A simulation comparison of imputation methods for quantitative data in the presence of multiple data patterns

Solaro, Nadia; Barbiero, Alessandro; Manzi, Giancarlo; Ferrari, Pier Alda

doi:10.1080/00949655.2018.1530773

Cited by 12 publications

(6 citation statements)

References 26 publications

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“…Moreover, Solaro et al demonstrated that the relative performance of missForest varied with the MCAR data patterns and did not show a clear advantage. Overall, the imputation accuracy and applicability of missForest is still unclear [ 49 ]. We initially did not include patients with more than 50% missing data as it will require data imputation, which may affect our result.…”

Section: Discussionmentioning

confidence: 99%

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

et al. 2021

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Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

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Section: Discussionmentioning

confidence: 99%

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

et al. 2021

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“…Yet Shah et al [3] reported that missForest produced substantially biased estimates for variables missing at random (MAR) and poor coverage of confidence intervals compared with CALIBERrfimpute. Solaro et al [8] demonstrated that the relative performance of missForest varied with the MCAR data patterns and did not show a clear advantage. Overall, the imputation accuracy and applicability of missForest is still unclear.…”

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confidence: 99%

Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction

Hong

Lynn

2020

BMC Med Res Methodol

140

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Background: Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research. Unlike standard imputation approaches, RF-based imputation methods do not assume normality or require specification of parametric models. However, it is still inconclusive how they perform for non-normally distributed data or when there are non-linear relationships or interactions. Methods: To examine the effects of these three factors, a variety of datasets were simulated with outcomedependent missing at random (MAR) covariates, and the performances of the RF-based imputation methods missForest and CALIBERrfimpute were evaluated in comparison with predictive mean matching (PMM). Results: Both missForest and CALIBERrfimpute have high predictive accuracy but missForest can produce severely biased regression coefficient estimates and downward biased confidence interval coverages, especially for highly skewed variables in nonlinear models. CALIBERrfimpute typically outperforms missForest when estimating regression coefficients, although its biases are still substantial and can be worse than PMM for logistic regression relationships with interaction. Conclusions: RF-based imputation, in particular missForest, should not be indiscriminately recommended as a panacea for imputing missing data, especially when data are highly skewed and/or outcome-dependent MAR. A correct analysis requires a careful critique of the missing data mechanism and the interrelationships between the variables in the data.

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“…Other R packages that implement RF for missing data imputation include the CALIBERrfimpute, randomForest, randomSurvivalForest and randomForestSRC packages [60,61]. Several researchers compare various missing data imputation methods from these packages and conclude that missForest gives lower imputation error [61][62][63].…”

Section: Random Forests Methodsmentioning

confidence: 99%

Comparing Single and Multiple Imputation Approaches for Missing Values in Univariate and Multivariate Water Level Data

Umar

Gray

2023

Water

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Missing values in water level data is a persistent problem in data modelling and especially common in developing countries. Data imputation has received considerable research attention, to raise the quality of data in the study of extreme events such as flooding and droughts. This article evaluates single and multiple imputation methods used on monthly univariate and multivariate water level data from four water stations on the rivers Benue and Niger in Nigeria. The missing completely at random, missing at random and missing not at random data mechanisms were each considered. The best imputation method is identified using two error metrics: root mean square error and mean absolute percentage error. For the univariate case, the seasonal decomposition method is best for imputing missing values at various missingness levels for all three missing mechanisms, followed by Kalman smoothing, while random imputation is much poorer. For instance, for 5% missing data for the Kainji water station, missing completely at random, the Kalman smoothing, random and seasonal decomposition methods had average root mean square errors of 13.61, 102.60 and 10.46, respectively. For the multivariate case, missForest is best, closely followed by k nearest neighbour for the missing completely at random and missing at random mechanisms, and k nearest neighbour is best, followed by missForest, for the missing not at random mechanism. The random forest and predictive mean matching methods perform poorly in terms of the two metrics considered. For example, for 10% missing data missing completely at random for the Ibi water station, the average root mean square errors for random forest, k nearest neighbour, missForest and predictive mean matching were 22.51, 17.17, 14.60 and 25.98, respectively. The results indicate that the seasonal decomposition method, and missForest or k nearest neighbour methods, can impute univariate and multivariate water level missing data, respectively, with higher accuracy than the other methods considered.

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A simulation comparison of imputation methods for quantitative data in the presence of multiple data patterns

Cited by 12 publications

References 26 publications

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction

Comparing Single and Multiple Imputation Approaches for Missing Values in Univariate and Multivariate Water Level Data

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