First Aid - Epidemiology and Biostatistics Flashcards
1
Q
Cross-sectional study
Measures what?
A
Cross-sectional study
Disease prevalence.
Can show risk factor association with disease, but
does not establish causality.
2
Q
Cross-sectional study
Design of the Study?
A
Cross-sectional study
Frequency of disease and frequency of riskrelated
factors are assessed in the present.
Asks, “What is happening?”
3
Q
Case-control study
Design of the Study?
A
Case-control study
Compares a group of people with disease to a
group without disease.
Looks to see if odds of prior exposure or risk
factor differ by disease state.
Asks, “What happened?”
4
Q
Case-control study
Measures what?
A
Case-control study
Odds ratio (OR).
Patients with COPD had higher odds of a
smoking history than those without COPD.
5
Q
Cohort study
Measures What?
A
Cohort study
Relative risk (RR).
Smokers had a higher risk of developing COPD
than nonsmokers.
Cohort = relative risk.
6
Q
Cohort study
Measures What?
A
Cohort study
Relative risk (RR).
Smokers had a higher risk of developing COPD
than nonsmokers.
Cohort = relative risk.
7
Q
Crossover study
Design of the Study?
A
Crossover study
Compares the effect of a series of ≥2 treatments
on a participant.
Order in which participants receive treatments
is randomized. Washout period occurs
between each treatment.
8
Q
Crossover study
Advantage of the Study?
A
Crossover study
Allows participants to serve as their own
controls.
9
Q
Twin concordance study
Measures What?
A
Twin concordance study
Measures heritability and influence of environmental factors (“nature vs nurture”).
10
Q
Twin concordance study
Design of the Study?
A
Twin concordance study
Compares the frequency with which both
monozygotic twins vs both dizygotic twins
develop the same disease.
11
Q
Adoption study
Measures What?
A
Adoption study
Measures heritability and influence of
environmental factors.
12
Q
Adoption study
Design of Study?
A
Adoption study
Compares siblings raised by biological vs
adoptive parents
13
Q
Clinical Trials
Experimental study involving humans. Compares therapeutic benefits of ≥2 treatments, or of
treatment and placebo. Study quality improves when study is randomized, controlled, and _________
(ie, neither patient nor doctor knows whether the patient is in the treatment or control
group). Triple-blind refers to the additional blinding of the researchers analyzing the data.
Four phases (“Does the drug SWIM?”).
A
Clinical Trials
Experimental study involving humans. Compares therapeutic benefits of ≥2 treatments, or of
treatment and placebo. Study quality improves when study is randomized, controlled, and doubleblinded
(ie, neither patient nor doctor knows whether the patient is in the treatment or control
group). Triple-blind refers to the additional blinding of the researchers analyzing the data.
Four phases (“Does the drug SWIM?”).
14
Q
Clinical Trials
Experimental study involving humans. Compares therapeutic benefits of ≥2 treatments, or of treatment and placebo. Study quality improves when study is randomized, controlled, and doubleblinded. \_\_\_\_\_\_\_\_ refers to the additional blinding of the researchers analyzing the data. Four phases (“Does the drug SWIM?”).
A
Clinical Trials
Experimental study involving humans. Compares therapeutic benefits of ≥2 treatments, or of treatment and placebo. Study quality improves when study is randomized, controlled, and doubleblinded. **Triple-blind** refers to the additional blinding of the researchers analyzing the data. Four phases (“Does the drug SWIM?”).
15
Q
Clinical Trials
Four phases (“Does the drug ____?”).
A
Clinical Trials
Four phases (“Does the drug SWIM?”).
Safe, Works, Improves, postMarketing safe
16
Q
Clinical Trials - Phase I
Typical study sample?
A
Clinical Trials - Phase I
Small number of either healthy volunteers or
patients with disease of interest
17
Q
Clinical Trials - Phase I
PURPOSE?
(SWIM)
A
Clinical Trials - Phase I
“Is it Safe?” Assesses safety, toxicity,
pharmacokinetics, and pharmacodynamics.
18
Q
Clinical Trials - Phase II
PURPOSE
(SWIM)
A
Clinical Trials - Phase II
“Does it Work?” Assesses treatment efficacy,
optimal dosing, and adverse effects
19
Q
Clinical Trials - Phase II
Typical study sample?
A
Clinical Trials - Phase II
Moderate number of patients with disease of
interest.
20
Q
Clinical Trials - Phase III
Typical study sample?
A
Clinical Trials - Phase III
Large number of patients randomly assigned
either to the treatment under investigation or
to the standard of care (or placebo).
21
Q
Clinical Trials - Phase III
PURPOSE?
(SWIM)
A
Clinical Trials - Phase III
“Is it as good or better?” Compares the new
treatment to the current standard of care (any
Improvement?).
22
Q
Clinical Trials - Phase IV
PURPOSE?
A
Clinical Trials - Phase IV
“Can it stay?” Detects rare or long-term adverse
effects (eg, black box warnings). Can result in
treatment being withdrawn from Market.
23
Q
Clinical Trials - Phase IV
Typical Study Sample?
A
Clinical Trials - Phase IV
Postmarketing surveillance of patients after
treatment is approved.
24
Q
Evaluation of Diagnostic tests
Sensitivity and specificity are ____ properties
of a test
A
Evaluation of Diagnostic tests
Sensitivity and specificity are fixed properties
of a test
25
**Evaluation of Diagnostic tests**
PPV and NPV vary depending on
disease prevalence in population being tested.
