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ebp > Comps review > Flashcards

Comps review Flashcards

1
Q

When/why ask an FQ?

A

Uncertain about clinical issue, want an answer

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2
Q

What is a PICO?

A

4 required elements of FQ (in any order)

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3
Q

What does PICO stand for?

A
  • Patient/problem
  • Intervention
  • Comparison/contrast
  • Outcome
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4
Q

What to use PICO for?

A
  • Research about treatment
  • about diagnoses/screening tools
  • How well one of the treatments/diagnosis tools worked for a client
  • How you would gather patient preferences about their treatment options
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5
Q

Oxford Hierarchy (top to bottom)

A
  • Systematic review and meta analyses of RCTs
  • RCTs
  • Cohort studies
  • Case control studies
  • Cross sectional surveys
  • Case studies
  • Ideas, expert opinions, editorials
  • Anecdotal
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6
Q

Lit reviews

A
  • Systematic review
  • Meta-analysis
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7
Q

Systematic review

A

Gather and summarize all relevant studies on a topicM

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8
Q

Meta-analysis

A

If the studies have similar enough methods, pool them and do stats over everything
- Numerical support for the conclusions across studies

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9
Q

Individuals works

A
  • Lit reviews
  • Original research
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10
Q

Professional association journals and websites

A
  • ASHA
  • American Academy of Audiology
  • American Psychological Association
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11
Q

Documenting steps

A
  • Heading: Where you searched + search terms
  • List full citations for articles that look relevant
  • List notes below the citation about the article’s usefulness for your current purpose
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12
Q

Citation parts

A
  • Authors
  • Year
  • Title (article, chapter)
  • Source (journal, book)
  • Publication details
  • Page numbers
  • DOI or website
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13
Q

Clinical Studies: Phase Model

A
  • Phase I & II: Exploratory, small groups
  • Phase III: Hypothesis testing, big samples
  • Phase IV: Translate to practice
  • Phase V: Practical matters
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14
Q

Phase I & II: Exploratory, small groups

A
  • Treatment effect
  • Refine operations, populations, methods, effects
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15
Q

Phase III: Hypothesis testing, big samples

A
  • Treatment efficacy
  • Pretest-posttest
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16
Q

Phase V: Practical matters

A
  • Cost-benefit
  • Quality of life
  • Satisfaction
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17
Q

Review Articles

A
  • Summarize results from Phase IV & V studies w/ common hypotheses
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18
Q

Review Styles

A
  • Narrative
  • Meta-analysis (quantitative)
  • Best evidence
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19
Q

Narrative Review - Traditional lit review

A
  • Thorough search
  • Describe results qualitatively
  • Overall conclusion
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20
Q

Narrative Review - Drawbacks

A
  • Subjective bias
  • Subjective interpretations
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21
Q

Systematic Review

A
  • Clear protocol for selecting and evaluating studies before beginning review
  • Has 6 steps
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22
Q

Steps to Systematic Review

A
  1. Formulate problem/question
  2. Locate, select studies (selection criteria)
  3. Assess study quality (uniform standards)
  4. Collect data (across studies, quantitative or qualitative methods)
  5. Analyze results
  6. Interpret results
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23
Q

Early Meta-Analysis Methods

A
  • Vote counting
  • Combined-probability
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24
Q

Vote Counting

A
  • Number of studies with positive, negative, null results/conclusions
  • Drawback: no effect size
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25
Q

Combined-probability

A
  • Incorporate probabilities (account for different sample sizes)
  • But still no effect size
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26
Q

Modern Meta-Analysis Outcome

A

Overall effect size and significance across studies w/ similar quantitative methods

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27
Q

Modern Meta-Analysis

A
  • Good way to combine results of studies on different populations, small samples, etc
  • Strong evidence for clinical decisions
  • Identify gaps, ideas for future research
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28
Q

Best Evidence Approach

A
  • Combines “best” of narrative & meta-analysis
  • Attempts to avoid drawbacks
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29
Q

Combines “best” of narrative and meta-analysis

A
  • Narrative intro, discussion, conclusion
  • Objectivity in selection criteria, evaluating quality
  • May use quantitative/meta-analysis
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30
Q

