Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146847/1/rssc02669.pd
BACKGROUND: Multiple imputation is a popular approach to handling missing data in medical research, ...
Contains fulltext : 88952.pdf (publisher's version ) (Closed access)OBJECTIVE: We ...
Abstract Background Multiple imputation is frequently...
Analysis of matched case-control studies is often complicated by missing data on covariates. Analysi...
Most epidemiologic studies will encounter missing covariate data. Software packages typically used f...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
grantor: University of TorontoMissing data or incomplete data are very common in almost ev...
This paper studies a non-response problem in survival analysis where the occurrence of missing data ...
Missing observations are common in cluster randomised trials. The problem is exacerbated when modell...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
Sample selection arises when the outcome of interest is partially observed in a study. Although soph...
A practical guide to analysing partially observed data. Collecting, analysing and drawing inference...
In survival analysis, censored observations can be regarded as missing event time data. For analysis...
Epidemiologic studies are frequently susceptible to missing information. Omitting observations with ...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...
BACKGROUND: Multiple imputation is a popular approach to handling missing data in medical research, ...
Contains fulltext : 88952.pdf (publisher's version ) (Closed access)OBJECTIVE: We ...
Abstract Background Multiple imputation is frequently...
Analysis of matched case-control studies is often complicated by missing data on covariates. Analysi...
Most epidemiologic studies will encounter missing covariate data. Software packages typically used f...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
grantor: University of TorontoMissing data or incomplete data are very common in almost ev...
This paper studies a non-response problem in survival analysis where the occurrence of missing data ...
Missing observations are common in cluster randomised trials. The problem is exacerbated when modell...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
Sample selection arises when the outcome of interest is partially observed in a study. Although soph...
A practical guide to analysing partially observed data. Collecting, analysing and drawing inference...
In survival analysis, censored observations can be regarded as missing event time data. For analysis...
Epidemiologic studies are frequently susceptible to missing information. Omitting observations with ...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...
BACKGROUND: Multiple imputation is a popular approach to handling missing data in medical research, ...
Contains fulltext : 88952.pdf (publisher's version ) (Closed access)OBJECTIVE: We ...
Abstract Background Multiple imputation is frequently...
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