Supplemental Material - High robustness does not always imply low uncertainty of treatment rankings: An empirical study of 60 network meta-analyses

Supplemental Material for High robustness does not always imply low uncertainty of treatment rankings: An empirical study of 60 network meta-analyses by Yun-Chun Wu and Yu-Kang Tu in Research Methods in Medicine & Health Sciences</p

A matroid analysis of the MetS data.

<p>A labelled Hasse diagram (LHD) produced using a minimum selection criteria (i.e. any subset with an greater than the threshold value is labelled dependent). Abbreviation: Fasting insulin (<i>Ins</i>), postchallenge insulin (<i>PCIns</i>), fasting glucose (<i>Glu</i>), postchallenge glucose (<i>PCGlu</i>), body mass index (<i>BMI</i>), waist/hip ratio (<i>WHR</i>), high density lipoprotein cholesterol (<i>HDL</i>), triglycerides (<i>Trig</i>), systolic blood pressure (<i>SBP</i>), diastolic blood pressure (<i>DBP</i>).</p

A table of measures demonstrating the ‘quality’ of each cluster component.

<p>The demonstrate the of the variable when regressed on the remaining variables in the cluster to which it is assigned. The is the greatest when the variable is regressed on any other cluster produced in the analysis. The is a measure of cluster ‘quality’. When a variable has a high within its own cluster and low to any other, the variable demonstrates a strong fit to the cluster in whch it is assigned. Abbreviation: Fasting insulin (<i>Ins</i>), postchallenge insulin (<i>PCIns</i>), fasting glucose (<i>Glu</i>), postchallenge glucose (<i>PCGlu</i>), body mass index (<i>BMI</i>), waist/hip ratio (<i>WHR</i>), high density lipoprotein cholesterol (<i>HDL</i>), triglycerides (<i>Trig</i>), systolic blood pressure (<i>SBP</i>), diastolic blood pressure (<i>DBP</i>).</p

The correlations between cluster components.

<p>The correlations between cluster components are analogous to inter-cluster correlations in a factor analysis with oblique rotation. Cluster 3 and cluster 4 demonstrate the strongest correlation (0.414), indicating an association between obesity and insulin resistance risk factors.</p

The factor pattern from an exploratory factor analysis.

<p>A principal factor analysis is selected with four factors retained and an oblique promax rotation used. Significance is highlighted in bold text and is determined by a factor loading >0.3. The significant loadings suggest a blood pressure factor (factor 3) and a lipid factor (factor 4). Factor 1 and factor 2 demonstrate some overlap with <i>PCIns</i> loading significantly on each. Abbreviation: Fasting insulin (<i>Ins</i>), postchallenge insulin (<i>PCIns</i>), fasting glucose (<i>Glu</i>), postchallenge glucose (<i>PCGlu</i>), body mass index (<i>BMI</i>), waist/hip ratio (<i>WHR</i>), high density lipoprotein cholesterol (<i>HDL</i>), triglycerides (<i>Trig</i>), systolic blood pressure (<i>SBP</i>), diastolic blood pressure (<i>DBP</i>).</p

A dendrogram of the cluster structure produced by VARCLUS.

<p>A hierarchical clustering produced from the VARCLUS analysis with four cluster components selected. Abbreviation: Fasting insulin (<i>Ins</i>), postchallenge insulin (<i>PCIns</i>), fasting glucose (<i>Glu</i>), postchallenge glucose (<i>PCGlu</i>), body mass index (<i>BMI</i>), waist/hip ratio (<i>WHR</i>), high density lipoprotein cholesterol (<i>HDL</i>), triglycerides (<i>Trig</i>), systolic blood pressure (<i>SBP</i>), diastolic blood pressure (<i>DBP</i>).</p

Magnitude of the influence (in percentage) of different levels of risk of bias on the treatment effect estimates (only comparisons with at least 5 studies in each ROB group).

<p>Magnitude of the influence (in percentage) of different levels of risk of bias on the treatment effect estimates (only comparisons with at least 5 studies in each ROB group).</p

Inter-factor correlations from the exploratory factor analysis solution.

<p>An oblique solution produces correlated factors. The inter-factor correlations demonstrate a high correlation between factor 1 and factor 2 (0.54). There is also a large correlation between factor 1 and factor 4 (0.49).</p

Meta-regression assessment of the influence of different levels of risk of bias on the treatment effect estimates.

<p>* p-value<0.05</p><p>(1): Sequence generation; (2): Allocation concealment; (3) Blinding of outcome assessment; (4) Sequence generation OR Allocation concealment; (5) Sequence generation OR Blinding of outcome assessment; (6) Allocation concealment OR Blinding of outcome assessment; (7) Sequence generation OR Allocation concealment ORBlinding of outcome assessment. NA = not available</p

An example labelled Hasse diagram.

<p>The ellipses in the labelled Hasse diagram (LHD) demonstrate near dependencies and any variables not involved in a linear dependency are displayed as squares. The rank of each subset (illustrated on the left of the LHD) demonstrates the dimensionality of the flat. Lines between objects are used to show the sources of any dependency. An measure is displayed in brackets alongside each variable to demonstrate the fit to the flat in which it is assigned. Abbreviations: Fasting insulin (<i>Ins</i>), postchallenge insulin (<i>PCIns</i>), fasting glucose (<i>Glu</i>), postchallenge glucose (<i>PCGlu</i>), body mass index (<i>BMI</i>), waist/hip ratio (<i>WHR</i>), high density lipoprotein cholesterol (<i>HDL</i>), triglycerides (<i>Trig</i>), systolic blood pressure (<i>SBP</i>), diastolic blood pressure (<i>DBP</i>).</p