Biostats HY Flashcards by Stephanie Escobar

Give an intervention to one group and give placebo to other group then compare /record outcomes

Random Controlled Clinical Trial
(RCT)

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Compare group of ppl with an uncommon dz (or characteristic) and a group of ppl w/o the dz and look back in time for exposures

Case-Control
(calculate odds Ratio)

Cohort is opposite it looks at exposure first then future dz.
Case control looks dz first then back in time for exposure!

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prominent issue with Case Control Studies?

Recall Bias

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To do a study on a rare phenomena (or disease)
_____ studies are typically the best option on NBME exams.

case-control study

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Which study looks at 2 groups; one with a risk factor/exposure and one w/o risk factor and then follow into future to see if they develop a particular outcome (disease/adverse effect)

Cohort studies

(calculate relative risk)

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Lower P value (<0.05) = higher (2)

confidence & power
(that results are not by chance)

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which P-value is better?

P<0.05 or <0.01?

P<0.01

means 1% chance that results were due to chance

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__% of population with normal distribution should fall within 2 Standard deviations below & above of the mean (average)

95%
Example: SD is 100 and mean is 1000.
2SD below mean = 800
2SD above mean = 1200
95% of population falls within 800-1200
5% must fall outside this range
2.5% less than 800
2.5% higher than 1200

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95% of population with normal distribution should fall within __ Standard deviations below & above of the mean (average)

2
Example: SD is 100 and mean is 1000.
2SD below mean = 800
2SD above mean = 1200
95% of population falls within 800-1200
5% must fall outside this range
2.5% less than 800
2.5% higher than 1200

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__% of population falls outside 2 Standard deviations below & above of the mean (average)
___% falls above 2 SD of average
___% falls below 2 SD of average

5%
2.5% above 2SD of mean
2,5% below 2SD of mean

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Whenever you have 2 confidence intervals overlap in value (or cross each other) that means results are

not significant

(no difference in effectivity between those two things)

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In Ratio derived confidence intervals (Relative risk, Odds ratio) if the confidence interval includes (crosses) the number ___ = not significant

(can get ONE, if you divide two of the SAME number)

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In Difference derived confidence intervals (Average/ percents/proportions, RRR, Attributable Risk, ARR) if the confidence interval includes (crosses) the number ___ = not significant

(can get zero, if you subtract two numbers that are the SAME)

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3 Rules for figuring out what a value’s Confidence interval is.
Example: Confidence interval of a Relative Risk of 3.5

Is it a ratio or a difference?
Relative risk is a ratio so CI can’t include #1 (eliminate those ans)
ARR is a difference so CI can’t include #0
Value cannot start or end the CI
(ex: confidence interval can’t start or end with 3.5)
Value must fall within the CI range of numbers & be nearest the center of the range.
(eliminate all ans that do not include 3.5 within the range)

CI must include the value (ex: 3.5) at the center within the range of numbers, but the value must not start or end the interval and the interval can’t include the number 1 or 0

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Calculate Number Needed to Treat & ARR

ARR = (% of pts who died getting DRUG) minus (% of pts who died getting PLACEBO)
──

NNT= 1 ÷ ARR

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Calculate Number Needed to Harm

1 ÷ (% of pts harmed by Placebo) minus (% of pts harmed by Drug)

NNH= 1 ÷ AR

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Calculate Relative Risk

& Relative Risk Reduction (Decreased Relative Risk)

Relative Risk
(% exposed/intervention + dz) ÷ (% unexposed/control + dz)

(ex: 20% of smokers got Lung cancer/ 10% nonsmoker got lung cancer = 2 → aka smoking increases risk of lung cancer 2-fold)
─
RR = rate of outcome in exposed/ rate of outcome of control
RRR= (1– RR)

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What is the Positive & Negative Likelihood ratio formula?

Positive= (Sensitivity/1– Specificity)
Negative= (1– Sensitivity/Specificity)

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When to use the positive or negative likelihood ratio on exam to calculate correct answer?

+ve LRs tell you how much more likely a phenomenon is when you have a +ve test result.

-ve LRs tell you how much less likely a phenomenon is when you have a -ve test result.

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Quick way to calculate Odds ratio

Odds ratio
(Expected Outcomes )÷ (Odd Outcomes)
─

Expected: (exposed got disease) x (unexposed no disease)
÷
Odd: (exposed no disease) x (unexposed got disease)

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How to Calculate Confidence Interval

90% Z-Score = 1.5
95% Z-Score = 2
99% Z-score = 2.5

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ROC curves (how well a test can distinguish b/w 2 groups)
The best test (highest sensitivity & specificity) lies at the _____ of the graph.

top left corner

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Cohort study, 2 groups of individuals are initially identified as “exposed” or “nonexposed” according to their exposure status to a specific risk factor and then followed into future to assess development of the outcome (incidence of disease).

