Brainscape's Knowledge GenomeTM

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


See full index

IASM + CPRS Practicals > EBP > Flashcards

EBP Flashcards

1
Q

Practice guideline, Colleague, Google, Pubmed, Dynamed

A

Practice guideline: vested interest
Colleague: unsystematic observation
Google: unfiltered information
Pubmed: difficult to locate most valid and up-to-date studies
Dynamed: most valid and up-to-date evidence

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

Evidence based medicine

A
  • Conscientious, explicit and judicious use of ***current best practice in making decisions about care of individuals
  • Integration of best **research evidence with our **clinical expertise and our ***patient’s unique values and circumstances

Why:
1. Best available evidence and tailor interventions to patient
—> Improve effectiveness of patient care
2. Evidence may be weak, contradictory, incomplete / vested interests
—> need to acquire most relevant evidence and appraise its quality

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

5A approach to EBP

A

Assess clinical scenario
Ask PICO question
- patient/population group
- intervention
- comparison group
- outcome
Acquire the best evidence for the question
Appraise the validity of evidence (external / internal)
Apply with patienyts’ unique values and circumstances

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

Best research evidence

A
  1. Relevant (patient-oriented outcomes)
  2. Valid (critical appraisal)
  3. Up-to-date
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Attributes of causality

A
  1. Part of scientific theory (can be tested / form hypothesis)
  2. Reversible (no cause no outcome)
  3. Consistent (same effect across settings)

—> associations are not always causal —> therefore can only make inferences

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

Variables

A
  1. Qualitative (categorical)
    - Binary (dichotomous)
    - Nominal (unordered: several distinct categories cannot be ordered e.g. ethnicity)
    - Ordinal (ordered: several distinct categories that can be ordered e.g. smoking)
  2. Quantitative (numerical)
    - Discrete (integers)
    - Continuous (within a range)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Both qualitative and quantitative data can be presented in frequency distribution

A
  1. Summarise the data
    —> identify outliers, reveal possible errors
  2. Visualised using graphical display
    —> Qualitative: pie chart, bar chart
    —> Quantitative: Histogram (frequency polygon), Box-and-whisker plot
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Central tendency

A

Mode:

  • not particular good indicator of central tendency
  • only means to measure central tendency when data is NOMINAL values

Median:

  • literal measure of central tendency
  • LESS SENSITIVE to outliers

Mean:

  • arithmetic mean
  • SENSITIVE to outliers (extreme values)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Dispersion

A
  1. Range (difference between largest and smallest)
    - VERY SENSITIVE to outliers
  2. Quartile
    - Inter-quartile range (difference between first and third quartile)
    - summarise variation of data
    - can be estimated from cumulative frequency curve
  3. Percentile
    - give information on spread
    - can be estimated from cumulative frequency curve
  4. Standard deviation
    - how dispersed the data are
    - average difference between mean and dat value
    - square root of variance
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Sample variance vs Population variance

A

Sample variance:
- sum of squared difference / (number of values - 1)

Population variance:
- sum of squared difference / number of values

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

Sampling

A

Different samples —> Sampling variation
Sample many times —> Sampling distribution

More samples we draw —> mean of sampling distribution closer to population mean

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

Effect measures

A

Judging whether exposure causes outcome - strength of possible association

  1. Risk ratio / Relative risk: how much more likely exposed will become cases
    - RATIO of probabilities
    * **- incidence of outcome in exposed / incidence of outcome in unexposed
    - a/(a+b) / c/(c+d)
    - RR < 1 —> Protective factor, lower risk
    - RR = 1 —> no evidence of association between exposure and outcome
    - RR > 1 —> Risk factor, higher risk
    - further from 1, stronger the association
  2. Odds ratio: odds of outcome in exposed to odds of outcome in unexposed
    - Odds is not probability
    - but RATIO of probability of getting to probability of not getting
    - odds = 兩個ratio相除
    * **- odds of outcome in exposed / odds of outcome in unexposed
    - a/(a+b) / b/(a+b) / c/(c+d) / d/(c+d)
    - OR < 1 —> exposed group less likely to have the outcome, lower odds of outcome
    - OR = 1 —> No association between exposure and outcome
    - OR > 1 —> exposed group more likely to have the outcome, higher odds of outcome
    * **- OR is always further away from 1 than corresponding RR, except for rare outcome (incidence is very low, RR / OR approximately equal)

