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RANZCP exam preparation > Critical appraisal course > Flashcards

Critical appraisal course Flashcards

1
Q

What is EBM

A

The conscientious, explicit and judicious use of current best evidence in making decisions about the care of a patient

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2
Q

5 steps of EBM

A
  1. Clinical question
  2. EVidence
  3. Critical appraisal
  4. Application
  5. Implementation and monitoring
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3
Q

Stages of critical appraisal

A
  1. The clinical question
  2. Methodology: study design, recruitment, variables and outcomes
  3. Results: data analysed and differences between groups assessed for significance
  4. Applicability
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4
Q

Internal and external validity

A

Internal: extent to which the results from the study reflect the true results
External: extent to which study results can be generalised

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5
Q

Efficacy and effectiveness

A

Efficacy is the impact of interventions under optimal (research) setting

Effectiveness is whether the interventions have the intended or expected effect under ordinary clinical settings

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6
Q

The efficacy of an intervention is almost always better than the effectiveness

A

True

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7
Q

The acceptance of EBM means all clinicians practice the same

A

False

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8
Q

Patient values have no role to play in EBM

A

False

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9
Q

The effectiveness is almost always better than the efficacy

A

False

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10
Q

A research project taking place in the outpatient clinic is almost always going to give effectiveness data rather than efficacy data

A

True

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11
Q

An RCT can answer any type of clinical question

A

False

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12
Q

The clinical question determines which study designs are suitable

A

True

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13
Q

A clinical question can usually only be answered by one type of study design

A

False

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14
Q

The critical appraisal should start by examining the study design

A

False

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15
Q

Broad categories of studies and what they can achieve

A
  1. Observational descriptive
    - survey, qualitative, case report/series
    - Generate hypothesis
  2. Observational analytical
    - case-control (outcome->exposure)
    - cohort (exposure->outcome)
    - test hypothesis
  3. Experimental
    - RCT, crossover, N of 1
    - intervention
  4. Others
    - Ecological: information about the population
    - Pragmatic: real life environment. Example- all people in clinical location, outpatient, randomised to receive particular treatment. More reflective of everyday practice
    - Economic
    - Systematic review/MA
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16
Q

Case series is observational analytic

A

False

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17
Q

Case control is observational descriptive

A

False

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18
Q

Qualitative study

A

Opinions are elicited from a group of people with emphasis on subjective meaning and experience. Complex issues can be identified

Data gathering and data analysis develops iteratively-> results inform further samplig

Inductive-> knowledge generated through data sampling and gathering as opposed to other scientific methods where research is deductive

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19
Q

Other names for case control

A

Retrospective or case comparison

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20
Q

Case control- type, advantages and disadvantages

A

People with outcome variable, are compared to those without outcome variable, to determine risk factors they have been exposed to in the past

Adv: 
cheap and easy
good for rare outcomes
few subjects required
good for diseases with long duration between exposure and outcome

Dis:
not for rare exposures
Recall problems
Control groups can be difficult to select

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21
Q

Cohort study- design, retrospective type, other names, adv, disadv

A

A group of people with exposure are followed up to see the development of an outcome

Also called prospective or follow-up
Retrospective type is using cohort data that already exists (say from 20 years ago, with exposure)

Adv:
Good for rare exposures
multiple outcomes
temporal relationship
estimation of outcome incidence rates
Dis:
May take ++time from exposure to outcome
expensive
attrition rates
unsuitable for rare outcomes
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22
Q

Recall bias is a bigger issue fir CC or Cohort

A

Case control

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23
Q

Which is better study design for rare exposures

A

Cohort

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24
Q

Whch study design is betten when long time from exposure to outcome

A

CC

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25
Q

Disadvantages of RCT

A

Expensive

Time consuming

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26
Q

When might you use a crossover trial

A

When unable to get enough subjects for RCT

Given one intervention, then switched half way through

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27
Q

Disadvantages of cross-over trials

A 
The order f interventions might be important
Carryover effects (drugs with long half lives) or prolonged discontinuation/withdrawal
May be difficulty using historical controls if conditions were different
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28
Q

N of 1 trials

A

Experimental version of case report
Single person
Given randomised treatments
Report on response

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29
Q

Historical control bias is an issue in which type of study design

A

Crossover

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30
Q

Which types of biases are an issue in open label

A

Selection and observation bias

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31
Q

Two sources of methodological error

A

Bias

Confounding factors

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32
Q

Definition of bias

A

Any process at any stage of inference, which tends to produce results or conclusions that differ (systematically) from the truth

Not by chance
Researchers must try to reduce bias

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33
Q

categories of bias

A
  1. Selection bias= recruitment of sample
  2. Performance bias= running of the trial
  3. Observation bias= data collection
  4. Attrition bias

2+3= measurement bias

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34
Q

Bias versus confounding

A

Confounders are real life relationships between variables that already exist, and so are not introduced by the researcher

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35
Q

Selection bias

A

Error in recruitment of sample population

Introduced by:
Researchers= sampling bias
- admission or berkson
- diagnostic purity
- neyman bias
- membership bias
- historical control
Subjects= response bias
- volunteers differ in some way from the population
- example if more motivated to improve their health, and adhere ++to the trial
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36
Q