**Evaluation of Diagnostic tests**
PPV and NPV vary depending on
disease _______ in population being tested.
26
**Evaluation of Diagnostic tests**
Positive Predictive Value is the number of ____ Positives Devided by the Number of ____ Positives and the Number of ____ Positives
**Evaluation of Diagnostic tests**
Positive Predictive Value is the number of True Positives Devided by the Number of True Positives and the Number of False Positives
**(PPV=TP/(TP+FP)**
27
**Evaluation of Diagnostic tests**
Positive Predictive Value is the number of ____ Positives Devided by the Number of ____ Positives and the Number of ____ Positives
**Evaluation of Diagnostic tests**
Positive Predictive Value is the number of True Positives Devided by the Number of True Positives and the Number of False Positives
**(PPV=TP/(TP+FP)**
28
**Evaluation of Diagnostic tests**
Negative Predictive Value - Definition
**Evaluation of Diagnostic tests**
Probability that a person with a negative test
result actually does not have the disease.
29
**Evaluation of Diagnostic tests**
Positive Predictive Value - Definition
**Evaluation of Diagnostic tests**
Probability that a person with a positive test
result actually has the disease.
30
**Evaluation of Diagnostic tests**
Sensitivity (True Positive Rate) is actually number of True Positives devided by the number of True ____ and the Number of ____ Negatives.
**Evaluation of Diagnostic tests**
Sensitivity (True Positive Rate) is actually number of True Positives devided by the number of True positives and the Number of False Negatives.
Sensitivity = TP / (TP + FN)
31
**Evaluation of Diagnostic tests**
Specificity (True Negative Rate) is actually number of True Negatives devided by the number of True ____ and the Number of ____ Positives.
**Evaluation of Diagnostic tests**
Specificity (True Negative Rate) is actually number of True Negatives devided by the number of True Negatives and the Number of False Positives.
Specificity = TN / (TN + FP)
32
**Evaluation of Diagnostic tests**
High Specificity indicates a Low ____ \_\_\_\_\_ Rate!
**Evaluation of Diagnostic tests**
High Specificity indicates a Low False Positive Rate!
(Specificity = True Negative Rate)
33
**Evaluation of Diagnostic tests**
High Sensitivity indicates a Low ____ \_\_\_\_\_ Rate!
**Evaluation of Diagnostic tests**
High Sensitivity indicates a Low False Negative Rate!
(Sensitivity = True Positive Rate)
34
**Evaluation of Diagnostic tests**
\_\_\_ varies inversely with prevalence or pretest
probability
**Evaluation of Diagnostic tests**
NPV varies inversely with prevalence or pretest
probability
35
**Evaluation of Diagnostic tests**
Prevelance = (TP+FN)/(TP+FP+TN+FN)
**Evaluation of Diagnostic tests**
Prevelance = (TP+FN)/(TP+FP+TN+FN)
36
**Evaluation of Diagnostic tests**
\_\_\_ varies directly with pretest probability
(baseline risk, such as prevalence of disease):
high pretest probability → high \_\_\_
**Evaluation of Diagnostic tests**
PPV varies directly with pretest probability
(baseline risk, such as prevalence of disease):
high pretest probability → high PPV
37
**Evaluation of Diagnostic tests**
A?
**Evaluation of Diagnostic tests**
A = 100% Sensitivity cutoff value
38
**Evaluation of Diagnostic tests**
C?
**Evaluation of Diagnostic tests**
C = 100% specificity cutoff value
39
**Evaluation of Diagnostic tests**
B?
**Evaluation of Diagnostic tests**
B = practical compromise cutoff between Specificity and Sensitivity
40
**Evaluation of Diagnostic tests**
Lowering the cutoff value: B→A
Will cause FP↑ and FN↓
What will happen to the Sensitivity?
**Evaluation of Diagnostic tests**
Sensitivity Rises!
Sensitivity↑ = TP / (TP + FN↓)
41
**Evaluation of Diagnostic tests**
Lowering the cutoff value: B→A
Will cause FP↑ and FN↓
What will happen to the Specificty?
**Evaluation of Diagnostic tests**
Specificty Falls!
Specificty↓ =TN / (TN + FP↑)
42
**Evaluation of Diagnostic tests**
Lowering the cutoff value: B→A
Will cause FP↑ and FN↓
What will happen to the NPV?
**Evaluation of Diagnostic tests**
NPV Rises!
NPV↑ = TN / (TN + FN↓)
43
**Evaluation of Diagnostic tests**
Lowering the cutoff value: B→A
Will cause FP↑ and FN↓
What will happen to the PPV?
**Evaluation of Diagnostic tests**
PPV Falls!
PPV↓ = TP / (TP + FP↑)
44
**Evaluation of Diagnostic tests**
Raising the cutoff value: B→C
Will cause FP↓ and FN↑
What will happen to the Specificity?
**Evaluation of Diagnostic tests**
Specificity Rises!
Specificity = TN / (TN + FP↓)
45
**Evaluation of Diagnostic tests**
Raising the cutoff value: B→C
Will cause FP↓ and FN↑
What will happen to the NPV?
**Evaluation of Diagnostic tests**
NPV Falls!
NPV = TN / (TN + FN↑)
46
**Evaluation of Diagnostic tests**
Raising the cutoff value: B→C
Will cause FP↓ and FN↑
What will happen to the PPV?
**Evaluation of Diagnostic tests**
PPV Rises!
