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Step 3 > BIOSTAT & EPIDEMIOLOGY > Flashcards

BIOSTAT & EPIDEMIOLOGY Flashcards

1
Q

What information do you get from an ecologic (correlational) study?

A

Ecologic studies give population-level information, not individual-level information. Applying population-level information to an individual level = ecologic fallacy bias.

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2
Q

Kappa statistic

A

quantitative measure of inter-rater reliability (aka inter-rater concordance). reflect extent to which inter-rater agreement represents an improvement on chance agreement alone. Kappa values range from -1 (perfect disagreement) to +1 ( perfect agreement), with kappa = 0 suggesting agreement due to chance. kappa<0 suggests less than chance agreement, kappa > 0 greater than chance agreement.

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3
Q

Kaplan-Meier Curve

A

depicts probability of survival at various time points during the study. calculated based on proportion of subjects who are alive (“at risk”) at a given time. the event-free survival rates of 2+ groups can be compared. they are statistically different only if p-value is <0.05

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4
Q

what do overlapping stand error of measurement (SEM) bars suggest in general

A

overlapping SEM error bars suggest a non-statistically significant difference .

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5
Q

Case fatality rate

A

proportion of people with a particular condition who end up dying from the condition

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6
Q

Attack rate

A

proportion of people in whom an illness develops out of the total population at risk from the disease

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7
Q

median survival time

A

measure of prognosis when studying big groups. defined as the length of time that it takes for half of the study population to die

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8
Q

Quality adjusted life years & Disability adjusted life years

A

both are burden of disease measures evaluating the impact of specific diagnoses/treatments on individual patients of the economic impact of health interventions on populations . time-trade-off is commonly used to QALY calculations, while years of life lost and years lived with disability are used for DALY calculations . DALYs represent a loss to be minimized and reflect the difference between the current situation and an ideal situation

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9
Q

Meta-analysis

A

conducted by pooling data from several studies to increase statistical power (ability to detect difference in outcome of interest between groups, if such a difference exists). meta-analysis increases sample size and therefore increases power. major disadvantage: concomitant “pooling” of the biases and limitations of individual studies into one analysis

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10
Q

Standardized Mortality Ratio

A

SMR=observed # of deaths divided by expected # of deaths

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11
Q

NNT

A

NNT the number of patients who need to be treated in order to prevent one add’l bad outcome. NNT is the inverse of absolute reduction (ARR). ARR=control group event rate-experimental group event rate. NNT = 1/ARR

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12
Q

Likelihood Ratio

A

-assesses value of diagnostic test.

Positive LR
LR+ =sensitivity/ (1-specificity)
probability of patient with dx testing +/probability of pt without dx testing +

Negative LR
LR- = (1-sensitivity) /specificity
probability of patient with the dx testing neg / probability of patient without the disease testing neg

  • LR range 0 to infinity. LR> 1 suggest disease present. higher the LR more likely dx presence. LR<1 argues against disease. in general smaller LR less likely dx
  • LRs are independent of prevalence
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13
Q

Standardized Incidence Ratio

A

used to determine if the occurrence of cancer in a small population is high or low relative to the an expected value derived from a larger comparison population. SIR=observed cases/expected cases

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14
Q

Standard deviation

A

1 SD: 68%
2SD: 95%
3 SD: 99.7%

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15
Q

Standard Error of the Mean

A
  • SEM is a measure of how tightly grouped a data set is

- smaller the SEM the more precise the data

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16
Q

z-score

A

shows how far above or below a score is compared to the mean

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17
Q

confidence intervals

A
  • indication of precision of data
  • if CI of outcome crosses 1, the results aren’t significant
  • narrow 95% CI is more precise
  • wide CI may be due to insufficient power
  • if 95% CI reflects lack of statistical significance then corresponding p-value >.05; if 95% CI is significant then p < .05
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18
Q

T-score (t-test) & Analysis of Variance (ANOVA)

A

both are used to assess different groups of data between different sets of data that are in more than one group.

  • T-test used for 2 groups of data. t-test answers questions “are means between two groups different?”
  • ANOVA used when there are 2+ groups
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19
Q

chi-square test

A

answers questions “are these groups related (or not)?”

compares multiple groups and indicates whether or not they are statistically significant. used for categorical data.

20
Q

Cohort Study

A
  • observational, prospective; see what happens to groups of patients with certain exposures/underlying illnesses over time. no intervention
  • results are assessed with relative risk calculations
21
Q

Relative Risk

-equation

A

-looks at risk of dx based on previous exposure to potential danger
RR=risk of exposed group / risk of unexposed group

RR = a/(a+b)
———–
c/(c+d)

RR>1 indicates increased risk in the group in the numerator. RR<1 indicates decreased risk in the group p in the numerator. RR=1 (null value) means no difference in risk between the ferrous. CI that excludes null value reflects statistically significant results.