Types of sampling bias

A
  1. Berkson bias
    - sample taken from hospital setting, and therefore rates/severity of condition is different compared to target population
  2. Diagnostic purity bias
    - co-morbidity are excluded from sample population, and therefore does not reflect the complexity in the target population
  3. Neyman bias
    - prevalence of condition, does not reflect the incidence, due to time gap between exposure and actual selection. such that some with exposure are not selected (have died)
    - example, giving treatment following MI. Some may die shortly after MI and therefore not be selected, therefore there is better prognosis already
  4. Membership bias
    - members of group selected may not be representative of target population
  5. Historical control bias
    - subjects and controls chosen across time, so definitions, exposures, diseases and treatments may mean they cannot be compared to one another
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37
Q

Protecting against performance bias

A

Systematic differences in the care provided, apart from intervention evaluated
Standardisation of care protocol and blinding protects

Randomisation and blinding

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38
Q

Types of observation bias

A

Failure to measure or classify the exposure or disease correctly. Can be due to researcher or participant.

Researcher

  1. Interviewer (ascertainment) bias
    - when researcher not blinded, may approach subject differently depending on if they know they are taking the treatment or the placebo
  2. Diagnostic/exposure suspicion bias
  3. Implicit review bias
  4. Outcome measurement bias
  5. Halo effect- knowledge of patient characteristics influences the impression of patients with respect to other aspects

Subject

  1. Recall bias
  2. Response bias- answers questions in a way they think the researcher would want
  3. Hawthorne effect- behaving in a way, usually positively, as aware being studied
  4. Social desirability bias
  5. Bias to middle and extremes
  6. Treatment unmasking
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39
Q

Attrition bias

A

The numbers of individuals dropping out differs significantly between the groups
Those left may not reflect the sample or target population

Intention to treat analysis will need to be conducted

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40
Q

Bias occurs by chance

A

F

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41
Q

lack of blinding could lead to ascertainment bias

A

T

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42
Q

What is a confounder, positive and negative

A

When there is a relationship between two variables, that is attributable to, or confounded by the presence of a third. May make it seem like the two variables are associated when they are not (positive confounder eg coffee and lung cancer *smoking, overestimating association), or fail to show association when there is (negative confounder poor diet and CVD *exercise, underestimating association)

To be a confounder

  1. Must be associated with the exposure, but not the consequence
  2. The outcome, independently from the exposure
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43
Q

Controlling for confounders

A
  1. Restriction
    - inclusion and exclusion criteria
  2. Matching
  3. Randomisation
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44
Q

Accounting for confounders using statistical methods

A
  1. Stratified analyses
    - can only control for a few
  2. Multivariable analysis
    - Accounts for many, need at least 10 subjects, in a logistic regression
    If matching was done, McNemar test or conditional logistic regression
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45
Q

Simple randomisation

A

Subjects are randomised to groups as they enter the trial

Selected independently of each other

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46
Q

Block randomisation

A

Differs from simple randomisation in that subjects are not allocated independently
Subjects are assigned to “blocks”, which as they fill, then distributed evenly to intervention or control group

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47
Q

Stratified randomisation

A

Subgroups are formed in relation to a confounding factor, then in each stratum, block randomisation occurs, so the confounders are equally distributed

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48
Q

Concealed allocation and methods

A

When the treatments being administered in the different arms of the study remain secret

Part of the randomisation process

Methods:

  1. Centrally controlled
  2. Pharmacy concealed
  3. Sequentially numbered, opaque, sealed envelops
  4. Numbered/coded bottles or containers
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49
Q

Allocating an equal number of subjects in each groups is possible with simple randomisation

A

Yes- possible by chance

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50
Q

Pygmalion effect (Rosenthal effect)

A

Subjects perform better than others because they are expected to

Power of positive expectations

George Bernard Shaw- Pygmalion

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51
Q

Placebo effect

A

In healthcare, the pygmalion effect is often called the placebo effect

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52
Q

Latin meaning of placebo

A

“I shall please”

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53
Q

2 methods to make expectations equal

A
  1. Allocation concealment
    - at time of selection
  2. Blinding
    - once the subjects start treatment
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54
Q

Problem with single blinding

A

If the subject is blind, the researcher is still in full possession of the facts

The subject may still be influenced by the behaviour of the researcher who may have expectation about the outcome

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55
Q

Blind assessment

A

Assessment of the outcome measures during and at the end of the study is made without any knowledge of what the treatment groups are

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56
Q

Placebo factors

A

Multiple pills
Large pills
Capsules

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57
Q

Reliability definition and subtypes

A

Consistency of results on repeat measurements by one or more raters over time

  1. Inter-rater
    - level of agreement by 2+ assessors at the same time
  2. Intra-rater
    - one rater, same material, different time
  3. Test-retest
    - level of agreement from initial test results to repeat measures at later date
  4. Alternate form reliability
    - reliability of similar forms of the test
  5. Split-half reliability
    - reliability of test divided in two, with each half being used to assess the same material under similar circumstances
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58
Q

Quantifying reliability

A

Compare the proportion of scores which agree, with the proportion that would be expected to agree by chance

