However, in some cases, analysts may need more information or statistics from the imputation process to help them with their analyses. The purpose of this paper is to demonstrate how to use SAS/STAT and SAS/IML to build model-based multiple imputation macros such that analysts can streamline ...
A model-based multiple imputation approach for analyzing sample data with non-detects is proposed. The imputation approach involves randomly generating observations below the detection limit using the detected sample values and then analyzing the data using complete sample techniques, along with suitable ...
(1998) `Comparison of model- and multiple imputation-based approaches to longitudinal analyses with partial missingness', Struc- tural Equation, 51(1), pp. 1 - 21.Duncan, T.E., Duncan, S.C., & Li, F. (1998). A comparison of model- and multiple imputation-based approaches to ...
This approach was found toout-perform the three most commonly used imputation methods for missing data handling inpavement management: linear interpolation method, substitution by mean method, andregression method. However, the SMI approach estimates missing data values purely basedon statistical ...
(2005), and the idea of converting binary and ordinal longitudinal outcomes to multivariate normal outcomes in a sensible way so that re-conversion to the original scale yields the original specified marginal expectations and correlations after performing multiple imputation (Demirtas and Hedeker, 2007...
propose an estimation method for the semiparametric AFTMC model based on the multiple imputation (MI) method. Both the rank estimation method (Jin et al., 2003) and the profile likelihood method (Zeng and Lin, 2007) for the semiparametric AFT model are considered in the MI approach. The pap...
When there are missing censoring indicators (MCIs), the SRC approach employs a model-based estimate of the conditional expectation of the censoring indicator given the observed time, where the model parameters are estimated using only the complete cases. The multiple imputations approach, on the ...
Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but achieving fine-resolution and high-accuracy deconvolution remains a challenge. Here we introduce MeDuSA, a mixed model-based method that le
Robust Model-Based Inference for Incomplete Data via Penalized Spline Propensity Prediction Parametric model-based regression imputation is commonly applied to missing-data problems, but is sensitive to misspecification of the imputation model. Li... H An,RJA Little - Communications in Statistics - Simu...
aRobustness of the MMRM model to departure from the missing-at-random assumption will be explored using sensitivity analysis based on the pattern mixture model plus multiple imputation approach. MMRM模型的强壮到离开从缺掉在任意假定使用根据样式混合物模型的灵敏度分析将被探索加上多种归咎方法。[translate...