Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Physicianship > Biostatistics and Research Design > Flashcards

Biostatistics and Research Design Flashcards

1
Q

what test to run?

comparing blood pressure to patients before and after taking their meds

A

paired t-test
DV: BP (continuous)
IV: before/after meals (2 paired observations)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

what test to run?

comparing blood pressure values with patients on vs off their medication

A

2-sample t-test
DV: BP (continuous)
IV: on/off medication (2 samples, binary)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

what test to run?

comparing length of hospital stay to age

A

correlation
DV: length of stay (continuous)
IV: age (continuous)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

what test to run?

comparing length of hospital stay to age and also mobility

A

linear regression
DV: length of stay (continuous)
IV: age (continuous) and mobility (confounding factor)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

what test to run?

comparing mortality of hospital patients by age and also mobility

A

logistic regression
DV: mortality (binary)
IV: age (continuous) and mobility (confounding variable)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

what test to run?

comparing mortality of patients with high vs low blood glucose

A

chi-squared (2x2 table)
DV: mortality (binary)
IV: high vs low glucose (binary)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

what test to run?

comparing patient Hgb A1c levels to one of 3 types of diet

A

ANOVA
DV: Hgb A1c (continuous)
IV: type of diet (more than 2 samples)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

what test to run?
DV: binary
IV: binary

A

chi-squared (2x2 table)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what test to run?
DV: binary
IV: continuous or categorical/binary + confounding variables

A

logistic regression

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

what test to run?
DV: continuous
IV: 2 paired observations

A

paired t-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

What test to run?
DV: continuous
IV: 2 samples (binary)

A

2-sample t-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

what test to run?
DV: continuous
IV: continuous

A

correlation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

what test to run?
DV: continuous
IV: continuous or categorical/binary + confounding variables

A

linear regression

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

what test to run?
DV: continuous
IV: more than 2 samples

A

ANOVA

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

type 1 error

A

false positive - reject the null hypothesis (detect a difference) when the null hypothesis is true

alpha: probability of false positive

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

type 2 error

A

false negative - fail to reject null hypothesis when there is truly a difference

beta: probability of false negative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

how to calculate power?

A

1 - beta
aka
1 - the probability of a false negative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

what is the probability of a true negative in relation to alpha,
and the probability of a true positive in relation to beta?

A

probability of true negative = 1 - alpha (probability of false positive)

probability of true positive = 1 - beta (probability of false negative)

*false positive = type 1 error, false negative = type 2 error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

what does the p value represent?

A

probability of finding an outcome more extreme than your findings (closer to being an outlier, outer edges of bell curve), assuming null hypothesis is true

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

study is “statistically significantly” if p value is below a certain level of ____

A

alpha - probability of false positive

alpha cutoff is usually 0.05

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

what is meant by power? what is statistically significant power? when is it especially important?

A

the probability of rejecting the null hypothesis by obtaining a p value less than 0.05% (alpha)

power should be at least 0.80 (80% chance of rejecting null hypothesis is difference truly exists)

power is very important only if experiment fails to reject null hypothesis (assuming a meaningful difference actually exists) … but an experience that with low power can still be statistically significant if p value is < 0.05

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

give an example of when negative studies might be used (looking for evidence to support null hypothesis)

A

testing side effects of a new drug compared to a conventionally used drug - hoping the new drug will not cause effects more adverse than current drug

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

contrast nominal categorical data to ordinal categorical data

A

nominal data: categories with no hierarchy (ex - ethnicity)

ordinal data: data does not have numeric assignment, but rather falls into specific bucket with some rank or order (ex - level of schooling)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

define:
Bernoulli distribution
log-normal distribution
binomial distribution

A

Bernoulli distribution: proportions expected in binary outcome (think pie chart with 2 options), ex: infected v uninflected

log-normal distribution: continuous data that cannot be negative (think right skewed bell curve), ex: income v age

binomial distribution: counting up multiple binary (Bernoulli) outcomes in discrete observations (think bell curve made of bars), ex: number of positive tests per batch of 100 tests

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

if mean > median, what direction is the data skewed?

A

RIGHT (positive)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

if mean < median, what direction is the data skewed?

A

LEFT (negative - tail towards left)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

what is the golden rule of standard deviation?

A

68 - 95 - 99.7

first SD contains 68% of data
second SD contains 95% of data
third SD contains 99.7% of data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

what does a Z distribution show?

what is the z cutoff for 95% CI?

A

bell curve of standard deviation values - each point on the x-axis is number of SD away from mean

z = 1.96 for cutoff of 95% CI

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

how does sample size and variance affect CI?

