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REVIEW 2 major objections 8 minor 36 references

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data

T0 review · 2 major / 8 minor · reviewed 2026-07-09 · glm-5.2

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Pith's one-line read Simple kNN beats MICE for missing clinical data at high rates

desk verdict Solid simulation study on imputation methods for clinical prediction models; the kNN advantage at extreme missingness is real but partly mechanical and needs careful interpretation. read the letter →

arxiv 2607.07247 v1 pith:ZQDKXTM4 submitted 2026-07-08 stat.ME

Pakpoom Wongyikul , Noraworn Jirattikanwong , Natthanaphop Isaradech , Wuttipat Kiratipaisarl , Arintaya Phrommintikul , Wachiranun Sirikul , Phichayut Phinyo This is my paper
classification stat.ME
keywords missingnessimputationpredictioncomplexdatadevelopmentexternalk-nearest
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks a practical question: when developing clinical prediction models from real patient data with missing values, which imputation method should you use? The authors take a fully observed cardiac cohort of 8,245 patients, artificially introduce missingness under a missing-at-random (MAR) mechanism across 18 scenarios (varying variable type, predictor-outcome relationship, and missingness proportion from 30% to 90%), and compare five strategies: complete case analysis, MICE-FCS, MICE-PMM, missForest, and kNN. The core finding is that when missingness involves non-linear variables or reaches extreme levels (90%), the standard multiple imputation approaches (MICE) become unstable and overfit, while the simplest method tested, k-nearest neighbors imputation, maintains stable predictions, performs better on external validation, and runs 2-5 times faster than MICE. The paper argues that for sufficiently large development samples, kNN offers a computationally efficient and robust alternative to multiple imputation, particularly in complex missingness scenarios.

What carries the argument

The key machinery is the simulation pipeline itself: a fully observed real-world cardiac dataset is split into development and validation cohorts, missingness is artificially induced under MAR using multivariate amputation across 18 scenarios, five imputation methods are applied, logistic regression models with backward stepwise elimination are developed on bootstrap samples, and performance is evaluated using optimism-corrected AUC, calibration slope, mean absolute prediction error (MAPE), external validation, and computation time. The comparison of these metrics across methods and scenarios is the mechanism that carries the argument.

What would settle it

If a different real-world clinical dataset with naturally occurring missingness (rather than artificially induced) showed that MICE methods maintained superior prediction stability and external validation performance compared to kNN in non-linear or extreme missingness scenarios, the paper's central claim would be undermined.

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Extended reading notes

Core claim

The central discovery is that the relative performance of imputation methods depends critically on the structure of the missing data. When missingness is isolated to linear or categorical variables at moderate levels (30-60%), all methods perform comparably. However, when missingness involves non-linear variables or reaches 90%, MICE methods show increased prediction instability and overfitting, missForest overfits substantially in external validation, while kNN maintains the most consistent performance with the shortest computation time. This suggests that the complexity of the imputation method is not always beneficial, and a simpler deterministic approach may be preferable in complex, non

Load-bearing premise

The study assumes that the artificial missingness generation process, which creates missing data under a Missing at Random (MAR) mechanism with specific weight configurations, accurately reflects real-world clinical missingness patterns. If real-world missingness follows different mechanisms, such as Missing Not at Random (MNAR), the relative performance of the imputation methods may not generalize.

Editorial extensions

If this is right

  • Clinical prediction model developers may consider kNN imputation as a default strategy when dealing with large datasets and complex or extreme missingness, rather than defaulting to MICE.
  • The finding that method performance depends on variable characteristics (linear vs. non-linear) suggests that imputation strategy selection should be guided by exploratory data analysis of the missing variables' relationships.
  • The computational efficiency of kNN (2-5x faster than MICE) could enable more rapid model development and validation cycles in clinical research settings with large datasets.
  • The observed overfitting of missForest in external validation under non-linear scenarios suggests caution when using machine learning imputation methods that do not account for imputation uncertainty.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 8 minor

Summary. This simulation study compares five missing-data strategies (complete case analysis, MICE-FCS, MICE-PMM, missForest, and kNN) for clinical prediction model development using a real-world cardiac cohort of 8,245 patients. Missingness was artificially induced under a missing-at-random (MAR) mechanism across 18 scenarios varying by variable type, predictor-outcome relationship, and missingness proportion (30%, 60%, 90%). The study evaluates imputed-value integrity, model-structure stability, optimism-corrected performance, prediction stability (MAPE), external validation performance, and computation time. The central finding is that while all methods perform comparably in simple scenarios at moderate missingness, kNN demonstrates the most consistent prediction stability and computational efficiency in complex non-linear scenarios at extreme (90%) missingness, where MICE methods show increased instability and missForest overfits externally. The simulation design is thorough in its scenario coverage and evaluation metrics, and the use of both internal bootstrap validation and external validation is commendable.

