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FND I - Summative III > EBPS > Flashcards

EBPS Flashcards

(104 cards)

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1
Q

p-value

A

determines the strength of evidence against a null hypothesis

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2
Q

null hypothesis (H0)

A

assumption that there is no significant difference, effect, or relationship between two or more groups or variables being studied

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3
Q

Alternative Hypothesis (Ha)

A

It usually suggests the presence of a significant effect, difference, or relationship.

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4
Q

hyperlipidemia

A

elevated levels of lipids in the bloodstream, including cholesterol and triglycerides, which can increase the risk of cardiovascular diseases

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5
Q

Normal blood pressure for adults is

A

120/80 or lower

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6
Q

Retrospective Study

A

looks at past data or events to examine the relationships between variables to draw conclusions about potential associations or outcomes.

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7
Q

Prospective Study

A

gathers data from participants moving forward in time, starting from the present and following them into the future to observe and measure outcomes as they occur, often through the design of cohort studies or clinical trials.

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8
Q

Cohort studies

A

follow a group of individuals, known as a cohort, and track their experiences and health outcomes over an extended period

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9
Q

confidence interval

A

a range of values that is calculated from sample data and is used to estimate the range within which a population parameter, such as a mean or proportion, is likely to fall with a certain level of confidence.

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10
Q

Epidemiology

A
  • the study of the distribution and determinants of disease frequency in human populations
  • the application of this study to control health problems and improve public health
  • understand and to control its causes
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11
Q

Biostatistics

A

concerns with analysis and summarization of raw data in interpretable messages related to human health

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12
Q

Evidence-based medicine (EBM)

A

using the current best evidence in decision making in medicine in conjunction (together) with expertise of the decision-makers and
expectations and values of the patients/people

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13
Q

clinical research

A
  • studying groups of people who are ill
  • studies humans in clinical facilities such as outpatient clinics or inpatient facilities
  • the interventions are often about therapy in sick people
  • experimental design
  • small to moderate size
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14
Q

epidemiological studies

A
  • study people in communities
  • preventive interventions
  • observational studies
  • large sample size
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15
Q

the “Big 6”

A
  • description
  • causation
  • attribution
  • mediation
  • interaction
  • prediction
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16
Q

Description

A

addresses how frequent or common are various risk factors, exposure, conditions, or diseases

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17
Q

Causation

A

addresses establishing causal relationships among biological, behavioral, environmental and other factors within humans.

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18
Q

Attribution

A

addresses what fraction or how many cases of disease Y can be eliminated if a causal exposure X is eliminated or reduced?

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19
Q

Mediation

A
  • addresses the mechanisms of causal relationships
  • Given that X does cause Y, how does X cause Y? What is the mechanism?
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20
Q

Interaction

A
  • addresses when and for whom does X cause/predict Y?
  • closely related to causation
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21
Q

Prediction

A

addresses as to whether some feature A or a combination of features A, B, and C predict the concurrent presence or future occurrence of Y?

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22
Q

How could we determine causes of diseases?

A

Conduct population studies using epidemiological methods

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23
Q

Why should pharmacists care about Epidemiology?

A
  • practice evidenced based medicine (EBM)
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24
Q

randomized controlled trials (RCTs)

