When faced with missing data, researchers should assess missingness mechanisms, report both complete-case and multiple-imputation estimates under MCAR/MAR while accounting for power-versus-bias tradeoffs, and employ pattern-mixture sensitivity analyses to test robustness when MNAR is plausible.
To mitigate bias caused by selective attrition in cohort studies, researchers have been advised to link cohort data to administrative data sources, as these are not prone to attrition and provide more objectively measured information. However, consent for data linkage is often influenced by mental health status and...
C. Driessens, P. W. Smith· BMJ Connections Mental Healt...· 0 citations
Missing data poses a great challenge in much research, and if not appropriately handled, can negatively impact the analysis and bias the results and study conclusions. This article assesses the performance of numerous methods used to impute missing data by using Root Mean Squared Error (RMSE) under various missing data...
Rahibu A. Abassi, R. J. Akarro· Tanzania Journal of Science· 0 citations
Patient-reported outcomes from longitudinal clinical trials on hemodialysis typically have missing data, yet there is a lack of empirical evidence to guide appropriate analysis and interpretation.
To compare approaches for analysis and interpretation of patient-reported outcomes in the Frequent Hemodialysis...
Victoria Zdunek, Guang-Yong Zou, Amit X. Garg et al.· Canadian Journal of Kidney H...· 0 citations
Generalized pairwise comparisons are increasingly being used in randomized clinical trials. This study evaluates how missing data strategies impact the operating characteristics of endpoints combining overall survival and a longitudinal outcome. A simulation study was conducted to evaluate the impact of censoring and m...
R. V. van Eijk, Ying Lu· Statistics in Medicine· 0 citations
Introduction
Real-world clinical registry data from addiction treatment centers are a valuable source for investigating the association between substance use disorder (SUD) and cardiovascular disease (CVD), but are frequently affected by missing data arising from irregular attendance and heterogeneous data collection....
Letizia Lorusso, N. Bartolomeo, Francesco Ciccirillo et al.· Epidemiology Biostatistics a...· 0 citations
Differences between item-level and score-level imputation were generally modest, although score-level imputation tended to yield slightly lower RMSE and bias magnitudes under more severe missingness conditions.
C. Veltri, U. Grittner, P. Gebert· BMC Medical Research Methodo...· 0 citations
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