Ch 12: Biostatistics and Pharmacoeconomics Flashcards

1
Q

spread

A

how similar or how varied values are. uses range and std deviation

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2
Q

gaussian distribution

A

normal dist, bell shaped curve of continuous values. symmetrical with 1/2 values on left and 1/2 on right. mean = median = mode. 68% of values are within 1 SD, 95% are in 2 SDs of mean. otherwise not gaussian –> skewed.

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3
Q

null vs alt hypothesis

A

null = no difference, trying to reject with statistical significance. alt = difference, trying to prove.

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4
Q

confidence interval

A

if narrow, high precision. if wide, low precision. % chance of no error with tx effect (range). if alpha is 0.05, CI is 95%. if alpha is 0.01, CI is 99%. if CI contains 0 - not significant.

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5
Q

alpha level

A

max permissible error margin (% chance of error), usually set at 5% or 0.05 as threshold for rejecting null hypothesis. smaller can be chosen but requires more data, pts, and money or larger tx effect

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6
Q

p value

A

if less than alpha - reject null. if equal or more than - accept null (failed to reject null, not statistically significant)

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7
Q

type I error

A

alpha. false pos, when null is rejected in error.

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8
Q

type 2 error

A

beta. false neg. null is accepted when should have been rejected.

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9
Q

study power

A

probability that a test will reject the null hypothesis correctly, power to avoid a type 2 error. as power inc, type 2 error dec. power = 1 - beta. power determined by number of outcome values, different in outcome rates between groups, and significance level.

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10
Q

relative risk (RR)

A

aka risk ratio. %risk tx / %risk control. 1 is no diff. >1 more risk of outcome. <1 less risk of outcome. calculated as decimal.

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11
Q

risk (R)

A

pts with event (regardless of tx or control) / #total in study

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12
Q

relative risk reduction (RRR)

A

how much risk is reduced in tx group compared to control group. (%risk control - %risk tx)/%risk control. OR 1-RR. calculated as decimal.

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13
Q

absolute risk reduction (ARR)

A

%risk control - %risk tx. includes reduction in risk and incidence rate. net effect beyond the effect obtained from placebo.

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14
Q

number needed to treat (or harm)

A

1/ARR. # patients that need to receive tx for 1 patient to be harmed or receive benefit of tx. always round up to next whole number for NNT and down for NNH.

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15
Q

odds ratio

A

estimate risks with tx in case-controls. calculate odds that outcome will occur with exposure compared to odds without exposure. (see flash where OR = AD/BC). = 1 no diff in events (CI does not cross 0). >1 tx has more events. <1 tx has less events.

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16
Q

hazard ratio

A

survival analysis uses hazard instead of risk - ex. cancer. rate of an unfavorable event occurring within short period of time. ratio between hazard rate tx and hazard rate control. = 1 no diff in events (CI does not cross 0). >1 tx has more events. <1 tx has less events.

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17
Q

discrete data

A

nominal or ordinal

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18
Q

nominal

A

categories like male and female

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19
Q

ordinal

A

has logical order like pain scale or NYHA class, steps on scale are not divisible (unlike ratio and interval data)

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20
Q

continuous data

A

interval and ratio. both continuously inc by same amt.

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21
Q

interval data

A

no meaningful zero (zero does not equal none). ex. fahrenheit temp scale, 0 is not “no temp” its cold.

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22
Q

ratio data

A

has meaningful zero (zero equals none). majority of med studies. ex. HR, 0 is death.

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23
Q

student t tests

A

significance in studies with continuous data values. commonly if studies have 2 independent sample groups (tx and control)

24
Q

ANOVA

A

aka F test. used for 3 or more samples of continuous data. (similar to student t test but more than 2 groups)

25
Q

chi square test

A

tests discrete (nominal or ordinal) data for significance. usually for observational studies for 1-2 groups.

