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MRCPsych Part B > Secondary Research > Flashcards

Secondary Research Flashcards

1
Q

What is secondary research?

A

Research conducted using data from other studies

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2
Q

What is recruited instead of patients in secondary studies?

A

Eligible studies

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3
Q

Types of secondary research

A

Narrative reviews
Systematic Reviews

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4
Q

Who carries out narrative reviews?

A

Experts in field of study

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5
Q

Structure of narrative reviews

A

Experts own opinion supported by selected research evidence
Broad, no specific clinical question
Subjective

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6
Q

Risk of narrative reviews

A

Prone to bias
Literature not searched methodically
Cannot be replicated due to above

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7
Q

Advantages of narrative reviews

A

Good introduction to topic
Stimulate interest and controversies
Collate existing data for new hypothesis

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8
Q

Question in narrative reviews

A

Broad in scope
General

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9
Q

Search methods in narrative reviews

A

Not usually specified
Comes from experts familiarity with literature

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10
Q

Appraisal of individual studies in narrative reviews

A

Variable
Usually based on experts opinion

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11
Q

Synthesis of results in narrative reviews

A

Usually only qualitative summary of studies

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12
Q

Resources in narrative studies

A

Less time consuming but requires expert in field

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13
Q

What are systematic reviews?

A

Follow rigorous steps in identifying relevant literature, appraising individual studies and analysing suitable data to synthesise a conclusion

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14
Q

Characteristics of systematic reviews

A

Focused narrow question
Comprehensive and specified data collection
Uniform criteria for study collection
Quantitative synthesis of data (optional)

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15
Q

What is meta-analysis?

A

Quantitative synthesis of individual study data in systematic reviews

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16
Q

What is GIGO?

A

Garbage in, garbage out

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17
Q

What does GIGO refer to?

A

If primary studies have poor quality, then even if they use sound methodology and statistical procedures, outcome will be meaningless

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18
Q

How is quality of individual trials assessed

A

Nature of patient sample
Outcome studied
Length of follow-up
Comparability of treatment
Methodological factors

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19
Q

What methodological factors are studied to assess quality of individual trials?

A

Adequacy of sample randomisation
Adequate concealment and control of intervention
Analysis with ITT
Objective, blinded outcome assessment

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20
Q

What should inclusion criteria for studies in systematic reviews consider?

A

Types of study designs
Types of subjects
Types of publications
Language restrictions
Types of interventions
Time frame for included studies

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21
Q

What characteristics must a literature review have?

A

Use of multiple databases
Cross checking of reference list of each study retrieved by direct search
Hand searching for materials unidentified online
Approaching experts to common on any missing studies
Identifying grey literature

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22
Q

What is grey literature?

A

Unpublished literature e.g. conference abstracts, presentations, posters

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23
Q

What is meta-analysis most often used for?

A

Assess clinical effectiveness of healthcare interventions

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24
Q

What data does meta-analysis use

A

Summary data or individual patient data

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25
Q

Why is summary data more often used?

A

Inability of investigators to supply patient data
Increased costs & time

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26
Q

What does quality of a meta-analysis or systematic review depend on?

A

Comprehensive literature search
Clearly defined eligibility criteria
Clearly defined & strictly adhered protocol
Predetermined criteria related to quality of trials
Thorough sensitivity analysis
Discussion and analysis of statistical and clinical heterogeneity

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27
Q

Why is thorough sensitivity analysis needed in meta-analysis and systematic reviews?

A

As assessing quality of study can be difficult since information reported is inadequate for this purpose

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28
Q

Steps for conducting meta-analysis

A

Literature search
Establish criteria for inclusion and exclusion
Record data from individual studies
Statistical analysis of data

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29
Q

What do meta-analysis use for combining individual trial data?

A

Weight average of results

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30
Q

What is weighting?

A

Significance attached to each study based on sample size, precision, external validity and methodological quality

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31
Q

Is weighting dependent on outcome of a study?

A

No

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32
Q

Modes of analysis in meta-analysis

A

Fixed effects model
Random effects model

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33
Q

What does fixed effects model assume?