PPV = TP / (TP + FP↓)
47
**Evaluation of Diagnostic tests**
Raising the cutoff value: B→C
Will cause FP↓ and FN↑
What will happen to the Sensitivity?
**Evaluation of Diagnostic tests**
Sensitivity Falls!
Sensitivity= TP / (TP + FN↑)
48
**Likelihood ratio**
What is it?
**Likelihood ratio**
Likelihood that a given test result would be
expected in a patient with the target disorder
compared to the likelihood that the same result
would be expected in a patient without the
target disorder.
49
**Likelihood ratio**
What does LR+\>10 indicates?
**Likelihood ratio**
LR+ \> 10 indicates a highly SPECIFIC test
50
**Likelihood ratio**
What does LR–\< 0.1indicates?
**Likelihood ratio**
LR– \< 0.1 indicates a highly SENSITIVE test.
51
**Odds ratio**
Typically used in Case-Control studies. Represents the odds of exposure among cases (\_/c) vs odds of exposure among controls (\_/d).
**Odds ratio**
Typically used in Case-Control studies. Represents the odds of exposure among cases (**a**/c) vs odds of exposure among controls (**b**/d).
52
**Odds ratio**
If in a case-control study, 20/30 lung cancer patients and 5/25 healthy individuals report smoking, the OR is \_; so the lung cancer patients are _ times more likely to have a history of smoking.
**Odds ratio**
If in a case-control study, 20/30 lung cancer patients and 5/25 healthy individuals report smoking, the OR is **8**; so the lung cancer patients are **8** times more likely to have a history of smoking.
**OR = (20/10)/(5/20) = 8**
53
**Relative risk**
Used in Cohort studies. Risk of developing disease in the
exposed group divided by risk in the unexposed group.
If RR = 1 than ____ ?
**Relative risk**
RR = 1 → NO association between
exposure and disease.
54
**Relative risk**
Used in Cohort studies. Risk of developing disease in the
exposed group divided by risk in the unexposed group.
If RR \< 1 than ____ ?
**Relative risk**
RR \< 1 → exposure associated with
↓ disease occurrence.
55
**Relative risk**
Used in Cohort studies. Risk of developing disease in the
exposed group divided by risk in the unexposed group.
If RR \< 1 than ____ ?
**Relative risk**
RR \< 1 → exposure associated with
↓ disease occurrence.
56
**Relative risk**
For rare diseases (low prevalence), \_\_
approximates RR.
**Relative risk**
For rare diseases (low prevalence), **OR**
approximates RR.
**OR = (a/c)/(b/d)**
**RR = (a/(a+b)/(c/(c+d)**
57
**Relative risk**
If 5/10 people exposed to radiation are diagnosed with cancer, and 1/10 people not exposed to radiation are diagnosed with cancer, the RR is _ ; so people exposed to radiation have a _ times greater risk of developing cancer.
**Relative risk**
If 5/10 people exposed to radiation are diagnosed with cancer, and 1/10 people not exposed to radiation are diagnosed with cancer, the RR is **5**; so people exposed to radiation have a **5** times greater risk of developing cancer.
**RR = (5/10)/(1/10) = 5**
58
**Relative Risk Reduction**
The proportion of risk reduction
attributable to the ________ as
compared to a control.
**RRR = 1 - RR**
**Relative Risk Reduction**
The proportion of risk reduction
attributable to the **intervention** as
compared to a control.
**RRR = 1 - RR**
59
**Relative Risk Reduction**
If 2% of patients who receive a flu
shot develop the flu, while 8% of
unvaccinated patients develop the flu,
then RR = _ , and RRR = _ .
**RRR = 1 - RR**
**Relative Risk Reduction**
If 2% of patients who receive a flu
shot develop the flu, while 8% of
unvaccinated patients develop the flu,
then **RR = 2/8 = 0.25, and RRR = 0.75.**
**RRR = 1 - RR**
60
**Attributable Risk**
The difference in risk between
\_\_\_\_\_\_\_\_ and ________ groups.
**Attributable Risk**
The difference in risk between
exposed and unexposed groups.
61
**Attributable Risk**
If risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then the attributable risk is \_\_\_.
**Attributable Risk**
If risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then the attributable risk is **20%**
62
**Absolute Risk Reduction**
The ______ in risk (not the proportion) attributable to the intervention as compared to a control.
**Absolute Risk Reduction**
The **difference** in risk (not the proportion) attributable to the intervention as compared to a control.
63
**Absolute Risk Reduction**
If 8% of people who receive a placebo vaccine develop the flu vs 2% of people who receive a flu vaccine, then ARR = \_.
**Absolute Risk Reduction**
If 8% of people who receive a placebo vaccine develop the flu vs 2% of people who receive a flu vaccine, then **ARR = 8%–2% = 6% = 0.06**.
64
**Number Needed to Treat**
Lower number = _____ treatment.
NNT = 1/ARR
**Number Needed to Treat**
Lower number = **better** treatment.
NNT = 1/ARR
65
**Number Needed to Treat**
Number of patients who need to be treated for \_\_\_.
NNT = 1/ARR
**Number Needed to Treat**
Number of patients who need to be treated for **1 patient to benefit.**
NNT = 1/ARR
66
**Number Needed to Harm**
Number of patients who need to be exposed to a risk factor for \_\_\_
NNH = 1/AR
**Number Needed to Harm**
Number of patients who need to be exposed to a risk factor for **1 patient to be harmed**.
