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Step 3 > Biostats > Flashcards

Biostats Flashcards

1
Q

How to interpret AUC

A

Larger AUC= better diagnostic accuracy

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2
Q

What does AUC of 1.0 mea?

A

100% sensitivity and specificity

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3
Q

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

A

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

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4
Q

How to interpret CI of odds ratios

A

If it runs over 1.0, its not significant

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5
Q

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

A

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)

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6
Q

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

A

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”

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7
Q

Define Study power

A

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

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8
Q

2 ways to increase the power of a study

A

Increase the sample size

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

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9
Q

What is the benefit of using Liklihood Ratios?

A

They are completely independent of a diseases prevalence

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10
Q

Susceptibility bias

A

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

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11
Q

What is verification bias and when does it happen?

A

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

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12
Q

Factorial study design

A

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

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

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13
Q

Crossover study

A

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

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14
Q

What is a pragmatic study

A

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

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15
Q

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

A

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

NCB = (greatest possible benefit - greatest possible risk)

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16
Q

What is intention to treat analysis

A

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

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17
Q

What are the 3 pillars of designing a randomized controlled trial

A

Randomization
Blinding
Intention to treat analysis

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18
Q

How does power of a study affect confidence intervals?

A

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)

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19
Q

Positive Likelihood Ratio definition

A

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

20
Q

Negative Likelihood ratio definition

A

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

21
Q

Positive LR equation

A

Sensitivity/ (1-Specificity)

22
Q

NEgative LR equation

A

(1-sensitivity) / Specificity

23
Q

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

A

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

24
Q

Effect Modification

A

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

25
Q

Difference between effect mod and confounding variable

A

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

26
Q

How to identify confounding vs effect mod

A

Stratification

Stratify confounding variable - no change between groups

Stratify effect modifying variable- Significant change in the relationship

27
Q

Ecological Fallacy

A

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

28
Q

What does Kappa represent and how to interpret it

A

measures reliability between multiple observers

k = -1, means total disagreement
k= 1 means total agreement
29
Q

Interpreting Neg Likelihood ratio

A

Range 0 to infinity

If >1, liklihood of disease is increased
If <1, liklihood of disesase is decreased

30
Q

Numerator and denominator for Incidenc

A

new cases / # of people at risk for getting disease

Exclude people who already have the disease or who have died etc.

31
Q

Standardized Mortality Ratio

A

Observed deaths / Expected Deaths

32
Q

Relationship of sample size to standard deviation

A

Inverse

More population = less variance in results

33
Q

Ecological study design

A

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”

34
Q

3 main categories for bias

A

Selection Bias
Measurement Bias
Confounding bias

35
Q

2 subclasses of measurement bias

A

Recall Bias

Observer Bias

36
Q

What does selection bias mean?

A

Your participants are not representative of the study population

37
Q

Berkson bias

A

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

38
Q

Referral Bias

A

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

39
Q

Prevalence (neyman) bias

A

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

40
Q

Susceptiblity Bias

A

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?

41
Q

Observer Bias aka

A

Ascertainment bias

Detection bias

42
Q

If you see a funnel plot, think:

A

Publication bias

43
Q

Hazard ratio >1 vs <1 interpretation

A

> 1 = the exposure has a detrimental effect

<1 means it has a protective effect

44
Q

Relationship between Rate and confidence interval (interpretation)

A

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

45
Q

Multiplicity

A

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

46
Q

Length time bias

A

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

47
Q

Lead time bias

A

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

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