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Biostats > Exam 1 > Flashcards

Exam 1 Flashcards

1
Q

Nominal

A

Qualitative or categorical
Examples: Ethnicity, race and gender

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2
Q

Nominal in medicine

A

categorical variable with only two categories
Example: Outcomes of medical treatment or surgical procedure: succesful/not

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3
Q

Ordinal

A

ranks-order cases in terms of quantity of a particular characteristic.
Examples: 1st, 2nd, 3rd….

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4
Q

Ordinal Scales in Medicine

A

Determines a patients amount of risk or the appropriate type of therapy.
Percentages and proportions are often used

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5
Q

Numerical Scales

A

Continuous-quantitative, interval, dimensional
Examples: height, weight, IQ, Age

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6
Q

Numerical Scales-Discrete

A

A variable in which possible scores have limited values.
Examples: Numbers of brothers, sisters, operations, and bone fractures

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7
Q

Descriptive Statistics

A

Central tendency gives us an idea of the typical value of a distribution
Measures of the middle -center of distribution

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8
Q

Dispersion (or Variabiliity)

A

tells us how different the data is from eachother, how spread out it is

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9
Q

Inferential Statistics

A

tells us if the difference we see is likely caused by chance (if real or not)
if descriptive statistics are probably accurate
if sample represents population

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10
Q

Variance

A

A variable that causes variance: experimentation or observation

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11
Q

Mean

A

a measure of central tendency
Average, Xbar

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12
Q

mode

A

a measure of central tendency.
the value occurs frequently

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13
Q

median

A

a measure of central tendency
the middle observation or the point at which half the observations are smaller and half are larger

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14
Q

A score at 10th percentile is

A

higher than 10% of all scores

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15
Q

A score at 75th percentile is

A

higher than 75% of all scores

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16
Q

The median is the score at

A

the 50th percentile

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17
Q

mean > median

A

positively skewed

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18
Q

mean < median

A

negatively skewed

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19
Q

Standard Deviation

A

used to measure dispersion with medical and health data
average of the spread of the observations around the mean
average distance of scores from their own mean

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20
Q

Variance (mean square)

A

the mean of the squared deviations

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21
Q

Correlation Coefficient

A

How strong is the relationship between two continuous variables?

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22
Q

if slope of SD line is positive

A

R is positive

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23
Q

Postive Correlation

A

always lie between 0 and 1
if r is close to 1=strong relationship

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24
Q

Negative correlation

A

Always lie between -1 and 1=if SD line goes down and r<0
if r=-1, perfect negative relationship

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25
Q

Randomized Control Trials

A

measures primary outcome of randomly assigned participants
participants have an equal likelihood of being assigned to intervention
strongest study desgin
required for FDA and NDA

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26
Q

In RCT

A

blinding is important

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27
Q

Validitty

A

the extent to which an instrument measures what it is intended to measure
degree to which findings are correct

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28
Q

Internal Validity

A

The outcome of interest (dependent variable) caused by the treatment (independent variable)

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29
Q

How to strengthen internal validity?

A

include a control group
random assignment-equally distributed across groups

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30
Q

why is internal validity important?

A

establishes cause and effect relationship
strong evidence of causality
low degrees of internal validity=little or no evidence of causality

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31
Q

threats to internal validity

A

RAMSHIT
regression, attrition, maturation, selection, history, instrumentation, testing

32
Q

Selection Bias

A

systematic error in the estimate of the effect due to procedures used to select subjects or factors that influence study participation

33
Q

differences in patients baseline characteristics in two groups can lead to

A

selection bias

34
Q

Suppose that a weight loss drug is given to individuals who volunteered to be part of a weight loss program and that the comparison condition includes only individuals who were not volunteered in the weight loss program.
* What differences related to selection would you expect between the two groups?

A

The volunteering individuals might have more motivation to eat healthier, exercise more often, or otherwise differ from non-volunteers in ways that might affect their weight loss achievement. So individuals who were given the drug that volunteered might have lost more weight, even without the new drug because of their motivation.

