scholarly journals Joint Longitudinal Models for Dealing With Missing at Random Data in Trial-Based Economic Evaluations

2021 ◽  
Author(s):  
Andrea Gabrio ◽  
Rachael Hunter ◽  
Alexina J. Mason ◽  
Gianluca Baio
Keyword(s):  
Missing At Random ◽  
Economic Evaluations ◽  
Longitudinal Models ◽  
Random Data

scholarly journals Development of a Models of Interpersonal Competencies as a Complex System

2020 ◽  
Vol 44 (2) ◽  
pp. 247-255
Author(s):  
Boris Blažinić ◽  
Lovorka Gotal Dmitrović ◽  
Marko Stojić
Keyword(s):  
Simulation Modeling ◽  
Human Error ◽  
Data Entry ◽  
Missing At Random ◽  
Practical Skills ◽  
Metacognitive Skills ◽  
Ethical Values ◽  
Random Data ◽  
Not Missing At Random ◽  
Interpersonal Competencies

Competencies represent a dynamic combination of cognitive and metacognitive skills, knowledge and understanding, interpersonal and practical skills, and ethical values. Since there are many entities, as well as many activities between entities, according to system theory, the whole system belongs to complex systems. The paper develops a conceptual and computational model of interpersonal competences for the process of optimization and methodology design, using simulation modeling. The developed model enables: faster data collection, more accurate results, avoiding human error in data entry and processing, survey time can be measured and more easily restricted, NMAR (Not Missing at Random) data is avoided and socially desirable responses are more easily avoided.

scholarly journals Diagnosing missing always at random in multivariate data

Biometrika ◽  
2019 ◽  
Vol 107 (1) ◽  
pp. 246-253
Author(s):  
Iavor I Bojinov ◽  
Natesh S Pillai ◽  
Donald B Rubin
Keyword(s):  
Missing Data ◽  
Diagnostic Tests ◽  
Missing Values ◽  
Multivariate Data ◽  
Missing At Random ◽  
Sensitivity Analyses ◽  
Necessary Condition ◽  
Regularity Conditions ◽  
Random Data

Summary Models for analysing multivariate datasets with missing values require strong, often unassessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable, which is a two-fold assumption dependent on the mode of inference. The first part, which is the focus here, under the Bayesian and direct-likelihood paradigms requires that the missing data be missing at random; in contrast, the frequentist-likelihood paradigm demands that the missing data mechanism always produce missing at random data, a condition known as missing always at random. Under certain regularity conditions, assuming missing always at random leads to a condition that can be tested using the observed data alone, namely that the missing data indicators depend only on fully observed variables. In this note we propose three different diagnostic tests that not only indicate when this assumption is incorrect but also suggest which variables are the most likely culprits. Although missing always at random is not a necessary condition to ensure validity under the Bayesian and direct-likelihood paradigms, it is sufficient, and evidence of its violation should encourage the careful statistician to conduct targeted sensitivity analyses.

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