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CM Pulmonary Boards > Stats > Flashcards

Stats Flashcards

1
Q

How to relate prevalence and incidence

A

Prevalence = incidence x duration

Prevalence- diseased / everyone
Incidence- new cases over certain time period

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2
Q

Formula for incidence vs. prevalence

A

Incidence = new cases over a specific time frame

Prevalence = rate of disease / all population
ex] sick / (sick plus well)

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3
Q

When does mortality start to equal incidence

A

Mortality approaches incidence if high case rate mortality and short duration of illness

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4
Q

Formula for sensitivity

A

Sensitivity= (SnOut)- chance of positive test if you have disease
- probability test will be positive when disease is present

Sensitivity = True Pos / (True pos plus false neg)
SN = a / (a + c)

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5
Q

Formula for specificity

A

Sensitivity = SpOut- chance of not having disease given a negative test
- probability test result negative when disease not present

Specificity = (true neg) / (true neg plus false pos)
SP = d / b + d

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6
Q

How to change the

(a) Sensitivity/specificity
(b) PPV/ NPV

A

(a) Changing the cutoff of positive or negative value
-otherwise is fixed!

(b) While PPV/NPV will change based on prevalence of disease in a population

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7
Q

Formula for positive predictive value

A

PPV = chance disease is present when test is positive

PPV = true pos / (true pos plus false pos)
PPV = a/ a + b

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8
Q

Formula for negative predictive value

A

NPV = chance disease not present when test is negative

NPV = true neg / (true neg plus false neg)

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9
Q

Increased prevalence would increase or decrease

(a) PPV of a test
(b) NPV
(c) Sensitivity
(d) Specificity

A

Increase prevalence (amount of ppl with illness)

(a) Increases positive predictive value = chance of having disease if test is positive
(b) Decreases NPV (chance of not having disease is test is negative
(c, d) does not change sens/spec b/c those are specific to the test

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10
Q

What is a positive likelihood ratio?

(a) Formula with respect to true pos/neg
(b) Formula with respect to Sn/Sp

A

PLR = ratio of positive test result given presence vs. absence of disease

PLR = (true pos rate) / (false pos rate) = sensitivity / (1- specificity)

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11
Q

What is a negative likelihood ratio?

(a) Formula with respect to true pos/neg
(b) Formula with respect to Sn/Sp

A

NLR = chance of negative test with presence vs. absence of disease

NLR = (false neg rate) / (true neg rate) = (1- sensitivity) / specificity

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12
Q

Differentiate lead-time and length-time bias

A

Biases with screening tests

Lead-time bias: survival (time from diagnosis to death) prolonged only because you diagnosed earlier, not b/c you delayed death

-lead time due to earlier Diagnosis, no real delay in survival

Length time bias- overestimation of survival (ppl who survive / ppl with disease) b/c detecting more earlier/slowly progressive cases (increasing the denominator)

-ex: more ppl with breast CA survive if you include DCIS cases that you detect early, but they weren’t going to die from the DCIS anyway

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13
Q

Gold standard for types of trials

(a) Observational
(b) Experimental

A

Gold standard for the two types of trials: experimental when the researcher alters the exposure

(a) Observational- no intervention, just see what happens based on different exposures- cohort study (prospective, take group exposed and those not then look forward to see outcome) preferred over case-control (retrospective, collect outcome and controls then look backwards at who was exposed to risk factor)
(b) Experimental- you change something about their exposure- RCTs (duh)

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14
Q

Differentiate case-control and cohort studies

A

Cohort (gold standard of observational): take ppl with and without exposure and see who gets disease prospectively
ex] take some patients given them statins, others not, follow with time and see who gets ASCVD
ex] fellows who used and didn’t use ultrasound, compare CVL complication rate

Case-control study: see ppl who do and don’t have disease, then look back (retrospectively) and see who was exposed
ex] lung CA and non-lung CA, look back to see who was exposed to agent orange