22
Q

Attributable Risk Percentage

A

measures excess risk & estimates proportion of dx among exposed subjects that is attributed to exposure status
ARP = risk in exposed -risk in unexposed)
————————————————–
risk in exposed
= RR-1
——–
RR

23
Q

Population Attributable Risk

A

excess risk in total population
PARP = risk in population - risk in exposed
—————————————————
risk in total population
OR
PARP = (prevalence)(RR-1)
——————————
(prevalence)(RR-1) + 1

24
Q

Case Control

A
  • retrospective study looking for the odds of previous exposure on the development of a rare dx manifestation
  • subject to recall bias which leads to misclassifcation of exposure
  • assessed with odd ratio
25
Q

Odds Ratio

A
  • assesses case control studies
  • starts with those that have disease and looks at the chance of them having had exposure in the past

OR = cases exposed / cases unexposed
———————————————————
controls exposed / controls unexposed

OR = a/c = ad
——- ——
b/d bc

null value for OR = 1 . if CI includes 1 then result isn’t statistically significant

  • OR > 1 exposure is associated with higher odds of the outcome. OR < 1 means that the exposure is associated with lower odds of the outcome.
  • odds that an outcome will occur in the presence of a particular exposure divided by the odds that the outcome will occur in the absence of that exposure
26
Q

Berkson Bias

A

hospitalized patients as trial subjects instead of general population . minimize by randomization

27
Q

Hawthorne Effect

A

those being studied know they are being watched for the effect of a drug or intervention . minimize this by using placebo control and double blinding

28
Q

Lead-Time bias

A

in this bias, early detection is confused with increased survival based on treatment . ex early detection

29
Q

Null Hypothesis & p-value

A

null hypothesis = intervention no better than random chance
reject null hypothesis = intervention works
p

30
Q

post market surveillance

A

if in RCT adverse events occurred and p > 0.05 (insignificant) then need to do post market surveillance for new drug/intervention. may not have been enough power.

31
Q

type 1 error

A
  • false positive result
  • rejecting null hypothesis
  • ex. saying drug works when it really doesn’t
  • called alpha error = p-value
32
Q

type II error

A
  • false negative result
  • called beta error
  • power = 1-beta, power and better inversely related
33
Q

Receiver Operating Curve

A

plot sensitivity as function of 1-specificity at various cut off points for a test. a negative result on highly sensitive test helps r/o disease

34
Q

Sensitivity

A

likelihood that TEST will detect all ppl with dx
a/a+c
TP/TP+FN

35
Q

specificity

A

likelihood that ppl w/o dx are correctly identified as dx negative
=b/b+d
=TN/TN+FP

36
Q

Negative Predictive Value

Positive Predictive Value

A

both change with prevalence. need to know pre-test probability.

Negative Predictive Value:
probability that dx is absent given negative test result
NPV = d/c+d = TN/TN+FN

Positive Predictive Value
PPV = a/a+b= TP/TP+FP

37
Q

Absolute Risk Reduction (ARR)

Number needed to treat (NNT)

A

ARR = % decrease in risk of death or dx from treatment compared with 100% of the people in the population

NTT = 1/ARR

in general, RRR is larger number & often used to exaggerate effectiveness of medication

38
Q

Attributable Risk (AR)/ARI

A

NNH = 1/AR

39
Q

Test for heterogeneity

A
  • useful for meta-analysis or comparing trials
  • commonly used is Q test. no heterogeneity is p >0.05 and the I2 index (25%, 50%, 75% are traditional considered cutoffs for low, moderate, high heterogeneity respectively)
40
Q

Hazard Ratio

A

measure of effect used in survival analysis (time-to-event)
null value HR= 1.00–>no difference
HR< 1.00–>protective
HR > 1 –>detrimental

41
Q 
Prevention Strategies 
primordial
primary 
secondary 
tertiary 
quaternary
A

primordial: prevention of risk factors themselves
primary: action taken before patient develops dx
secondary: axn halts/delays dx progression/complications
tertiary: limit impairments/disabilities from advanced dx
quaternary: lim consq’s of unnecessary/extra intervention

42
Q

Correlation coefficient (r)

A

indicates positive/negative direction of association between 2 variables. closer r is to margins [-1, +1] the stronger the association

43
Q

intention-to-treat analysis

A
  • used in RCTs
  • participants analyzed in groups to which they were randomized, regardless of whether they received/achieved the allocated intervention and regardless of if they withdrew
44
Q

Sensitivity Analyses

A

refers to repeating primary analysis calculations in a study by modifying certain criteria or variable ranges to determine whether such modifications significantly affect the results initially obtained

45
Q

Non-inferiority Trials

A

goal: prove new drug isn’t unacceptably worse than comparator by a given margin

-----------------0------------------------
#1 vertical line at zero: right of 0 = non-inferior &amp; superior 
#2 vertical line for inferiority.  right of line non-inferior, left of line not non-inferior
46
Q

Funnel Plot

A

triangle is centered on a summary treatment effect (pooled estimates of ORs in log) with y-axis being standard error. w/o bias 95% points should be inside triangle. should be symmetric

publication bias and heterogeneity–>asymmetry

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