A two-part mixed-effects pattern-mixture model to handle zero-inflation and incompleteness in a longitudinal setting

2011 ◽  
Vol 53 (5) ◽  
pp. 716-734 ◽  
Author(s):  
Antonello Maruotti
Keyword(s):  
Mixture Model ◽  
Mixed Effects ◽  
Zero Inflation ◽  
Pattern Mixture Model

A pattern-mixture model for the analysis of censored quality-of-life data

2006 ◽  
Vol 25 (9) ◽  
pp. 1533-1546 ◽  
Author(s):  
Huiling Li ◽  
Daniel F. Heitjan
Keyword(s):  
Quality Of Life ◽  
Mixture Model ◽  
Pattern Mixture Model ◽  
Life Data

scholarly journals Data Missing Not at Random in Mobile Health Research: Assessment of the Problem and a Case for Sensitivity Analyses

10.2196/26749 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e26749
Author(s):  
Simon B Goldberg ◽  
Daniel M Bolt ◽  
Richard J Davidson
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Mixture Model ◽  
Mobile Health ◽  
Sensitivity Analyses ◽  
Research Assessment ◽  
Missing Not At Random ◽  
Pattern Mixture Model

Background Missing data are common in mobile health (mHealth) research. There has been little systematic investigation of how missingness is handled statistically in mHealth randomized controlled trials (RCTs). Although some missing data patterns (ie, missing at random [MAR]) may be adequately addressed using modern missing data methods such as multiple imputation and maximum likelihood techniques, these methods do not address bias when data are missing not at random (MNAR). It is typically not possible to determine whether the missing data are MAR. However, higher attrition in active (ie, intervention) versus passive (ie, waitlist or no treatment) conditions in mHealth RCTs raise a strong likelihood of MNAR, such as if active participants who benefit less from the intervention are more likely to drop out. Objective This study aims to systematically evaluate differential attrition and methods used for handling missingness in a sample of mHealth RCTs comparing active and passive control conditions. We also aim to illustrate a modern model-based sensitivity analysis and a simpler fixed-value replacement approach that can be used to evaluate the influence of MNAR. Methods We reanalyzed attrition rates and predictors of differential attrition in a sample of 36 mHealth RCTs drawn from a recent meta-analysis of smartphone-based mental health interventions. We systematically evaluated the design features related to missingness and its handling. Data from a recent mHealth RCT were used to illustrate 2 sensitivity analysis approaches (pattern-mixture model and fixed-value replacement approach). Results Attrition in active conditions was, on average, roughly twice that of passive controls. Differential attrition was higher in larger studies and was associated with the use of MAR-based multiple imputation or maximum likelihood methods. Half of the studies (18/36, 50%) used these modern missing data techniques. None of the 36 mHealth RCTs reviewed conducted a sensitivity analysis to evaluate the possible consequences of data MNAR. A pattern-mixture model and fixed-value replacement sensitivity analysis approaches were introduced. Results from a recent mHealth RCT were shown to be robust to missing data, reflecting worse outcomes in missing versus nonmissing scores in some but not all scenarios. A review of such scenarios helps to qualify the observations of significant treatment effects. Conclusions MNAR data because of differential attrition are likely in mHealth RCTs using passive controls. Sensitivity analyses are recommended to allow researchers to assess the potential impact of MNAR on trial results.

scholarly journals Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Loukia M. Spineli ◽  
Katerina Papadimitropoulou ◽  
Chrysostomos Kalyvas
Keyword(s):  
Mixture Model ◽  
Simulation Study ◽  
Meta Analysis ◽  
Credible Interval ◽  
Outcome Data ◽  
Systematic Bias ◽  
Two Stage ◽  
Pattern Mixture Model ◽  
One Stage ◽  
The One

Abstract Background Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The performance of the one-stage and the two-stage approaches has been documented extensively in the literature. However, little is known about how these approaches behave in the presence of missing outcome data (MOD), which are ubiquitous in clinical trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis using Bayesian methods to handle MOD appropriately. Methods We used 29 published networks to empirically compare the two approaches concerning the relative treatment effects of several competing interventions and the between-trial variance (τ2), while considering the extent and level of balance of MOD in the included trials. We additionally conducted a simulation study to compare the competing approaches regarding the bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and τ2 in the presence of moderate and large MOD. Results The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. For these networks, the empirical results revealed slightly higher τ2 estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and τ2 when compared with the two-stage approach for large MOD. Conclusions Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

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