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15
Q

Compare cross-sectional study to case-control study

A

Cross-sectional: exposure and outcome measured at same time
ex: sample of nonsmoking vets asked if they had exposure to burn pits and if have been diagnosed with lung CA

Case-control: retrospective observational, certain disease (cases) and controls (w/o disease) then look back to see if exposed to risk factor
ex: vets with lung CA, look back to see if exposed to agent orange f

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16
Q

Differentiate relative risk and odds ratio

A

Relative risk- (risk in exposed/treated) / (risk in unexposed/untreated)
-used in cohort study (

ex: RR over 1- higher chance of disease if exposed
ex: RR less than one- lower chance of disease in exposed (ex: rate of ASCVD in ppl who exercise frequently)
- derived from prospective case-control, not from retrospective cohort studies

vs.
odds ratio- odds of disease in exposed vs. unexposed
-used for both cohort and case-control studies

Odds ratio approaches relative risk if the sampled population is representative of the general population

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17
Q

How are the following observational studies’ results reported

(a) Case-control
(b) Cohort study

A

(a) Case control reported in odds ratio
-ratio of odds of disease in exposed group: odds of disease in nonexposed group

(b) Cohort (gold standard) can be reported in either relative risk or odds ratio (RR or OR)

18
Q

Explain relative risk in something protective, ex: relative risk of heart disease in ppl who exercise daily

A

Relative risk (risk in treated / risk in control) under 1 (ex: 0.8) in something protective

19
Q

Describe what the following mean

(a) RR over 1
(b) RR under 1

A

(a) RR over 1: positive association
-risk in exposed greater than risk in nonexposed
ex: lung cancer higher in smokers than nonsmokers

(b) RR under 1: risk in exposed less than nonexposed
ex: ASCVD lower in pts who exercise

20
Q

ex] type II error is 10%

(a) Explain what this means
(b) What is the power of the study
(c) How to increase power

A

ex] type II error (false negative rate) is 10%

(a) means .1 chance of accepting null hypothesis even though null hypothesis is false
-10% chance of not finding an association when one in fact exists
(b) power = 1 - beta = 90% (probability of finding an association if one exists, are rejecting an association when one doesn’t exist = probability of being right!)
(c) increase sample size

21
Q

Difference btwn type I and type II error

A

Type I error (alpha) = false positive rate- reject the null hypothesis when null hypothesis is true
-say there’s an association (reject null hypothesis) when outcome is actually just due to chance, generally ~5% is acceptable

Type II error (beta) = false negative rate- accept the null hypothesis when null hypothesis is false
-say the outcome is due to chance when there actually is an association

22
Q

Efficacy vs. effectiveness

A

Efficacy- benefit under ideal circumstances (ex: in RCT)

Effectiveness- benefit under real life/clinical circumstances

23
Q

Formula for absolute risk reduction

A

Absolute risk reduction = control event rate - experimental event rate

24
Q

Relate absolute risk reduction to number needed to treat

A

NNT is the inverse of the absolute risk reduction

NNT = 1 / ARR

25
Q

Ex: 50% of pts taking ICS alone had exacerbation, while 40% taking ICS/LABA had exacerbation

NNT to prevent an exacerbation?

A

NNT = 1 / ARR

Absolute risk reduction = control event rate - experimental event rate
ex: ARR = .5-.4 = .1

NNT = 1/.1 = 10

26
Q

Describe what the following mean

(a) RR over 1
(b) RR under 1

A

(a) OR over 1: exposure or disease positively related with outcome
ex: exposure and disease are positively related

(b) RR over 1: positive association, risk in exposed is greater than risk in nonexposed
ex: smoke

27
Q

Compare cross-sectional study to case-control study

A

Cross-sectional: exposure and outcome measured at same time
ex: sample of nonsmoking vets asked if they had exposure to burn pits and if have been diagnosed with lung CA

Case-control: retrospective observational, certain disease (cases) and controls (w/o disease) then look back to see if exposed to risk factor
ex: vets with lung CA, look back to see if exposed to agent orange f

28
Q

Confidence interval better at guessing for heterogeneous or homogeneous population

A

More heterogeneous the population- less likely that the sampled population represents the entire population appropriately

=> sampling bias produces a more flawed confidence interval in a heterogeneous population

29
Q

What does the confidence interval tell you?

