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1
Q

Selection bias

A

How were patients selected, are groups similar?

Big with case control, also see with meta analysis

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2
Q

Allocation bias

A

Group assignments, randomization

*groups not representative of population being studied

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3
Q

Misclassification bias

A

Participant placed in the wrong category

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4
Q

Measurement bias

AKA detection bias

A

Data collection issue

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5
Q

Attrition bias

A

Patient drop out

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6
Q

Compliance bias

A

Was compliance assessed/ could compliance effect results

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7
Q

As treated analysis

A

All patients randomized according to therapy they actually received

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8
Q

Per protocol

A

Only pts who followed protocol were analyzed

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9
Q

ITT

A

Analyzed according to intended therapy

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10
Q

Nominal data examples

A

ADR rate Yes/no, gender, race, presence or absence of dx, death, hospitalization

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11
Q

Ordinal examples

A

Likert, NYHA functional class, years of therapy 0-5 5-10 10-20, age <50 50-75 75-100

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12
Q

Continuous data examples

A

Lab values, age, weight, time to event

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13
Q

HR vs RR

A

HR is for survival analysis

HR is weight RR over time

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14
Q

Kaplan meirer

A

How survival analysis (log rank test) is presented - paired with HR

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15
Q

Cox proportional

A

Most common stat test for survival analysis - used for mutivariate analyses

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16
Q

Mx comparisons procedure issue and fix

A

Increased risk type 1 error

Correct: bonferroni, tukeys, scheffe, dunnetts, hochberg

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17
Q

Funnel plot shows what?

A

Publication and selection bias

Want symmetry around the middle (shows no bias)

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18
Q

Cohort v case control

A

Cohort is prospective, case control is retrospective

Cohort: start w/ risk factory

Case: starts w/ cases

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19
Q

Hawthorne effect

A

People modify behavior bc they know they are being observed

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20
Q

Cost of illness

A

Cost of dx for define population

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21
Q

Cost minimization

A

Intervention cost differences b/w similar alternatives

Identifies least costly alternative when consequences are the same

Ex. Losartan vs valsartan

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22
Q

Cost benefit

A

Identifies net cost impact of an interventions

Compares programs or agents with different objectives

Strengths and weakness of interventions (cost of intervention vs cost that we get back via benefit)

Example: building cost $100 to make but will yield $200 in profit ($200-$100)

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23
Q

Cost effectiveness

A

Net cost divided by health outcomes

Ex. Cost per case of dx prevented or per death averted,

Ex. Years if life saved, number symptom free days, BG, BP

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24
Q

Cost utility

A

Sunset of cost effectiveness

When tx affects quality of life QALY

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25
Which type of data should not report means and SDs?
Ordinal
26
Types of quantitative data
Discrete : 1, 2, 3 etc Continuous: -interval: zero is arbitrary (degrees F) -ratio: absolute zero (HR, BP, distance, kelvin)
27
When can you use mean
Continuous and normally distributed NOT ordinal!
28
When to use median?
Ordinal or continuous Good for skewed data
29
When to use SD
Continuous and normally distributed
30
SD meaning
1: 68% 2: 95% 3: 99%
31
Kolmogorov-smirnov
Formal test for normal distribution
32
CI vs p-values *important slide!
CI helps us determine importance of findings! Clinical significance P-value tells us nothing of importance, only of certainty
33
F test
Difference in variance
34
Mantel haenszel
For independent nominal 3+ groups Controls for confounders
35
Pearson v spearman
Pearson: correlation of continuous normally distributed Spearman: correlation of non-normally distributed continuous data or ordinal data
36
r vs r2
R is correlation and r2 is regression -Correlation cannot show causality but regression can -correlation can be negative or positive but regression cannot show direction
37
Log rank test
For survival analysis of two independent groups (presented via Kaplan Meier curve)
38
Cox proportional hazards
Survival analysis for more than 3+ groups or paired groups Or for prediction..?
39
Which type of bias do we see with case control studies?
Recall bias Also selection
40
When can you not assess NNT /NNH?
When results are not significant!
41
Observation or information bias
Incorrect determination of outcomes or exposure Ex. Inaccurate recording of risk factor
42
Recall bias
Recollection of past events. Cases more likely to recall exposure than controls (big for case-control studies)
43
Interview bias
Interviews not conducted in uniform manner
44
Publication bias
Positive results more likely to be published
45
Differential vs non-differential bias
Differential effects one group more Non-diff effects both equally (systematic error)
46
Observational studies
Case control and cohort *shows correlation but not causation
47
Cross sectional
Snapshot in time- prevalence study Not as good as case control or cohort
48
Incidence vs prevalence
Incidence: # of new cases per time period Prevalence: number of cases at a given time
49
Pragmatic trial
More real life conditions- less internal validity
50
ITT, per protocol, as treated
ITT: underestimates benefit. Preferred in superiority trial Per protocol: overestimates benefit (results only for adherent pts- reduced external validity). Preferred in non-inferiority trial As tx: interpret with caution! Destroys randomization for non-adherent patients
51
Systematic review vs meta analysis
Meta analysis uses mathematic analysis
52
SEM calulcario
SD/sqrt of n
53
How to get 95% CI from SEM
SEM x1.96 Add and substract that from the mean
54
When to use fischers exact over chi squared
All values in 2x2 table are at least 5 to use chi squared- otherwise uses Fischers exact
55
Kendal
Correlation of ordinal values
56
Linear vs logistic regression
Linear: continuos outcome Logistic: categorical outcome
57
Why are composite outcomes even used?
Increase power and decrease sample size requirements Note: components should be physiologically linked!!!
58
Effect size (delta) relationship to power
Increased effect size increases power and allows for smaller sample size
59
Another benefit of survival analysis
Loss to follow up- you can still use data up til the loss to follow up
60
Why are systematic reviews highest level of evidence
They reduce bias
61
Confounding by indication
Lipid lowering drug compared to non lipid lowering drug…therefore actually kinda comparing high cholesterol to low cholesterol People who take a drug are inherintly different from those who don’t d/t dx it’s treating, not the drug itself
62
95% CI equation
2x SEM in both directions SEM= SD/sqrt of n
63
Coefficient if variation equation
SD/mean X 100
64
Uspstf
Recommends health screening
65
FEMA
Federal emergency management agency For emergency preparedness
66
Emergency response groups
American Red Cross, national pharmacy response team, disaster medical assistance team
67
Ethnics framework
For cultural competence Explanation, treatment, healers, negotiation, intervention, collaboration, spirituality
68
CLAS
Culturally and linguistically appropriate services
69
When to use mantel-haenszel
It control for confounding- like Ancova
70
Regression- simple vs mx, linear vs logistical
Simple - 1 dependent variable Mx- 2+ dependent variables Linear- continuous Logistic- categorical
71
Measurement of collection for cohort vs case control
Cohort: RR Case control: OR
72
Reporting guidelines for clinical studies
Consort, strobe (observational), prisma (meta analysis) was previously quorum, equator
73
OR
(A/B)/(C/D) Outcome I No outcome Intervention. A. B No intervention C. D
74
RR
(A/A+B)/(C/C+D) Outcome I No outcome Intervention. A. B No intervention C. D
75
ARR
(C/C+D) - (A/A+B) Outcome I No outcome Intervention. A. B No intervention C. D
76
RRR
1-RR
77
NNT/ NNH
1/ARR round down for NNT and up for NNH
78
Cochran Q
Traditional test for heterogeneity P< 0.05= high heterogeneity
79
I^2 (I squared)
Degrees of heterogeneity 0-25% = low 26-50%= moderate >50%= high Don’t want high heterogeneity in a meta analysis
80
Fixed effect vs random effect
Use fixed effect if low heterogeneity and random effect if high heterogeneity
81
Non-inferiority margins questions
Words question: to be NI, CI cannot cross the bound if the NI margin -for Superiority CI can’t contain 0 or 1 (duh) Pictures: -superiority: doesn’t cross middle line -NI: can cross middle line but not the upper or lower limit of the CI -equivalent: upper and low limit are the same as upper upper and low limit of CI
82
Specificity and sensitivity
Specificity: True negative rate, low Number of false positives ***D/B+D*** Sensitivity: True positive rate, low number of false negatives ***A/A+C+++ Truth (+) I Truth (-) Test (+). A. B Test (-). C. D
83
Positive and negative predictive value
PPV: probability that person with positive test has a the condition ***A/A+B*** NPV: probability that person with negative test does not have the condition ***D/C+D*** Truth (+) I Truth (-) Test (+). A. B Test (-). C. D
84
PFS bias types
-measurement bias -assessment time bias- difficult to determine exact progression date
85
Oncology endpoints
ORR (objective response rate): dx activity ***(in metastatic setting)***- does drug effect the dx after pCR (pathological response): determines dx activity ***(in neoadjuvant setting)***- does drug effect dx before RFS (relapse free) or DFS: determination of dx recurrence or death PFS: determination of dx progression ***(in metastatic setting)***- time until progression or death OS- survival
86
Correlation coefficient and coefficient of determination
Correlation coefficient: r (correlation)- strength and direction of relationship Coefficient of determination: r2 (regression)- estimates the variation in the outcome due to the independent variable

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