NNH = 1/AR
67
**Number Needed to Harm**
Higher number = ___ exposure.
NNH = 1/AR
**Number Needed to Harm**
Higher number = **Safer** exposure.
NNH = 1/AR
68
**Case Fatality Rate**
Percentage of deaths occurring among \_\_\_.
**Case Fatality Rate**
Percentage of deaths occurring among **those with disease.**
**CFR% = (Deaths/Cases)x100%**
69
**Case Fatality Rate**
If 4 patients die among 10 cases of
meningitis, case fatality rate is \_\_\_.
**Case Fatality Rate**
If 4 patients die among 10 cases of
meningitis, case fatality rate is **40%**.
**CFR% = (Deaths/Cases)x100%**
70
**Prevelance Vs. Incidence**
What is the Difference?
**Prevelance Vs. Incidence**
Incidence looks at new cases (incidents) while Prevalence looks at all current cases.
71
**Prevelance Vs. Incidence**
Incidence = # of new cases / # of people \_\_\_
(per unit of time)
**Prevelance Vs. Incidence**
Incidence = # of new cases / # of people **at risk**
(per unit of time)
72
**Prevelance Vs. Incidence**
Prevalence = # of ___ cases / Total # of people
(at a point in time)
**Prevelance Vs. Incidence**
Prevalence = # of **existing** cases / Total # of people
(at a point in time)
73
**Prevelance Vs. Incidence**
Prevalence/ (1-Prevelance) = ___ x Average Duration of the Disease
**Prevelance Vs. Incidence**
Prevalence/ (1-Prevelance) = **Incidence rate** x Average Duration of the Disease
74
**Prevelance Vs. Incidence**
Prevalence ≈ ____ for short duration disease
(eg, common cold).
**Prevelance Vs. Incidence**
Prevalence ≈ **Incidence** for short duration disease
(eg, common cold).
75
**Prevelance Vs. Incidence**
\_\_\_ \> incidence for chronic diseases, due to
large # of existing cases (eg, diabetes).
**Prevelance Vs. Incidence**
**Prevalence** \> incidence for chronic diseases, due to
large # of existing cases (eg, diabetes).
76
**Prevelance Vs. Incidence**
Prevalence ∼ ______ probability.
**Prevelance Vs. Incidence**
Prevalence ∼ **pretest** probability.
77
**Prevelance Vs. Incidence**
↑ \_\_\_\_= ↑ PPV and ↓ NPV.
**Prevelance Vs. Incidence**
↑ **Prevalence** = ↑ PPV and ↓ NPV.
78
**Prevelance Vs. Incidence**
Survival Rate ↑ → Prevalence ↑/↓
**Prevelance Vs. Incidence**
Survival Rate↑ → **Prevalence↑**
79
**Prevelance Vs. Incidence**
Mortality ↑ → Prevalence ↑/↓
**Prevelance Vs. Incidence**
Mortality↑ → **Prevalence**↓
80
**Prevelance Vs. Incidence**
Recovery Time ↑ → Prevalence ↑/↓
**Prevelance Vs. Incidence**
Recovery Time↑ → **Prevalence**↓
81
**Prevelance Vs. Incidence**
Extensive Vaccine Administration ↑ → Prevalence ↑/↓
**Prevelance Vs. Incidence**
Extensive Vaccine Administration ↑→ **Prevalence**↓
82
**Prevelance Vs. Incidence**
Extensive Vaccine Administration ↑ → Incidence ↑/↓
**Prevelance Vs. Incidence**
Extensive Vaccine Administration ↑→ **Incidence**↓
83
**Prevelance Vs. Incidence**
Risk Factor ↓ → Incidence ↑/↓
**Prevelance Vs. Incidence**
Risk Factor ↓ → **Incidence**↓
84
**Prevelance Vs. Incidence**
Risk Factor ↓ → Prevelance ↑/↓
**Prevelance Vs. Incidence**
Risk Factor ↓ → **Prevelance** ↓
85
**Accuracy Vs. Precision**
The consistency and reproducibility of a test.
The absence of random variation in a test.
True for?
**Accuracy Vs. Precision**
**Precision (Relability)**
86
**Accuracy Vs. Precision**
Test Precision (Relability)↑ → Random Error↓/↑
**Accuracy Vs. Precision**
Test Precision (Relability)↑ → **Random Error↓**
87
**Accuracy Vs. Precision**
Test Precision (Relability)↑ → Standard Deviation↓/↑
**Accuracy Vs. Precision**
Test Precision (Relability)↑ → **Standard Deviation↓**
88
**Accuracy Vs. Precision**
Test Precision (Relability)↑ → Statistical Power (1 − β)↓/↑
**Accuracy Vs. Precision**
Test Precision (Relability)↑ → **Statistical Power (1 − β)↑**
89
**Accuracy Vs. Precision**
The closeness of test results to the true values.
The absence of systematic error or bias in a test.
True for?
**Accuracy Vs. Precision**
**Accuracy (Validity)**
90
**Accuracy Vs. Precision**
Test Accuracy (Validity)↑→ Systemic Error↓/↑
**Accuracy Vs. Precision**
Test Accuracy (Validity)↑→ **Systemic Error↓**
91
**Receiving Operating Characteristic Curve**
ROC curve demonstrates how well a diagnostic
test can ________ between 2 groups (eg,
disease vs healthy).
**Receiving Operating Characteristic Curve**
ROC curve demonstrates how well a diagnostic
test can **distinguish** between 2 groups (eg,
disease vs healthy).
