Random Effects Pattern Mixture Model

13:31 Feb 29, 2020

This question was closed without grading. Reason: No acceptable answer

English to Russian translations [PRO]
Medical - Medical: Pharmaceuticals / Summary of Clinical Efficacy

English term or phrase: Random Effects Pattern Mixture Model

REM
For the sensitivity analysis based on the Random Effects Pattern Mixture Model (REM) with two patterns, study subjects are categorized as completers and dropouts to further examine the possible effects of missing data on study results.
Results of the sensitivity analysis based on PMM using a placebo-based multiple imputation method and based on the REM were consistent with those obtained for the MMRM model for both lurasidone 40 and 80 mg/day treatment groups, and therefore support the robustness of the primary efficacy analysis.
Results of the sensitivity analysis based on the PMM using a placebo-based multiple imputation method (Study D1050301 Table 14.2.2.1.4) and based on the REM (Study D1050301 Table 14.2.2.1.5) were consistent with those obtained for the MMRM model for both lurasidone 40 and 80 mg/day treatment groups, and therefore support the robustness of the primary efficacy analysis.

Julia Berezina KudoZ activity Questions: 180 (1 open) (1 without valid answers) (6 closed without grading) Answers: 0
Local time: 10:58

Summary of answers provided

3 -1

Случайных Эффектов Смешения-Паттернов Модели

Navid Azizi Pirmohamadi

Discussion entries: 2

Answers

1 hr confidence:

peer agreement (net): -1

Случайных Эффектов Смешения-Паттернов Модели

Explanation:
this is how it goes.
ABSTRACT
Random-effects models are popular for the analysis of longitudinal data in part because they easily handle missing
response data. If whether data are missing or not, known as missingness, is independent of the missing data, then
the missingness is ignorable. Random-effects pattern-mixture models are models that allow one to evaluate if the
missingness is ignorable. In these models, one or more between-subjects variables are created to represent patterns
of missing data, such as dropout, and are added to the model. Within each missing data pattern the missingness is
assumed to be ignorable. Typically, these models rely on only a small number of pattern indicators to represent the
missing data patterns, and the effects of the pattern indicators are fixed across subjects. Less common are
applications of models in which the missing data patterns are random effects. This paper considers both types of
models and their extensions to address participant dropout and intermittent patterns of missing data. In particular,
beginning with SAS/STAT version 9.4 TS1M20, it is possible to estimate a 3-level model using NLMIXED, making it
possible to fit a random pattern-mixture model that includes nonlinear coefficients. Empirical longitudinal psychiatric
data are used to illustrate these models.

https://www.researchgate.net/publication/232475500_Application_of_Random-Effects_Pattern-Mixture_Models_for_Missing_Data_in_Longitudinal_Stu
https://www.lexjansen.com/wuss/2015/43_Final_Paper_PDF.pdf

Navid Azizi Pirmohamadi
Germany
Local time: 09:58
Native speaker of:

English

Login or register (free and only takes a few minutes) to participate in this question.

You will also have access to many other tools and opportunities designed for those who have language-related jobs (or are passionate about them). Participation is free and the site has a strict confidentiality policy.

Return to KudoZ list

KudoZ™ translation help

The KudoZ network provides a framework for translators and others to assist each other with translations or explanations of terms and short phrases.

See also:

ProZ.com term search

Search millions of term translations

Random Effects Pattern Mixture Model

Answers

KudoZ™ translation help

Your current localization setting

Select a language

Random Effects Pattern Mixture Model

Answers

KudoZ™ translation help

You have native languages that can be verified

Your current localization setting

Select a language