Reliability co-efficient

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59
Q

Kappa (Cohen’s) statistic k

A

Kappa or Cohens is used to test measures of categorical variables

Inter rater reliability in qualitative

Also known as chance-corrected proportional agreement statistic
Measures the proportion of agreement over and above that expected by chance
If agreement is no more than expected by chance k=0
To be significant, k> 0.7 is normally necessary

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60
Q

Strength of agreement or association

A 
0= chance agreement only
<0.2 poor agreement beyond chance
0.21-0.4 Fair agreement beyond chance
0.41-0.6 Mod
0.61-0.8 Good agreement
0.81-1.0 Very good 
1 Perfect agreement
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61
Q

Crohnbach’s alpha is

A

Used in complicated tests with several parts measuring several variables
When you have multiple Likert questions

Internal consistency/reliability
No formal test statistic
>0.5 mod
>0.8 excellent

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62
Q

Intraclass coefficienct

A

Used for tests measuring quantitative variables, such as BP

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63
Q

Validity and subtypes

A

Extent to which a test measures what it is supposed to measure

  1. Criterion- predictive, concurrent, convergent, discriminant
  2. Face
    3, Content
  3. Construct
  4. Incremental
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64
Q

Criterion validity

A

demonstrates the accuracy of a measure or procedure by comparing it with another measure or procedure that has been demonstrated to be valid

  1. Predictive: extent to which the test can predict what it theoretically should be able to predict
  2. Concurrent validity: extent to which the test can distinguish between two groups it theoretically should be able to distinguish
  3. Convergent validity: the extent to which the test is similar to other tests that it theoretically should be similar to
  4. Discriminant validity: the extent to which the test is not similar to other tests that it theoretically should not be similar to.
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65
Q

Other types of validity

A
  1. Face validity: superficially looks to measure what it should
  2. Content: measures variables that are related to that which should be measured
  3. Construct validity: extent to which a test measures a theoretical concept by a specific measuring device or procedure
  4. Incremental validity: the extent to which the test provides a significant improvement in addition to the use of another approach. A test has incremental validity if it helps to the use of another approach.
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66
Q

Intention to treat analysis

A

All the subjects are included in the analyses as part of the groups to which they are randomised, regardless of whether they completed the study or not.

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67
Q

Last observation carried forward, disadvantages

A

Way of accounting for subjects that drop out before the end

Disadvantages:
1. Underestimation of treatment effects
- intervention expected to lead to an improved outcome
2. Over estimation of treatment effects
Intervention expected to slow down a progressively worsening condition

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68
Q

per protocol analysis

A

only those subject remaining in the study are used in the analyses

introduces bias through exclusion of participants who dropped out

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69
Q

when is incidence preferred over prevalence and vice versa

A

When disease is frequent and short duration-> incidence

When long duration, slow, rare-> prevalence more useful to indicate impact of disease on the population

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70
Q

Mortality rate

A

Type of incidence rate that expresses the risk of death in a population over a period of time

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71
Q

Standardised mortality rate

A

Adjusted for confounding factors

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72
Q

Standardised mortality ratio

A

Ratio of observed mortality rate compared to expected mortality rate

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73
Q

Types of data summary

A 
1. Categorical
Nominal
Ordinal
2. Quantitative
Discrete
Continuous
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74
Q

Categorical data

A

No numerical value
Not measured on scale
No in between values

  1. Nominal= unordered
    - binary, dichotomous= only 2 mutually exclusive categories (dead/alive, male/female)
    - multi-category= mutually exclusive categories, bearing no relationship to each other (married, engaged, single, divorced)
  2. Ordinal= numbered
    - order inherent, but not quantified
    - can assume non-parametric
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75
Q

Quantitative data (numerical)

A
  1. Discrete
    - counts (number of children, asthma attacks)
  2. Continuous
    - can have a value within the range of all possible values
    (age, body weight, ht, temp)
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76
Q

Another term for normal distribution

A

Gaussian distributioni

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77
Q

Statistical tests to describe samples with different data samples: categorical, quantitative

A

Categorical= mode, frequency
Quantitative=
1. Non-normal distributed-> median, range
2. Normally distributed-> mean SD

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78
Q

Advantages and disadvantages of median

A 
Adv:
robust to outliers
Dis:
does not use all data
not easy to manipulate mathematically
79
Q

Explain interquartile range

A

Median = 2nd quartile (50%)
First quartile is at 25%
Third quartile is at 75%
So interquartile = 3rd quartile- 1st quartile

80
Q

the midpoint of a perfect ND also represents

A

Mean value of the concerned parameter in the population

81
Q

SD from mean, values contained, adv and disadv

A 
1 SD = 68 &amp; of values
2 SD (1.96) = 95%
3 SD = 99% (2.?58)

SD= calculated as square root of variance
Variance is the sum of all differences between all the values and the mean, squared and divided by total number of observations = 1 (degree of freedom)

Adv:
uses all the data, when distribution normal, mean and SD summarise entire distribution

Dis:
vulnerable to outliers, not useful for skewed data

82
Q

Standard error

A

SE of a mean is an estimate of the SD that would be obtained from the means of a large number of samples from that population

If we measure ht from sample of population, calculate mean. Get another sample from people of same population, and measure hts of people, calculating another mean. Unlikely to be the same as before. Can carry on recruiting samples and calculating means. Series of means. If we calculate the mean heights for each samply, the plot the frequency of means, end up with ND. Mean is population mean. Spread of observations around population mean is known as SE.

