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USMLE Step 1 > Biostats - First Aid > Flashcards

Biostats - First Aid Flashcards

1
Q

Cross Sectional Study

A

Observational.
Collect data from a group of people to assess frequency (f) of disease at a particular time.
It’s a screenshot in time that looks at disease prevalence.
Can show RF assoc. w/ disease but doesn’t establish causality.

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2
Q

Case Control Study

A

Observational. Retrospective.
Compares group of people with disease to group without disease, then looks for prior exposure/RF.

measures odds ratio.

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3
Q

Cohort study

A

Observational.
Prospective: groups with RF/exposure compared to group w/o RF/exposure. Later down the line look for disease vs. no disease development.

Retrospective: looks at exposure vs. no exposure, then looks forward to see who eventually developed the disease vs did not develop the disease.

measures relative risk.

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4
Q

Twin concordance study

A

compares frequency w/which both MZ twins or DZ twins develop same disease.
Measure heritability and influence of environmental factors (nature v. nurture)

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5
Q

Adoption study

A

compares siblings raised by biological vs adoptive parents.

Measure heritability and influence of environmental factors.

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6
Q

Ecologic Study

A

The unit of analysis in ecological studies are populations, not individuals!
Studied using population data - they are useful to generate hypotheses but should not be used to make conclusions regarding individuals w/in these populations (ecological fallacy)

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7
Q

Clinical Trial

A

Experimental Study.
Compares benefits of 2 or more treatments or tx vs. placebo.
Study quality increases if randomized, controlled, and double blinded.
Triple blinding offers additional benfit, in with researchers analyzing data are also unaware.

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8
Q

Phases of Clinical Trials

A

Pre-clinical: tested on animals
Phase 1: Small number of healthy volunteers to test safety, toxicity, PKs, and PDs
Phase 2: small # pts with disease of interest. Tests if drug works - assess efficacy optimal dosing, and AEs.
Phase 3: large # pts randomly assigned to either tx under investigation or best available tx/placebo. Asks if the drug is good or better.
Phase 4: Postmarketing surveillance of pts after tx approved. Asks if the drug can stay on market, bc it can detect rare/long term AEs. Can result in drug withdrawal if harmful.

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9
Q

Sensitivity

A

Given you have disease, what’s the probability that you test positive?
High sensitivity when negative, rules disease out (SNOUT).
Sensitivity is fixed property of a test (stays fixed regardless of prevalence).

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10
Q

Specificity

A

Given you do not have the disease, what’s the probability that you test negative?
High specificity when positive, rules disease in (SPIN).
Specificity is fixed property of a test (stays fixed regardless of prevalence)

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11
Q

Positivity predictive value (PPV)

A

Given a positive test result, what’s the probability that you truly have the disease?
PPV varies directly with prevalence - as the prevalence of disease increases, so does the PPV.

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12
Q

Negative predictive value (NPV)

A

Given a negative test result, what’s the probability that you truly do not have the disease?
NPV varies directly with prevalence - as the prevalence of disease increases, the NPV decreases!

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13
Q

Incidence

A 
# new cases / # people at risk 
Looks new cases during a period of time.
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14
Q

Prevalence

A 
#existing cases / #people at risk
Looks at all current cases.
*For a short duration disease (like flu), prevalence can equal incidence
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15
Q

Odds ratio (OR)

A

Quantifying Risk.
Typically used in case - control studies.
Odds that the group with the disease (cases) was exposed to a RF, divided by odds that group w/o disease (control) was exposed to the same risk factor.

OR = (a/c) / (b/d)

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16
Q

Relative Risk (RR)

A

Quantifying Risk.
Typically used in cohort studies.
Risk of developing disease in the exposed group divided by risk in unexposed group.
Note that if prevalence is low, then OR = RR.

RR = (a/a+b) / (c/c+d)

Ex. If 2% of pts who get flu shot develop the flu vs. 8% of pts who do not get the flu shot develop the flu, then RR = 2/8 = .25
Ex. If 21% smokers develop lung cancer vs. 1% nonsmokers develop lung cancer, then RR = 21/1 = 21.

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17
Q

Attributable Risk (AR)

A

Difference in risk between exposed and unexposed groups, or the proportion of disease occurrence that are attributable to the exposure.

AR = (a/a+b) - (c/c+d)
AR = (RR - 1)/RR

Ex. If 21% smokers get lung cancer, vs. 1% nonsmokers, then 21-1 = 20% lung cancer in smokers is attributable to smoking

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18
Q

Relative Risk Reduction (RRR)

A

Proportion of risk reduction attributable to the intervention compared to control.
Ex. If 2% of pts who get flu shot develop the flu vs. 8% of pts who do not get the flu shot develop the flu, then RR = 2/8 = .25, and RRR = .75

RRR = 1 - RR

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19
Q

Absolute Risk Reduction (ARR)

A

Difference in risk attributable to intervention as compared to control. Ex. if 8% people who get placebo vaccine get flu, vs 2% who get flu vaccine get flu, then ARR = 6%.