A

All studies share same common treatment effect

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34
Q

What does random effects model assume?

A

Studies do not share common treatment efefct

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35
Q

What happens in fixed effect analysis?

A

Inference restricted to included set of studies
Assumes that only random error within studies can explain observed differences
Ignores between-study variations

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36
Q

When can fixed effect analysis be applied?

A

If heterogeneity can be safely excluded by testing for it

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37
Q

What happens in random effects analysis?

A

Assumes that each study shows a different effect which are normally distributed around true mean
This gives proportionally greater weight to smaller studies

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38
Q

Disadvantages of random effects analysis

A

Susceptible to publication bias
Results in wider less precise confidence intervals

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39
Q

What is used in fixed effect analysis?

A

Mantel-Haenszel and Peto ratios

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40
Q

When is Mantel-Haenszel ratio useful?

A

When wide differences exist between studies in ratios of the size of two groups

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41
Q

What type of study designs is Mantel-Haenszel helpful for?

A

Cohort/case control

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42
Q

What is Peto ratio used for?

A

RCTs

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43
Q

Why is Peto ratio only used in RCTs?

A

Can produce bias results in unequal groups

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44
Q

When do both fixed and random effects analysis have similar confidence intervals?

A

In the absence of heterogeneity

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45
Q

How is heterogeneity calculated?

A

Q statistic

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46
Q

Advantage of random effects model

A

Wider confidence interval which may be more representative

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47
Q

Give examples of clinical heterogeneity

A

Diverse interventions
Differences in selection of patients
Severity of disease
Dose or duration of treatment

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48
Q

Is clinical heterogeneity measurable?

A

No

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49
Q

What is methodical heterogeneity?

A

Heterogeneity resulting from differential use of study methodology

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50
Q

Name the types of heterogeneity

A

Clinical
Methodological
Statistical

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51
Q

What is statistical heterogeneity?

A

Studies may report same outcome but results that are not consistent with each other

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52
Q

What is a homogenous sample?

A

Set of studies which have comparable outcomes without much variation

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53
Q

What is a heterogenous sample?

A

Studies with significant variation amongst them

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54
Q

What can be used to test for statistical heterogeneity?

A

Forest Plot
L’Abbe plot
Galbraith plot
Chi Square
I2 statistic
Cochrans Q

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55
Q

What is a L’Abbe plot?

A

Modified scatter plot where CER is plotted against EER from individual trials

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56
Q

How will data appear in L’Abbe plot?

A

Trials that report experimental treatment to be superior to control will be in upper left of plot
If experimental is no better than control, point will fall on line of equality
If control is better than experimental point will be in lower right of plot

57
Q

Axis of a Galbraith plot?

A

Horizontal: 1/standard error of study effect estimate
Vertical: study effect estimate divided by its standard error (log odds ratio/SE)

58
Q

What is 1/standard error equal to?

A

Precision

59
Q

What is the standard normal deviate?

A

Study effect estimate divided by its standard error

60
Q

What can statistical procedures test in meta-analysis re individual studies?

A

Whether results of study reflect single underlying effect (homogenous) or distribution effect (heterogenous)

61
Q

Limitation of studying homogeneity/heterogeneity of single studies?

A

Statistical tests lack power to detect heterogeneity in most meta-analyses

62
Q

What can Chi square test in meta-analysis?

A

Test of significance for heterogeneity - does not measure it

63
Q

How can Chi square test studies in meta analysis for heterogeneity?

A

Q test
I2 test

64
Q

What is the rule re Chi square when used to test heterogeneity in meta-analyses?

A

Its value on average is equal to its degree of freedom, which is n-1 (number of studies -1)

65
Q

What would Chi Square of 7 for a meta-analysis of 8 studies mean?

A

N-1 = 7
Chi square = 7
Thus, chi square of 7 or less means no significant heterogeneity

66
Q

Disadvantages of chi square use for meta-analysis

A

Can only tell us heterogeneity
Does not tell us anything about homogeneity

67
Q

Why can Chi square not tell us anything about homogeneity?

A

Low power

68
Q

How can one quantify heterogeneity?

A

Cochrans Q

69
Q

How does one calculate Cochrans Q?