83
Q

Confidence intervals

A

Tells us the range within which a true magnitude of effect lies with a certain degree of assurance, usually 95%

CI for population mean=
mean +/- 1.96 x SE (SD/sqRn))

84
Q

Positive and negatively skewed data

A

Positively skewed has longer tail to the right

Negatively skewed has longer tail to the left

85
Q

Null hypothesis

A

There is no difference, no association between two or more sets of data
Any observed association can occur by chance. The probability of results occuring by chance can be calculated. If unlikely to be due to chance, the null is rejected

86
Q

Alternate hypothesis

A

Experimental hypothesis

87
Q

Probability

A

Likelihood of an event occurring as a proportion of the total number of possibilities
Expressed as P values

88
Q

P values

A

Probability of getting the observed results or more extreme, given a true null hypothesis

Significant= result deemed unlikely to have occurred by chance, thus rejecting the null

<0.05 probability to obtain result by chance is <1 in 20
<0.1 by chance <1 in 10
<0.5 chance 1 in 2

Statistical significance is very sensitive to sample size and study power

89
Q

Comparing statistical significance and clinical signifiicance

A

Statistical significant tells use whether results are due to chance
Clinical significance tells us whether the results are worthwhile or even noticeable

90
Q

One tailed and two tailed significance testing

A

1-tailed examines in only one direction, ignoring the other

In 2 tails examines both directions

91
Q

Type 1 error

A

Null hypothesis rejected when in fact true= false positive
Usually attributable to bias or confounding
Avoided by using stats to generate value
P value <0.05 signifies null can be rejected. >0.05 null cannot be rejected, therefore minimising type 1 error
Significance level a= pre chosen probability
P value = probability of making type 1 error

Not affected by sample size
More likely with increasing number of tests/end points

92
Q

Type 2

A

Null hypothesis is accepted when it is in fact false= false negative
Usually because sample size not big enough
Probability of type 2 error= b
B depends on sample size and alpha
B gets smaller as sample size gets bigger
B gets smaller as the number of tests of end points increases

93
Q

Power

A

Probability that a type 2 error will NOT be made in that study
A power of 0.8 generally accepted= probability of finding a real difference when one truly exists
Probability of rejecting the null hypothesis when true difference = 1-B

94
Q

Unpaired and paired data

A

Unpaired comes from two different groups/subjects

Paired data comes from the same subjects at different times

95
Q

To identify type of statistical test to use

A
  1. Consider if descriptive, comparing two groups or comparing >2 groups
  2. Then consider if categorical, non-normal or normal
  3. Paired or unpaired
Therefore:
1. Descriptive-
categorical= mode, freq
non-normal= median, inter-quartile
normal= mean, SD
  1. Comparing two groups
    categorical= chi square for large sample, fisher’s exact for small
    non-normal= Mann-Whitney U (unpaired), Wilcoxon’s rank sum (paired)
    normal= students T, either paired or unpaired
  2. Comparing >2 groups
    categorical= chisq (unpaired), McNemars (paired)
    Non-normal= Kuskal-Wallis ANOVA (unpaired), Friedman (paired),
    normal= ANOVA (paired or unpaired)
96
Q

Contingency table

A

Categorical
Row= treatment groups, exposure
Columns= outcomes/disease status

97
Q

What would be the appropriate test for comparing 2 groups of unpaired data that is not normally distributed

A

Mann-Whitney U

98
Q

What would be the appropriate test for comparing 2 groups of paired data that is not normally distributed

A

Wilcoxon’s rank sum test

99
Q

What would be the appropriate test for comparing > 2 groups of paired categorical

A

McNemar’s

100
Q

What would be the appropriate test for comparing more than 2 groups of unpaired data that is not normally distributed

A

Krushkal willis ANOVA

101
Q

Measuring BP in a subject before and after is an example of paired

A

Yes

102
Q

What would be the appropriate test for comparing 2 groups of unpaired categorical data

A

Chi square

103
Q

Risk definition

A

Risk has the same meaning as probability
Probability is the number of times we believe it is likely to occur divided by the total number of events possible

For exposure +
EER= a/a+b

For control -
CER= c/c+d

104
Q

Absolute risk reduction

A

CER-EER
Absolute risk difference is the absolute change in risk that is attributable to experimental intervention
Can range from -1 to +1

105
Q

RR or Risk ratio

A

Ratio of risk in experimental to risk in control
RR= EER/CER

Assuming outcome is undesirable
If RR= 1, experimental as likely as control
>1 more likely in experimental
<1 less likely in experimental

106
Q

Relative risk reduction

A

Proportional reduction in rate of outcomes between experimental and control
RRR= CER- EER / CER

107
Q

NNT

A

Number needed to be treated compared with control, for one subject to experience beneficial effect
NNT= 1/ARR (CER-EER)`