A
  • scientific experiments in which participants are randomly assigned to receive different interventions or treatments
  • assess the efficacy and safety of these interventions while minimizing bias
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25
Case-control studies
observational research designs that compare individuals with a specific outcome or condition (cases) to those without it (controls) in order to identify factors associated with the development of that outcome or condition
26
Steps in Practicing EBM
1. identify a good question 2. find relevant literature 3. critically evaluate data 4. synthesize and apply to patients 5. recognize gaps and design solutions
27
Two types of study designs
1. experimental 2. observational
28
Experimental study can be categorized as | two
1. randomized control trial (RCT) 2. non-randomized control trial
29
observational studies can be grouped as | two
1. analytical 2. descriptive
30
analytical studies can be | two
- case-control - cohort
31
descriptive studies can be
cross-sectional
32
internal validity
How well do the study estimates represent what was intended in the study plan?
33
external validity
- How relevant are the study estimates to the research question? - AKA: Generalizability - Are study results applicable to the patient/population/problem in front of me?
34
three threats to validity
1. chance (random error) 2. bias (systematic error) 3. confouding
35
chance (random error)
- errors that occur by chance - improved by increasing sample size - measured by CI - can affect precision - Lots of random error/chance = poor precision - There can be random error in both sampling and measurement
36
bias (systematic error)
- can include selection bias, volunteer bias, measurement bias - can affect accuracy - errors caused by choices, compromises and mistakes we make in how we conduct our study and not by random processes - Lots of systematic error/bias = poor accuracy - There can be systematic error in both sampling and measurement
37
confounding
third variable associated with both exposure and outcome
38
prevalence
currently have a disease
39
incidence rate | definition
new cases per unit person-time
40
point prevalence
proportion with disease at a particular point in time
41
period prevalence
proportion with disease at any point in time during the period (short lived ex: COVID-19, migraine)
42
point prevalence formula
number of people with a disease or trait / total # of people in a study population
43
period prevalence formula
number of people with a disease or trait in specific time period / total # of people in study population
44
cumulative incidence formula
number of new cases of the disease during a specific time period / number of people in study population
45
incidence rate formula
number of new cases of a disease / number people in population * time period
46
RCT scheme
Participants are randomly assigned to different groups: one group receives the intervention being tested (the treatment group), and another group (the control group) receives either a placebo or a standard treatment, depending on the study design.
47
RCT strengths
- Provides strongest causal evidence - Randomization minimizes confounding and blinding minimizes measurement bias
48
RCT weaknesses
- $$$ and time-consuming - Low external validity - Ethics: some exposures impossible to study
49
RCT association measures
relative risk
50
cohort scheme
- Follow 2+ groups with different exposures over an extended period of time and compare outcomes - Ideal for common diseases and rare exposures
51
cohort strengths
- Can measure incidence of multiple outcomes - Can study effects of multiple risk factors - Provides strong causal evidence due to time sequence
52
cohort weaknesses
- $$$ and time-consuming - Vulnerable to confounding because we know the exposure beforehand - Difficult to assess rare outcomes - Loss to follow up
53
association measures
relative risk
54
case-control scheme
- Identify 2 groups with and w/o an outcome interest, collect data and compare odds of exposure - Ideal for rare outcomes and outcomes with a long latency
55
case-control strengths
- $ and not time-consuming, quick - Can assess multiple outcomes and study rare outcome
56
case-control weaknesses
- Recall and selection biases → low internal validity - Vulnerable to confounding - Can only study one outcome
57
association measures
odds ratio
58
cross-sectional scheme
- Sample at one point in time to describe the distribution of exposures and outcomes - Ideal for hypothesis generation
59
cross-sectional strengths
- $ and not time-consuming, quick - Can measure prevalence - Can assess several exposures & outcomes
60
cross-sectional weaknesses
- No temporal ordering → weak causal evidence - Vulnerable to confounding - Cannot measure incidence
61
association measures
odds ratio
62
PICO
Population Intervention Comparison Outcome
63
Relative-risk (RR)
64
Odd ratio (OR)
65
RR >1
exposed are X times **more likely** to have a disease compared to unexposed
66
OR > 1
cases have X times **higher** odds of exposure compared to controls
67
RR = 1
exposed and the unexposed are equally likely to have a disease
68
OR = 1
odds of exposure in cases and controls are the same
69
RR < 1
exposed are (1-X)% **less likely** to have a disease compared to unexposed
70
OR < 1
odds of exposure in cases is (1-X)% lower than in controls
71
cumulative incidence | definition
new cases during time period
72
The frequency of of new COPD diagnosis in smokers is 33 per 1,000 person-years | incidence or prevalence
Incidence rate
73
In the same study, 5 had active wheezing at baseline exam | incidence or prevalence
Period prevalence
74
In the same study, 11 reported at baseline that they had taken opioid medications for pain at some point in the last year | prevalence or incidence
Point prevalence
75
In a study of 1,000 young adults, 24 developed diabetes over 10 years | prevalence or incidence
Cumulative incidence
76
measurement
making observations about the individuals who are sampled for the study
77
measurement can be
numeric (quantitative) thematic (qualitative)
78
intervention
intentionally expose people to something
79
stages of conducting a research study
1) Specifying a research question 2) Making a study plan 3) Implementing that plan
80
point estimate
best guess about what the truth is
81
95% confidence interval
The interval within which the TRUE parameter will be found 95% of the time (this helps us understand the precision of an estimate)
82
bias is a problem with
accuracy
83
Lots of random error =
chance is playing a large role = poor precision
84
Lots of systematic error =
bias is playing a large role = poor accuracy
85
Sampling
Choosing particular individuals from a population
86
Census
we study every individual in a population
87
Experimental study
You manipulate an exposure by doing an “intervention” (usually with one or more control groups), and then see what happens
88
Observational study
You just observe without any intervention
89
Three types of Observational study
Cross-sectional Cohort Case-control
90
Target population
The population for whom the research question is relevant
91
Accessible population
The population the researchers have access to and plan to study
92
Study sample
the actual study subjects who were included in the study and whose data were analyzed and included in the study estimates
93
Target phenomenon
The thing you want to learn about
94
Intended variables
The things you think you can realistically measure in a research study
95
Actual measurements
The measurements that are actually made (with error) for a study
96
Inference
A conclusion reached on the basis of evidence and reasoning
97
Estimate
Numerical best guess informed by data
98
prevalence
current disease
99
incidence
new disease
100
average incidence rate
number of events / person-time
101
Kaplan-meier
- the graph can be for the rate of mortality or rate of survival
102
time-to-event analysis
cumulative incidence over time
103
rate
- must have time in denominator - ex: incidence rate = events/person-year
104
proportion
- numerator is subset of denominator (between 0-1) - ex: cumulative incidence = # developing disease / # total

FND I - Summative III (5 decks)

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