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research methods > Secondary research > Flashcards

Secondary research Flashcards

1
Q

Narrative review

A

-carried out by experts in the field of study but are guided by their own opinion. These are broad reviews and provide a qualitative summary of the studies

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2
Q

Systematic reviews

A
  • focus on a narrow question
  • comprehensive and specific data collection
  • uniform criteria for study selection
  • quantitive synthesis of data- not always possible
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3
Q

Meta-analysis

A
  • statistical combination of individual study data into a quantitive summary
  • extended systematic review
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4
Q

Literature search

A
  1. must use multiple databases
  2. cross check the reference list of each individual study retrieved
  3. hand search for materials unidentfied online
  4. approach experts to comment on any missing studies
  5. identify grey literature- e.g conference abstracts, presentations and posters
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5
Q

Inclusion criteria for studies in a systematic review should consider

A
  1. types of study designs to include
  2. types of subjects to include
  3. types of publications
  4. language restrictions
  5. types of interventions
  6. time frame for included studies
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6
Q

Basic steps for meta-analysis

A
  1. literature search
  2. establishing criteria for including and excluding studies
  3. recording of data from the individual studies
  4. statistical analysis of the data
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7
Q

Combining individual trial data in meta-analysis

A
  • methods used for meta-analysis use a weighted average of results
  • weighting refers to the significance attached to each study based on multiple factors
  • may used fixed or random effects model to combine outcomes from different studies
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8
Q

Fixed effects model

A
  • assumes that all the studies share the same common treatment effect (homogeneous)
  • assumes only random error within studies could explain observed differences
  • ignores between-study variations
  • can only be applied if heterogeneity can be safely excluded by testing for it
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9
Q

Random effects model

A
  • assumes all studies do not share the same common treatment effect
  • assumes each study shows a different effect which are normally distributed around the true mean
  • gives greater weight to small studies
  • susceptible to publication bias and results in wide less precise confidence intervals
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10
Q

Q statistic

A

-significant heterogeneity

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11
Q

Which model to use?

A
  • if there is an absence of significant heterogeneity (Q statistic), both fixed and random effects model have similar confidence issues
  • if very heterogenous then random effects model will yield wider confidence intervals
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12
Q

Fixed effects statistics

A
  • Mantel-Haenszel and Peto ratios are used in fixed effect analysis
  • Mantel-Haenszel is useful even when wide differences exist between individual studies in the ratios of the size of two groups
  • can be used in cohort/case control designs too
  • Peto- mostly restricted to reviewing RCTs as it can produce biased results in unequal groups
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13
Q

Clinical heterogeneity

A

-differences in the study that results in uneven outcomes and is describable but not measurable

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14
Q

Methodological heterogeneity

A
  • refers to heterogeneity resulting from the differential use of study methodology
  • these may lead to different conclusions in different studies, even though the clinical characteristics are the same
  • methodological heterogeneity is describable but does not need quantification
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15
Q

Statistical heterogeneity

A
  • variation in trial outcomes
  • homogeneous sample refers to a set of individual studies that have comparable outcomes without much variation
  • heterogeneity refers to the presence of significant variation among the individual studies in a sample
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16
Q

Test for statistical heterogeneity

A
  • can be graphical or statistically
  • graphical = forest plot or L’Abbe plot or Galbraith plot
  • statistically is chi squared, chochran’s Q,
17
Q

Forest plot

A

-blobbogram

18
Q

L’Abbe plot

A
  • modified scatter plot wherein CER is plotted against EER from individual trials included in the meta-analysis
  • treatment better than control will be above the line and control better than treatment will be below the line of equality (diagonal)
19
Q

Galbraith plot

A
  • an alternative to a forest plot
  • on the horizontal axis one plots 1/standard error of the study effect estimate
  • on vertical axis one plots the study effect estimate divided by the standard error (log odds ration/SE=standard normal deviate)
  • distance from the middle line indicates SD
  • major limitation of this approach is that the statistical tests lack the power to detect heterogeneity in most meta-analyses
20
Q

Forest plot

A
  • horizontal axis is the odds ratio or relative risk
  • vertical line at 1.0 is the line of null effect
  • numbers below 1 favour treatment
  • numbers above 1 favour control
  • a box and line represents the study
  • the bigger the box the bigger the study
  • the line represents the 95% confidence interval
21
Q

Chi squared

A
  • tests of heterogeneity can be tested with a Q test or I2

- chi squared tests provide a test of significance for heterogeneity but do not measure it

22
Q

Cochran’s Q

A

-calculates the weighted sum of the squared differences between individual study effects and pooled effects across studies

23
Q

I2 statistic

A
  • describes the percentage of variation across studies that are due to heterogeneity rather than chance
  • high P suggests that the heterogeneity is insignificant and that one can perform meta-analysis
24
Q

Funnel plot

A
  • detects publication bias
  • shows relation between the effect size and precision of the individual studies in a meta-analysis
  • large studies get published, small ones dont which can produce an asymmetrical funnel
25
Q

Failsafe N

A

-calculates the number of zero effect studies that would be required to nullify the mean effect seen in a meta-analysis

26
Q

Cumulative meta-analysis

A
  • this can be used to assess the potential impact of publication bias in tilting or nullifying the effect
  • sort the studies from largest to smallest, plot forest plot as you go along
27
Q

The trim and fill procedure

A
  • developed by Duvall and Tweedie
  • helps to assess whether the effect would change if the bias were to be removed
  • sensitivity analysis where missing studies are imputed and added to the analysis and then the effect size is recomputed
28
Q

Location bias

A
  • not being located due to citation habits, the database used, the key terms used or multiple replications of data
  • location of information not the studies!
29
Q

Inclusion bias

A

-refers to reviewers tendency to include studies they agree with

30
Q

Sensitivity analysis

A
  • e.g failsafe N

- publication bias can be examined

31
Q

Blobbogram

A
  • Forest plot
  • the combined or pooled effect size is given by various stats including OR, RR, standardised mean differences, Cohens d
  • confidence interval of individual line width estimates and combined estimates(lozenge width) should be noted
  • rectangle size is noted as it shows assigned weight
  • heterogeneity results should be noted -absence of heterogeneity is indicated by vertical linerarity of rectangles or non signdicant chi square test for heterogeneity
32
Q

Effect sizes for continuous variables

A

-there are two effect size measures for continuous variables
1. simple difference between the mean values (DM)- mean value of group X minus the mean value of group Y
2. standardised difference between the mean values (SMD)- carried out by dividing the DM by the pooled standard deviation of the two groups in the basic formula
SMD=(mean value of groupX/mean value of group Y

33
Q

Meta-analyses standard format

A

-must follow QUORUM statement

34
Q

Standard format for RCTs

A

-CONSORT statement

35
Q

Meta-regression analyses

A

-refers to 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

research methods (14 decks)

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