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A Multiple Imputation Workflow for Handling Missing Covariate Data in Pharmacometrics Modeling [PDF]
CPT: Pharmacometrics & Systems PharmacologyCovariate missingness is a prevalent issue in pharmacometrics modeling. Incorrect handling of missing covariates can lead to biased parameter estimates, adversely affecting clinical practice and drug development dosing decisions.
My‐Luong Vuong +2 more
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Model checking in multiple imputation: an overview and case study
Emerging Themes in Epidemiology, 2017 Background Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values.
Cattram D. Nguyen +2 more
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Multiple Imputation for Longitudinal Data: A Tutorial. [PDF]
Stat MedABSTRACTLongitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for this clustering by individual is required.
Wijesuriya R +5 more
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Multiple Imputation: Theory and Method
International Statistical Review, 2003 SummaryIn this review paper, we discuss the theoretical background of multiple imputation, describe how to build an imputation model and how to create proper imputations. We also present the rules for making repeated imputation inferences. Three widely used multiple imputation methods, the propensity score method, the predictive model method and the ...
exaly +4 more sources
Model-based standardization using multiple imputation [PDF]
BMC Medical Research MethodologyBackground When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates.
Antonio Remiro-Azócar +2 more
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Multiple Imputation for Bounded Variables [PDF]
Psychometrika, 2018 Missing data are a common issue in statistical analyses. Multiple imputation is a technique that has been applied in countless research studies and has a strong theoretical basis. Most of the statistical literature on multiple imputation has focused on unbounded continuous variables, with mostly ad hoc remedies for variables with bounded support. These
GERACI Marco, MCLAIN Alexander
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Methods to Handle Incomplete Data
MAMC Journal of Medical Sciences, 2020 Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny +2 more
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JAMA, 2015 In this issue of JAMA, Asch et al1 report results of a cluster-randomized clinical trial designed to evaluate the effects of physician financial incentives, patient incentives, or shared physician and patient incentives on low density lipoprotein cholesterol (LDL-C) levels among patients with high cardiovascular risk.
Peng, Li +2 more
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Practical strategies for handling breakdown of multiple imputation procedures
Emerging Themes in Epidemiology, 2021 Multiple imputation is a recommended method for handling incomplete data problems. One of the barriers to its successful use is the breakdown of the multiple imputation procedure, often due to numerical problems with the algorithms used within the ...
Cattram D. Nguyen +2 more
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BMJ Open, 2022 Objectives Despite the possible large number of missing values on the 25-question Geriatric Locomotive Function Scale (GLFS-25), how we should treat them is unknown.
Sakae Tanaka +9 more
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