Case-Control = 1 Uncommon diseases are followed back in time to assess exposure(s)

Cohort = Exposures are followed into future for development of common diseases

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68%, 95%, and 99.7% of a normal population lie b/w __, __, & __ SDs of the mean respectively.

1 (68% → 16%)
2 (95% → 2.5%)
3 (99.7%)

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Both test require measuring a **quantitative** (numerical) outcome Between 2+ **qualitative** (Intervention/Risk Factor) groups compares **means** of 2 groups, ___ test. compares **means** of 3+ groups, ___ test.

T test ANOVA (or F) test Chi test has qualitative terms for both intervention and outcome

When you incorrectly **reject** the null (state there is an effect when there is **not** an effect) = a Type __ error.

Type 1 error (alpha error) (aka **false positive** error)

When you incorrectly **accept** the null (state there is **no** effect when there is an effect) = a Type ___ error.

Type 2 error (beta error) (aka **false negative** error)

Power = ___

1– beta Statistical power is the probability of stating that there is an association & it's actually true. (aka rejecting a **false null** hypothesis)

Narrower CIs tell you study is more ___.

precise However, you should feel a lot **less confident** in the results of the study bc the CIs are too narrow (less room for error).

Ways to Increase the power of a study (HY!)

Studies with larger sample sizes have greater statistical power, consequently a lower probability of a type II error 1. Recruit more people for a study (larger sample size). 2. Have a large difference b/w 2 quantities you’re trying to measure (larger effect size). 3. Increase measurement precision (how consistent values are) 4. lower P values = more power (P<0.01) 5. Increase data for a measured qty cluster around 1 value.

FYI

The fact that something is statistically significant does not mean that it is clinically significant

study compares 2+ treatment on one pt and allows them to serve as **own controls**

Crossover study

This test has 2 groups divided by ≥2 categorical/**qualitative** factors (exposure or intervention) and measure the categorical/**qualitative** outcome observed in each group

Chi squared test Qualitative = characteristic Quantitative = numerical values (Temp, BGL, Percentages)

Mean is the average. Median represents: 1. the ___ # 2. the ___#s Mode represents the # that ____ in the data set **TIP** Arrange data in ascending/descending order before making these determinations.

Middle # (odd # data set) Avg of the 2 middle #s (even # data set) Mode = # that is repeated most

For a normal distribution, _____

mean = median = mode

**HY** bimodal distributions found in (3 illnesses)

Hodgkin’s lymphoma Suicide slow/fast acetylators in metabolism

erroneously thinking that survival has been improved when in fact the “apparent survival improval” arose bc you found disease earlier.

Lead time bias

80% sensitivity = 20% ____ test result

False negative test result (tested negative but have dz)

Screening test: High _____ test if Negative rules **out** dz Confirmatory test: High ____ test if Positive rules **in** dz

High **Sensitivity** test if Negative rules out dz (SN-N-OUT) High **Specificity** test if Positive rules in dz (SP-P-IN)

Of all the population with the disease what % will have a (+) test result = ___

Sensitivity High se**N**sitivity = Low False **N**egative rate Missed ones are False Negative (pt has disease, but result was negative)

Of all the population without the disease, what % have (–) test results = ___

Specificity A highly s**P**ecific test has a low false **P**ositive rate.

90% specificity = 10% ____ test result

False positive test result (tested + but have no disease)

Which of the following points best represents the spot with the **highest positive predictive value (PPV)** (aka % of people with +ve tests who have disease)

C The highest **P**PV region on a graph, corresponds to the region with the highest s**P**ecificity (C)

Sensitivity of a test represents the **(% of pts with disease)** & have _____ PPV of a test represents the **(% of pts with +ve test)** & have _____

(+ve test result) → SN = % of ppl w/dz the test marks positive (disease) → PPV= % of True Positives the test reports

Equation for calculating Sensitivity

Sensitivity = (True Positive Test)/ (TP test + FN test) aka (# of diseased & [+] test ) over (# of diseased regardless of test result)

Equation for calculating Specificity

Specificity = (True Negative test)/ (TN test + FP test) aka (# of healthy & Neg test) over (# of healthy regardless of test result)

Equation for calculating PPV%

PPV= (TP test)/ (TP test + FP test) aka [# of pts w/ (dz & + test)] over [total # of positive test regardless if true or not] NPV= TN/(TN+FN)

Which of the following points best represents the region of the graph with the highest negative predictive value (NPV) (aka % of healthy pts w/ NEG test results)

B The highest NPV region on a graph, corresponds to the region with the highest seNsitivity (B), which corresponds to the region that DOES NOT miss anyone with disease.