Significance. The study addresses a practically important question for clinical prediction model development: how imputation method choice interacts with predictor-outcome complexity and missingness severity to affect model stability and transportability. The multi-dimensional evaluation framework—spanning imputed-value fidelity, model-structure stability, individual-level prediction stability, and external validation—is a genuine strength that goes beyond standard discrimination/calibration reporting. The inclusion of computation time is practically relevant for deployment considerations. The finding that method performance depends on variable characteristics and missingness complexity is useful for practitioners. However, the significance of the central claim regarding kNN's superiority is tempered by a fundamental confound between deterministic single imputation and stochastic multiple imputation in the measurement of prediction stability, which must be addressed before the conclusions can be considered well-supported.

major comments (2)
  1. The central claim that kNN provides 'superior prediction stability' in complex scenarios at 90% missingness is confounded by a mechanical property: kNN is a deterministic single-imputation method, so it will necessarily show lower bootstrap-to-bootstrap variability than MICE's stochastic multiple imputation. The MAPE metric, as defined in §Model evaluation, measures the absolute difference between bootstrap-derived and original-development-model predictions; a deterministic imputation method will mechanically produce smaller such differences. More critically, Supplementary Table S3 shows that at 90% missingness in the NLinCon scenario, kNN dramatically shrinks the variance of imputed eGFR (SD=14.8 vs. CCA reference 25.1) and substantially biases the median (89.2 vs. true 72.9, per Supplementary Table S4). MICE methods preserve variance much better (SD ~24-26) and produce medians closer (
  2. The external validation results in Supplementary Table S7 at 90% missingness for NLinCon show that kNN achieves external AUC of 0.68 (vs. reference 0.71), while missForest achieves 0.56 and MICE-PMM achieves 0.58. While kNN is indeed the least degraded, an AUC of 0.68 represents a meaningful loss from the 0.71 reference. The manuscript's framing of kNN as providing 'better external validation results' (Abstract) and 'superior prediction stability' (Conclusion) without adequately emphasizing that all methods are substantially degraded at 90% missingness risks overstating the practical implications. The authors should explicitly state the magnitude of performance degradation relative to the complete-data reference and discuss whether 'stable but biased' imputation is genuinely preferable for clinical practice, particularly given that the biased imputed values (e.g., eGFR median overestimat
minor comments (8)
  1. The Declaration of Generative AI states that ChatGPT-5 and Gemini were used for language editing. ChatGPT-5 does not appear to be a released model as of the manuscript date; the authors should verify and correct this reference.
  2. In the Methods (§Handling of missing data), the outcome variable was excluded from imputation to prevent data leakage. While this is a defensible choice, it is at odds with standard recommendations for multiple imputation in prediction modeling (e.g., references 10, 17), where including the outcome in the imputation model is generally advised to preserve predictor-outcome associations. The rationale for this choice should be briefly discussed.
  3. Supplementary Table S2 describes amputation weights but the text in the Missing Data Simulation Mechanics appendix uses slightly different variable lists (e.g., 'dbp' appears in some descriptions but not others). The mapping between the prose description and the table should be made consistent.
  4. Figure 4 caption references 'Red dashed vertical line indicates optimal MAPE % threshold at 20%' but the text in §Model evaluation does not explicitly define a 20% MAPE threshold. This threshold should be defined in the Methods or the caption should be made more self-contained.
  5. The sample size justification (§Sample size calculation) cites a minimum of 1,647 participants based on 18 predictors and 13% prevalence, then uses 4,123 as approximately 2.5-fold. The rationale for choosing 2.5-fold specifically (rather than 2-fold or 3-fold) is not provided and should be briefly discussed.
  6. Reference 21 (Albu et al., 2025) is cited for missForest implementation but appears to describe a different package ('missForestPredict'). The original missForest reference (Stekhoven & Bühlmann 2012, ref 15) is also cited; clarification on which implementation was used would be helpful.
  7. In the Results §3, the text states 'the mean and SD of the linear predictor (LP) were similar across most scenarios; however, at 90% missingness in the LinMix, NLinCon, NLinMix, and All conditions, MICE, particularly MICE-FCS, produced a notably high variance of mean and SD of the LP.' Supplementary Table S5 should be referenced here, and the specific values illustrating this high variance should be cited.
  8. The term 'prediction stability' is used throughout but is defined somewhat indirectly via the MAPE metric. A brief, explicit definition in the Introduction or Methods (e.g., 'the degree to which a CPM produces consistent risk estimates for the same individual when developed on different samples from the same target population') would improve clarity for readers unfamiliar with the concept.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: simulation design is self-contained with independent evaluation