In Case-control studies: only OR can be calculated because no incidence (prevalence is inflated by selecting cases)
New cases involved time: use RR

In general: RR are better since take into account incidence rate + represent likelihood of outcome
However, in case-control study, only OR can be calculated

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

Prevalence vs Incidence

A

Prevalence: for Cross-sectional studies
Incidence: for Cohort studies

Prevalence: no. of EXISTING cases at a designated time
- depends on: incidence, duration (chronic / acute)
- proportion, not rate
—> Point prevalence: at a time point
—> Period prevalence: during specified time period

Incidence: frequency of OCCURRENCE of NEW cases in a given time period
- measure of risk
- not directly measurable unless population is followed over time
- frequency count / proportion / rate
—> Cumulative incidence (incidence proportion): new cases/population at start period
—> Incidence rate (incidence density): new cases/total person time at risk

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

Reliability vs Validity

A

Reliability: produce same results if repeated
Validity: extent to which measures true value of variable

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

Hierarchy of evidence

A

Evidence summaries > Systematic reviews / Meta-analyses > individual studies

  1. Randomised Controlled Trial (control intrinsic and confounding factors)
  2. Cohort studies
  3. Case-control studies / cross-sectional / ecological
  4. Expert opinion
  5. Pathophysiologic reasoning

RCT not always ethical / feasible
—> then use observational studies
—> use guides to assess causation:
E.g. Bradford Hill’s criteria, Koch’s postulate

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

Internal validity: Can we trust the results?

A

Null hypothesis: (Default)
- a variable has NO association with another variable / 2 population distributions do not differ from each another

Alternative hypothesis:
- a variable has an association with another variable / 2 populations differ from each other

P-value:

  • probability that a result that is extreme from observed result is obtained, assuming that the null hypothesis is true
  • probability that a result is affected by random error, assuming null hypothesis is true
  • smaller p-value —> stronger confidence that we are correctly rejecting null hypothesis
  • p < 0.05: observed results is unlikely due to chance, statistically significant

Power:

  • probability of correctly rejecting the null hypothesis, given that null hypothesis is wrong
  • (1-β)
  • ability of a study to demonstrate an association
  • Good: as high as possible (80%)
17
Q

Confidence interval

A

More informative than p-value:
- gives range of plausible values for true value
(E.g. 95% confident that true value lies within specific range)
- tell precision of estimate width decreases with increasing sample size
- suggest whether an association exist (if the confidence interval crosses the null e.g. risk ratio = 1)

18
Q

Random errors

A

Random error (chance): (lack of reliability —> cannot produce same results if repeated)

  1. Type 1 (α error / p-value): rejecting null hypothesis when it is true (false positive)
    - probability of committing Type 1 error: p-value
  2. Type 2 (β error / 1-power): accepting null hypothesis when it is false (false negative)
    - probability of avoiding Type 2 error: power

When try to reduce Type 2 error (increase power) —> Type 1 error will increase consequently, to keep both errors to acceptable level:

  • increase effect size —> large effect size —> small p-value
  • increase sample size —> smaller sampling variation —> smaller p-value
  • standard procedures
  • regularly calibrate
19
Q

Systematic errors

A

consistently wrong results due to problems with study design (lack of validity: cannot measure true value)
- must be identified early since cannot controlled for in the analysis
- reduce systematic error
—> appropriate instrument
—> reduce biological variations

  1. Information bias
    —> Observer bias (by investigators)
    —> Recall bias (by subjects, particular problem if public is aware of exposure-disease association)
    - reduce bias:
    —> Blind assessment of outcome status (investigator blinded)
    —> Prospective assesssment of outcome (no need to recall)
    —> Use objective measures
  2. Selection bias:
    - systematic difference between subjects and target population
    - subjects not representative of target population —> affects EXTERNAL validity
    - subjects groups are not comparable —> affects INTERNAL validity
    - reduce bias:
    —> random sample
    —> maximise response rate
    —> minimise attrition rate
    —> select case and control from same underlying population
20
Q

***Confounding

A

Type of bias
—> Distortion of observed association due to other factors that are common causes of both exposure and outcome (confounders)
—> leading to estimated association is not the true causal effect