108
Q

Why might absolute be valuable when given relative only

A

Relative can be misleading

109
Q

Odds

A

The odds of an event is the ratio of number of times we believe it is likely to occur divided by the number of times it is likely NOT to occur

Someone expecting a baby-
Probability (risk) of it being a girl= 1/2 = 50%
Odds of it being a girl= 1/1, it is as likely to be a girl, as it is not to be a girl

110
Q

Odds ratio

A

Odds of the event occuring in one group divided by odds of event in another group
OR= ad/bc
a/b /c/d

In case control:
exposure is often presence or absence of risk factor, and outcome is disease presence of absence

OR 1= same outcome rates
OR >1 estimated likelihood of developing disease is greater in exposed than not exposed
OR<1 likelihood of disease is less in exposed than unexposed

111
Q

When do risk ratios and odds ratios differ

A

In general OR will always be further from the point of no effect, where OR = 1, RR= 1

If the event rate increases in treatment group, OR and RR will both be >1, OR>RR
If event decreases in treatment group both OR and RR will be <1 (OR

112
Q

Odds of cases in exposed

A

a/b

113
Q

Odds of cases in non-exposed

A

c/d

114
Q

What does correlation tell us

A

How strong the association between variables is

115
Q

Describe scatter graph

A

Compare data on two variables
Positive correlation= on graph points will slope from bottom lef to upper right

Negative correlation= on graph from upper left to lower right

Can be quantified by r= correlation coefficient

116
Q

Correlation co-efficient

A

If r +ve- directly correlated, var 1 increases, var 2 increase

If r = -ve, inversely correlated
as var 1 increases, var 2 decreases

The closer to -1 or +1, the more strong the correlation

R does not correlate with the gradient of the line, rather how close the points fall in line

correlation coefficients used depends on the type of data used: categorical, non-normal or normal

117
Q

Types of correlation co-efficients

A

Pearson’s= quantifies relationship between 2 continuous variables, normally distributed

Spearman’s rank= non-normal, when r is calculated using ranks. for two categorical ordinal variables, one continuous normally distributed variable and one categorical or non-normally distributed

Kendall’s correlation (Tau)= used for two categorical or non-normally distributed

Do not establish causality

118
Q

Regression

A

Used to find out how one set of data relates to another

Regression line gives relationship between variables, on a scatter graph

119
Q

Simple linear regression

A

Straight line that explains relationship between x and y data sets, so for a given value of x, a y value can be predicted

Y= outcome variable (dependent)
x= independent variable
a= intercept if the regression on the y axis
b= regression coefficient, slope of the lie, gives strength of association
120
Q

Multiple linear regression

A

Regression model in which the outcome variables is predicted from two or more independent variables. The independent variables may be continuous or categorical

If researchers knows outcome likely to be affected by one or more confounders, not eliminated from sampling, multiple linear regression may be used

121
Q

Logistic regression

A

When the outcome variable Y is binary

122
Q

Proportional cox regression

A

Proportional hazards ratio, is used to assess survival or other time related event

123
Q

Factor analysis

A

This is used to analyse the interrelationships between a large number of variables, and can be used to explain these variables in terms of underlying factors

124
Q

Cluster analysis

A

Multivariate analysis technique that tries to organise information about variables so that relatively homogenous groups, clusters can be formed

125
Q

ANOVA multivariate extensions

A 
ANCOVA= analysis of covariance, similar to multiple regression
MANOVA= multiple analyses of variable. Multiple dependent variables, multiple hypotheses testing
MANCOVA= multiple dependent and independent variables
126
Q

Critical appraisal in aetiological studies: case-control, cohort

A
  1. Methodology
    clearly defined group?
    except for exposure/outcome studied, were groups similar?
    - selection bias, matching, restriction criteria, randomisation
    Did the exposure predict the outcome?
    - recall in cohort is issue
    was the follow-up complete and of sufficient duration?
    - attrition bias
    - power calculations
    - if too many drop outs, Type 2 error may occur
    were the exposures.outcomes measured in the same way in both groups?
2. Results
what is the RR or OR?
what is the confidence limit of the estimate
NNT/NNTH
dose -response gradient?
association make biological sense?
3. Applicability
my patients similar to target?
risk factors similar?
patient's risks of adverse outcomes
should exposure to risks be stopped or minimised?
127
Q

Critical appraisal in diagnostic tests

A
  1. Methodology
    ?was the test applied to appropriate spectrum of patients
    were the diagnostics test results compared to gold standard
    was the comparison with the gold standard test blind and independent
  2. Results
    is the new test valid?
    Is it reliable?
    what was the outcome when patients underwent the new and gold standard test
  3. Can I use this study for caring for my patients
    - test acceptable, available, affordable, accurate, precise in this setting
    - consequences of the test help your patient
128
Q

Contingency table for diagnostic studies

A 
Rows= test + / -
Columns= outcome by gold standard (disease present/absent)
a= true +
b= false +
c= false -
d= true -
Sensitivity
Specificity
PPV
NPV
LR +ve
LR-ve
Pre-test and post-test probabilities and odds
129
Q

