Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.07247.

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pith.paper-citation-record.v1
2607.07247 v1

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Outbound references

Observation 573ad0f6-8d6f-4e8c-93d5-3de692f7cffe · outbound

This paper cites Riley RD, van der Windt D, Croft P, Moons KGM, editors: Oxford University Press; 2019 01 Feb 2019.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Riley RD, van der Windt D, Croft P, Moons KGM, editors: Oxford University Press; 2019 01 Feb 2019

Reference 1

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This paper cites Development and validation of a prediction model with missing predictor data: a practical approach.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Development and validation of a prediction model with missing predictor data: a practical approach

Reference 2

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Observation 94c756c9-7f63-4b1d-8261-802484b9500c · outbound

This paper cites Combining multiple imputation with internal model validation in clinical prediction modeling: a systematic methodological review.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Combining multiple imputation with internal model validation in clinical prediction modeling: a systematic methodological review

Reference 3

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This paper cites Bias arising from missing data in predictive models.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Bias arising from missing data in predictive models

Reference 4

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This paper cites Missing data should be handled differently for prediction than for description or causal explanation.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Missing data should be handled differently for prediction than for description or causal explanation

Reference 5

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Observation 9ca63bd2-b34b-4874-84a6-e1aee977c288 · outbound

This paper cites Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches

Reference 6

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Observation 7c331c4d-5dc1-49ac-b206-2437d849f612 · outbound

This paper cites The performance of prognostic models depended on the choice of missing value imputation algorithm: a simulation study.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data The performance of prognostic models depended on the choice of missing value imputation algorithm: a simulation study

Reference 7

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This paper cites Stability of multivariable fractional polynomial models with selection of variables and transformations: a bootstrap investigation.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Stability of multivariable fractional polynomial models with selection of variables and transformations: a bootstrap investigation

Reference 8

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This paper cites On stability issues in deriving multivariable regression models.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data On stability issues in deriving multivariable regression models

Reference 9

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Observation 0c5591bb-90ec-4e83-b319-6584067c8e65 · outbound

This paper cites Combining Missing Data Imputation and Internal Validation in Clinical Risk Prediction Models.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Combining Missing Data Imputation and Internal Validation in Clinical Risk Prediction Models

Reference 10

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Observation d548ea16-8451-492d-84c4-9625dc5c6af7 · outbound

This paper cites Multiple imputation of discrete and continuous data by fully conditional specification.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Multiple imputation of discrete and continuous data by fully conditional specification

Reference 11

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This paper cites Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified

Reference 12

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Observation 70f69c49-40e1-468b-b03b-dabe177015e9 · outbound

This paper cites Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Reference 13

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Observation d6c45716-5df2-4b2a-ba08-5482e3589c14 · outbound

This paper cites Comparison of imputation methods for missing laboratory data in medicine.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Comparison of imputation methods for missing laboratory data in medicine

Reference 14

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This paper cites MissForest—non-parametric missing value imputation for mixed-type data.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data MissForest—non-parametric missing value imputation for mixed-type data

Reference 15

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Observation f523722c-83d0-4af7-9297-500f3963f2ac · outbound

This paper cites Missing value estimation methods for DNA microarrays.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Missing value estimation methods for DNA microarrays

Reference 16

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Observation 298f98e4-19c5-447a-aa62-ac403c73fec8 · outbound

This paper cites Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study

Reference 17

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This paper cites Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes

Reference 18

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Observation d783f06c-86a3-45ba-a6f5-0256d375bd02 · outbound

This paper cites Generating missing values for simulation purposes: a multivariate amputation procedure.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Generating missing values for simulation purposes: a multivariate amputation procedure

Reference 19

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Observation 2a20a5fa-7268-4ccb-a4f8-2247f9e44f02 · outbound

This paper cites Evaluation of Four Multiple Imputation Methods for Handling Missing Binary Outcome Data in the Presence of an Interaction between a Dummy and a Continuous Variable.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Evaluation of Four Multiple Imputation Methods for Handling Missing Binary Outcome Data in the Presence of an Interaction between a Dummy and a Continuous Variable

Reference 20

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This paper cites missForestPredict—Missing data imputation for prediction settings.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data missForestPredict—Missing data imputation for prediction settings

Reference 21

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Observation 699d17a1-b6b2-46ed-b24b-ca18d8f46dd9 · outbound

This paper cites Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis

Reference 22

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This paper cites psfmi: Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data psfmi: Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets

Reference 23

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Observation 33bdc63d-d19c-4dc5-9265-56eefae7cda1 · outbound

This paper cites Re-evaluation of the comparative effectiveness of bootstrap- based optimism correction methods in the development of multivariable clinical prediction models.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Re-evaluation of the comparative effectiveness of bootstrap- based optimism correction methods in the development of multivariable clinical prediction models

Reference 24

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Observation 148bab80-8fe5-4253-8d09-9a9d0a179b3b · outbound

This paper cites Population median imputation was noninferior to complex approaches for imputing missing values in cardiovascular prediction models in clinical practice.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Population median imputation was noninferior to complex approaches for imputing missing values in cardiovascular prediction models in clinical practice

Reference 25

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This paper cites Neural Networks and the Bias/Variance Dilemma.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Neural Networks and the Bias/Variance Dilemma

Reference 26

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Observation cabc02cf-96b9-499e-8006-7f1cc43c1a86 · outbound

This paper cites Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T16:36:21.690162Z

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Observation e305b7b5-6ae6-4331-834f-a18c5cdb0d67 · outbound

This paper cites Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

Reference 28

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This paper cites Missing Data in Clinical Research: A Tutorial on Multiple Imputation.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Missing Data in Clinical Research: A Tutorial on Multiple Imputation

Reference 29

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This paper cites Developing prediction models for clinical use using logistic regression: an overview.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Developing prediction models for clinical use using logistic regression: an overview

Reference 30

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Observation 31d9ae95-fcf5-4fd7-86ef-a953aed72486 · outbound

This paper cites Missing data and prediction: the pattern submodel.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Missing data and prediction: the pattern submodel

Reference 31

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

Reference 32

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Observation e48f33d8-247f-408f-b7d1-19eaf632cb25 · outbound

This paper cites Just Another Variable.

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

Reference 33

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This paper cites Predictive Mean Matching (pmm) was applied to both continuous and categorical variables, identifying the 10 closest donors for imputation.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Predictive Mean Matching (pmm) was applied to both continuous and categorical variables, identifying the 10 closest donors for imputation

Reference 34

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This paper cites To rigorously prevent data leakage during model training, the primary outcome was deliberately excluded from the imputation predictor pool.

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data To rigorously prevent data leakage during model training, the primary outcome was deliberately excluded from the imputation predictor pool

Reference 35

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Observation 1c6a921d-59bb-431f-b310-702bc745e3ac · outbound

This paper cites Missing continuous variables were imputed using the median of the neighbors, while missing categorical variables were imputed using the maximum category (mode).

Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data Missing continuous variables were imputed using the median of the neighbors, while missing categorical variables were imputed using the maximum category (mode)

Reference 36

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