Positive confounders: overestimate associations
Negative confounders: underestimate associations

Confounders:

  • must cause the exposure
  • must cause the outcome
  • must not be on causal pathway between exposure and outcome (confounders cannot be result of exposure), otherwise called mediator

Reduce confounding:

  1. Randomisation (randomly distribute confounders, only possible when you assign people to groups i.e. interventional studies)
  2. Restriction (not allow certain confounders to be present)
  3. Matching (equal representation of subjects with certain confounders between study group —> may introduce selection bias)
  4. Stratifying (into sub-samples according to specified confounder during analysis)
  5. Multivariable analysis (e.g. regression analysis: adjust for a number of confounders simultaneously)
  6. Quantitative analysis (multiple linear regression)
  7. Time to occurrence (cox regression)
21
Q

Reporting bias

A

Dissemination of research findings is influenced by nature of results
—> distort results of systematic reviews
—> biased / incomplete picture

  1. Publication bias (positive results more likely to be published)
  2. Time lag bias (positive results published sooner)
  3. Language bias
  4. Citation bias
  5. Selective outcome reporting bias (secondary outcome more favourable —> report as major outcome want to look for in the beginning)

Reduce:
- Register clinical trials in public registries before carrying out
—> Ensure transparency and subject to public scrutiny
—> Pre-specified protocol
—> Pre-specified outcomes
- Fully report all methods and results including negative results
- Share individual patient data so other researchers can replicate

22
Q

Epidemiology study design

A

Observational (without intervention)
- Group
—> descriptive (Disease mapping)
—> analytical (Ecological studies)
- Individual
—> descriptive (Cross-sectional + Cohort)
—> analytical (Cross-sectional + Case-control + Cohort)

Interventional (RCT and other experiments)

23
Q

Observational group descriptive

A

Disease mapping (Routine data, Surveillance data):

  • Comparison of health indicators across populations
  • Monitoring of time trends
  • Evaluate and planning public health programmes
24
Q

Observational group analytical

A

Ecological studies
- analyse associations of exposure with outcome
- provide preliminary evidence for association of interest
- examine possible exposure-disease relationship on population level
- ECOLOGICAL FALLACY (以全概偏)
—> inferences about individual risks can be erroneous

25
Q

Cross-sectional study

A

Descriptive: prevalence survey
Analytical: assess association with outcome

Strength:

  • quick and economical
  • useful for generation hypothesis
  • can simultaneously assess multiple associations

Limitations:

  • cannot measure incidence
  • cannot establish temporal sequence
26
Q

Cohort studies

A 
Assess OUTCOME
Prospective / retrospective
Strength:
- can estimate incidence
- have temporal sequence
- can select RARE exposure

Limitation:

  • time-consuming
  • costly
  • cannot select for rare outcomes
27
Q

Case-control study

A

Assess EXPOSURE
Select controls and compare with cases

Strength:

  • can look at multiple exposures simultaneously
  • cheaper and quicker
  • suited for RARE outcomes

Limitation:

  • cannot measure incidence
  • inefficient for rare exposure
28
Q

Interventional studies

A

Modify an exposure
Examine effect on outcome

RCT: randomisation

  • Randomly allocate participants to minimise confounding
  • allocation concealment
  • blinding
  • intention to treat analysis to minimise attrition, moving to other group, compliance issues

Strength:

  • less prone to confounding
  • strongest level of evidence

Limitation:

  • time-consuming
  • costly
  • not ethical / feasible
29
Q

Systematic reviews + Meta-analysis

A

Forest plot: summarise results from individual studies and combined estimate

Funnel plot: detecting reporting biases
—> Asymmetry due to: 
- Reporting bias
- Heterogeneity (clinical and methodological diversity)
- Chance
30
Q

Background vs Foreground question

A

Background: general knowledge about a topic
- use textbooks

Foreground: specific to a clinical decision

  • use research data base e.g. Pubmed
  • use point-of-care information tools —> pre-appraised evidence e.g. Dynamed plus, Uptodate, Essential evidence plus
  • beware of reporting bias and vested interest
31
Q

Disease-oriented evidence vs Patient-oriented evidence

A

Disease-oriented evidence: disease markers, risk factors

Patient-oriented evidence: mortality, morbidity, quality of life

IASM + CPRS Practicals (9 decks)

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