Background: Estrogen receptor β (ERβ) is abundantly expressed in colorectal tissue, but its role in colorectal carcinogenesis remains elusive. ER novel co-regulator, proline-, glutamic acidand leucine-rich protein 1 (PELP1/MNAR) has been characterized, but its expression in colorectal carcinomas has not been investigated. Methods: ERα, ERβ and PELP1/MNAR protein expression were evaluated by imm...

For studies with missing data, it is being increasingly recommended to provide sensitivity analyses that assume data are Missing Not at Random (MNAR). In certain therapeutic areas, MNAR assumptions about the missingness mechanism are even recommended for the primary analysis. MNAR assumptions can be modeled within several statistical frameworks, one of which is known as pattern-mixture models (...

In the analyses of incomplete longitudinal clinical trial data, there has been a shift, away from simple methods that are valid only if the data are missing completely at random, to more principled ignorable analyses, which are valid under the less restrictive missing at random assumption. The availability of the necessary standard statistical software nowadays allows for such analyses in pract...

Psychologists often use latent transition analysis (LTA) to investigate state-to-state change in discrete latent constructs involving delinquent or risky behaviors. In this setting, latent-state-dependent nonignorable missingness is a potential concern. For some longitudinal models (e.g., growth models), a large literature has addressed extensions to accommodate nonignorable missingness. In con...

In addition to their well-studied ability to transactivate the expression of many genes, estrogen receptors (ERs) also effect cytoplasmic changes occurring too quickly to be accounted for by gene expression. Indeed, these immediate, "nongenomic" effects have been intensely studied, but the identification of important protein partners in quick ER-mediated signaling has lagged behind. Now, Wong e...

The missing not at random (MNAR) mechanism may bias parameter estimates and even distort study results. This study compared the maximum likelihood (ML) selection model based on missing at random (MAR) mechanism and the Diggle-Kenward selection model based on MNAR mechanism for handling missing data through a Monte Carlo simulation study. Four factors were considered, including the missingness m...

The past decade has seen a noticeable shift in missing data handling techniques that assume a missing at random (MAR) mechanism, where the propensity for missing data on an outcome is related to other analysis variables. Although MAR is often reasonable, there are situations where this assumption is unlikely to hold, leading to biased parameter estimates. One such example is a longitudinal stud...

BACKGROUND Most epidemiological studies have missing information, leading to reduced power and potential bias. Estimates of exposure-outcome associations will generally be biased if the outcome variable is missing not at random (MNAR). Linkage to administrative data containing a proxy for the missing study outcome allows assessment of whether this outcome is MNAR and the evaluation of bias. We ...

We propose a probabilistic matrix factorization model for collaborative filtering that learns from data that is missing not at random (MNAR). Matrix factorization models exhibit state-of-the-art predictive performance in collaborative filtering. However, these models usually assume that the data is missing at random (MAR), and this is rarely the case. For example, the data is not MAR if users r...