Attempts to avoid drawbacks

A
  • Bias in study selection
  • Balance between big picture and important points from individual studies
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31
Q

Good review features

A
  • Clear scope, purpose, theories
  • Systematic, thorough evidence search
  • Systematic appraisal of all studies for relevance, quality/rigor
  • Sound synthesis across studies
  • Reasonable conclusions based on synthesis
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32
Q

Reading Article Order

A
  1. Abstract: is this article relevant?
  2. Introduction: find the research question
  3. Find answers in conclusion
  4. Start from the top: get context from lit review
  5. Methods: evaluate study quality
  6. Results: evaluate rigor
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33
Q

Research Ethics

A
  • Fair treatment of research participants
  • Honesty, accuracy in reporting
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34
Q

Fair treatment of research participants

A
  • Minimized harm, maximized benefit
  • Informed consent
  • Protected private data
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35
Q

Honesty, accuracy in reporting

A
  • Describing procedures
  • Minimizing subjective bias
  • Giving credit
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36
Q

Participant Rights

A
  • First, do no harm
  • Nuremberg code
  • Institutional review boards
  • Belmont Reports
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37
Q

Nuremberg Code (1947)

A
  • Voluntary consent: Free choice to participate
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38
Q

Institutional Review Boards (IRBs)

A
  • Review research proposals BEFORE they begin
  • Participants’ rights, protections; risks, benefits
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39
Q

Belmont Report (1979)

A

Codes for research with human subjects
- Medical
- Behavioral

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40
Q

Belmont Report

A
  • Applies to human research participants
  • Applies to research, not practice
  • Respect for persons: informed consent
  • Beneficence: risk-benefit assessment
  • Justice: selection of participants
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41
Q

Respect: Informed Consent

A
  • Informed of procedures, risks, alternatives
  • Understand, make free choice to participate
  • No coercion: Rewards can’t be too enticing
  • Can quit any time and still get compensation
  • Extra protections for vulnerable populations
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42
Q

Deception

A
  • Only when the truth upfront would make the experiment impossible
  • Must minimize risks of harm due to deception
  • Must debrief at end
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43
Q

Beneficence: Risk-Benefit

A
  • Ensure well-being of participants
  • Do no harm
  • Minimize risks, maximize benefits to participants
  • Risk to participants doesn’t exceed benefit to science
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44
Q

Justice: Participant Selection

A
  • Fair distribution of risks and benefits
  • Minimize selection bias
  • Subject should correspond to research purpose
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45
Q

Convenience Sampling

A
  • Easy-access populations
  • Prisoners
  • Students
  • Family members
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46
Q

Vulnerable populations

A
  • Institutionalized
  • Children
  • Disabled
  • Students
  • Patients
  • Immigrants
  • Poor
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47
Q

Participants

A
  • Anyone involved who’s not a researcher
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48
Q

Distributive Justice

A
  • Participant pool should match the purpose of the study
  • Inclusion/exclusion of participants based on need
  • Purposefully exclude people who may benefit
  • Purposefully include/select samples based on convenience or vulnerabilities
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49
Q

Other Issues

A
  • Honoring commitments to participants
  • Withholding treatment
  • Conflicts of interest
  • Privacy, confidentiality
  • Data management, ownership, security
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50
Q

Honoring Commitments to Participants

A
  • Compensation
  • Continued therapy
  • Summary of results
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51
Q

Withholding Treatment

A
  • No treatment control groups: may feel unfair to “let people go untreated”
  • Risk-benefit: is no-treatment harmful?
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52
Q

Conflicts of Interest

A
  • When researcher has another role/interest related to the research/outcomes
  • Teacher can’t recruit own current students
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53
Q

Privacy, confidentiality

A
  • Identifying into = confidential unless stipulated in consent form
  • Anonymize data: Use subject code w/ all data, store name-code key under lock and key
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54
Q

Data Management, Ownership, Security

A
  • Follow collection, analysis protocols systematically
  • Store securely
  • Later: who “owns”
55
Q

Reporting

A
  • Honest, accurate description of study
  • Responsibility to publish publishable results
56
Q