92
**Receiving Operating Characteristic Curve**
Plots the \_\_\_\_-positive rate (sensitivity) against the \_\_\_\_-positive rate (1 – specificity).
**Receiving Operating Characteristic Curve**
Plots the **True**-positive rate (sensitivity) against the **False**-positive rate (1 – specificity).
93
**Receiving Operating Characteristic Curve**
The better performing test will have a higher \_\_\_, with the curve closer to the upper left corner.
**Receiving Operating Characteristic Curve**
The better performing test will have a higher **AUC (area under the curve)**, with the curve closer to the upper left corner.
94
**Bias and Study Errors - Recruiting Participants**
Selection Bias - Nonrandom sampling or treatment allocation of subjects such that study population is not representative of _____ population.
Most commonly a sampling bias.
**Bias and Study Errors - Recruiting Participants**
Selection bias - Nonrandom sampling or treatment allocation of subjects such that study population is not representative of **target** population.
Most commonly a sampling bias.
95
**Bias and Study Errors - Recruiting Participants**
Selection Bias Example : cases and/
or controls selected from **hospitals** are **less** healthy and
have different exposures than general population.
This true for ______ bias
**Bias and Study Errors - Recruiting Participants**
Selection Bias Example : cases and/
or controls selected from **hospitals** are **less** healthy and
have different exposures than general population.
This true for **Berkson** bias
96
**Bias and Study Errors - Recruiting Participants**
Selection Bias Example : Participants **lost** to follow up have a **different prognosis** than those who complete the study.
This true for ______ bias
**Bias and Study Errors - Recruiting Participants**
Selection Bias Example : Participants **lost** to follow up have a **different prognosis** than those who complete the study.
This true for **Attrition** bias
97
**Bias and Study Errors - Recruiting Participants**
Selection Bias Reduction is done by : __________ and
Ensuring the choice of the right comparison/reference group
**Bias and Study Errors - Recruiting Participants**
Selection Bias Reduction is done by : **Randomization** and
Ensuring the choice of the right comparison/reference group
98
**Bias and Study Errors - Performing study**
\_\_\_\_\_ bias is when Awareness of disorder alters
recall by subjects; common in retrospective studies.
**Bias and Study Errors - Performing study**
**Recall** bias is when Awareness of disorder alters
recall by subjects; common in retrospective studies.
99
**Bias and Study Errors - Performing study**
\_\_\_\_\_ bias is when Awareness of disorder alters
recall by subjects; common in retrospective studies. For example Patients with disease recall exposure after learning of similar cases.
**Bias and Study Errors - Performing study**
**Recall** bias is when Awareness of disorder alters
recall by subjects; common in retrospective studies. For example Patients with disease recall exposure after learning of similar cases.
100
**Bias and Study Errors - Performing study**
Recall bias Reduction is done by ___ time from exposure to follow-up
**Bias and Study Errors - Performing study**
Recall bias Reduction is done by **Decreasing** time from exposure to follow-up
101
**Bias and Study Errors - Performing study**
\_\_\_\_\_\_\_\_ effect—participants change behavior upon awareness of being observed. This is an exampele for Measurement Bias.
**Bias and Study Errors - Performing study**
**Hawthorne** effect—participants change behavior upon awareness of being observed. This is an exampele for Measurement Bias.
102
**Bias and Study Errors - Performing study**
\_\_\_\_\_\_\_\_ effect—participants change behavior upon awareness of being observed. This is an exampele for Measurement Bias.
**Bias and Study Errors - Performing study**
**Hawthorne** effect—participants change behavior upon awareness of being observed. This is an exampele for Measurement Bias.
103
**Bias and Study Errors - Performing study**
Reduction of Measurement bias is done by Using objective, \_\_\_\_\_\_, and previously tested methods of data collection that are planned ahead of time and Use of \_\_\_\_\_group
**Bias and Study Errors - Performing study**
Reduction of Measurement bias is done by Using objective, **standardized**, and previously tested methods of data collection that are planned ahead of time and Use of **placebo** group
104
**Bias and Study Errors - Performing study**
Procedure bias is when _______ in different groups are
not treated the same. For Example Patients in treatment group spend more time in highly specialized hospital units
**Bias and Study Errors - Performing study**
Procedure bias is when **Subjects** in different groups are
not treated the same. For Example Patients in treatment group spend more time in highly specialized hospital units
105
**Bias and Study Errors - Performing study**
Reuction of Procedure bias is done by \_\_\_\_\_\_\_(masking) and use of ______ reduce influence of participants and
researchers on procedures and interpretation of outcomes as neither are aware of group aassignments
**Bias and Study Errors - Performing study**
Reuction of Procedure bias is done by **Blinding** (masking) and use of **placebo** reduce influence of participants and
researchers on procedures and interpretation of outcomes as neither are aware of group aassignments
106
**Bias and Study Errors - Performing study**
Reuction of Observer-expectancy
bias is done by \_\_\_\_\_\_\_(masking) and use of ______ reduce influence of participants and
researchers on procedures and interpretation of outcomes as neither are aware of group aassignments
**Bias and Study Errors - Performing study**
Reuction of Observer-expectancy
bias is done by **Blinding** (masking) and use of **placebo** reduce influence of participants and
researchers on procedures and interpretation of outcomes as neither are aware of group aassignments
107
**Bias and Study Errors - Performing study**
\_\_\_\_\_\_\_\_\_\_\_\_\_ bias is when Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (aka, Pygmalion effect). For Example An observer expecting treatment group to show signs
of recovery is more likely to document _______ outcomes.
