Nonignorable Missing Data, Single Index Propensity Score and Profile Synthetic Distribution Function
نویسندگان
چکیده
منابع مشابه
Joint modeling of event time and nonignorable missing longitudinal data.
Survival studies usually collect on each participant, both duration until some terminal event and repeated measures of a time-dependent covariate. Such a covariate is referred to as an internal time-dependent covariate. Usually, some subjects drop out of the study before occurrence of the terminal event of interest. One may then wish to evaluate the relationship between time to dropout and the ...
متن کاملPropensity score based data analysis
For some time, propensity score (PS) based methods have been frequently applied in the analysis of observational and registry data. The PS is the conditional probability of a certain treatment given patient’s covariates. PS methods are used to eliminate imbalances in baseline covariate distributions between treatment groups and permit to estimate marginal effects. The package nonrandom is a too...
متن کاملA Semiparametric Approach for Analyzing Nonignorable Missing Data
In missing data analysis, there is often a need to assess the sensitivity of key inferences to departures from untestable assumptions regarding the missing data process. Such sensitivity analysis often requires specifying a missing data model that commonly assumes parametric functional forms for the predictors of missingness. In this paper, we relax the parametric assumption and investigate the...
متن کاملBayesian quantile regression for longitudinal studies with nonignorable missing data.
We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introduc...
متن کاملParametric fractional imputation for mixed models with nonignorable missing data
Inference in the presence of non-ignorable missing data is a widely encountered and difficult problem in statistics. Imputation is often used to facilitate parameter estimation, which allows one to use the complete sample estimators on the imputed data set. We develop a parametric fractional imputation (PFI) method proposed by Kim (2011), which simplifies the computation associated with the EM ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Business & Economic Statistics
سال: 2021
ISSN: 0735-0015,1537-2707
DOI: 10.1080/07350015.2020.1860065