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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
Randomized Control Trials
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
26
In RCT
blinding is important
27
Validitty
the extent to which an instrument measures what it is intended to measure degree to which findings are correct
28
Internal Validity
The outcome of interest (dependent variable) caused by the treatment (independent variable)
29
How to strengthen internal validity?
include a control group random assignment-equally distributed across groups
30
why is internal validity important?
establishes cause and effect relationship strong evidence of causality low degrees of internal validity=little or no evidence of causality
31
threats to internal validity
RAMSHIT regression, attrition, maturation, selection, history, instrumentation, testing
32
Selection Bias
systematic error in the estimate of the effect due to procedures used to select subjects or factors that influence study participation
33
differences in patients baseline characteristics in two groups can lead to
selection bias
34
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?
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
history
changes in the outcomes of a study due to the occurrence of external events during the course of the study
36
Maturation
normal changes in study participants over time
37
Attrition/Experimental Mortality
caused by differential drop out of patients in treatment and control groups in RCT
38
Testing
changes in outcomes due to repeated prior assesments
39
instrumentation
changes in the outcomes due to instrumentation or technique used to measure the outcome
40
Regression
shift in the initial extreme measures towards the mean or average in subsequent measures due to statistical variability extreme groups
41
External Validity
the extent which the results of a study can be generalized to other populations or settings
42
threats to external validity
treatment interaction with subject selection, settings of the study, historical factors
43
External Validity examples:
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
Hawthorn Effect
modifications in a study subject's behavior because of the fact that she is being studied or observed
45
Multiple treatments
multiple treatments can have a significant effect on the results
46
Bias
IPADS investigator, performance, attrition, detection, selection
47
What is Bias?
systematic error in study design in the way subjects are selected, measured and analyzed leading to incorrect findings
48
Investigator Bias
can minimize by binding errors in study design, implementation, or analysis after study-ascertain bias could be present
49
ascertain bias
due to differences in assessing or analyzing outcomes by the researcher due to awareness of which participants received the active versus control interventions
50
Performance Bias
due to systematic differences in care between treatment groups or in exposure to factors other than the intervention being studied
51
attrition bias
can be minimized to intent to treat dropouts of patients if the data is analzyed only including smaller groups (excluding the drop-outs)
52
detection bias
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
selection bias
can be minimized by random assignment preferential enrollment of specific patients into one treatment group over another
54
randomization
assigning patients randomly/by chance highly effective in reducing biases and confounding factors
55
confounding factors
a factor that is associated with both exposure (treatment) and the outcome influences treatment
56
simple randomization
random number generator to allocate participants leads to unequal sample sizez
57
block randomization
process of dividing subjects into a specified number of blocks to be randomized at the beginning of the trial ensuring groups are equal
58
stratified randomzation
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
single blind
only 3 categories of individuals (usually participant) is unaware of the intervention assignment
60
double blind
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
Triple Blind
most objective design-patients and investigators are blinded, as well as the external group of individuals monitoring the study
62
Open Label
least objective label-a study that involves unblinded participants, investigators, and assessors. EVERYONE is AWARE
63
statistical power
likelihood to detect an affect in a sample, if the effect truly exists in the population studies typically have 80% power
64
inclusion criteria
the characterisitics the invesitgator is most interested in studying
65
exclusion criteria
factors that would confound or impair the ability to interpret the study results
66
placebo control
group of patients only receive an inert pill that includes all the extraneous conditions except the active ingredients
67
active control
known or accepted standard of care in a RCT
68
Historical Control
external group who were observed at different times internal valditiy concerns=need large sample size
69
non-inferiority trials
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
parallel study design
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
cross over design
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
factorial randomized trials
multiple dose levels and multiple drug regimens
73
cluster randomization
selection of a specific group of subjects for randomization such as those enrolled in a clinic or hospital
74
adaptive designs
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
Drug Efficacy Study
determines the effects of intervention under tight control examples: BP, seizures, survival , quality of life (survey-yes/no)
76
Drug effectiveness
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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