**Bias and Study Errors - Performing study**
**Observer-expectancy bias** is when Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (aka, Pygmalion effect). For Example An observer expecting treatment group to show signs
of recovery is more likely to document **positive** outcomes.
108
**Bias and Study Errors - Interpreting results**
\_\_\_\_\_\_\_ bias is when Factor related to both exposure and outcome (but not on causal path) distorts effect of exposure on outcome (vs effect modification, in which the exposure leads to different outcomes in subgroups stratified by the factor)
**Bias and Study Errors - Interpreting results**
**Confounding** bias is when Factor related to both exposure and outcome (but not on causal path) distorts effect of exposure on outcome (vs effect modification, in which the exposure leads to different outcomes in subgroups stratified by the factor)
109
**Bias and Study Errors - Interpreting results**
An uncontrolled study shows an association between
drinking coffee and lung cancer. However, coffee
drinkers **also smoke more**, which can account for the
association - this is an example for __________ bias.
**Bias and Study Errors - Interpreting results**
An uncontrolled study shows an association between
drinking coffee and lung cancer. However, coffee
drinkers **also smoke more**, which can account for the
association - this is an example for **Confounding** bias.
110
**Bias and Study Errors - Interpreting results**
Multiple/**repeated** studies, **Crossover** studies (subjects act as their own controls), **Matching** (patients with
similar characteristics in both treatment and control groups)these are reduction methods for __________ bias.
**Bias and Study Errors - Interpreting results**
Multiple/**repeated** studies, **Crossover** studies (subjects act as their own controls), **Matching** (patients with
similar characteristics in both treatment and control groups)these are reduction methods for **Confounding** bias.
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**Bias and Study Errors - Interpreting results**
\_\_\_\_-time bias is when Early detection is confused
with elevated survival.
**Bias and Study Errors - Interpreting results**
**Lead-time bias** is when Early detection is confused
with elevated survival.
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**Bias and Study Errors - Interpreting results**
Early detection makes it seem like survival has increased,
but the disease’s natural history has not changed - this is an example for \_\_\_\_\_\_\_\_
**Bias and Study Errors - Interpreting results**
Early detection makes it seem like survival has increased,
but the disease’s natural history has not changed - this is an example for **Lead-time bias**
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**Bias and Study Errors - Interpreting results**
Early detection makes it seem like survival has increased,
but the disease’s natural history has not changed - this is an example for \_\_\_\_\_\_\_\_
**Bias and Study Errors - Interpreting results**
Early detection makes it seem like survival has increased,
but the disease’s natural history has not changed - this is an example for **Lead-time bias**
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**Bias and Study Errors - Interpreting results**
Measure “back-end” survival (adjust survival according to the severity of disease at the time of diagnosis) this is an example for Reduction of a Bias - \_\_\_\_\_\_\_\_
**Bias and Study Errors - Interpreting results**
Measure “back-end” survival (adjust survival according to the severity of disease at the time of diagnosis) this is an example for Reduction of a Bias - **Lead-time bias**
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**Bias and Study Errors - Interpreting results**
\_\_\_\_\_\_\_\_\_ bias is reduced by a randomized controlled trial assigning subjects to the **screening** program or to **no
screening.**
**Bias and Study Errors - Interpreting results**
* *Length-time bias** is reduced by a randomized controlled trial assigning subjects to the **screening** program or to **no
screening. **
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**Bias and Study Errors - Interpreting results**
\_\_\_\_\_\_\_\_\_\_\_\_\_ is when Screening test detects diseases with long **latency** period, while those with shorter latency period become symptomatic earlier. For Example a slowly progressive cancer is **more likely detected** by a screening test than a rapidly progressive cancer.
**Bias and Study Errors - Interpreting results**
**Length-time bias** is when Screening test detects diseases with long **latency** period, while those with shorter latency period become symptomatic earlier**.** For Example a slowly progressive cancer is **more likely detected** by a screening test than a rapidly progressive cancer.
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**Measures of central tendency**
\_\_\_\_ = (sum of values)/(total number of values). Most affected by outliers (extreme values).
**Measures of central tendency**
**Mean** = (sum of values)/(total number of values). Most affected by outliers (extreme values).
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**Measures of central tendency**
\_\_\_\_\_ = middle value of a list of data sorted
from least to greatest. If there is an even number of values, the median will be the average of the middle two values.
**Measures of central tendency**
**Median** = middle value of a list of data sorted
from least to greatest. If there is an even number of values, the median will be the average of the middle two values.
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**Measures of central tendency**
\_\_\_\_\_ = most common value. Least affected by outliers.
**Measures of central tendency**
**Mode** = most common value. Least affected by outliers.
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**Measures of Dispersion**
## Footnote
Standard ______ = how much variability exists in a set of values, around the mean of these values.
**Measures of Dispersion**
**Standard Deviation** = how much variability exists in a set of values, around the mean of these values.
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**Measures of Dispersion**
Standard ____ = an estimate of how much variability exists in a (theoretical) set of sample means around the true population mean.
**Measures of Dispersion**
**Standard Error** = an estimate of how much variability exists in a (theoretical) set of sample means around the true population mean.
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**Statistical distribution**
**Normal** Distribution is a Gaussian Disturbtion where
Mean = ___ = \_\_\_.
