|
When to identify covariates in the review process
|
A priori (e.g., in protocol)
|
17
|
76, 92,93,95, 100, 98, 18, 26, 39, 40, 30, 59, 29, 31, 46, 94, 114
|
|
How to find important clinical covariates from trial information
|
Looking at forest plots (variation in point estimates/CI overlap/ adding a vertical line for levels of some clinical variable)
|
6
|
92, 98, 93, 97, 98, 94
|
| |
Proceed regardless of formal testing of statistical heterogeneity
|
5
|
35, 92, 97, 98, 29
|
| |
Looking at L’Abbe plots
|
4
|
98, 45, 93, 98
|
| |
Influence plot
|
3
|
98, 54, 85
|
| |
Looking at summary tables
|
2
|
92, 24
|
| |
Looking at funnel plots
|
2
|
49, 98
|
| |
Use conceptual frameworks to facilitate choice of covariates (i.e., using taxonomies for active ingredients)
|
2
|
98, 112
|
| |
I2 (look at the change in statistical heterogeneity by adding subgroups)
|
2
|
87, 100
|
| |
Plot of effect size against individual covariates
|
1
|
48
|
| |
Using an adaptation of multidimensional scaling (CoPlot)
|
1
|
55
|
| |
Plot of normalized z-scores
|
1
|
93
|
| |
Radial/Galbraith plot
|
1
|
93
|
| |
Frequency distributions
|
1
|
98
|
| |
Dose-response graph
|
1
|
3?
|
| |
Use P.I.C.O. model to guide choice of characteristics
|
1
|
115
|
| |
Use causal mediating processes
|
1
|
113
|
| |
Treat strata within trials as separate studies; these subgroups if similar across studies can be combined
|
1
|
46
|
|
Rationale for choice of covariate
|
Scientific (e.g., pathophysiological, pharmacologic argument)
|
10
|
7,76,92,93, 100, 18, 26, 59, 31, 115
|
| |
Previous research (e.g., large RCT)
|
3
|
76, 68, 100
|
| |
Clinical grounds
|
2
|
96, 100
|
| |
Indirect evidence
|
1
|
59
|
|
Personnel
|
Use of clinical experts
|
2
|
21, 115
|
| |
Blind to results of trials
|
1
|
35
|
|
Number of covariates/trials needed
|
Small number of covariates
|
7
|
92, 95, 100, 18, 26, 31, 94
|
| |
Each covariate investigation should be based on an adequate number of studies (e.g., 10 for every moderator)
|
6
|
100, 59, 50, 94, 115
|
| |
Investigators must report actual number of covariates investigated for reader to determine the potential for false-positives
|
1
|
115
|
|
Number of outcomes to investigate
|
Restrict investigations to small number of outcomes (e.g., primary)
|
1
|
26
|
| |
Limit to central question in the analysis
|
1
|
94
|
|
Interpretation of results of investigations
|
Use caution (4 resources note especially with post hoc testing)
|
12
|
100, 18, 29, 31, 85, 16, 20, 23, 25, 61, 32, 35
|
| |
Observational only
|
6
|
59, 23, 94, 98, 100, 114
|
| |
Exploratory or hypothesis generating only
|
4
|
32, 100, 40, 94
|
| |
Consider confounding between covariates
|
4
|
100, 50, 115, 59
|
| |
Consider artifactual causes of between-study variation
|
2
|
6, 98
|
| |
Consider biases (e.g., misclassification, dilution, selection)
|
2
|
93, 115
|
| |
Look at magnitude of the effect and the 95% CI; not just effect and p-value; consider precision of the subgroup effects (e.g., sample sizes in the studies dictate precision of the subgroup effects)
|
2
|
100, 115
|
| |
Seek evidence to justify claims of subgroup findings
|
1
|
26
|
| |
Identify knowledge gaps in the investigations
|
1
|
24
|
| |
Consider effect of variability within studies
|
1
|
19
|
| |
Consider if the magnitude is clinically important (i.e., differences in effect between subgroups)
|
1
|
100
|
| |
Think through causal relationships, especially directionality
|
1
|
113
|
| |
Use caution with variables grouped after randomization
|
1
|
23
|
| |
Consider parabolic relationships (i.e., beyond linear regression)
|
1
|
115
|
| |
Be cautious not to say there is a consistency of effect if no subgroup effects are found
|
1
|
115
|
|
Descriptive methods
|
Perform a narrative synthesis of these investigations
|
4
|
115, 98, 27, 100
|
| |
Other: 1. idea webbing, 2. qualitative case descriptions, 3. investigator/methodological/conceptual triangulation
|
1
|
98
|
|
Use of types of data
|
Aggregate patient data for trial level covariates
|
4
|
23, 25, 118, 46
|
| |
Only group characteristics derived prior to randomization (e.g., stratifying)
|
2
|
23, 46
|
| |
Individual patient data for participant level covariates
|
1
|
59
|
| |
Individual patient data only for all covariates where possible
|
1
|
59
|