A

95% sure that the true value of the entire population falls within the interval
-using the study sample to make an inference
about the entire population
-larger study sample more representative of the entire population => can narrow the confidence interval

30
Q

ARDSNet NEJM 2000 mortality for low TV 30% vs. high TV 40%

(a) What is the relative risk reduction?
(b) What is the absolute risk reduction?

A

(a) Relative risk reduction: (40-30) / 40 = 25%
-proportional reduction in rates of bad events in experimental vs. control group

(b) 40-30 = 10% absolute risk reduction
-absolute difference in event rates

31
Q

Differentiate relative and absolute risk reduction

(a) Which typically makes a drug effect sound better

A

Relative- proportional reduction in rates of bad events vs. absolute arithmetic difference

(a) Relative risk reduction typically larger number and makes reduction seem more impressive

32
Q

Calculate the number needed to harm with thrombolytics if chance of major bleed is 9% w/ thrombolytics vs. 3% without

A

NNT = (1 - ARR)
while NNH = (1 - absolute harm added)

ex: risk of major bleed is 6% higher with thrombolytics, so 1 / 0.06 = 16
so need to give 16 ppl thrombolytics to cause one major bleed

33
Q

Which statistical variable describes:

(a) Chance of having the disease if you have a positive test result
(b) Chance test comes positive if have the disease
(c) Ratio probability of positive test if have disease vs. positive test if don’t have disease

A

(a) Positive predictive value = chance of having the disease if there is a positive test result
(b) Sensitivity = chance of positive test if disease is present
(c) Positive likelihood ratio = probability of positive test with disease vs. probability of positive test without disease

34
Q

Differentiate PLR and NLR

A

PLR- chance of positive test with disease / chance of positive test without disease
-increases with increased prevalence of disease

NLR = chance of negative test with disease / chance of negative test without disease
-decreases with increase in prevalence of disease

35
Q

Clinically how do we use likelihood ratios?

A

Use likelihood ratio to guide our pre and post-test probability of disease

+LR: increase between pre and post-test probability (aka more likely pt has disease if est is positive)

36
Q

4x4 table

(a) Formula for sensitivity
(b) Formula for PPV

A

(a) Sensitivity = chance pt has a positive test if disease is present
= a / a + c

(b) PPV- given a positive result, how likely disease is present
= a / a + b

37
Q

4x4 table

(a) Formula for specificity
(b) Formula for negative predictive value

A

(a) Specificity- chance test comes back negative if disease absent
= d / b + d

(a) NPV- how likely disease absent if test negative
= d / c + d

38
Q

Differentiate null and alternative hypothesis

A

H0 (null hypothesis) = sample observation is due purely to chance

Ha (alternate hypothesis) = observation due to a non-random cause (aka some correlation)

39
Q

Differentiate power and confidence level

(a) Rejects the null hypothesis when it’s false
(b) Accepts the null hypothesis when it’s true

A

(a) Power = rejects null hypothesis when it’s false- study says this is not due to chance when there is in fact an association

(b) Confidence level = accept null hypothesis (that sample observation results purely from chance) when null hypothesis is true (no association exists)

40
Q

Differentiate type I and II error

(a) Rejects null hypothesis when it’s true
(b) Accepts the null hypothesis when its false

A

(a) Type I error- rejects null hypothesis when it’s true
think there’s a correlation when it’s actually just due to chance

(b) Type II error- accepts null hypothesis when it’s false
think it’s just chance when there is actually an association

41
Q

Mounier-Kuhn syndrome

A

Congenital form of tracheobronchomegaly- enlargement of central airways (trachea and main bronchi)
can allow for airway diverticula => retained secretions => bronchiectasis

CM Pulmonary Boards (11 decks)

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