full rationale

This is a simulation study with a straightforward, non-circular derivation chain. The authors take a fully observed real-world cardiac dataset, artificially induce missingness under MAR via multivariate amputation, apply five standard imputation algorithms (CCA, MICE-FCS, MICE-PMM, missForest, kNN), develop logistic regression models with backward elimination, and evaluate performance on an independent external validation cohort using optimism-corrected AUC, calibration slope, MAPE, and computation time. No step in this chain reduces to its inputs by construction. Critically, the outcome variable was explicitly excluded from imputation ('the primary outcome was excluded from imputation to prevent data leakage'), so the imputed values are not fitted to the target. The performance metrics (AUC, calibration slope, MAPE) are computed on held-out or external data, not on the same data used to fit imputations. There are no self-citations that are load-bearing for the central claim—the references are to standard methodological works by independent authors (van Buuren, Stekhoven, Troyanskaya, etc.). No uniqueness theorem or ansatz is invoked. The skeptic's concern that kNN's apparent stability advantage may be mechanically driven by its deterministic single-imputation nature (variance compression producing low bootstrap-to-bootstrap variability) is a validity and confounding concern, not a circularity concern—the paper does not define 'stability' in terms of kNN's properties and then claim kNN is stable. The MAPE metric is an independent measurement of prediction differences between bootstrap and original models, computed identically for all methods. The derivation is self-contained against external benchmarks.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The study uses standard statistical methods and does not invent new entities. The free parameters are tuning choices for the imputation algorithms and simulation design.

free parameters (5)
  • k in kNN = 10
    Chosen by the authors as the number of nearest neighbors for imputation.
  • m in MICE = 10
    Number of imputed datasets generated.
  • ntree in missForest = 50
    Number of trees used in the random forest imputation.
  • bootstrap iterations = 100
    Number of bootstrap resamples used for internal validation.
  • amputation weights = 0.2-0.4
    Weights assigned to variables to determine missingness probability under MAR.
assumptions (3)
  • domain assumption Missing at Random (MAR) assumption
    The simulation assumes missingness is related to observed data, not unobserved. This is a standard but untestable assumption in missing data research.
  • domain assumption Logistic regression is appropriate for the outcome
    The study uses logistic regression for model development, assuming it adequately captures the predictor-outcome relationship.
  • domain assumption The single dataset is representative
    The conclusions assume that the cardiac cohort used is representative of broader clinical prediction scenarios.

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Cite this review

Pith. "Pith review of Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data." pith.science (2026). https://pith.science/paper/ZQDKXTM4

@misc{pith2026260707247,
  author       = {Pith},
  title        = {Pith review of: Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZQDKXTM4}},
  note         = {Machine review of arXiv:2607.07247}
}
read the original abstract

Evidence remains limited on how missing-data strategies affect the stability of clinical prediction models across different predictor-outcome relationships and degrees of missingness. We conducted a simulation study using a fully observed real-world cardiac cohort of 8,245 patients, equally divided into development and external validation cohorts. Missing data were induced under a missing-at-random mechanism across 18 scenarios varying by variable type, predictor-outcome relationship, and missingness proportion. Five strategies were compared: complete case analysis, multiple imputation by chained equations with fully conditional specification, multiple imputation using predictive mean matching, missForest, and k-nearest neighbours. Logistic regression models were developed using backward stepwise elimination. Outcomes included optimism-corrected AUC, calibration slope, mean absolute prediction error, external validation performance, and computation time. When missingness involved isolated linear or categorical variables at 30%-60%, all methods maintained discrimination comparable to the complete-data model, with median AUCs of about 0.75. When missingness involved isolated non-linear variables or more complex patterns, predictive performance and calibration worsened as missingness increased, especially at 90%. In complex scenarios, multiple imputation showed greater prediction instability and overfitting, while missForest performed well internally but overfitted externally. k-nearest neighbours showed the most consistent performance, with stable predictions, better external validation results, and the shortest computation time. The optimal strategy may depend on the characteristics of variables with missing data. In sufficiently large development samples, k-nearest neighbours may provide a computationally efficient alternative.

Figures

Figures reproduced from arXiv: 2607.07247 by the authors.

Figure 1
Figure 1. Study simulation flow diagram. The figure summarizes the full analytical pipeline of the simulation study. After splitting datasets into development and validation datasets. The upper section illustrates the generation of missing data using different amputation scenarios and missingness proportions. The middle sections show the model development workflow, including bootstrap resampling, application of each imputatio… view at source ↗
Figure 2
Figure 2. Comparison of number of retained predictors across all scenarios and imputation methods [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Comparison of standard deviation (A) and median (B) of linear predictors (LP) across all scenarios and imputation methods. Abbreviation CCA, complete-case analysis; FOREST, missForest imputation; kNN, k-nearest neighbors; MAPE, mean absolute prediction error; MICE-FCS, multiple imputation by chained equations using fully conditional specification; MICE-PMM, multiple imputation by chained equations using predictive m… view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Comparison of AUC, calibration slope across all scenarios and imputation methods based on external validation dataset. Panel (A) shows external validated discrimination, assessed by the area under the receiver operating characteristic curve (AUC). Red dashed vertical l…

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