We discuss inference for longitudinal clinical trials subject to possibly informative dropout. A selection of available methods is reviewed for the simple case of trials with two timepoints. Using data from two such clinical trials, each with two treatments, we demonstrate that different analysis methods can at times lead to quite different conclusions from the same data. We investigate propert...

We propose a modified version of the three-step estimation method for the latent class model with covariates, which may be used to estimate a latent Markov (LM) model with individual covariates and possible dropout. We illustrate the proposed approach through an application finalized to the study of the health status of elderly people hosted in Italian nursing homes. This application is based o...

We consider estimation in generalized linear mixed models (GLMM) for longitudinal data with informative dropouts. At the time a unit drops out, time-varying covariates are often unobserved in addition to the missing outcome. However, existing informative dropout models typically require covariates to be completely observed. This assumption is not realistic in the presence of time-varying covari...

Semicompeting risks data are often encountered in clinical trials with intermediate endpoints subject to dependent censoring from informative dropout. Unlike with competing risks data, dropout may not be dependently censored by the intermediate event. There has recently been increased attention to these data, in particular inferences about the marginal distribution of the intermediate event wit...

This study is part of a strategy aimed at using fluorescent polymerase chain reaction (PCR) on informative genetic microsatellite markers as a diagnostic tool in preimplantation genetic diagnosis (PGD) of severe monogenic disease. Two couples, both of whom had previously had children who were compound heterozygote for severe cystic fibrosis mutations, were offered PGD using fluorescent PCR of t...

Pattern mixture models are frequently used to analyze longitudinal data where missingness is induced by dropout. For measured responses, it is typical to model the complete data as a mixture of multivariate normal distributions, where mixing is done over the dropout distribution. Fully parameterized pattern mixture models are not identiied by incomplete data; Little (1993) has characterized sev...

Deep convolution neural networks are going deeper and deeper. However, the complexity of models is prone to overfitting in training. Dropout, one crucial tricks, prevents units from co-adapting too much by randomly dropping neurons during It effectively improves performance deep but ignores importance differences between neurons. To optimize this issue, paper presents a new dropout method calle...