( also called bell-shaped)
**Statistical distribution**
* *Normal** Distribution is a Gaussian Disturbtion where
* *Mean = Median = Mode.**
( also called bell-shaped)
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**Non-Normal distributions**
\_\_\_\_\_\_ Distibution Suggests two different populations (eg, metabolic polymorphism such as fast vs
slow acetylators; age at onset of Hodgkin
lymphoma; suicide rate by age).
**Non-Normal distributions**
**Bimodal** Distibution Suggests two different populations (eg, metabolic polymorphism such as fast vs
slow acetylators; age at onset of Hodgkin
lymphoma; suicide rate by age).
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**Non-Normal distributions**
\_\_\_\_\_\_ skew distribution is where:
Typically, mean \> median \> mode.
Asymmetry with longer tail on right.
**Non-Normal distributions**
**Positive skew distribution** is where:
Typically, mean \> median \> mode.
Asymmetry with longer tail on right.
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**Non-Normal distributions**
\_\_\_\_\_\_ skew distribution is where:
Typically, mean \< median \< mode.
Asymmetry with longer tail on left
**Non-Normal distributions**
**Negative skew distribution** is where:
Typically, mean \< median \< mode.
Asymmetry with longer tail on left
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**Statistical hypotheses**
\_\_\_\_\_ is a Hypothesis of no difference or relationship (eg, there is no association between the disease and the
risk factor in the population).
**Statistical hypotheses**
**Null (H0)** is a Hypothesis of no difference or relationship (eg, there is no association between the disease and the
risk factor in the population).
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**Statistical hypotheses**
\_\_\_\_\_\_\_\_\_ is Hypothesis of some difference or relationship (eg, there is some association between the disease and the risk factor in the population).
**Statistical hypotheses**
**Alternative Hypothesis (H1)** is Hypothesis of some difference or relationship (eg, there is some association between the disease and the risk factor in the population).
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**Outcomes of statistical hypothesis testing**
\_\_\_\_\_\_ result can State that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis).
**Outcomes of statistical hypothesis testing**
**Correct** result can State that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis).
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**Outcomes of statistical hypothesis testing**
\_\_\_\_\_\_ result can State that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis).
**Outcomes of statistical hypothesis testing**
**Correct** result can State that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis).
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**Incorrect result**
Type _ error (\_) - Stating that there is an effect or difference when none exists (null hypothesis incorrectly
rejected in favor of alternative hypothesis).
**Incorrect result**
**Type I error (α)** - Stating that there is an effect or difference when none exists (null hypothesis incorrectly
rejected in favor of alternative hypothesis).
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**Incorrect result**
\_ is the probability of making a type I error. p is
judged against a preset _ level of significance
(usually set as 0.05). If p \< 0.05 for a study outcome,
the probability of obtaining that result purely
by chance is \< 5%.
**Incorrect result**
**α** is the probability of making a type I error. p is
judged against a preset **α** level of significance
(usually set as 0.05). If p \< 0.05 for a study outcome,
the probability of obtaining that result purely
by chance is \< 5%.
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**Incorrect result**
Type _ Error - Also called false-positive error.
**α** = you **a**ccused an innocent man.(You can never “prove” the alternate hypothesis, but you can reject the null hypothesis as being very unlikely)
**Incorrect result**
* *Type I Error** - Also called false-positive error.
* *α** = you **a**ccused an innocent man.(You can never “prove” the alternate hypothesis, but you can reject the null hypothesis as being very unlikely)
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**Incorrect result**
Type _ error (\_) - Stating that there is not an effect or difference when one exists (null hypothesis is not rejected when it is in fact false).
**Incorrect result**
**Type II error (β)** - Stating that there is not an effect or difference when one exists (null hypothesis is not rejected when it is in fact false).
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**Incorrect result**
\_ is the probability of making a type II error. _ is related to statistical power (1 – \_), which is the probability of rejecting the null hypothesis when it is false.
**Incorrect result**
**β** is the probability of making a type II error. **β** is related to statistical power (**1 – β**), which is the probability of rejecting the null hypothesis when it is false.
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**Incorrect result**
Type II Error is Also called \_\_\_\_-negative error.
**β** = you **b**lindly let the guilty man go free. If you ↑ sample size, you ↑ power. There is power in numbers.
**Incorrect result**
Type II Error is Also called **False-negative error.**
**β** = you **b**lindly let the guilty man go free. If you ↑ sample size, you ↑ power. There is power in numbers.
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**Statistical Power**
Equals to (1 – β), to ↑ Power and ↓ β:
1) Sample size → ↓/↑
2) Expected Effect size → ↓/↑
3) Precision of Measurement → ↓/↑
**Statistical Power**
Equals to (1 – β), to ↑ Power and ↓ β:
* *1) Sample size ↑
2) Expected Effect size ↑
3) Precision of Measurement ↑**
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**Confidence interval**
CI is a Range of values within which the true \_\_\_\_\_
of the population is expected to fall, with a
\_\_\_\_\_\_\_ probability.
**Confidence interval**
CI is a Range of values within which the true **Mean**
of the population is expected to fall, with a
**Specified** probability.
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**Confidence interval**
CI for sample mean = x¯ ± Z(SE)
The 95% CI (corresponding to α = .05) is often
used. As sample size increases, CI \_\_\_\_\_\_\_.
**Confidence interval**
CI for sample mean = x¯ ± Z(SE)
The 95% CI (corresponding to α = .05) is often
used. As sample size increases, CI **Narrows**.
