Epidemiology and biostatistics Flashcards
1
Q
Types of observational studies
A
- Cross-sectional
- Case-control
- Cohort
- Twin concordance
- Adoption
2
Q
OR is used in
A
Case-control studies
3
Q
RR is used in
A
Cohort studies
4
Q
-Frequency of disease
-Frequency of risk related factors
Assessed in the present
A
Cross-sectional study: What is happening now?
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
6
Q
Smokers had a higher risk of developing COPD than nonsmokers. What observational study did we use?
A
Cohort study
7
Q
Compares frequency with wich both monozygotic twins vs both disygotic twins develop the same disease
A
Twin concordance study
8
Q
Compares siblings raised by biological vs adoptive parents
A
Adoption study
9
Q
Blinding of the researchers analyzing the data
A
Triple-blind
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?
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
12
Q
Measures heritability and influence of environmental factors
A
Twin concordance study
Adoption study
13
Q
Study sample used in phase I
A
Small number of healthy volunteers
14
Q
Study sample used in phase II
A
Small number of patients with disease of interest
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
16
Q
Study sample used in phase IV
A
Postmarketing surveillance of patients after treatment is approved
17
Q
Phase I of a clinical trial assesses…
A
Assesses safety, toxicity, pharmacokinetics and pharmacodynamics
18
Q
Phase II of a clinical trial assesses…
A
Treatment efficacy
Optimal dosing
Adverse effects
19
Q
Phase III of a clinical trial assesses…
A
Compares the new treatment to the current standard of care
20
Q
Phase IV of a clinical trial assesses…
A
Rare or long term adverse effects
21
Q
Formula of sensitivity
A
TP/(TP+FN)
22
Q
If sensitivity test is negative, it…
A
rules OUT a disease
23
Q
Proportion of all the people with disease among all those who test positive
A
Sensitivity
24
Q
True positive rate
A
Sensitivity
25
True negative rate
Specificity
26
If specificity test is positive, we...
Rule IN disease
27
If sensitivity is 100%, what is the % of false negatives?
0% of false negatives. So all negatives are True Negatives
28
If specificity is 100%, what is the % of false positives?
0% of false positives. So all positives are True positives
29
The probability that when the disease is absent the test is negative
Specificity
30
The probability that when the disease is present the test is positive
Sensitivity
31
Test used for screening in diseases with low prevalence
Test with high Sensitivity
32
Test used for confirmation after a positive screening test
Test with high specificity
33
Fixed properties of a test
Sensitivity
| Specificity
34
Variables that change depending on disease prevalence in population being tested
Positive and negative predictive values (PPV and NPV)
35
Define PPV
Proportion of all positive tests that are true positive.
| If my results are positive, what are my chances of truly having the disease?
36
Define NPV
Proportion of all negative tests that are true negative.
| If my results are negative, what are my chances of truly not having the disease?
37
Varies directly with pretest probability
PPV
38
Varies inversely with prevalence or pretest probability
NPV
39
Prevalence in low NPV
High
40
Prevalence in high PPV
High
41
Lowering the cutoff point of a test
Increases sensibility
Decreases specificity
Decreases PPV
Increases NPV
42
Rising the cutoff point of a test
Increases specificity
Decreases sensibility
Rises PPV
Decreases NPV
43
1 - false negative rate
Sensibility
44
1 - false positive rate
Specificity
45
LR+
= True positive rate / False positive rate
46
LR -
= False negative rate / True negative rate
47
LR+ and LR- indicators of a very useful diagnostic test
LR+ >10
| LR- <0.1
48
RR>1
Exposure associated with more disease occurrence
49
RR<1
Exposure associated with less disease occurrence
50
RR=1
No association between exposure and disease
51
Risk of developing disease in the exposed group divided by the risk of developing disease in the non exposed group
Relative Risk
52
What happens with RR and OR when disease prevalence is low?
OR approximates RR
53
Attributable risk, Definition
The difference in risk between exposed and unexposed groups:
| the % of disease occurrences that are attributable to the exposure
54
Relative Risk Reduction (RRR). Formula
RRR= 1 - RR
55
RRR definition
The proportion of risk reduction attributable to the intervention as compared to a control
56
Calculate relative risk reduction:
| 3% of pacients who recieve a flu shot develop the flu, while 10% of unvaccinated patients develop the flue
```
3/10= 0.3
1-0.3= 0.7= 70% of relative risk reduction
```
57
Absolute risk reduction definition
The difference in risk (in absolute terms, not in proportion) attributable to the intervention as compared to a control).
