Ch 12: Biostatistics and Pharmacoeconomics Flashcards
spread
how similar or how varied values are. uses range and std deviation
gaussian distribution
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.
null vs alt hypothesis
null = no difference, trying to reject with statistical significance. alt = difference, trying to prove.
confidence interval
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.
alpha level
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
p value
if less than alpha - reject null. if equal or more than - accept null (failed to reject null, not statistically significant)
type I error
alpha. false pos, when null is rejected in error.
type 2 error
beta. false neg. null is accepted when should have been rejected.
study power
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.
relative risk (RR)
aka risk ratio. %risk tx / %risk control. 1 is no diff. >1 more risk of outcome. <1 less risk of outcome. calculated as decimal.
risk (R)
pts with event (regardless of tx or control) / #total in study
relative risk reduction (RRR)
how much risk is reduced in tx group compared to control group. (%risk control - %risk tx)/%risk control. OR 1-RR. calculated as decimal.
absolute risk reduction (ARR)
%risk control - %risk tx. includes reduction in risk and incidence rate. net effect beyond the effect obtained from placebo.
number needed to treat (or harm)
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.
odds ratio
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.
hazard ratio
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.
discrete data
nominal or ordinal
nominal
categories like male and female
ordinal
has logical order like pain scale or NYHA class, steps on scale are not divisible (unlike ratio and interval data)
continuous data
interval and ratio. both continuously inc by same amt.
interval data
no meaningful zero (zero does not equal none). ex. fahrenheit temp scale, 0 is not “no temp” its cold.
ratio data
has meaningful zero (zero equals none). majority of med studies. ex. HR, 0 is death.
student t tests
significance in studies with continuous data values. commonly if studies have 2 independent sample groups (tx and control)
ANOVA
aka F test. used for 3 or more samples of continuous data. (similar to student t test but more than 2 groups)
chi square test
tests discrete (nominal or ordinal) data for significance. usually for observational studies for 1-2 groups.
independent variable
changed by researcher to see if there is effect on dependent variable
dependent variable
effect. ex. disease progression or a1c
composite endpoint
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.
spearman’s rank-order correlation
aka rho. tests correlatino in ordinal, ranked data.
pearson’s correlation coefficient
aka r. calculated score to indicate strenth and direction of relationship between 2 variables.
regression
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
equivalence trial
demonstrate new tx has similar effect as the old tx. 2 way margin.
non inferiority trials
demonstrate new tx is not much wore than old tx. 1 way margin. more common
sensitivity
true positive. how efficiently test IDs pts with condition. #pos results with condition/#with condition
specificity
true negative. how accurately a test IDs pts without condition. #neg results wihtout condition/#without condition.
forest plot
meta analysis. if 95% CI used, then result is sig if at 0.05 level aka does not cross 0.
case control study
compares pts with disease to those without disease. retrospective. chart review.
cohort study
compares outcomes of group of pts exposed and not exposed to a tx. prospective or retrospective.
cross sectional study
relationship between variables and outcomes (prevalence) at 1 time point in defined population. hypothesis generating.
parallel study
most common RCT. pts are given tx or placebo during entire study.
crossover study
pts are given tx or placebo then switched after a washout period. pts serve as own control.
factorial study
randomizes to more than the usual 2 groups to test a number of interventions
meta analysis
combines results from multiple studies to form greater statistical power than any individual study alone.
systematic review
summary of clinical lit focusing on specific topic or question.
If using conventionally accepted standards, for any given study result you’ve got 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
ECHO model
economic, clinical, humanistic outcomes
economic outcomes
direct, indirect, and intangible costs of drug compared to medical intervention
clinical outcomes
medical events that occur as result of tx or intervention
humanistic outcomes
consequences of disease or tx as reported by the pt or caregiver (qol, satisfaction)
humanistic outcomes
consequences of disease or tx as reported by the pt or caregiver (qol, satisfaction)
incremental cost ratio
(cost2 - cost1)/ (effect2 - effect1)
average cost effectiveness ratio
cost per outcome of 1 tx alt (independent of other alts)
average cost effectiveness ratio
cost per outcome of 1 tx alt (independent of other alts)
cost minimization analysis
limited to comparing alts with demonstrated equiv outcomes (2 diff ACEI ex.). comparing cost of each intervention in $$.
cost benefit analysis
calculating and comparing benefits and costs of intervention in $$. costs and benefits of all kinds are translated into dollars and adjusted for present day. hard to quanitify qol.
cost effectiveness analysis
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)
cost utility analysis
includes qol assoc with morbidity using quality-adjusted life years (QALYs) and disability adjusted life years (DALYs)