26
Q

independent variable

A

changed by researcher to see if there is effect on dependent variable

27
Q

dependent variable

A

effect. ex. disease progression or a1c

28
Q

composite endpoint

A

combines multiple endpoints into 1 measurement, must be similar in magnitude and have similar/meaningful importance to pt. sum of individual endpoints will not correlate to composite endpoint.

29
Q

spearman’s rank-order correlation

A

aka rho. tests correlatino in ordinal, ranked data.

30
Q

pearson’s correlation coefficient

A

aka r. calculated score to indicate strenth and direction of relationship between 2 variables.

31
Q

regression

A

3 types: linear for continuous data, logistic for categorical data, cox for categorical data in survival analysis. describes the relationship between dependent variable and 1+ independent variables OR how much the dependent variable changes when independent variable/s changes. common in observational studies to control for confounding factors

32
Q

equivalence trial

A

demonstrate new tx has similar effect as the old tx. 2 way margin.

33
Q

non inferiority trials

A

demonstrate new tx is not much wore than old tx. 1 way margin. more common

34
Q

sensitivity

A

true positive. how efficiently test IDs pts with condition. #pos results with condition/#with condition

35
Q

specificity

A

true negative. how accurately a test IDs pts without condition. #neg results wihtout condition/#without condition.

36
Q

forest plot

A

meta analysis. if 95% CI used, then result is sig if at 0.05 level aka does not cross 0.

37
Q

case control study

A

compares pts with disease to those without disease. retrospective. chart review.

38
Q

cohort study

A

compares outcomes of group of pts exposed and not exposed to a tx. prospective or retrospective.

39
Q

cross sectional study

A

relationship between variables and outcomes (prevalence) at 1 time point in defined population. hypothesis generating.

40
Q

parallel study

A

most common RCT. pts are given tx or placebo during entire study.

41
Q

crossover study

A

pts are given tx or placebo then switched after a washout period. pts serve as own control.

42
Q

factorial study

A

randomizes to more than the usual 2 groups to test a number of interventions

43
Q

meta analysis

A

combines results from multiple studies to form greater statistical power than any individual study alone.

44
Q

systematic review

A

summary of clinical lit focusing on specific topic or question.

45
Q

If using conventionally accepted standards, for any given study result you’ve got a…

A
  • 5% chance Type I Error
  • 20% chance Type II Error
  • 5% degree of uncertainty (from the 95% CI) that the study result applies to the entire tx pop
46
Q

ECHO model

A

economic, clinical, humanistic outcomes

47
Q

economic outcomes

A

direct, indirect, and intangible costs of drug compared to medical intervention

48
Q

clinical outcomes

A

medical events that occur as result of tx or intervention

49
Q

humanistic outcomes

A

consequences of disease or tx as reported by the pt or caregiver (qol, satisfaction)

50
Q

humanistic outcomes

A

consequences of disease or tx as reported by the pt or caregiver (qol, satisfaction)

51
Q

incremental cost ratio

A

(cost2 - cost1)/ (effect2 - effect1)

52
Q

average cost effectiveness ratio

A

cost per outcome of 1 tx alt (independent of other alts)

53
Q

average cost effectiveness ratio

A

cost per outcome of 1 tx alt (independent of other alts)

54
Q

cost minimization analysis

A

limited to comparing alts with demonstrated equiv outcomes (2 diff ACEI ex.). comparing cost of each intervention in $$.

55
Q

cost benefit analysis

A

calculating and comparing benefits and costs of intervention in $$. costs and benefits of all kinds are translated into dollars and adjusted for present day. hard to quanitify qol.

56
Q

cost effectiveness analysis

A

outcomes are easier to quantify. most common . inability to directly compare different types of outcomes. outcomes need to be the exact same to be compared (ex. cannot compare asthma exacerbations to BG values)

57
Q

cost utility analysis

A

includes qol assoc with morbidity using quality-adjusted life years (QALYs) and disability adjusted life years (DALYs)