Combining scores from different patient reported outcome measures in meta-analyses: when is it justified?
Bibliographic record
Abstract
BACKGROUND: Combining outcomes and the use of standardized effect measures such as effect size and standardized response mean across instruments allows more comprehensive meta-analyses and should avoid selection bias. However, such analysis ideally requires that the instruments correlate strongly and that the underlying assumption of similar responsiveness is fulfilled. The aim of the study was to assess the correlation between two widely used health-related quality of life instruments for patients with chronic obstructive pulmonary disease and to compare the instruments' responsiveness on a study level. METHODS: We systematically identified all longitudinal studies that used both the Chronic Respiratory Questionnaire (CRQ) and the St. George's Respiratory Questionnaire (SGRQ) through electronic searches of MEDLINE, EMBASE, CENTRAL and PubMed. We assessed the correlation between CRQ (scale 1-7) and SGRQ (scale 1-100) change scores and compared responsiveness of the two instruments by comparing standardized response means (change scores divided by their standard deviation). RESULTS: We identified 15 studies with 23 patient groups. CRQ change scores ranged from -0.19 to 1.87 (median 0.35, IQR 0.14-0.68) and from -16.00 to 3.00 (median -3.00, IQR -4.73-0.25) for SGRQ change scores. The correlation between CRQ and SGRQ change scores was 0.88. Standardized response means of the CRQ (median 0.51, IQR 0.19-0.98) were significantly higher (p < 0.001) than for the SGRQ (median 0.26, IQR -0.03-0.40). CONCLUSION: Investigators should be cautious about pooling the results from different instruments in meta-analysis even if they appear to measure similar constructs. Despite high correlation in changes scores, responsiveness of instruments may differ substantially and could lead to important between-study heterogeneity and biased meta-analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".