Sensitivity

A

Proportion of subjects with disorder who have positive result

a/a+c (positive by gold standard)

True positive

Sensitive test when Negative rules Out disorder
SnOut

Sensitive test for screening

130
Q

Specificty

A

Proportion of subjects without disorder who have a negative= true negative

D/b+d (negative by gold standard)

Sensitive test when Negative rules Out disorder

Specific test when Positive rules In disorder

Specific test for diagnosis

131
Q

Generally what is pre-test probability

A

Prevalence

Only put a person through diagnostic test if a positive result will be greater than pre-test probability

132
Q

PPV

A

Proportion of subjects who have a positive result with the disease
same as post - test probability of a positive result

Positive test= a+b
PPV = a/a+b
Want PPV to be substantially higher than pre-test probability

133
Q

NPV

A

proportion of subjects with negative result, who do not have the disorder

NPV= d/c+d

134
Q

LR +

for a positive result

A

How much more likely is a positive test to be found in a person with , as opposed to without, the condition

sensitivity/1-Specificity
True positive/1-true negative

135
Q

LR -

for a negative result

A

How much more likely is negative test to be found in a person with, as opposed to without, the condition

1-sensitivity /specificity
when < 1 means, negative test more likely to come from someone without the disease

136
Q

Pre-test probability

A

a+c/a+b+c+d

Probability that a subject will have the disorder before the test

137
Q

Pre-test odds

A

Odds subject with have the disorder before the test

Pre-test probability/1-pre-test

138
Q

Post-test odds

A

Odds that subject has disorder after test

Pre-test odds x LR for +ve

139
Q

Post-test probability for + results

A

Probability subject will have disorder after test

Post-test odds/post-test odds+1

140
Q

Does sensitivity and specificity depend on prevalence

A

No

141
Q

PPV and NPV depend on prevalence/

A

Yes, will change as disorder becomes rarer in the population
PPV will decrease
NPV will increase
Post-test prob will also change

142
Q

Post test probability of negative test

A

not the same as NPV (probability of disorder being absent in negative test)

PTP -ve= probability of disorder being present in those with negative result

NPV + PTP = 100%
therefore PTP -ve = 1-NPV

143
Q

Serial testing and relationship between sensitivity and specificity

A

leads to increase in specificity and decrease in sensitivity

useful when treatment for disorder is hazardous and inappropriate treatment costs need to be reduced

144
Q

Receiver operating characteristic curve

A

The closer the line to top left hand corner, the better the performance of the test will be= true positive high, false positive low

Line of unity- a test that is no better than chance at discriminating individuals with or without disease lies on line of unity

Plots true positive (sens) versus false positive (1-specificity)

The larger the area under the curve, the better the test is

Area of 1= perfect, 0.5 = worthless

145
Q

Critical appraisal for treatment studies

A
  1. Methodology
    clearly focused clinical question and primary hypothesis?
    randomisation process clearly described?
    concealed allocation?
    groups similar at the start of the study?
    groups treated equally apart from the experimental intervention?
    blinding used effectively?
    trial of sufficient duration?
    follow up complete?
    intention to treat study?
2. Results
CER
EER
ARR
RRR
RR
OR
NNT
Precision of the estimate of treatment effect- confidence limits
  1. Applicability
    pts similar to target population?
    were all the relevant outcome factors considered?
    will the intervention help your patients?
    benefits worth the risks and costs?
    patient’s values and preferences been considered?
    what alternatives are available?
146
Q

Prognostic studies

A

looks at prognostic factors and the likelihood that different outcome events may occur

Most are

  1. Cohort
    - most prognostic
    - one or more followed up to see who develops the outcome
    - groups may classified according to the presence or absence of prognostic
  2. Case-control
    - group with outcome are compared with a group who do not have the outcome, for the presence of prognostic factors
147
Q

Prognostic factors vs risks

A

Prognostic factors are a characteristic of the patient

RF increase the probability of getting a disease

PF predict the course and outcome of a disease once it has developed

148
Q

Critical appraisal for prognostic studies

A
  1. Methodology
    was the sample clearly defined?
    sample population recruited at common point in the course of the disease? selection bias?
    was there adjustment for important prognostic factors>
    was the follow up duration sufficiently long and complete>
    was there blind assessment of objective outcome criteria?
2. Results
AR/odds
RR/OR
Survival analysis, survival curves
Precision of prognostic estimates- confidence limits
149
Q

Survival analysis, disadv

A

Time between entry into a study and a subsequent occurrence of an event.
Technique used in longitudinal cohort studies, in which one interested in the time interval until an outcome occurs

Disadv:
likely not normally distributed
unequal distribution periods
people leave the study early, and be lost to follow up

150
Q

Kaplan-Meier survival analysis

A

Looks at event rates over a study period, rather than a specific time point

data presented in life tables and survival curves

The survival curve will not change at the time of censoring, but only when the next event occurs

151
Q

median survival time

A

time taken until 50% of the population survive

152
Q

survival time

A

time fron entering into the study to developing the endpoint- time to relapse, time to death

153
Q

survival probability

A

probability that an individual will not have developed an end point event over a given time duration

