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Latent class pattern mixture models (LCPMMs) are emerging tools for modeling data from open-enrollment groups with membership turnover in recently proposed treatment trials.
For MNAR, pattern mixture models can be used [ 22].
The novel application of pattern mixture modelling for refining survey estimates using record-linked data.
Few sensitivity analysis methods using pattern mixture models and selection models are available for this scenario [ 23, 24].
21 According to the results of the pattern mixture model, patient dropout did not notably influence the results.
Statistical methodological papers: The novel application of pattern mixture modelling for refining survey estimates using record-linked data.
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48 The key innovations of this approach are the simulation of observations for non-responders, and the explicit incorporation of differential associations for non-responders and responders for any given age/sex/deprivation/region combination by factoring in an alternative hospital admission/death rate for the non-responders by implementation of a pattern mixture-based approach.
Imputation of missing survival times was based on a pattern-mixture modelling approach, in which we stratify subjects by their pattern of missing values, in our case LTFU status, and formulate distinct imputation models for each stratum [17].
Pattern-mixture models were used to account for data missing-not-at-random [ 20].
Our primary approach to dealing with this monotone missing data pattern will be a pattern-mixture modeling approach [ 60].
Difference in change from baseline between treatment groups was evaluated using linear mixed and pattern-mixture models.
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