ARR = (c/c+d) - (a/a+b)

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20
Q

Number needed to treat (NNT)

A

Number of pts who need to be treated for 1 pt to benefit.

NNT = 1/ARR

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21
Q

Number needed to harm (NNH)

A

Number of pts who need to be exposed to risk factor for 1 pt to be harmed.
NNH = 1/AR

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22
Q

Precision

A

Reliability.
The consistency and reproducibility of a test.
*The absence of random variation in a test.

Note that presence increases as standard deviation decreases, and as statistical power increases.
Random error decreases precision of a test.

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23
Q

Accuracy

A

Validity.
The trueness of test measurements (as compared to the “gold standard”)
The absence of systemic error bias in a test.

Note that systemic error decreases accuracy of a test.

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24
Q

Selection Bias

A

Sampling/Referral bias: Sample doesn’t represent the population.

Susceptibility bias: Intervention based on disease severity.

Attrition bias: Different growth withdrawals - ex. if one intervention gives bad SE, then that group may have greater # participants leave.

Berkson bias: study population selected from hospital is less healthy than general population

Healthy worker effect: study population is healthier than general population.

Non-response bias: participating subjects differ from nonrespondants in meaningful ways.

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25
Recall bias
Inaccurate recall of past exposures by subject. Awareness of d/o alters recall by subjects - this is common in retrospective studies Pts with disease recall exposure after learning of similar cases!
26
Measurement bias
Info is gathered in a way that distorts it - miscalibrated scale consistently overstates weight, etc. Need to use a standardized data collection method.
27
Procedure bias
Subjects in different groups are not treated the same. | Ex. pts in treatment groups may spend more time in highly specialized hospital units
28
Observer expectancy bias/Pygmalion effect
Self fulfilling prophecy. Researcher’s beliefs in efficacy of treatment can be potentially affect the outcome. Or if observer expects treatment group to show signs of recovery, then he is more likely to document those types of outcomes. Ex. teachers told students have higher IQs and expect more from the group, but the kids in “higher IQ” groups performed better probably bc the teachers unconsiously behaved in a way that would facilitate their success.
29
Hawthorne Effect / Observer Effect
Tendency of study subjects to change their behavior as result of their awareness that they are being studied. Affects the validity of the study. Commonly seen in studies concerning behavioral outcomes or outcomes that can be influenced by behavioral changes.
30
Confounding bias
When a factor is related to both the exposure and outcome, but not on the causal pathway...the factor distorts or confuses effect of exposure. Ex. pulmonary disease is more common in coal workers than general population, but people who work in coal mines also smoke more frequently than general population.
31
Lead time bias
Apparent prolongation of survival after applying screening test, but really just detects disease earlier w/o adding to prognosis
32
Effect Modification
When effect of exposure on outcome is modified by another variable. *This is not a bias! It's a nl phenomenon that exists and should be described, not corrected! Ex. smoking status of pt modifies the effect of a new estrogen rs. agonist on DVTs.
33
Qualitative measures
Measures of central tendency: Mean, median, mode | Measures of dispersion: standard deviation
34
Categorical measures
Frequency (n) and percentages
35
Normal distribution
Continuous. Gaussian ie. bell shaped. Mean = median = mode.
36
Student T
similar to normal distribution, but with more extreme values (longer tails)
37
Nonnormal Distributions
``` Bimodal (discrete) Positive skew (continuous) Negative skew (continuous) ```
38
Biomodal
Discrete. Suggests 2 different populations Ex. metabolic polymorphism like fast vs slow acetylator
39
Positive skew
mean > median > mode | Asymmetry with longer tail on the right
40
Negative skew
mean
41
Null (Ho) hypothesis
Hypothesis of no difference or relationship, meaning there is no association between the disease and risk factor in the population. Researchers are trying to disprove this.
42
Alternative (H1) hypothesis
Hypothesis of some difference or relationship, meaning there is some assoc. between disease and RF in population. Researchers trying to prove this.
43
Type 1 error (alpha)
Alpha is the level that we're allowing ourself error. It's the probability of making a type 1 error. Stating that there is an effect or difference when none exists (the null hypo is incorrectly rejected). ie you saw a difference that did not exist!
44
Type 2 error (ß)
ß is the probability of making a type 2 error. The probability of making this error decreases as sample size increases, expected effect size increases, and precision of measurement increases. Stating there is no association when there really is. Null hypothesis not rejected although it really is false.
45
Power (1 - ß)
Is the probability of rejecting the null hypothesis when it truly is false! Power increases as sample size, expected effect size, precision of measurement increase.
46
Correct result
Stating that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis). Stating that there is not an effect or difference when one does not exist (null hypothesis accepted/not rejected)