35
Q

history

A

changes in the outcomes of a study due to the occurrence of external events during the course of the study

36
Q

Maturation

A

normal changes in study participants over time

37
Q

Attrition/Experimental Mortality

A

caused by differential drop out of patients in treatment and control groups in RCT

38
Q

Testing

A

changes in outcomes due to repeated prior assesments

39
Q

instrumentation

A

changes in the outcomes due to instrumentation or technique used to measure the outcome

40
Q

Regression

A

shift in the initial extreme measures towards the mean or average in subsequent measures due to statistical variability
extreme groups

41
Q

External Validity

A

the extent which the results of a study can be generalized to other populations or settings

42
Q

threats to external validity

A

treatment interaction with subject selection, settings of the study, historical factors

43
Q

External Validity examples:

A

interactions with treatment and:
subject selection
pre-testing–leads to it cannot be generalized
settings
setting-could have low external validity
study in past may not apply to future

44
Q

Hawthorn Effect

A

modifications in a study subject’s behavior because of the fact that she is being studied or observed

45
Q

Multiple treatments

A

multiple treatments can have a significant effect on the results

46
Q

Bias

A

IPADS
investigator, performance, attrition, detection, selection

47
Q

What is Bias?

A

systematic error in study design in the way subjects are selected, measured and analyzed leading to incorrect findings

48
Q

Investigator Bias

A

can minimize by binding
errors in study design, implementation, or analysis
after study-ascertain bias could be present

49
Q

ascertain bias

A

due to differences in assessing or analyzing outcomes by the researcher due to awareness of which participants received the active versus control interventions

50
Q

Performance Bias

A

due to systematic differences in care between treatment groups or in exposure to factors other than the intervention being studied

51
Q

attrition bias

A

can be minimized to intent to treat
dropouts of patients
if the data is analzyed only including smaller groups (excluding the drop-outs)

52
Q

detection bias

A

can be minimized by the use of non-study personnel to assess patient outcomes
overestimation
when the investigator is aware of the study treatment and makes an assessment of the outcome

53
Q

selection bias

A

can be minimized by random assignment
preferential enrollment of specific patients into one treatment group over another

54
Q

randomization

A

assigning patients randomly/by chance
highly effective in reducing biases and confounding factors

55
Q

confounding factors

A

a factor that is associated with both exposure (treatment) and the outcome
influences treatment

56
Q

simple randomization

A

random number generator to allocate participants
leads to unequal sample sizez

57
Q

block randomization

A

process of dividing subjects into a specified number of blocks to be randomized at the beginning of the trial
ensuring groups are equal

58
Q

stratified randomzation

A

ensures balance of participants for predefined strata based on prognostic factors such as disease severity
like age, race, gender, and disease severity differences -in small samples stratification helps more than in large groups

59
Q

single blind

A

only 3 categories of individuals (usually participant) is unaware of the intervention assignment

60
Q

double blind

A

both participants and investigators are unaware of the randomization schedule
for studies involving investigational agents (phase 3 trials), at least 2 DB trials are required for the drug to be approved

61
Q

Triple Blind

A

most objective design-patients and investigators are blinded, as well as the external group of individuals monitoring the study

62
Q

Open Label

A

least objective label-a study that involves unblinded participants, investigators, and assessors. EVERYONE is AWARE

63
Q

statistical power

A

likelihood to detect an affect in a sample, if the effect truly exists in the population
studies typically have 80% power

64
Q

inclusion criteria

A

the characterisitics the invesitgator is most interested in studying

65
Q

exclusion criteria

A

factors that would confound or impair the ability to interpret the study results

66
Q

placebo control

A

group of patients only receive an inert pill that includes all the extraneous conditions except the active ingredients

67
Q

active control

A

known or accepted standard of care in a RCT

68
Q

Historical Control

A

external group who were observed at different times
internal valditiy concerns=need large sample size

69
Q

non-inferiority trials

A

seeks to determine whether a new therapy is no worse than a standard therapy
does not asess if one is better than the other.
they see if they are equivalent

70
Q

parallel study design

A

each subject is randomized to either treatment group or placebo group only
strong design and shorter time period needed but requires a large sample size

71
Q

cross over design

A

subjects receive intervemtions on a specified sequence of events (washout period)
most statistical power with fewer subjects but need to enroll patients with stable disease states

72
Q

factorial randomized trials

A

multiple dose levels and multiple drug regimens

73
Q

cluster randomization

A

selection of a specific group of subjects for randomization such as those enrolled in a clinic or hospital

74
Q

adaptive designs

A

changes the condition of study plan over time based on the results of the preliminary analysis at interim points of time
can reduce number of subjects needed

75
Q

Drug Efficacy Study

A

determines the effects of intervention under tight control
examples: BP, seizures, survival , quality of life (survey-yes/no)

76
Q

Drug effectiveness

A

determines the effects of the intervention under the conditions that the drug is most often used in the clinical setting

Biostats (4 decks)

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