A

when n (sample size) increases, CI gets narrower (more confident)

when variance (SD) increases, CI gets wider (less confident)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

when comparing 2 binary variables (2x2 contingency table), when should you use chi-squared statistic vs fisher’s exact test?

A

chi-squared statistic - assumes n is large

fisher’s exact test - any cell <10

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

what is the ordinary lease squares (OLS) in linear regression?

A

computed line (slope) with the least amount of error

when comparing continuous data IV with continuous data DV

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

contrast absolute risk difference with risk ratio (relative risk, rate ratio, RR)

A

absolute risk difference: probably 1 - probability 2

risk ratio: probability 1 / probability 2

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

what does a larger chi-squared (x^2) signify?

A

larger x^2 signifies a larger difference between expected and measured outcomes/values - signifies more error with a no-difference assumption

each x^2 value is associated with p value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

describe the Hawthorne Effect

A

people change their behavior in a study, form of subject bias

minimize via subject blinding (placebo)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

give 4 time points during a RCT that blinding can occur

A
  1. treatment allocation (randomization)
  2. patient blinding (placebo)
  3. clinician blinding (blind to what treatment being provided)
  4. outcome assessor blinding (investigator assessing outcome blinded to which group)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

describe intention to treat vs explanatory/ “as treated” protocol and why this difference is important

A

intention to treat: analysis of outcome according to treatment assigned, regardless of drop out or lack of compliance —> preserves randomization

“as treated”: analysis of outcome according to the treatment actually received, irrespective of what group subjects were originally assigned to

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
37
Q

describe the difference between “as treated” protocol and “per-protocol” in RCT

A

“as treated” protocol would not remove non-compliant subjects
“per-protocol” would remove non-compliant subjects from the results

in both cases, analysis of outcome is according to what treatment was actually received, regardless of original group assignment

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
38
Q 
describe these variations on RCT:
parallel 
stratified 
crossover
cluster
non-inferiority
A

parallel: classic, intervention group v control
stratified: randomization into stratified groups if there is a variable that will likely have a large influence on outcome (such as stage of cancer, wealth, etc)
crossover: subjects undergo intervention, then control after wash-out period (can serve as their own controls - reduces variability, increases power) - doesn’t work when the order matters or intervention has lasting effect
cluster: randomize care systems rather than individual patients (ex - testing 2 different disinfectants in a number of different emergency rooms) - does not work if individual consent is required

non-inferiority: basically testing if new treatment is not excessively less effective than current treatment, if new treatment is more favorable in other ways (cost, convenience, availability, etc)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
39
Q

what is therapeutic equipoise

A

refers to genuine uncertainty about which treatment is better in RCT

ethical concern in RCT

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
40
Q

describe secondary, composite, and surrogate outcomes

A

secondary: outcomes of interest other than primary, ideally also designated a priori, require more stringent p value to be considered significant (otherwise you can find a difference anywhere if you look hard enough)
composite: combining multiple outcomes into one (ex: a OR b OR c - achieving any of these would be considered a primary outcome)
surrogate: some number outcome, often a lab measurement, that doesn’t necessarily speak to patient’s experience or quality of life

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
41
Q

describe internal vs external validity

A

internal validity: how much results reflect reality for patients in study

external validity = generalizability

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
42
Q

describe the features of cohort studies, outcomes, and key strength

A

observational, from exposure to disease
exposures can be beneficial or harmful
can be prospective (“will x exposure causes disease?”) or retrospective (“did x exposure cause disease”?)

outcomes: absolute risk (aka incidence), absolute risk difference (subtraction), and relative risk (risk ratio)

best way to look at prognosis and incidence

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
43
Q

“big data” studies are a subtype of what kind of study?

what are the strengths and weaknesses?

A

subtype of retrospective cohort study

strength: can use very large populations, most practical method for looking for rare side effects/outcomes
weakness: data often gathered for intent other than medicine (billing, etc), important data may not have been collected on outcome date (weakness of all retrospective cohort studies)

44
Q

describe features and outcomes of case-control studies

A

working backwards to explore possible causes of developed disease (“what exposure may have caused x disease?”) - can be hypothesis generating

cases: newly-incident cases of outcome
control: persons without outcome who “had the opportunity” to be exposed

outcomes: odds ratio

45
Q

contrast retrospective cohort study to case control study

A

retrospective cohort - “did x exposure cause disease?”
outcome: absolute risk (incidence), absolute risk difference, relative risk ratio

case control - “what exposure may have caused x disease?”
outcome: odds ratio

46
Q

describe case series study and its strength

A

case-control without the control - may be no way to know who had the opportunity to be exposed

best for describing emergent diseases, rare outcomes, odd exposures

47
Q

describe features of cross-sectional study and its strength

A

snapshot of individuals in particular population at particular time

best way to determine prevalence

48
Q

describe features of ecological study

A

aka correlational study

snapshot of population and/or environment

mostly hypothesis generating

49
Q

what type of study is best for examining prognosis and incidence?

