CRQ Flashcards

1
Q

Hierarchy of evidence

A
Meta analysis
Rct
Non randomised CT
Cohort study
Case control study
Cross sectional surgery
Case series
Case report
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2
Q

Sensitivity

A

Chance of it being correctly positive
Ie does have the disease and is positive on the test

A/ a+c

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3
Q

Positive predictive value

A

If positive on a test what is the chance they actually are positive for the disease?

A/a+b

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4
Q

Specificity

A

If they don’t have the disease what is the chance the test correctly identifies them as negative?

D/b+d

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5
Q

Negative predictive value

A

If the patient is negative on the test what is the chance they actually are a true negative without the disease

D/c+d

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6
Q

What does CONSORT diagram stand for?

A

Consolidated standards of reporting trials

To improve quality of rct
Patient flow
Includes those lost to follow up

Evidence based minimum set of recommendations for reporting rcts
Standardised way to present report findings

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7
Q

What does PICO stand for?

A

Formulating a question for a study

Patient or problem (population)
Intervention
Comparison
Outcome

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8
Q

Case report

A

Experience of one person
Anecdotal
Prone to chance and bias

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9
Q

Case series

A

Group of people studied
Useful for rare diseases

5 children who presented to EF with abdo epilepsy etc

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10
Q

Cohort study

A

Consequences of exposure to a risk factor
Same group of people followed
One with risk exposure and one without
No one has the disease at start of the trial
Can do retrospectively
Takes long time (usually years)

E.g 500 people who smoke cannabis monitored for 15 years to see if at greater risk of developing schizophrenia

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11
Q

Case control study

A

Compares those with disease and those without
and looks at a risk factor

50 women with hepatitis and 50 women without queries about ear piercing to see if a risk factor

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12
Q

Cross sectional study

A

Surveyed group of people at one point in time
Prevalence of exposure and outcome in a population

E.g population questionnaire examining prevalence of stroke risk factors

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13
Q

RCT

A

Minimises bias
Measures efficacy
Compare two treatments

Kids with fever given either paracetamol or ibuprofen to determine which is better at reducing fever

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14
Q

Endpoints

A

Study should say how endpoint selected e.g morbidity, plaque developed, major CV risk factor

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15
Q

Validity

A

How well does it measure what it was supposed to

Measures variable
Criteria compared to what is know to be valid

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16
Q

Reliability

A

How consistent a test is on repeated measurement

How true is it to true value

Consistency of measurement

Measurement error either:
systematic (dif readings on same machine)
Random error- dif in weight readings

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17
Q

Incidence

A

Rate of occurrence of new cases over a period of time

Measures risk of disease

Number of new cases over time period divided by population size

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18
Q

Prevalence

A

Proportion with disease at a given time

Number with disease divided by population size

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19
Q

Variance

A

Sum of all the differences between the values plus the mean squared

Divided by

Total number of observations - 1

( work out mean
Minus mean from each value
Square each result
Sum of squared results
Divide by number of results -1
20
Q

Standard deviation

A

Degree of spread from the mean

Square root of variance

2 standard deviations above and below =95%

21
Q

Z score

A

Value of an observation into number of standard deviations frommean where it lies

Observation -mean divided by standard deviation

22
Q

95% confidence interval

A

95% of means in the sample like in 1.96 standard error

So 95% of the time the population mean would lie in this range

23
Q

Equation for risk

A

Number of times an event likely to occur

Divided by

Total number of events possible

Ie one in 6 fall ill
Risk = 1/6 = 0.167 or 16.7%

24
Q

Equation for odds

A

Ratio of number of times likely to occur

Divided by number of times not likely to occur

1/5 = 0.2 odds of falling ill

25
Control event rate equation in 2x2
C/c+d
26
Experimental event rate Absolute risk of outcome in experimental group
A/a+b
27
Absolute risk reduction
CER - EER If 0.4 shows 40% reduction in risk from control group
28
Relative risk
EER/CER If <1 reduced risk
29
Relative risk reduction
CER-EER/ CER
30
Number needed to treat
1/ARR
31
Odds ratio
A x d Divided by B x c >1 more likelihood of developing that condition with risk factor
32
P value
Usually less than 0.05 | Probability of obtaining the result by chance
33
Type 1 error
False positive result Wrongful rejection of null hypothesis Usually bias, confounders
34
Type 2 error
False negative Wrongful acceptance of null hypothesis Small sample size
35
Power
Probability that a type 2 error will not be made Ability to detect smallest difference between the two groups Bigger sample size = more power 0.8 power generally accepted so only 20% chance of type 2 error Sample size Alpha value that is as Variability if decreased this decreases power
36
Chi squared test
Compare significance of categorical data The 2x 2 boxes Unpaired data Tests hiv acquisition between iud or dmpa Ie tests null hypothesis that the two groups are the same
37
Test for normal distribute data
T test | Anova
38
Tests for non normal data
Wilcoxons rank Mann Whitney u Anova Kruskal Wallis
39
Four stages listed in consort diagram for rct
Enrolment Intervention allocation Follow up Analysis of data
40
Forest plot
Used to present results of meta analysis
41
Funnel plot
Used to identify publication bias
42
Fishers exact test
Significance of independent categorical data | Alternative to chi squared test when small sample size
43
Test from funnel plot and in metaanalysis to test for asymmetry
Egger test
44
Therapeutic papers | Eg rct looking at high flow oxygen on mortality in copd
``` Objective Design Setting Population Method Intervention Control Outcome Results Conclusions ```
45
Diagnostic papers | Eg diagnosis of intersussepyipn by uss in ed
``` Objective Design Setting Population Methods Test under ix Gold standard Results Conclusion ```
46
Meta analysis
``` Paper identification Secelection Funnel plot Forest plot Stat analysis (heterogeneity) ```