47
Confidence Interval
Range of values in which a specified probability of the means of repeated samples would be expected to fall. "We feel confident that 95% of the time, the true value of the parameter lies w/in the bounds of the confidence interval" CI = mean +/- Z (SEM) For 95% CI, the Z = 1.96 For 99% CI, the Z = 2.58
48
Standard deviation (SD)
How much variability exists from mean in a set of values. 1 SD from mean: 68% 2 SD from mean: 95% 3 SD from mean: 99.7%
49
Standard error of mean (SEM)
SEM = SD/(square root of n) note that n is the sample size An estimate of how much variability exists between the sample mean and the true population mean.
50
t-test
Checks difference between means of 2 groups
51
ANOVA
Analysis of variance. | Checks differences between means of 3 or more groups
52
Chi-square
Checks differences between 2 or more percentages or proportions of categorical outcomes (not mean value!!) ex. frequency, percentages
53
Pearson Correlation Coefficient (r)
r is always between -1 and +1. The closer the absolute value of r is to 1, the stronger the linear correlation between 2 variables. Positive r value means positive correlation Negative r valvue means negative correlation Usually reported as coefficient of determination (r^2)
54
Primary prevention
Prevent disease from occuring. | Ex. HPV vaccination
55
Secondary Prevention
Screening early for disease | Ex. Pap smear
56
Tertiary Prevention
Treatment to reduce disability from disease | Ex. Chemotherapy
57
Quaternary Prevention
ID pts at risk of unnecessary tx, and protect them from harm of new interventions
58
Medicare
Pts >/= 65 | Pts
59
Medicaid, and 4 parts
Joint federal and state health assistance for people with very low income. 4 Parts: A: Hospital insurance B: Basic medical bills (ex. doctor's fees, diagnostic testing) C: (parts a + b) delivered by approved private companies D: Prescription drugs
60
Core Ethical Principles
Autonomy Beneficence Nonmaleficence Justice
61
Informed Consent
Disclosure (discuss pertinent info w/pt) Understanding (ability to comprehend) Capacity (ability to reason, make decisions. - NOT same as legal determination of competence) Voluntariness (freedom from coercion and manipulation) *Pts must be informed that they can revoke written consent at any time, event orally
62
Exceptions to informed consent
Pt lacks decision making capacity/is legally incompetent Implied consent in an emergency Therapeutic privilege (withhold info when disclosure would severely harm pt or undermine informed decision making capacity) Waiver (pt explicitly waives right to informed consent)
63
Situations in which parental consent is usuallly not required
Sex (contraception, STIs, pregnancy) Drugs (addiction) Rock and roll (emergency / trauma) * Consent for abortion is required in people younger than 18, who are not legally emancipated * Physicians should always encourage healthy minor-guardian communication
64
Advance directives
Oral advance directives Living will (written advance directive) Medical Power of attorney (can be revoked anytime pt wishes - regardless of competence)
65
Priority of surrogate decision maker
spouse > adult children > parents > adult siblings > other relatives
66
General Principles for Exceptions to Confidentiality
- Potential physical harms to self/other is serious and imminent - No alternative means exist to warn or protect those at risk - Physicians can take steps to prevent harm
67
Examples of exceptions to patient confidentiality (many are state specific)
- Reportable disease (STIs, TB, hepatitis, food poisoning) - physicians have duty to warn public officials, who will notify people at risk - Tarasoff decision: physician directly inform and protect potential victim from harm - Child or elder abuse - Impaired automobile drivers (epileptics) - suicidal / homicidal patients
68
Child wishes to know more about illness
Parents of child decide what info is relayed about the illness
69
Apgar score
Assessment of newborn at 1 minute and 5 minutes, via 10 pt scale. Appearance, Pulse, Grimace, Activity, Respiration >/= 7 is good 4-6 is assist and stimulate
70
Low birth weight
71
What things do NOT decrease in elderly
``` Sexual interest (ew) Intelligence ``` I think HR stays the same as well...
72
Changes in the elderly
Sexual: slower erection and longer refractory period in men; vaginal thinning/shortening/drying in females Sleep patterns: decreased REM and slow wave sleep ; sleep onset latency and early waking Decreased vision, hearing, immune response, and bladder control Decreased renal, pulmonary, and GI function Decreased Muscle mass Increased fat Increased suicide rates
73
Common causes death
Congenital malformations Preterm births SIDS
74
Common causes death 1 - 14 yrs
Unintentional injury Cancer Congenital malformations
75
Common causes death 15 - 34 yrs
Unintentional Injury Suicide Homicide
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Common causes death 35 - 44 yrs
Unintentional Injury Cancer Heart disease
77
Common causes death 45 - 64 yrs
Cancer Heart disease Unintentional injury
78
Common causes death 65 + yrs
Heart disease Cancer Chronic respiratory disease
79
What is the p-value if the 95% CI is 1.02 - 1.85?
Note that the CI and p-value are 2 measures for statistical significance that help strengthen RR (since RR by itself doesn't account for possibility that chance alone is responsible for the results). For a result to be statistically significant, its corresponding CI must NOT contain the null value. What 95% CI does not include the null value, then the corresponding p-value will be

USMLE Step 1 (9 decks)

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