58
Calculate absolute risk reduction:
| 3% of pacients who recieve a flu shot develop the flu, while 10% of unvaccinated patients develop the flue
10%-3%= 7%=0.07
59
Number needed to treat, definition
Number of patients who need to be treated for 1 single patient to benefit
60
Number needed to treat, formula
NNT=1/ARR
61
Number needed to harm, definition
Number of patients who need to be exposed to a risk factor for 1 patient to be hARmed
62
Number needed to hARm, formula
NNH=1/AR
63
NNT for a good treatment
Low
64
NNH for a good treatment
High
65
Incidence rate
Number of new cases / Total people at risk
| During a specific period
66
Prevalence
Number of existing cases / Total population
| During a point in time
67
For a short duration disease, prevalence and incidence...
offer similar results
68
For chronic diseases, prevalence and incidence
differ, being prevalence larger than incidence
69
The consistency and reproducibility of a test
Precision
70
The truness of test measurments
Accuracy
71
The absence of systematic error
Accuracy
72
The absence of random error
Precision
73
Validity
Accuracy
74
Reliability
Precision
75
Higher precision
Less standard deviation
| More statistical power
76
Statistical power
1- beta
77
Selection bias: definition
Non random sampling: study population is not representative of target populated
78
Berkson bias
Selection bias: study population selected from hospital is less healthy than general population
79
Healthy worker effect
Selection bias: study population is healthier than the general population
80
Non response bias
Selection bias: participating subjects differ from nonrespondents in meaningful ways
81
Strategies to reduce selection bias
Randomization
| Ensure the choice of the right comparison/reference group
82
Bias performing the study
1. Recall bias
2. Measurment bias
3. Procedure bias
4. Observer-expectancy bias
83
Recall bias
Awareness of disorder alters recall by subjects
84
Recall bias common in
Retrospective studies
85
Strategies to reduce recall bias
Decrease time from exposure to follow-up
86
Measurment bias
Information is gathered in a systematically distorted manner
87
Hawthorne effect
Measurment bias: participants change their behavior in response to their awareness of being observed
88
Strategies to reduce measurment bias
Use objective, standardized and previously tested methods of data collection that are planned ahead of time
Use placebo group
89
Procedure bias
Subjets in different groups are not treated the same
90
Observer-expectancy bias
Researcher's belief in the efficacy of a treatment changes the outcome of that treatment
91
Strategies to reduce procedure bias and observer expectancy bias
Blinding
| Use of placebo
92
Bias interprenting results
1. Confounding bias
| 2. Lead-time bias
93
Confounding bias
When a factor is related to both the exposure and the outcome but not the causal pathway
94
Lead-time bias
Early detection is confused with increase in survival
95
Strategies to reduce confounding bias
```
Multiple studies
Crossover studies
Matching
Restriction
Randomization
```
96
Crossover studies
Subjects act as their own controls
97
Strategies to reduce time lead bias
Measure back-end survival: adjust survival according to the severity of disease at the time of diagnosis
98
Measures of central tendency
Mean
Median
Mode
99
Mean
Sum of all values / total number of values
100
Mean affects mostly
Outliers: extreme values
101
Mode
Most common value
102
Least affected by outliers
Mode
103
Median
Middle value of a list of data sorted from least to greatest
104
Measures of dispersion
Standard deviation
| Standard error
105
Standard deviation
how much variability exists in a set of values, around the mean of these values
106
An estimate of how much variability exists in a theoretical set of sample means around the true population mean
Standard error
107
Mean=Median=Mode
Normal distribution, Gaussian
108
Non normal distributions
Bimodal
Positive skew
Negative skew
109
Non normal distribution that suggests two different populations
Bimodal
110
Mean > Median >Mode
Positive skew
111
Mean
Negative skew
112
Null hypothesis (H0)
Hypothesis of no difference or relationship
113
Alternative hypothesis (H1)
Hypothesis of some difference or relationship
114
Type I error
Stating that there is an effect when none exists
115
Null hypothesis rejected in favor of alternative hypothesis
Type I error
116
Alpha
The probability of making a typpe I error
117
False-positive error
Type I error
118
Type II error
Stating that there is not an effect when one exists
119
Null hypothesis not rejected when it is in fact false
Type II error
120
Beta
The probability of making a type II error
121
Related to statistical power
Beta: 1- beta = statistical power
122
Probability of rejecting the null hypothesis when it is false
Statistical power= 1 - beta
123
Lower beta - Higher statistical power
1. Higher precision of the test!
2. Higher sample size
3. Higher expected effect size
124
Confidence interval
Range of values within which the true mean of the population is expected to fall, with a specified probability
125
Standard error formula
Standard deviation / √n
126
With a higher sample number, SE
Decreases
127
Z for 95% CI
1.96
128
Z for 98% CI
2.58
129
t-test
Checks differences between means of 2 groups
| Tea is meant for 2
130
Comparing mean blood pressure between men and women
t-test
131
ANOVA
Checks differences between eans of 3 or more groups: Analysis of Variance
132
Chi-square
Checks differences between 2 or more percentages of categorical outcomes
133
Positive r value in pearson correlation coefficient
Positive correlation
134
Negative r value in pearson correlation coefficient
Negative correlation
135
Coefficient of determination
r square: amount of variance in one variable that can be explained by variance in another variable
