scholarly journals A pattern-mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized trials

2017 ◽  
Vol 36 (26) ◽  
pp. 4094-4105 ◽  
Author(s):  
Mallorie H. Fiero ◽  
Chiu-Hsieh Hsu ◽  
Melanie L. Bell
Keyword(s):  
Mixture Model ◽  
Randomized Trials ◽  
Outcome Data ◽  
Cluster Randomized Trials ◽  
Continuous Outcome ◽  
Pattern Mixture Model ◽  
Mixture Model Approach ◽  
Cluster Randomized ◽  
Model Approach

scholarly journals Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach

2021 ◽  
pp. 096228022098354
Author(s):  
Loukia M Spineli ◽  
Chrysostomos Kalyvas ◽  
Katerina Papadimitropoulou
Keyword(s):  
Mixture Model ◽  
Meta Analysis ◽  
Missing At Random ◽  
Outcome Data ◽  
Analysis Model ◽  
Continuous Outcome ◽  
Pattern Mixture Model ◽  
One Stage ◽  
Mixture Model Approach ◽  
Model Approach

Appropriate handling of aggregate missing outcome data is necessary to minimise bias in the conclusions of systematic reviews. The two-stage pattern-mixture model has been already proposed to address aggregate missing continuous outcome data. While this approach is more proper compared with the exclusion of missing continuous outcome data and simple imputation methods, it does not offer flexible modelling of missing continuous outcome data to investigate their implications on the conclusions thoroughly. Therefore, we propose a one-stage pattern-mixture model approach under the Bayesian framework to address missing continuous outcome data in a network of interventions and gain knowledge about the missingness process in different trials and interventions. We extend the hierarchical network meta-analysis model for one aggregate continuous outcome to incorporate a missingness parameter that measures the departure from the missing at random assumption. We consider various effect size estimates for continuous data, and two informative missingness parameters, the informative missingness difference of means and the informative missingness ratio of means. We incorporate our prior belief about the missingness parameters while allowing for several possibilities of prior structures to account for the fact that the missingness process may differ in the network. The method is exemplified in two networks from published reviews comprising a different amount of missing continuous outcome data.

Dichotomizing a continuous outcome in cluster randomized trials: impact on power

2012 ◽  
Vol 31 (24) ◽  
pp. 2822-2832 ◽  
Author(s):  
Agnès Caille ◽  
Clémence Leyrat ◽  
Bruno Giraudeau
Keyword(s):  
Randomized Trials ◽  
Cluster Randomized Trials ◽  
Continuous Outcome ◽  
Cluster Randomized

Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness

2019 ◽  
Vol 29 (5) ◽  
pp. 1338-1353
Author(s):  
Elizabeth L Turner ◽  
Lanqiu Yao ◽  
Fan Li ◽  
Melanie Prague
Keyword(s):  
Multiple Imputation ◽  
Randomized Trials ◽  
Simulated Data ◽  
Real Data ◽  
Cluster Randomized Trial ◽  
Outcome Data ◽  
Binary Outcomes ◽  
Cluster Randomized Trials ◽  
Cluster Randomized ◽  
Multilevel Multiple Imputation

The generalized estimating equation (GEE) approach can be used to analyze cluster randomized trial data to obtain population-averaged intervention effects. However, most cluster randomized trials have some missing outcome data and a GEE analysis of available data may be biased when outcome data are not missing completely at random. Although multilevel multiple imputation for GEE (MMI-GEE) has been widely used, alternative approaches such as weighted GEE are less common in practice. Using both simulations and a real data example, we evaluate the performance of inverse probability weighted GEE vs. MMI-GEE for binary outcomes. Simulated data are generated assuming a covariate-dependent missing data pattern across a range of missingness clustering (from none to high), where all covariates are measured at baseline and are fully observed (i.e. a type of missing-at-random mechanism). Two types of weights are estimated and used in the weighted GEE: (1) assuming no clustering of missingness (W-GEE) and (2) accounting for such clustering (CW-GEE). Results show that, even in settings with high missingness clustering, CW-GEE can lead to more bias and lower coverage than W-GEE, whereas W-GEE and MMI-GEE provide comparable results. W-GEE should be considered a viable strategy to account for missing outcomes in cluster randomized trials.

scholarly journals Covariate-constrained Randomization Routine for Achieving Baseline Balance in Cluster-randomized Trials

2017 ◽  
Vol 17 (2) ◽  
pp. 503-510 ◽  
Author(s):  
Eva Lorenz ◽  
Sabine Gabrysch
Keyword(s):  
Randomized Trials ◽  
Theoretical Background ◽  
Cluster Randomized Trials ◽  
Cluster Randomized ◽  
Baseline Covariates

In cluster-randomized trials, groups or clusters of individuals, rather than individuals themselves, are randomly allocated to intervention or control. In this article, we describe a new command, ccrand, that implements a covariate-constrained randomization procedure for cluster-randomized trials. It can ensure balance of one or more baseline covariates between trial arms by restriction to allocations that meet specified balance criteria. We provide a brief overview of the theoretical background, describe ccrand and its options, and illustrate it using an example.

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