A

cohort study - follow group from exposure to disease

50
Q

what type of study is most practical for looking for rare side effects?

A

“Big Data” studies

51
Q

what type of study is best for describing emergent diseases, rare outcomes, and odd exposures

A

case series - case-control without the control (unable to determine)

52
Q

what type of study is best for determining prevalence?

A

cross-sectional

53
Q

lead-time bias (observational studies)

A

testing increases perceived survival time without affecting the course of the disease

54
Q

ascertainment bias

A

aka measurement bias
the way data is collected is more likely to include some members of a population than others, such as more intense surveillance or screening among exposed individuals than unexposed individuals

55
Q

what type of bias is present here?

you are conducting cross-sectional study of last year’s urology clinic patients to determine the ability of elevated blood PSA to pick out patients with biopsy-proven prostate cancer

A

ascertainment/ measurement bias - people with high PSA are more likely to get the biopsy

56
Q

what type of bias is present here?

you are conducting case-control study of recently-diagnosed liver cancer patients compared to community controls, surveying them regarding if they ever used herbal supplements

A

recall bias - patients with liver cancer are probably thinking about what may have led to developing cancer and might remember their past supplement use more than average control

57
Q

describe modeling studies and the benefit of using modeling studies

A

incorporates all pertinent info of medical decision into computer model that simulates multiple patients

outcomes: overall mortality, cause-specific morbidity or mortality, utility (ex - QALYs or DALYs), cost

helpful for decision analysis of complicated decisions or cost-effective/cost-benefit analysis
(cost effective - compares cost to outcomes,
cost benefit - represents all outcomes in money terms)

58
Q

incidence vs prevalence (in ratio terms)

A

incidence: probability that unaffected people will develop disease during specific time period
—> new cases per time / unaffected at-risk people at beginning of time period

prevalence: proportion of people in population that have a disease at given time
—> affected persons / all persons

59
Q

how do chronic illness, incidence, and migration of people affect prevalence?

A

chronic illness - increases prevalence, even if incidence is small

incidence increase/decrease causes the same in prevalence (in acute illnesses, incidence and prevalence tend to track each other - not the case for chronic illness)

in-migration can increase prevalence, out-migration can decrease it

60
Q

what is the relative risk difference for a RR of 1.62? what about for RR 5?

A

relative risk difference = | RR - 1 |

RR 1.62 = 62% RR increase
RR 5 = 400% RR increase

61
Q

why is absolute risk more important for a patient than relative risk?

A

absolute risks makes risks and benefits sound smaller

relative risks makes risk or benefits sound bigger

62
Q

convert probability of 1%, 25%, and 80% into an odds ratio

A

1% —> 1:99
25% = 1/4 —> 1:3
80% = 4/5 —> 4:1

63
Q

contrast NNT to absolute risk decrease

A

NNT = # people treated / one additional good outcome

absolute risk decrease = additional good outcomes / # of people treated

NNT is the INVERSE of absolute risk decrease
(same goes for NNH and absolute risk increase)

64
Q

what are Bradford Hill’s criteria for causation

A
  1. strength
  2. dose-response
  3. specificity
  4. alternative explanations (have been considered)
  5. temporality (cause —> effect) ***
  6. reversibility
  7. consistency
  8. plausibility/ coherence
  9. analogy
65
Q 
which of the following are descriptive (hypotheses generating) or analytic (hypothesis testing) studies?
case reports
case-control studies
retrospective cohort studies
case series
prospective cohort studies
cross-sectional studies
ecological studies
RCT
meta-analysis
A

descriptive/ hypothesis generating:

  1. case reports
  2. case series
  3. cross-sectional
  4. ecological

analytic/ hypothesis testing:

  1. case control
  2. retrospective and prospective cohort studies
  3. RCt
  4. meta-analysis
66
Q

what is ecologic fallacy

A

ascribing relationships observed for groups (in ecological studies) to individuals members

67
Q

which of these variables is influenced by population or patient:
specificity
pre-test probability
sensitivity

A

pre-test probability: estimated probability of disease before you do the test
nearly synonymous with prevalence

specificity and sensitivity should not vary with population

68
Q

NNT/NNH is inverse of what

A

inverse of ARR (absolute risk ratio)

69
Q

given an absolute risk ratio (ARR) of 25%, what is the NNT?