A

Weighted sum of squared difference between individual study effects and pooled effect across studies

70
Q

What does I2 statistic do?

A

Describes percentage of variation across studies that are due to heterogeneity rather than chance

71
Q

What does high P mean in chi square tests for meta-analyses?

A

High P suggests that heterogeneity is insignificant and that one can perform meta-analyses

72
Q

If there is heterogeneity in meta-analysis, what can one do?

A

Study reasons behind it
Random effects analysis

73
Q

What must one consider when studying the reasons behind heterogeneity in meta-analysis?

A

Clinical differences
Methodological issues e.g. problems with randomisation
Early termination of trials
Use of absolute rather than relative measures of risk etc
Publication bias

74
Q

What procedures can give suggestions of the causes behind heterogeneity?

A

Meta-regression analysis
Subgroup analysis

75
Q

What is publication bias?

A

Tendency for authors to submit or editors to publish only those studies that yield statistically significant results

76
Q

What do methods that diagnose presence of publication bias work?

A

Based on the assumption that small studies are more susceptible to publication bias and may therefore show larger treatment effects

77
Q

When are methods for publication bias diagnosis not feasible?

A

When all available studies are equally large i.e. have similar precision
If heterogenous studies

78
Q

What happens if methods to diagnose publication bias are used in heterogenous studies?

A

May lead to false positives for publication bias

79
Q

Methods used to diagnose publication bias

A

Funnel plots
Failsafe N
Linear regression
Correlation method
Cumulative meta-analysis
Trim-and-fill procedure

80
Q

Most common method used to detect publication bias?

A

Funnel plot

81
Q

What does a funnel plot show?

A

Relation between effect size and precision of individual studies in a meta-analysis

82
Q

Reasoning behind funnel plots?

A

Small studies are more likely to remain unpublished if their results are not significant and larger studies get published regardless of producing asymmetrical funnel plots.

83
Q

Axis of funnel plots

A

X: measure of effect size
Y: measure of precision

84
Q

Measures of precision

A

Sample size
Inverse of standard error (1/SE)

85
Q

What can be used for measures of effect size?

A

OR
Log OR
Cohens d

86
Q

Reasoning behind Failsafe N?

A

Meta-analyses can be wrong due to non-significant studies that are not published.
Inclusion of these studies could nullify the statistical significance of an observed mean effect.

87
Q

Who developed Failsafe N?

A

Rosenthal and Cooper

88
Q

What does Failsafe N do?

A

Calculates the number of zero-effect studies (no effect) that would be required to nullify the mean effect seen in a meta-analysis

89
Q

What can be used to quantify publication bias?

A

Linear regression method
Correlation method (Tau)

90
Q

What can cumulative meta-analysis be used for?

A

Assess potential impact of publication bias in tilting or nullifying an effect

91
Q

How are cumulative meta-analysis carried out?

A

Studies sorted from largest to smallest
Meta-analysis is run with one study, then repeated with 2nd etc
Forest plot/blobbogram plotted; first row will show effect based on one study, 2nd row shows cumulative effect based on 2 studies etc.

92
Q

Results of cumulative meta-analysis

A

If the point estimate stabilizes based on the initially added larger studies, then there is no evidence that the smaller studies are producing a biased overall effect.

If point estimate shifts when smaller studies are added, may be bias - and one can also see direction of bias

93
Q

Who developed the trim-and-fill procedure?

A

Duval and Tweedie (2000)

94
Q

What does trim-and-fill procedure do?

A

Assess whether effect would change if bias were removed

95
Q

What happens in trim-and-fill procedure?

A

Type of sensitivity analysis where missing studies are imputed and added to the analysis, and then effect size is recomputed.

96
Q

What is location bias?

A

Studies not located due to citation habits, databases used, keywords used or multiple replication of same data

97
Q

What is inclusion bias?

A

Reviewers tendency to include studies they agree with

98
Q

What is sensitivity analysis?

A

Analysis of the extent to which an outcome derived from research will change when hypothetical data are introduced

99
Q

What does sensitivity analysis mainly apply to?

