On the use of multiple imputation in handling missing values in longitudinal studies

2004 ◽  
Author(s):  
Pui-shan Chan
Keyword(s):  
Multiple Imputation ◽  
Longitudinal Studies ◽  
Missing Values

scholarly journals Strategies for Multiple Imputation in Longitudinal Studies

2010 ◽  
Vol 172 (4) ◽  
pp. 478-487 ◽  
Author(s):  
M. Spratt ◽  
J. Carpenter ◽  
J. A. C. Sterne ◽  
J. B. Carlin ◽  
J. Heron ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Longitudinal Studies

scholarly journals Comparison of Methods for Handling Covariate Missingness in Propensity Score Estimation with a Binary Exposure

2019 ◽  
Author(s):  
Donna Coffman ◽  
Jiangxiu Zhou ◽  
Xizhen Cai
Keyword(s):  
Propensity Score ◽  
Multiple Imputation ◽  
Missing Values ◽  
Propensity Scores ◽  
Causal Effect ◽  
Nonparametric Approach ◽  
Split Method ◽  
Mean Imputation ◽  
Substantial Bias ◽  
Effect Estimation

Abstract Background Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values in covariates.Method Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP), and treatment mean imputation. However, there are other potentially useful approaches that have not been evaluated, including single imputation (SI) + prediction error (PE), SI+PE + parameter uncertainty (PU), and Generalized Boosted Modeling (GBM), which is a nonparametric approach for estimating propensity scores in which missing values are automatically handled in the estimation using a surrogate split method. To evaluate the performance of these approaches, a simulation study was conducted.Results Results suggested that SI+PE, SI+PE+PU, MI, and MIMP perform almost equally well and better than treatment mean imputation and GBM in terms of bias; however, MI and MIMP account for the additional uncertainty of imputing the missingness.Conclusions Applying GBM to the incomplete data and relying on the surrogate split approach resulted in substantial bias. Imputation prior to implementing GBM is recommended.

scholarly journals Improving Outcome Predictions for Patients Receiving Mechanical Circulatory Support by Optimizing Imputation of Missing Values

2021 ◽  
Author(s):  
Byron C. Jaeger ◽  
Ryan Cantor ◽  
Venkata Sthanam ◽  
Rongbing Xie ◽  
James K. Kirklin ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Risk Prediction ◽  
Random Forests ◽  
Missing Values ◽  
Prediction Models ◽  
Model Performance ◽  
Circulatory Support ◽  
Risk Prediction Models ◽  
Prognostic Accuracy ◽  
The Mean

Background: Risk prediction models play an important role in clinical decision making. When developing risk prediction models, practitioners often impute missing values to the mean. We evaluated the impact of applying other strategies to impute missing values on the prognostic accuracy of downstream risk prediction models, that is, models fitted to the imputed data. A secondary objective was to compare the accuracy of imputation methods based on artificially induced missing values. To complete these objectives, we used data from the Interagency Registry for Mechanically Assisted Circulatory Support. Methods: We applied 12 imputation strategies in combination with 2 different modeling strategies for mortality and transplant risk prediction following surgery to receive mechanical circulatory support. Model performance was evaluated using Monte-Carlo cross-validation and measured based on outcomes 6 months following surgery using the scaled Brier score, concordance index, and calibration error. We used Bayesian hierarchical models to compare model performance. Results: Multiple imputation with random forests emerged as a robust strategy to impute missing values, increasing model concordance by 0.0030 (25th–75th percentile: 0.0008–0.0052) compared with imputation to the mean for mortality risk prediction using a downstream proportional hazards model. The posterior probability that single and multiple imputation using random forests would improve concordance versus mean imputation was 0.464 and >0.999, respectively. Conclusions: Selecting an optimal strategy to impute missing values such as random forests and applying multiple imputation can improve the prognostic accuracy of downstream risk prediction models.

scholarly journals Enhancing the Human Health Status Prediction: the ATHLOS Project

2021 ◽  
Author(s):  
Panagiotis Anagnostou ◽  
Sotiris Tasoulis ◽  
Aristidis G. Vrahatis ◽  
Spiros Georgakopoulos ◽  
Matthew Prina ◽  
...  
Keyword(s):  
Longitudinal Studies ◽  
Regression Models ◽  
Missing Values ◽  
Data Imputation ◽  
High Complexity ◽  
Preventive Healthcare ◽  
Horizon 2020 ◽  
Research And Innovation ◽  
The Impact ◽  
Learning Data

AbstractPreventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized as part of solving the aforementioned challenges, respectively. Towards this direction, we focus on the development of a complete methodology for the ATHLOS (Ageing Trajectories of Health: Longitudinal Opportunities and Synergies) Project - funded by the European Union’s Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health. The inherent complexity of the provided dataset lie in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we particularly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicine’s crucial role.

scholarly journals Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies

2013 ◽  
Vol 03 (04) ◽  
pp. 26-40 ◽  
Author(s):  
Christophe Genolini ◽  
René Écochard ◽  
Hélène Jacqmin-Gadda
Keyword(s):  
Longitudinal Studies ◽  
Missing Values ◽  
New Method

scholarly journals Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation

2021 ◽  
Vol 18 (16) ◽  
pp. 8375
Author(s):  
Thelma Dede Baddoo ◽  
Zhijia Li ◽  
Samuel Nii Odai ◽  
Kenneth Rodolphe Chabi Boni ◽  
Isaac Kwesi Nooni ◽  
...  
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Real World ◽  
Missing Values ◽  
Total Error ◽  
Extensive Study ◽  
Error Measurement ◽  
Missing Data Imputation ◽  
Single Station ◽  
Real World Datasets

Reconstructing missing streamflow data can be challenging when additional data are not available, and missing data imputation of real-world datasets to investigate how to ascertain the accuracy of imputation algorithms for these datasets are lacking. This study investigated the necessary complexity of missing data reconstruction schemes to obtain the relevant results for a real-world single station streamflow observation to facilitate its further use. This investigation was implemented by applying different missing data mechanisms spanning from univariate algorithms to multiple imputation methods accustomed to multivariate data taking time as an explicit variable. The performance accuracy of these schemes was assessed using the total error measurement (TEM) and a recommended localized error measurement (LEM) in this study. The results show that univariate missing value algorithms, which are specially developed to handle univariate time series, provide satisfactory results, but the ones which provide the best results are usually time and computationally intensive. Also, multiple imputation algorithms which consider the surrounding observed values and/or which can understand the characteristics of the data provide similar results to the univariate missing data algorithms and, in some cases, perform better without the added time and computational downsides when time is taken as an explicit variable. Furthermore, the LEM would be especially useful when the missing data are in specific portions of the dataset or where very large gaps of ‘missingness’ occur. Finally, proper handling of missing values of real-world hydroclimatic datasets depends on imputing and extensive study of the particular dataset to be imputed.

scholarly journals The efficiency of multiple imputation and maximum likelihood methods for estimating missing values

2018 ◽  
Vol 11 (16) ◽  
pp. 1-11
Author(s):  
Tlhalitshi Volition Montshiwa ◽  
Ntebo Moroke ◽  
Elias Munapo ◽  
◽  
◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Missing Values ◽  
Likelihood Methods ◽  
Maximum Likelihood Methods
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