154
Q

Log rank test

A

compare medical survival times to see any significance

155
Q

endpoint probability

A

1- survival probability

156
Q

cox regression (cox proportional hazards)

A

method for investigating the effect of several variables upon a time specified event takes to happen

assumes the effects of the predictor variables upon survival are constant over time and are additive in one scale

positive coefficient indicates a worse prognosis
negative coefficient represents a better prognosis

157
Q

Hazard

A

instantaneous probability of an end point event in a study

degree of increased or decreased risk of a clinical outcome due to a factor, over a period of time, with various lengths of follow-up

158
Q

Hazard ratio

A

comparison of hazards between two groups
<1 not statistically significant= factor decreases risk of death
>1 statistically significant= increased risk of death

159
Q

4 key steps to systematic reviews

A
  1. Specifying the question
    - type of study, subjects, inclusions, exclusions, intervention/exposure, outcome
  2. Identifying studies
    - reproducible, unbiased, comprehensive
  3. Extracting the data
    - standardised proforma, study methodology details, assessment of study quality
  4. Interpreting the results
    - fixed or random effects, publication bias, heterogeneity
160
Q

Why should a meta-analysis be done

A

Large sample size
Increases power
Reduces risk of Type 2 error
Smaller confidence intervals

161
Q

Weighted average

A

An average where the results of some studies make a greater contribution to the total than others

Large weighting when:
larger sample size
higher event rates (estimated more precisely)

Pooled result= size of combined studies

162
Q

Forest plot

A

Axes

  • vertical= list of studies
  • horizontal=outcome measures, may be odds or risk ratio, means, event rates

Line of no effect- for RR= 1, OR+1
Size of box= weighting

163
Q

Variability in MA between studies can be due to

A
  1. Chance
    - studies have similar and consistent results, and any differences are due to random variation
    - referred to as homogenous results
    - as a result of similarities in design/intervention/subjects, these studies merit combination
  2. Systematic differences
    - differences between studies not due to chance
    - here real differences exist between the results of the reviewed studies ever after allowing for random variation
    - referred to as heterogenous results
164
Q

How to determine heterogeneity

A
  1. Forest plot= if CI of studies don’t overlap, likely to be heterogeneity
  2. Funnel plots, quantified using Cochran’s Q, chi squared, sensitivity analysis, meta-regression
165
Q

Chi squared statistic on forest plots

A

Keep null hypothesis in mind

Probability of differences arising from chance= P. To calculate P, chi squared is calculated for meta-analysis
Chi squared, DF, P quoted on forest plot
If P<0.05, variability is not due to chance, that is, results are heterogenous. There is some methodological difference in the way that the individual studies were carried out

166
Q

Identifying heterogeneity quickly

A

Use statistical tables to look up P values, compare chi squared with its degrees of freedom

If statistic is bigger than DF, then there is evidence of heterogeneity

167
Q

Z statistic

A

If Z > 2.2 results are heterogenous- null hypothesis can be rejected

168
Q

Dealing with heterogeneity

A

If heterogeneity present, things that can be done

  • use a random effects model: assumes the true treatment effects in the individual studies may be different from each other. (In homogenous, used fixed effect- every study is evaluating a common treatment effect)
  • subgroup analysis
  • meta regression
169
Q

Approaches to publication bias

A 
Prevention
Trial registers
Trial amnesty
Identification
Funnel plots
Galbraith plot
170
Q

Shape of funnel plot

A

If SE on y-> larger studies at funnel on bottom, smaller sudies up top

If 1/SE-> opposite is true, larger studies at the top

Asymmetry of funnel plot suggests publication bias

171
Q

r2

A

In statistics, the coefficient of determination, denoted R2 or r2 and pronounced “R squared”, is the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

172
Q

Purpose for systematic review/meta-analysis

A

different studies can be formally compared to establish generalisability of findings and consistency of results.

reasons for heterogeneity (inconsistencies) can be identified and a new hypothesis can be generated for different subgroups

173
Q

Types of selection bias in SR/MA and ways to minimise

A 
Publication bias
Language bias
Indexing
Inclusion
Multiple publication bias

Search through databases to find relevant studies`

174
Q

How are trials weighted

A
  1. Allocation concealment
  2. Randomisation
    (1 and 2 minimise selection bias)
  3. Blinding (measurement bias)
  4. ITT analysis (attrition bias)
175
Q

Types of hetergeneity

A
  1. Clinical heterogeneity: introduced due to clinical differences in populations included in the study
  2. Statistical heterogeneity (can detect using statistics measures)
  3. Methodological heterogeneity
176
Q

Detecting heterogeneity

A
  1. Eye balling a forest plot- if CI all overlap, no heterG
  2. Galbraith plot: ratio of log odds ratio to SE for each study, against recipricol of SE. If no stat sig heterogeneity, 95% will be within a band 2 units above and below overakk log ratio. 5% will be outside, just by chance
    3.Stats:
    Chi squared or Q test heterogeneity
    df, I2 statistic calculated from cochrane Q test gives extent of heterogeneity
177
Q

Methods to manage heterogeneity

A

Fixed effects model-> assumes all studies measuring same thing
Random effects-> studies estimating different treatment effects
Sensitvity analysis= check robustness of results, by changing parameters within the study
Data transformation-> continuous to dichotomous
Subgroup analyses
Meta-regression analyses-can test if there’s different treatment effects in different subgroups.