Author Order

A
  • First author = did most work
  • Fields/labs differ on rest
  • Most to least work
57
Q

Validity

A

How closely something reflects reality

58
Q

Internal Validity

A

Accuracy of relation between observations and the subjects observed

59
Q

External Validity

A

Generalizability

60
Q

Generalizability

A

Applicability of patterns/results to a larger population

61
Q

Internal Validity Parts

A
  • Confounders
  • Subjective bias
62
Q

Confounders

A

Unintended, uncontrolled, or unknown facts that should affect the results
- Alternate explanation
- Nullification
- False conclusion

63
Q

Subjective Bias

A

Could influence any stage of research

64
Q

Ways to minimize subjective bias

A
  • Blinding
  • Outside observer
  • Reliability checks
65
Q

Blinding

A

Make involved people unaware of information that could bias findings

66
Q

Single-blind

A

Either patient or practitioner are unaware of the patient’s treatment group assignment

67
Q

Double/triple-blind

A
  • Other researchers are unaware of something
  • Researchers who interact with subjects, give treatments, evaluate progress, analyze data
68
Q

Norm-Referenced Tests

A
  • Standardized
  • Rank a score relative to normative sample
  • Not designed for multiple administrations
69
Q

Criterion-referenced

A

Compare performance to reaching an expected level

70
Q

Consistency of Measurement

A
  • Train examiners
  • Monitor consistency of test admin procedures
  • Check intra-examiner reliability
  • Check inter-examiner reliability
71
Q

Intra-examiner Reliability

A

Consistency of an examiner’s measurements across test subjects

72
Q

Inter-examiner Reliability

A
  • Consistency of measurements across examiners
  • When examiners score same person/take same measurements, do they agree?
73
Q

Randomized Controlled Trials (RCTs)

A

Best for causal inferences about average effects across a population

74
Q

What are RCTs not appropriate for?

A
  • Diagnostic accuracy
  • Etiology
  • Risk factors
  • Rare/slowly-progressing conditions
  • Risky/unethical experimental procedure
75
Q

Experimental Design

A

Include active manipulations

76
Q

Observational/Non-Experimental Design

A
  • No active manipulations
  • Observe systematically, don’t alter
77
Q

Controlled Studies

A

Include control comparison group

78
Q

Uncontrolled Studies

A

No control group

79
Q

Controlled Trial

A
  • One group receives treatment/manipulation, control group does not
80
Q

Multiple Baseline Controlled Trial

A

Treatment group/patient is its own control:
- Measure multiple times before treatment, part-way through, after, later follow-up

81
Q

Uncontrolled Trial

A

All participants receive treatment
- No control/comparison group

82
Q

Cohort

A

Groups differing on a variable are followed over time to observe differences in outcomes

83
Q

Case-control

A

Compare group with disorder to controls (w/o disorder), usually at one or a few points in time

84
Q

Cross-sectional

A

Examine relationships between variables in a sample at one point in time

85
Q

Case Study/Report

A

Describe single patient

86
Q

Case Series

A

Describe series of similar patients

87
Q

Prevalence/Surveillance Studies

A

Examine rates of occurrence in a sample

88
Q

Prospective

A
  • Hypothesis testing, methods planned out before data collection
  • Experimental studies must be prospective
89
Q

Retrospective

A
  • Analyze pre-existing data
  • Ranked lower than prospective: no control over systematic or unknown influences, can’t assess validity of procedures
90
Q

Random Assignment

A
  • All subjects have equal chance of being assigned to any condition, determined by chance
  • Applies to prospective, controlled, experiments
  • “Best way” to assure that groups don’t differ systematically before beginning
91
Q

Matched Assignment

A
  • Create groups that differ on the variable of interest but not others that are expected to influence results
  • Weaker validity than random assignment: unknown, unanticipated confounders possible
92
Q

Confounders

A

Unintended, uncontrolled, or unknown factors that could affect the results

93
Q

Statistical Significance

A

Math that says whether or not your result was probably a fluke

94
Q

P < .05

A

There is a 95% chance that the samples were not drawn from the same population

95
Q

Generalizability

A

Applicability of patterns/results to a larger population

96
Q

Subjective Bias

A

Minimize bias in participant selection

97
Q

Random Assignment in Observational Studies

A

All members of the population have equal chance of being selected for observation

