

67
Q

For a short duration disease, prevalence and incidence…

A

offer similar results

68
Q

For chronic diseases, prevalence and incidence

A

differ, being prevalence larger than incidence

69
Q

The consistency and reproducibility of a test

A

Precision

70
Q

The truness of test measurments

A

Accuracy

71
Q

The absence of systematic error

A

Accuracy

72
Q

The absence of random error

A

Precision

73
Q

Validity

A

Accuracy

74
Q

Reliability

A

Precision

75
Q

Higher precision

A

Less standard deviation

More statistical power

76
Q

Statistical power

A

1- beta

77
Q

Selection bias: definition

A

Non random sampling: study population is not representative of target populated

78
Q

Berkson bias

A

Selection bias: study population selected from hospital is less healthy than general population

79
Q

Healthy worker effect

A

Selection bias: study population is healthier than the general population

80
Q

Non response bias

A

Selection bias: participating subjects differ from nonrespondents in meaningful ways

81
Q

Strategies to reduce selection bias

A

Randomization

Ensure the choice of the right comparison/reference group

82
Q

Bias performing the study

A
  1. Recall bias
  2. Measurment bias
  3. Procedure bias
  4. Observer-expectancy bias
83
Q

Recall bias

A

Awareness of disorder alters recall by subjects

84
Q

Recall bias common in

A

Retrospective studies

85
Q

Strategies to reduce recall bias

A

Decrease time from exposure to follow-up

86
Q

Measurment bias

A

Information is gathered in a systematically distorted manner

87
Q

Hawthorne effect

A

Measurment bias: participants change their behavior in response to their awareness of being observed

88
Q

Strategies to reduce measurment bias

A

Use objective, standardized and previously tested methods of data collection that are planned ahead of time
Use placebo group

89
Q

Procedure bias

A

Subjets in different groups are not treated the same

90
Q

Observer-expectancy bias

A

Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment

91
Q

Strategies to reduce procedure bias and observer expectancy bias

A

Blinding

Use of placebo

92
Q

Bias interprenting results

A
  1. Confounding bias

2. Lead-time bias

93
Q

Confounding bias

A

When a factor is related to both the exposure and the outcome but not the causal pathway

94
Q

Lead-time bias

A

Early detection is confused with increase in survival

95
Q

Strategies to reduce confounding bias

A 
Multiple studies
Crossover studies
Matching
Restriction
Randomization
96
Q

Crossover studies

A

Subjects act as their own controls

97
Q

Strategies to reduce time lead bias

A

Measure back-end survival: adjust survival according to the severity of disease at the time of diagnosis

98
Q

Measures of central tendency

A

Mean
Median
Mode

99
Q

Mean

A

Sum of all values / total number of values

100
Q

Mean affects mostly

A

Outliers: extreme values

101
Q

Mode

A

Most common value

102
Q

Least affected by outliers

A

Mode

103
Q

Median

A

Middle value of a list of data sorted from least to greatest

104
Q

Measures of dispersion

A

Standard deviation

Standard error

105
Q

Standard deviation

A

how much variability exists in a set of values, around the mean of these values

106
Q

An estimate of how much variability exists in a theoretical set of sample means around the true population mean

A

Standard error

107
Q

Mean=Median=Mode

A

Normal distribution, Gaussian

108
Q

Non normal distributions

A

Bimodal
Positive skew
Negative skew

109
Q

Non normal distribution that suggests two different populations

A

Bimodal

110
Q

Mean > Median >Mode

A

Positive skew

111
Q

Mean

A

Negative skew

112
Q

Null hypothesis (H0)

A

Hypothesis of no difference or relationship

113
Q

Alternative hypothesis (H1)

A

Hypothesis of some difference or relationship

114
Q

Type I error

A

Stating that there is an effect when none exists

115
Q

Null hypothesis rejected in favor of alternative hypothesis

A

Type I error

116
Q

Alpha

A

The probability of making a typpe I error

117
Q

False-positive error

A

Type I error

118
Q

Type II error

A

Stating that there is not an effect when one exists

119
Q

Null hypothesis not rejected when it is in fact false

A

Type II error

120
Q

Beta

A

The probability of making a type II error

121
Q

Related to statistical power

A

Beta: 1- beta = statistical power

122
Q

Probability of rejecting the null hypothesis when it is false

A

Statistical power= 1 - beta

123
Q

Lower beta - Higher statistical power

A
  1. Higher precision of the test!
  2. Higher sample size
  3. Higher expected effect size
124
Q

Confidence interval

A

Range of values within which the true mean of the population is expected to fall, with a specified probability

125
Q

Standard error formula

A

Standard deviation / √n

126
Q

With a higher sample number, SE

A

Decreases

127
Q

Z for 95% CI

A

1.96

128
Q

Z for 98% CI

A

2.58

129
Q

t-test

A

Checks differences between means of 2 groups

Tea is meant for 2

130
Q

Comparing mean blood pressure between men and women

A

t-test

131
Q

ANOVA

A

Checks differences between eans of 3 or more groups: Analysis of Variance

132
Q

Chi-square

A

Checks differences between 2 or more percentages of categorical outcomes

133
Q

Positive r value in pearson correlation coefficient

A

Positive correlation

134
Q

Negative r value in pearson correlation coefficient

A

Negative correlation

135
Q

Coefficient of determination

A

r square: amount of variance in one variable that can be explained by variance in another variable

Step 1 - Public Health Sciences (4 decks)

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