A

NNT is inverse of ARR

1/0.25 = 4

70
Q

describe predictive value and how it differs from sensitivity

A

predictive value: how likely is it that this test result is correct?

PPV: among everyone with positive test, what percent have disease?
NPV: among everyone with negative test, what percent do not have disease?

sensitivity: # true positives / all with disease

PPV: # true positives / all positives

71
Q

pre-test probability (prevalence) alters predictive value of test -T or F

A

TRUE: prevalence of illness among certain populations differ, if for example, one population is screened more often (ex: blood donors and HIV)

72
Q

how do you express likelihood ratio for a positive and negative test?

A

LR+: likelihood of + test in diseased / likelihood of + test in non-diseased

LR-: likelihood of - test in diseased / likelihood of - test in non-diseased

73
Q

contrast the ratios of LR+, sensitivity, and PPV

A

LR+ = likelihood + test in disease/ likelihood + test in non-diseased

sensitivity = # true positives / everyone with disease

PPV = # true positives / # all positives

74
Q

how does LR+ relate to sensitivity and specificity?

A

LR+ = likelihood of + test in disease / likelihood of + test in non-diseased

this is the same as saying
LR+ = sensitivity / (1 - specificity)

aka LR+ = ratio of true positives out of everyone (sensitivity)
—————————————
1 - [ ratio of true negatives out of everyone (specificity) ]

75
Q

how does LR- relate to sensitivity and specificity

A

LR- = likelihood of - test in disease / likelihood of - test in non-diseased

aka LR- = 1 - sensitivity / specificity

aka LR- = 1 - [ratio of true positives out of everyone]
————————————
ratio of true negatives out of everyone

76
Q

what does a LR of 1 represent?

A

likelihood ratio of 1 means that a test result will be the same whether or not a patient has the disease - so the test is useless

large LR+ and low LR- values are associated with big changes between pre-test and post-test probabilities

77
Q

given a test with a 85% sensitivity and 90% specificity, what is the LR+?

A

LR+ = sensitivity / (1 - specificity)

LR+ = 0.85 / (1 - 0.9)
LR+ = 0.85 / 0.1
LR+ = 8.5
78
Q

what are receiver operating characteristic (ROC) curves used for?

A

portray the trade-off of various test cut-offs and sensitivity/specificity

diagonal line through the middle representes a slope of 0.5, where the test becomes useless

79
Q

name 3 “gold” reference standards that can be used to study characteristics of a new diagnostic test (need something to compare to)

A

another test, taken near-simulteaneously

clinical judgement, simultaneously

clinical outcomes over time, needs follow up

80
Q

define these types of outcome presentation in prognosis:
case-fatality rate
x-year survival
median survival

A

case-fatality rate = #deaths/#cases (usually used for disease that kill quickly)

x-year survival: show as a curve of survival probability spanning years, usually for more long-lasting disease

median survival: time at which half of the cohort has died

81
Q

describe censoring in prognosis studies

A

to compensate for competing mortality (unrelated to illness being studied), censored patients are removed from the denominator of patients at risk for an event - lowers the number of patients but does not affect survival rate

82
Q

describe:
hazard ratios
stratified survival curves
clinical prediction rules for prognosis

A

hazard ratios: similar to relative risk, evaluate one prognosis factor at at time (what’s the slope of survival over time comparing one group to another, such as patients with varying levels of immunoglobulins) - relative risk cares about outcome at the end, hazard risk cares about events along the way to the end outcome

stratified survival curves: survival curves comparing subjects grouped by criteria

clinical prediction rules for prognosis: patients are “scored” by the amount of criteria they meet to put them in a high/medium/low risk group (combining prognostic variables is more predictive than one alone)

83
Q

match:
validity and reliability
precision and accuracy

A 
accuracy = validity
precision = reliability
84
Q

Gaussian distribution

A

aka normal distribution curve

85
Q

why does running more tests on a patient decrease the likelihood that they will all be in the 95% CI range?