178
Q

Cost benefit analysis

A

All effects measured in dollars

Adv:
easy to interpret
when NB >0= new treatments extra benefits are worth more than the extra cost

Dis:
it is difficult to measure the value of all health outcomes in dollars
?some moral objection when not able to pay

179
Q

Cost UTILITY analysis

A

Two effect= quality and length of life
Product is taken as QALY

Adv:
outcomes involve both quality and length of life.
QALY is universal, so easily compared

Dis:
QALY measured vary by method
May vary by respondent
Society may value a QALY for different patient group differently

180
Q

Cost effectiveness analysis

A

Adv:
One effect measured in “natural units”
incremental cost effectiveness

Dis:
Only one outcome will represent the effect of treatment, however other outcomes may be relevant

181
Q

Cost-minimisation analysis

A

Not worried about outcomes

Adv: only need to collect cost data

Dis:
Few treatments have identical outcomes
Researchers likely need to collect the effect data to verify the equal effect assumption

182
Q

Advantages and limitations of economic analysis

A

Adv:
Systematic evaluation of costs and consequences
Answers the question:
Is the intervention worth the cost, including if it is cheaper than the other comparator, decision makers can render judgments
Better advocacy in health care
Makes decision making explicit
Can be used to guide priority setting

Limitations:
The primary limitation of summarising cost and consquences in an ICER ‘price tag’ is that decision makers, assuming they find the economic evaluation useful, may still decide whether the extra gain associated is worth the extra cost.

Does not explicitly consider decision makers budget
May over simplify health decisions

183
Q

CEA is only done when, and calculation

A

When the intervention is either more expensive and more effective, or less expensive and less expensive

ICER= C1-C1/E1-E2

All should have sensitivity analysis done, to test the extent to which changes in the parameters used in the analysis may affect the results obtained

184
Q

An intervention results in a patient living for an addition 4 years , rather than dying within one year, but where QOL reduced from 1 to 0.6 will generate

A
  1. 4 x 0.6= 2.4
  2. less 1 year @ reduced quality= 1-0.6= 0.4
  3. QALY’s generated = 2
185
Q

Advantages and disadvantages of QALY

A

Adv:
combine estimate of extra quantity and quality of life provided by the intervention in one measure
Can compare interventioins or programs in same therapy area
Making healthcare decisions and allocation of resources
Setting priorities with respect to healthcare interventions

Disadvantages:
Values assigned may not reflect values of patients receiving treatments
May lack sensitivity within disease area
May be derived from population studies that may not be generalisable to the population you are treating

20-40,000 fir 1 QALY or one DALY averted, usually considered cost effects

186
Q

How to calculate incremental net benefit

A

Cost effectiveness analysis

INB:
(extra effect x willingness to pay) - extra cost

187
Q

Interpreting CEAC

A

represents uncertainty in cost effectiveness analysis

Indicates the probability that the intervention is cost effective as compared with the alternative.

188
Q

Bootstrapping

A

Commonest method used to construct CEACs

Constructing CI and the visually representing it as a CEAC

189
Q

Methods in qualitative research

A
  1. Grounded theory- used to develop a theory, ‘grounded’ in the groups observable experiences
  2. Phenomenological approach- to gain a better understanding of everyday experiences of a group of people
  3. Ethnographic approach- learns about culture by observing people from that culture
190
Q

Data sampling methods in qualitative research

A
  1. Purposive: purposefully selecting wide range of informants to explore meanings and also select key informants with important sources of knowledge
  2. Theoretical sampling- type of purposive, developing a theory or explantion guides the process of sampling and data collection. Analyst makes initial sample, codes, collects and analyses data, and produces a preiliminary theory, before deciding which further data to collect
  3. Convenience sampling
  4. Snowball- target populations are elusive, participants asked to identify others with direct knolwedge relevant to the study
  5. Extreme case sampling- participants chosed because of their knowledge or experience is atypical or unusual in come way relevant to the study being conducted
191
Q

Data collection in qualitative, triangulation

A
  1. Interviews
  2. Focus groups
  3. Participant observation

Triangulation-> multiple data gathering techniques or multiple sources
1. Investigation
data
method

192
Q

Data saturation

A

despite further data gathering and analysis, understanding is not developed further. At this point, data collection and sampling ends

193
Q

Data analysis in qualitative

A
  1. Meaning focused: code relevant themes within data. Understand experiences and meaning
  2. Discovery focussed: analysis of segments of text, coded, sorted and organised, looking for patterns or connections
  3. Constant comparison: test and re-test

Clear transparent process= audit trait

194
Q

Minimising bias in qualitative

A
  1. Transparence
  2. Bracketing (exclusion of preconceptions)
  3. Reflexivity: researchers aware of own preconceptions. Reader can weigh researcher’s role in the conduct of the study
  4. Member checking: researcher returns to one of more participants to check the researcher’s interpretations of what the participants have said

RANZCP exam preparation (119 decks)

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