Specificity of a test represents the % of people ____ NPV of a test represents the % of people ____

SPECIFICITY: % people (w/o disease) who have (–ve test results) NPV: % people with (-ve test results) who are (w/o disease).

Lowering cutoff value dose what (6)

Lowering cutoff (B → A) ↑ SN & NPV & FP ↓ SP & PPV & FN

Increasing the cutoff value dose what (6)

Increasing cutoff (B → C) ↑ SP & PPV & FN ↓ SN & NPV & FP

As Prevalence goes up, ____ should increase too

PPV

As Prevalence goes up, ____ should decrease

NPV (Inversely related PPV/NPV)

Can Prevalance change Sensitivity or Specificity of a test?

No (but changing cut off values can)

Prevalence vs Incidence

Prevalence counts at all current cases of dz in total population (live longer stay in population longer incr prevalence) Incidence counts all new cases in the total population

Prevalence decreases with (4)

increased mortality faster recovery more vaccine/prevention Lowering risk factors

incidence decreases with (2)

more vaccine/prevention lowering risk factors

**TIP** place Mean, Median, Mode in alphabetical order. This should help you remember that: in a **Negatively** skewed curve (flat portion on the ___): ____ in a **Positively** skewed curve (flat portion on the ___): _____

Flat left → mean < median < mode. Flat Right → mean > median > mode. ─── *notice how arrow head's (<) flat part points in direction of the skew's flat part*

**Case-control** studies can consider only ____ per study but can evaluate exposure to **several risk factors**.

1 outcome (ie, disease)

3 actions used to control for **confounding** variables during the design stage of a study.

Randomization Matching (same # of pts w & wo risk factor) Restriction (participation criteria)

Nonrandom treatment assignment may lead to ___ bias

selection bias

Stratified analysis of the extraneous variable can help distinguish whether that variable is a confounding bias or an effect modifier. It is a confounding bias if ______.

Stratified analysis of both groups yields **similar** RR (relative risk) no significant difference If RR between 2 groups are significantly different → Effect Modifier

Because cohort studies measure **incidence** of disease, they provide a measure of

relative risk of disease

A case-control study compares the exposure status of people with & w/o a disease (ie, cases), they provide a measure of

odds ratio

Types of studies: A) 2 groups: Disease & no disease B) 2 groups: Exposed & not Exposed C) 2 groups: All subjects have disease or don't have dz (risk factor and outcome are measured simultaneously)

A) Case control B) Cohort C) Cross Sectional

By raising the cutoff value, it is harder to get a ___ test result and easier to get a ___ test result.

Harder: positive test result ( ↓ False Positives) Easier: negative test result ( ↑ False Negatives)

Equation for Accuracy (probability that an individual will be correctly classified by a test)

(True positives + True negatives) / Total number of individuals tested

**probability** of *having** the disease if pt gets *negative* test results. vs *not having* the disease if pt gets *positive* test results

100 – NPV 100- PPV

Increasing the CI from 95% to 99% does what to the range of numbers?

Makes the range larger/wider Mean 7 → **95%** CI= 4–10 Mean 7 → **99%** CI= 2– 12

P value of <0.05 in words mean

**If** there is **no real difference** between 2 groups, there is a 5% chance of finding a difference

The probability/likelihood that a patient with a **negative test** result **truly does not** have the disease.

Negative predictive value (NPV)

ANOVA test If p-value is **greater than** alpha (α) the results are ____.

Not significant (no difference/are similar) if p-value **less than** α → significant (difference exists)

correlation coefficient (r) r < 0 = r > 0 =

(NEG correlation #) as one variable increases, the other variable decreases (POS correlation #) both variables increase (or decrease) together.

Increasing power (guess before flipping card)

In a NORMAL DISTRIBUTION Pt's above/below the mean by 1SD → is within the __% of the distribution. 2 SD → is within the __% of the distribution. 3 SD → is within the __% of the distribution.

68% 95% 99.5%

The **narrower** the confidence interval range is, the more ____ the test/results are.

precise (FYI: Increasing the sample size increases the precision of the study, but does not affect accuracy.)

The best diagnostic accuracy represents the

best compromise between highest sensitivity & specificity (aka top left corner of ROC curve)

Odds Ratio cannot establish

risk (increase nor decrease)

Calculate Absolute risk reduction (ARR) & Attributable Risk (AR)

ARR = (Intervention % outcome) **–** (Control % outcome) AR = (Control % outcome) **–** (Intervention % outcome)

A ___ study design is best for determining the **incidence** of a disease. What about prevalance?

cohort Prevalence = Cross-Sectional

A ___ study is best for determining **odds** of developing a disease.

Case-control

The typical example of lead-time bias is prolongation of apparent survival in patients to whom a test is applied, without changing the prognosis of the disease.

FYI

Biostats HY Flashcards

(82 cards)