A

the chance of one test being in the 95% CI range is 0.95
so the probability of more than one test being in that range would be each of their probabilities multiplied together
each test has a 0.95 probability, so total probability they are all in that range would be 0.95^n where n is number of tests

hence a smaller probability

in other words, with too many tests there is a higher chance you’ll “find” something wrong

86
Q

what are the “gold standard” comparisons for each below?
x-ray
all imaging, blood tests
everything

A

x-ray —> compare to CT/MRI
all imaging, blood tests —> autopsy/ surgical study (biopsy)
everything —> autopsy (“ultimate” gold standard)

87
Q

carcinoma in situ

A

cancer in its “usual” place, not breaking through membrane barriers surrounding primary tumor

88
Q

cancer grade v stage

A 
grade = severity under a microscope 
stage = severity of physical extent (metastasis)
89
Q

cytology vs pathology specimens, and 3 subtypes of each

A

cytology: cells not in environment of origin - exfoliative (cells fall off), brush (cells brushed off), needle aspiration (cells pulled out with syringe)
pathology: cells with some surrounding environment - excisional biopsy (small volume), surgical resection (large volume), autopsy

90
Q

difference between medical autopsy and medical examiner autopsy?

A

medical: hospital or outpatient death, requiring next of kin consent

medical examiner: unexpected/ unusual death, possible legal issues involved (ex- homicide) or public health concern (outbreak), no consent needed

91
Q

what is the “perfect” study for:
intervention/ exposure questions
prognosis questions
diagnostic test characteristics

A

intervention/ exposure —> RCT

prognosis —> cohort

diagnostic test characteristics —> cross-sectional if using simultaneous reference, prospective cohort if using clinical follow-up reference

92
Q

for studies involving unknowns, important data not measured, or systematic differences in people (aka selection bias), what kind of study design is best and why?

A

RCT! because you can control for unknowns, confounding factors, bias, etc

93
Q

when is it important to know the odds ratio?

A

when you don’t have/ know the absolute or relative risk difference

94
Q

why are RCT studies often low-powered?

A

non-adherence to treatment and cross-over

95
Q

you’re an epidemiologist and you notice that in the past 3 months there has been a pattern of patients being treated in central America for a very strange and unique set of symptoms. What type of study should you employ to investigate this further?

A

case series (observational)

hypothesis generating, like case-control but without controls because it is difficult to determine who the controls would be

96
Q

you’re Dr. Fauci and you’ve been tasked with measuring how many people in the US currently have covid (it’s March 21, 2020 - good luck). what kind of study should you employ?

A

cross-sectional: best for establishing prevalence (how many people out of everyone currently have disease)

97
Q

can you look for trends over time with a cross-sectional study?

A

actually, yes, but not with the same group of people. comparing snapshots of different groups of people in different groups of time

98
Q

you’re reading a study showing the prevalence of XYZ disease over time. the data shows in 1995, 0.1% of Americans had XYZ, in 2002, 0.12%, and in 2019, 0.14%. What kind of study is this most likely to be?

A

cross-sectional, comparing snapshots of populations over time to establish trends in prevalence

99
Q

what type of study design is best for examining uncommon outcomes, risky or rare exposures, or outcomes and exposures not likely captured in routine care?

A

case-control: from disease to exposure (“what exposure might have caused x disease?”)

100
Q

what type of study is best for examining diagnostic test characteristics (with a long reference standard), prognosis, and incidence?

A

prospective cohort study

101
Q

you’re conducting a study that involves chart reviews of 500 patients who were known to be significantly exposed to radiation. you’re investigating the health effects of their radiation. What kind of study is this?

A

retrospective cohort study: from exposure to disease (“what disease did x exposure cause?”)

best for diagnosis or prognosis questions based on information usually captured in routine care, risky or rare exposures, long time-frame issues (such as radiation exposure)

102
Q

case control studies and retrospective cohort studies (“chart reviews”) are similarly advantageous for studying risky or rare exposures, but differ in how they are advantageous in another way - what is different about their strengths?

A

case control studies (from disease to exposure - searching for an exposure) are for examining outcomes or exposures not likely captured in routine care
(you’re looking for an unstudied exposure)

retrospective cohort studies (from exposure to disease - searching for a disease) are for examining diagnosis or prognosis questions based on information usually captured in routine care
(you’ll need this information to study the development of the disease)

103
Q

what type of observational study is best for evaluating a test with simultaneous reference?

A

cross-sectional

104
Q

your clinical research team wants to evaluate a new type of blood test for liver failure. every patient that is seen for liver failure is given the new test, and simultaneously the conventional blood test. what kind of test is this?

A

cross-sectional

best [observational study] for evaluating a test with a simultaneous reference

105
Q

what is the best study design for evaluating the efficacy of an intervention

A

RCT!

Physicianship (4 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions