Biostats

Question 1

How to interpret AUC

Answer 1

Larger AUC= better diagnostic accuracy

Question 2

What does AUC of 1.0 mea?

Answer 2

100% sensitivity and specificity

Question 3

What does odds ratio of >1 vs <1 mean?

Answer 3

OR > 1 means exposure is associated with higher odds of an outcome

Question 4

How to interpret CI of odds ratios

Answer 4

If it runs over 1.0, its not significant

Question 5

What is non-response bias and how does it affect study results?

Answer 5

In questionaires, people who don’t respond may be very different from those that do respond. (i.e. people with more severe disease may not be able to respond to the questionaire, thus skewing your results)

Question 6

How does stratifying help determine if a variable is confounding your results?

Answer 6

If there appears to be a significant difference between 2 groups, if you stratify the groups by one of their common a different variable and there is no signiicant difference, then the variable that you stratified is the confounding variable

“stroke is higher in obese than non obese, but when participants are separated into diabetes vs no diabetes, there isn’t a significant difference in rates of stroke in obese/non obese people”

Question 7

Define Study power

Answer 7

The ability for a study to detect a true difference between two groups

Question 8

2 ways to increase the power of a study

Answer 8

Increase the sample size

Increase the precision of measurement (more likely to detect a true difference)

Question 9

What is the benefit of using Liklihood Ratios?

Answer 9

They are completely independent of a diseases prevalence

Question 10

Susceptibility bias

Answer 10

The test and control groups have different prognosis based on some unforseen variable (i.e. confounding variables)

Question 11

What is verification bias and when does it happen?

Answer 11

Happens in studies that use the gold standard treatment method as the control, and test against a new method. SO, to reduce this, you have to use the gold standard method on a small random group of people from the new group to VERIFY that the new method is better/worse than gold standard

Question 12

Factorial study design

Answer 12

Study with >2 interventions and all the combinations of those interventions

Vit D alone
Calcium alone
Vit + calcium
Neither Vit or Cal

Question 13

Crossover study

Answer 13

Participants are exposed to different treatments as the study progresses…essentially, they CROSSover to a different study arm mid way through to assess how the other intervention works for them

Question 14

What is a pragmatic study

Answer 14

A study that aims to determine if an intervention works under real world conditions

i.e. NOT a randomized controlled trial, because in real life, people aren’t randomized and nothing is controlled

Question 15

What is net clinical benefit, and how do you measure it?

Answer 15

Basically, weighing the risks and benefits of an intervention and determine if there is net benefit

NCB = (greatest possible benefit - greatest possible risk)

Question 16

What is intention to treat analysis

Answer 16

Every participant is analyzed based on the group they were randomized to…regardless of whether they actually adhered to the treatment plan

Question 17

What are the 3 pillars of designing a randomized controlled trial

Answer 17

Randomization
Blinding
Intention to treat analysis

Question 18

How does power of a study affect confidence intervals?

Answer 18

The higher the power, the narrower the confidence interval

So, if a study as a really wide CI, it tells you the power of the study is poor (beause power means the study’s ability to detect a difference between 2 groups. If you have a wide CI, your ability to identify the difference isn’t very good since you have a huge range)

Question 19

Positive Likelihood Ratio definition

Answer 19

How likely is a person WITH disease to test positive, as compared to liklihood of a person without disease testing positive

Question 20

Negative Likelihood ratio definition

Answer 20

How likely is a person WITH disease to test negative, as opposed to likelihood of a person without disease testing negative

Question 21

Positive LR equation

Answer 21

Sensitivity/ (1-Specificity)

Question 22

NEgative LR equation

Answer 22

(1-sensitivity) / Specificity

Question 23

Time to event analysis (when and what its used for)

Answer 23

Commonly used in survival analysis where the outcome is death…it looks at both groups in the study and analyzes how much time occured prior to their death to determine if there was a significant difference

Question 24

Effect Modification

Answer 24

When an extraneous variable changes the strength or direction of of the effect that the Exposure has on the Outcome

Question 25

Difference between effect mod and confounding variable

Answer 25

Effect mod - extraneous variable changes the strength or direction of the effect whereas confounding masks it

Question 26

How to identify confounding vs effect mod

Answer 26

Stratification Stratify confounding variable - no change between groups Stratify effect modifying variable- Significant change in the relationship

Question 27

Ecological Fallacy

Answer 27

A type of bias that occurs when you try and apply population level data to an individual

Question 28

What does Kappa represent and how to interpret it

Answer 28

measures reliability between multiple observers ``` k = -1, means total disagreement k= 1 means total agreement ```

Question 29

Interpreting Neg Likelihood ratio

Answer 29

Range 0 to infinity If >1, liklihood of disease is increased If <1, liklihood of disesase is decreased

Question 30

Numerator and denominator for Incidenc

Answer 30

new cases / # of people at risk for getting disease Exclude people who already have the disease or who have died etc.

Question 31

Standardized Mortality Ratio

Answer 31

Observed deaths / Expected Deaths

Question 32

Relationship of sample size to standard deviation

Answer 32

Inverse More population = less variance in results

Question 33

Ecological study design

Answer 33

Use population level data to make conclusions about the population as a whole "Decrease in cigarette sales in USA is associated with decreaesed incidence of lung cancer"

Question 34

3 main categories for bias

Answer 34

Selection Bias Measurement Bias Confounding bias

Question 35

2 subclasses of measurement bias

Answer 35

Recall Bias | Observer Bias

Question 36

What does selection bias mean?

Answer 36

Your participants are not representative of the study population

Question 37

Berkson bias

Answer 37

Using sick individuals (hospitalized patients), and using the results to apply to the general population

Question 38

Referral Bias

Answer 38

Comparing patients at high specialized hospitals and community hospitals...the onces at specialized centers are likely sicker and have more comorbidities

Question 39

Prevalence (neyman) bias

Answer 39

When incidence of disease is estimated based on prevalence...thus, the selective survival is skewed in favor of the healthier people

Question 40

Susceptiblity Bias

Answer 40

When the intervention chosen for each participant is based on how sick they are. Ex- Sicker people with ACS get meds, while healthier people with ACS get cath. Results show cath is far superior to meds...is it just because the people that got the cath are healther in the first place?

Question 41

Observer Bias aka

Answer 41

Ascertainment bias | Detection bias

Question 42

If you see a funnel plot, think:

Answer 42

Publication bias

Question 43

Hazard ratio >1 vs <1 interpretation

Answer 43

>1 = the exposure has a detrimental effect <1 means it has a protective effect

Question 44

Relationship between Rate and confidence interval (interpretation)

Answer 44

Does not follow same rules as RR, OR etc...even if CI for a rate crosses 1, you can't say it isn't significant

Question 45

Multiplicity

Answer 45

Occurs when you test mulitple secondary end points. By doing this, you increase your risk of having type I error (false positive)

Question 46

Length time bias

Answer 46

Benefits of a screening tool are overestimated because they catch all the slow progressing, mild cases. However, they miss all the rapidly fatal causes of the same disease, so it makes it look like survival is way higher than it actually is The LENGTH of the illness is what causes the issue

Question 47

Lead time bias

Answer 47

Length of survival from time of diagnosis to death is falsely prolonged. Ex- everyone dies in year 10. If you catch the disase in year 2 vs year 7, it will make it look like survival is increased. But, its the same, you just caught it earlier