A Semiparametric Missing-Data-Induced Intensity Method for Missing Covariate Data in Individually Matched Case-Control Studies

Semiparametric Inference in Matched Case-Control Studies with Missing Covariate Data

An estimated-score approach for dealing with missing covariate data in matched case–control studies

Matched case–control designs are commonly used in epidemiological studies for estimating the effect of exposure variables on the risk of a disease by controlling the effect of confounding variables. Due to retrospective nature of the study, information on a covariate could be missing for some subjects. A straightforward application of the conditional logistic likelihood for analyzing matched ca...

Application of the missing-indicator method in matched case-control studies with incomplete data.

A common practice in matched case-control studies with incomplete data is to perform two analyses in parallel: a matched analysis of the complete pairs and an unmatched analysis of all subjects carried out after breaking the matching in the complete pairs. The missing-indicator method, which has the advantage of making use of the data in the incomplete pairs while still preserving the matching ...

Missing data imputation in multivariable time series data

Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...

A new semiparametric procedure for matched case-control studies with missing covariates