Missing Data in Marginal Structural Models: A Plasmode Simulation Study Comparing Multiple Imputation and Inverse Probability Weighting.
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TL;DR: A plasmode simulation study to compare the validity and precision of MSMs estimates using complete case analysis, multiple imputation, and inverse probability weighting in the presence of missing data on time-independent and time-varying confounders finds that MI seems to confer an advantage over IPW in MSMs applications.
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Abstract: Background:The use of marginal structural models (MSMs) to adjust for time-varying confounding has increased in epidemiologic studies. However, in the setting of MSMs, recommendations for how best to handle missing data are contradictory. We present a plasmode simulation study to compare the validit
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Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain, Iran
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TL;DR: In this paper, a substantial number of missing values are taken into consideration before using them for further analysis, particularly for numerical ground-watchers, in order to improve the accuracy of groundwater-level time series.
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Missing data reporting in clinical pharmacy research.
Sujita W. Narayan,Kar Yu Ho,Jonathan Penm,Barbara Mintzes,Ardalan Mirzaei,Carl R. Schneider,Asad E. Patanwala +6 more
TL;DR: Very few studies in clinical pharmacy literature report any handling of missing data, which has the potential to lead to biased results and advocate that researchers should report how missing data were handled to increase the transparency of findings and minimize bias.
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Dealing With Treatment-Confounder Feedback and Sparse Follow-up in Longitudinal Studies: Application of a Marginal Structural Model in a Multiple Sclerosis Cohort.
TL;DR: A cohort of patients with relapsing onset MS from British Columbia, Canada is accessed to examine the potential survival advantage associated with beta-interferon exposure using a marginal structural model and several single-level and multi-level multiple imputation approaches were considered.
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Statistical plasmode simulations-Potentials, challenges and recommendations.
N. Schreck,Alla Slynko,Maral Saadati,Axel Benner +3 more
TL;DR: The concept of statistical plasmodes is introduced as well as the proposed plasmode generation procedure by means of a public real RNA data set on breast carcinoma patients and a step-wise procedure for their generation is provided.
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References
mice: Multivariate Imputation by Chained Equations in R
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Missing data: Our view of the state of the art.
Joseph L. Schafer,John W. Graham +1 more
TL;DR: 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.
Statistical Analysis With Missing Data
TL;DR: Generalized Estimating Equations is a good introductory book for analyzing continuous and discrete correlated data using GEE methods and provides good guidance for analyzing correlated data in biomedical studies and survey studies.
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Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.
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Multiple Imputation For Nonresponse In Surveys
Lena Osterhagen
- 01 Jan 2016
TL;DR: The multiple imputation for nonresponse in surveys is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can download it instantly.
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