98
Q

Attrition

A
  • In studies with multiple measurement points, not all participants complete all steps
  • Must report in published results
99
Q

Replicability

A

Enough detail reported that another researcher could repeat the procedures? And get the same results

100
Q

Continuous

A

Can have infinitely small, intermediate values
- Interval
- Ratio

101
Q

Categorical/Discrete

A

Completely separate bins
- Nominal
- Ordinal
- Interval or Ratio data that has been binned into categories or ranges

102
Q

Nominal - Unordered, named category labels

A
  • Categories aren’t better/worse, higher/lower
  • Demographics, type
  • Best if every participant fits into just one category
103
Q

Nominal - Can’t do most stats

A
  • Even if assigning arbitrary numbers to categories
  • Can count numbers of members
104
Q

Ordinal

A
  • Categories are ordered but there’s no “amount” of difference between levels
  • Likert, rating, severity scales
  • Hard to do stats on these, must transform
105
Q

Interval

A
  • Ordered with equal intervals: can compute differences between scores but not ratios
  • Good for transformations and stats
106
Q

Ratio

A
  • Like interval plus true zero, can compute differences and ratios
  • Great for transformations and stats
107
Q

Frequency Distribution

A

How many data points fell in each interval
- AKA frequency polygon

108
Q

Skewed

A

Long tail
- Positive/right skew = positive tail
- Negative/left skew = negative tail

109
Q

If mean = mode

A

Skewness is 0

110
Q

If mean > mode

A

Skewness is positive

111
Q

If mean < mode

A

Skewness is negative

112
Q

Bimodal

A

Two modes
- Mean misleading
- Really represents 2 distributions

113
Q

Data Transformations - Modify raw values to simplify data structure

A
  • Make distribution more symmetrical/normal
    – Many stats require a normal distribution
  • Make validity more constant
  • Make relationships more linear
  • Convert ordinal data to interval/ratio scales
114
Q

Data Transformations - Inspect distribution of data

A
  • Looks normal? Outliers? Mistakes?
  • Calculate skew, kurtosis to confirm
115
Q

Nonlinear Transformations

A

Reduce relative spacing between values on the right more than left side of distribution

116
Q

Square Root

A

Take square root of each value
- First add constant to make lowest value > 1

117
Q

Log

A

Changes spread of distribution

118
Q

Inverse: 1/x

A
  • Makes big numbers small and small numbers big
  • Reverses order of values, so first multiply each value by
    – 1 and add a constant so the lowest value is > 1
119
Q

Descriptive Stats

A

Summarize characteristics of data set

120
Q

Counts

A
  • Frequency
  • Percentage
121
Q

Location/Central Tendency

A
  • Mean
  • Median
  • Mode
122
Q

Individual Location

A
  • Rank
  • Percentile rank
  • Standard score
123
Q

Variability (spread)

A
  • Range
  • Variance
  • Standard deviation
124
Q

Frequencies

A

How many subjects/items in each category

125
Q

Percentages, proportions

A

Divide each frequency count by total

126
Q

Location/Central Tendency

A
  • Single values that describe whole data set for one measure
  • Central location/tendency
  • Fractiles/Quantiles
127
Q

Central Location/Tendency

A
  • Mean (average)
  • Median (middle value)
  • Mode (most common value)
128
Q

Fractiles/Quantiles

A

Divide rank-ordered date into even-ish bins
- Median split (2)
- Quartiles (4)
- Deciles (10)
- Percentiles (100)

129
Q

Individual Location

A

Location of participant in relation to group

130
Q

Rank

A

The Xth best score (out of?)

131
Q

Percentile Rank

A
  • Rank-order scores
    – Divide individual’s rank by total number of participants
  • 80th percentile = scored better than 80% of class
132
Q

Standard Score (z-score)

A

Number of standard deviations from the mean
- X - mean/st dev

133
Q

Variability (Spread)

A
  • How spread out the data values are
  • Adds necessary meaning to central tendency and individual location
    – Number of categories
    – Range
    – Interquartile Range
134
Q
A

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