Analysis of multivariate responses in patient reported outcome measures: missing data and auxiliary variables
Bibliographic record
Abstract
Patient-reported outcomes measures (PROMs) are increasingly used in clinical registries and clinical trials to collect information about patient’s perspectives of their own health. Item non-response or missing data, which may occur when patients fail to complete or respond to PROMs question, threatens the validity of findings from the assessment of group differences or longitudinal change in PROMs. The goal of this research was to develop and evaluate methods for addressing item non-response in PROMs. Four related studies were undertaken using the population-based Winnipeg Regional Health Authority Joint Replacement Registry and simulated data. The first study compared the performance of non-negative matrix factorization (NNMF), which is an unsupervised machine-learning method that uses optimization techniques to detect a low-dimensional structure from the data, with full information maximum likelihood (FIML). The methods were applied to test for differential item functioning in multidimensional PROMs. The second study evaluated the performance of NNMF, FIML, and multiple imputation (MI) with conditional proportional odds model when estimating longitudinal change in latent variable means. The third study investigated the use of auxiliary variables, which are potential correlates of missingness in the data, in imputation model and compared the precision and bias of FIML, MI with and without auxiliary variable, when estimating longitudinal change in PROM scores. The fourth study proposed an enhanced weighted NNMF, which uses observed item responses as auxiliary variable to define weights for item-level imputation, and compares the performance with FIML, and NNMF. We found that the Type I error rates and statistical power for NNMF were comparable to the FIML method. The NNMF method is relatively efficient when sample size is large (i.e., >500) and the percentage of non-response is high, but less optimal under other data-analytic conditions. Also, we showed that including auxiliary information in the imputation model increased the precision and reduced the bias of the estimated parameters. This research contributes to the statistical literature on methods to address missing data in PROMs with potential applications in clinical and quality of life research. Also, it demonstrates the practicality of using observed item responses to define an auxiliary variable, which provides a basis for accessible approach of identifying auxiliary variable in PROMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".