139
**Confidence interval**
CI for sample mean = x¯ ± Z(SE)
For the 95% CI, Z = \_\_\_\_.
For the 99% CI, Z = \_\_\_\_.
(SE = SD/√n)
**Confidence interval**
CI for sample mean = x¯ ± Z(SE)
For the 95% CI, **Z = 1.96**.
For the 99% CI, **Z = 2.58**.
(SE = SD/√n)
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**Confidence interval**
If the 95% CI for a mean difference between 2
variables includes \_, then there is no significant
difference and H0 is \_\_\_\_.
**Confidence interval**
If the 95% CI for a mean difference between 2
variables includes **0**, then there is no significant
difference and H0 is **not rejected**.
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**Confidence interval**
If the 95% CI for odds ratio or relative risk
includes \_, H0 is \_\_\_\_\_.
**Confidence interval**
If the 95% CI for odds ratio or relative risk
includes **1**, H0 is **not rejected.**
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**Confidence interval**
If the CIs between 2 groups do **not** overlap → statistically significant difference \_\_\_\_\_.
**Confidence interval**
If the CIs between 2 groups do **not** overlap → statistically significant difference **exists**.
143
**Confidence interval**
If the CIs between 2 groups **overlap** → usually significant difference \_\_\_\_\_\_.
**Confidence interval**
If the CIs between 2 groups **overlap** → usually significant difference **doesn't exists**.
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**Meta-Analysis**
A method of statistical analysis that pools summary data (eg, means, RRs) from multiple studies for a more precise estimate of the ____ of an effect. Also estimates ________ of effect sizes between studies.
**Meta-Analysis**
A method of statistical analysis that pools summary data (eg, means, RRs) from multiple studies for a more precise estimate of the **size** of an effect. Also estimates **heterogeneity** of effect sizes between studies.
145
**Meta-Analysis**
Improves power, strength of evidence, and \_\_\_\_\_\_\_\_\_\_\_of study findings. Limited by quality of
individual studies and ____ in study selection.
**Meta-Analysis**
Improves power, strength of evidence, and **generalizability** of study findings. Limited by quality of
individual studies and **bias** in study selection.
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**Common Statistical Tests**
\_\_\_\_\_ - Checks differences between **means** of 2 groups. (Tea is meant for 2). Example: comparing the mean blood pressure between men and women.
**Common Statistical Tests**
**t-test** - Checks differences between **means** of 2 groups. (Tea is meant for 2). Example: comparing the mean blood pressure between men and women.
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**Common Statistical Tests**
\_\_\_\_\_ - Checks differences between **means of 3 or more** groups. (3 words: ANalysis Of VAriance.)
Example: comparing the mean blood pressure
between members of 3 different ethnic groups.
**Common Statistical Tests**
**ANOVA -** Checks differences between **means of 3 or more** groups. (3 words: ANalysis Of VAriance.)
Example: comparing the mean blood pressure
between members of 3 different ethnic groups.
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**Common Statistical Tests**
\_\_\_\_\_\_\_\_\_\_ - Checks differences between **2 or more
percentages or proportions of categorical** outcomes (not mean values). Pronounce Chi-tegorical. Example: comparing the percentage of members of 3 different ethnic groups who have essential hypertension.
**Common Statistical Tests**
**Chi-square (χ²)** Checks differences between **2 or more
percentages or proportions of categorical** outcomes (not mean values). (Pronounce Chi-tegorical). Example: comparing the percentage of members of 3 different ethnic groups who have essential hypertension.
149
**Common Statistical Tests**
\_\_\_\_\_\_\_\_\_\_\_\_- Checks differences between **2 percentages or proportions of categorical, nominal** outcomes. Use instead of chi-square test with small
populations. Example: comparing the percentage of 20 menand 20 women with hypertension.
**Common Statistical Tests**
* *Fisher’s exact test** - Checks differences between **2 percentages or proportions of categorical, nominal** outcomes. Use instead of chi-square test with small
populations. Example: comparing the percentage of 20 menand 20 women with hypertension.
150
**Pearson correlation Coefficient**
r is always between __ and \_\_.
**Pearson correlation Coefficient**
r is always between **−1 and +1**.
151
**Pearson correlation Coefficient**
The closer the absolute value of r is to \_, the stronger the linear correlation between the 2 variables.
**Pearson correlation Coefficient**
The closer the absolute value of r is to **1**, the stronger the linear correlation between the 2 variables.
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**Pearson correlation Coefficient**
\_\_\_\_\_\_\_ is how much the measured values differ from the average value of r in a data set.
**Pearson correlation Coefficient**
**Variance** is how much the measured values differ from the average value of r in a data set.
153
**Pearson correlation Coefficient**
\_\_\_\_\_\_ r value → \_\_\_\_\_correlation (as one variable ↑, the other variable ↑).
**Pearson correlation Coefficient**
**Positive** r value → **positive** correlation (as one variable ↑, the other variable ↑).
154
**Pearson correlation Coefficient**
\_\_\_\_\_\_ r value → \_\_\_\_\_correlation (as one variable ↓, the other variable ↑).
**Pearson correlation Coefficient**
**Negative** r value → **negative** correlation (as one variable ↓, the other variable ↑).
155
**Pearson correlation Coefficient**
Coefficient of determination = __ (amount of variance in one variable that can be explained by variance in another variable).
**Pearson correlation Coefficient**
Coefficient of determination = **r2 **(amount of variance in one variable that can be explained by variance in another variable).
