Comparison of methods to handle missing values in a continuous index test in a diagnostic accuracy study – a simulation study
Bibliographic record
Abstract
BACKGROUND: Most diagnostic accuracy studies have applied a complete case analysis (CCA) or single imputation approach to address missing values in the index test, which may lead to biased results. Therefore, this simulation study aims to compare the performance of different methods in estimating the AUC of a continuous index test with missing values in a single-test diagnostic accuracy study. METHODS: We simulated data for a reference standard, continuous index test, and three covariates using different sample sizes, prevalences of the target condition, correlations between index test and covariates, and true AUCs. Subsequently, missing values were induced for the continuous index test, assuming varying proportions of missing values and missingness mechanisms. Seven methods (multiple imputation (MI), empirical likelihood, and inverse probability weighting approaches) were compared to a CCA in terms of their performance to estimate the AUC given missing values in the index test. RESULTS: Under missing completely at random (MCAR) and many missing values, CCA gives good results for a small sample size and all methods perform well for a large sample size. If missing values are missing at random (MAR), all methods are severely biased if the sample size and prevalence are small. An augmented inverse probability weighting method and standard MI methods perform well with higher prevalence and larger sample size, respectively. Most methods give biased results if missing values are missing not at random (MNAR) and the correlation or the sample size and prevalence are low. Methods using the covariates improve with increasing correlation. CONCLUSIONS: Most methods perform well if the proportion of missing values is small. Given a higher proportion of missing values and MCAR, we would recommend to conduct a CCA and standard MI methods for a small and large sample size, respectively. In the absence of better alternatives we recommend to conduct a CCA and to discuss its limitations, if the sample size is small, and missing values are M(N)AR. Standard MI methods and the augmented inverse probability approach may be a good alternative, if the sample size and/or correlation increases. All methods are biased under MNAR and a low correlation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".