A

Economic analysis
Meta-analysis

100
Q

What are the nodal points in meta-analysis where hypothetical data could be introduced?

A

Significant heterogeneity (or absence) - impact of outcome?
Factors determining methodological quality
Publication bias

101
Q

Name a sensitivity analysis method where publication bias can be examined

A

Failsafe N

102
Q

What is the other name for a forest plot?

A

Blobbogram

103
Q

What do forest plots present?

A

Effect (point estimate) from each individual study as a blob/square with horizontal line across the square

104
Q

What is the square in forest blot?

A

Measured effect

105
Q

What is the horizontal line in forest plot?

A

9% CI - indicates precision

106
Q

What does size of square represent in forest plot?

A

Amount of information in that study - usually the sample size

107
Q

What does length of horizontal line in forest plot represent?

A

Degree of uncertainty of estimated treatment effect for study

108
Q

What is the mid vertical line in forest plots?

A

Represents null effect for difference in outcomes

109
Q

What does it mean when a square lies on one side of the vertical line on a forest plot?

A

Outcome of that study was favoured based on the side of the line the square it is

110
Q

What will be shown on a forest plot if an individual study result is statistically significant?

A

Horizontal lines representing uncertainty will lie entirely on the same size as the square and will not cross the vertical line

111
Q

What does the diamond mean in forest plots?

A

Final synthesis of individual studies

112
Q

What does the position of the diamond in forest plots depend on?

A

Whether the intervention rates favourably or not at the end of the meta-analysis

113
Q

Things to note in a forest plot

A

Combined/pooled effect size
CI of individual point estimates and combined estimates
Weighting assigned to studies
Heterogeneity of results

114
Q

What is the CI of individual point estimates on a forest plot?

A

Line width

115
Q

What is the CI of the combined estimate?

A

Diamond width

116
Q

What does the size of the squares refer to?

A

Weighting assigned to study

117
Q

What would absence of heterogeneity look like on a forest plot?

A

Vertical linearity rectangles

118
Q

Why are relative risk and odds ratio more advantageous than absolute risk for calculating effect size in meta-analysis?

A

Depend less on baseline risk

119
Q

When are OR and RR appropriate to use as effect size measure?

A

When both variables are binary

120
Q

What is conventionally used to show effect size when reporting treatment effects?

A

Odds ratio

121
Q

What can occur if one assumes OR and RR are the same?

A

One may overestimate the effect of treatment - but this overestimation will be small if experimental and control event rates are <20%

122
Q

At what point will OR be significantly greater than RR?

A

For frequent events i.e. >20%

123
Q

Why is relative risk used in Cochrane reviews?

A

For frequent events, OR will be greater than RR, leading to overestimation of risk

124
Q

What are the two effect size measures for continuous variables?

A

Simple difference between the mean values (DM)
Standardized difference between the mean values (SMD)

125
Q

What is DM?

A

Keeps original units and is the mean value of one group minus the mean value of the other group

126
Q

How does one carry out SMD?

A

Dividing DM by pooled standard deviation of the two groups using a formula

127
Q

Formula for SMD

A

SMD = (

128
Q

What is SMD also known as?

A

Cohens D
Hedges’ g

129
Q

What does SMD of 0 mean

A

Treatment and placebo have same effects

130
Q

Advantages of Hedges’ g?

A

Avoids a bias seen in Cohens d when there are small number of subjects

131
Q

Statistical procedures for fixed effects

A

Peto
Mantel-Haenszel

132
Q

Statistical procedures for random effects

A

DerSimonian-Laired

133
Q

Measured effects for fixed effects model

A

OR
Rate Ratios
Risk Ratios

134
Q

Measured effects of fixed effects model

A

Approximates OR

135
Q

Measured effects for random effects model

A

Ratios and rate differences

136
Q

What is a Quorum statement?

A

Consensus statement on standard format for meta-analysis

137
Q

What is a consort statement?

A

Consensus statement on standard format for RCTs

138
Q

What is meta-regression analysis?

A

Technique of regression wherein regression model is applied to meta-analysis to analyse which characteristics of the studies actually contributed to overall effect size

MRCPsych Part B (64 decks)

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