A monotone data augmentation algorithm for multivariate nonnormal data: With applications to controlled imputations for longitudinal trials

Tang, Yongqiang

doi:10.1002/sim.8062

Cited by 5 publications

(20 citation statements)

References 57 publications

(199 reference statements)

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“…We relax the normality assumption in MMRM by modeling the within subject dependence using the multivariate ST distribution 25 , which includes the multivariate SN 32 , normal and t distributions as special cases. This extension is different from our previous work 23 , where the data are modeled by a sequence of univariate ST regressions.…”

Section: Introductionmentioning

confidence: 81%

“…These covariates are typically completely observed. In case there are some missing covariates, the MDA algorithm can be adapted to impute both the missing covariates and responses based on their joint distribution 23 . In Lu 66 , the baseline covariates are constrained to have the same mean across treatment groups in randomized trials.…”

Section: Discussionmentioning

confidence: 99%

“…There are numerical evidences that MMRM-st tends to outperform MMRM-n for nonnormal longitudinal continuous outcomes. Inference based on a reduced model can be misleading if the corresponding assumption does not hold 23,27,73 . We will conduct a formal simulation study to compare these MMRMs after we find enough computational resources, and report the results elsewhere.…”

Section: Discussionmentioning

confidence: 99%

“…The inverse Wishart or Jeffrey's prior can be quite informative or inappropriate in the multivariate SN and ST regression for highly skewed data, and the argument is the same as that in the univariate regression. 23…”

Section: Mda Algorithm For Mmrm With No Restriction On Fixed Effectsmentioning

confidence: 99%

“…Update ρ j 's from its posterior distribution scriptG(n0+p2,n0∑k=j pγkβkj 2+1a0 2) P1. Update (dψj,θj,γj)'s by drawing dψj from scriptG(34,14+2π2γjψtrue¯j 2) , and (θj,γj) from the gamma-normal posterior distribution using Tang's method 23,51 …”

Section: Mda Algorithm For Mmrm With No Restriction On Fixed Effectsmentioning

confidence: 99%

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A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal ortdistributions

Tang

2019

Stat Methods Med Res

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The mixed effects model for repeated measures (MMRM) has been widely used for the analysis of longitudinal clinical data collected at a number of fixed time points. We propose a robust extension of the MMRM for skewed and heavy-tailed data on basis of the multivariate skew-t distribution, and it includes the multivariate normal, t, and skew-normal distributions as special cases. An efficient Markov chain Monte Carlo algorithm is developed using the monotone data augmentation and parameter expansion techniques. We employ the algorithm to perform controlled pattern imputations for sensitivity analyses of longitudinal clinical trials with nonignorable dropouts. The proposed methods are illustrated by real data analyses. Sample SAS programs for the analyses are provided in the online supplementary material.

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Section: Introductionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Mda Algorithm For Mmrm With No Restriction On Fixed Effectsmentioning

confidence: 99%

Section: Mda Algorithm For Mmrm With No Restriction On Fixed Effectsmentioning

confidence: 99%

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A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal ortdistributions

Tang

2019

Stat Methods Med Res

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Sequential modeling for a class of reference‐based imputation methods in clinical trials with quantitative or binary outcomes

Fang

Jin

2022

Statistics in Medicine

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We consider treatment effect estimation in a randomized clinical trial with longitudinally measured quantitative or categorical outcomes. To handle missing data, we usually assume missing at random and then conduct sensitivity analysis for missing not at random. In the literature, several reference‐based imputation methods, including “jump to reference” (J2R) and “copy reference” (CR), have been commonly used for conducting sensitivity analysis. J2R assumes the mean effect profile of patients who discontinue the investigative treatment jumps to that of the patients in the reference group after discontinuation, while CR assumes the conditional mean effect profile given the status prior to the time of discontinuation copies that of the patients in the reference group after discontinuation. In this article, we propose a novel, wide class of reference‐based imputation methods for conducting sensitivity analysis, which includes J2R and CR as two extreme ends. The framework is motivated by the thought that the investigative treatment may have no, partially, or fully carried‐over effect after deviation from the assigned treatment (eg, discontinue the assigned treatment or change to a different treatment). Further, we show that the proposed reference‐based imputation methods can be implemented through sequential modeling. This property ensures that the methods can be applied to clinical trials with either quantitative or categorical outcomes. We use both causal‐inference arguments and numerical examples to demonstrate the performance of the proposed methods.

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Modified reference based imputation and tipping point analysis in the presence of missing data due to COVID-19

Jin

Liu

Robieson

2021

Contemporary Clinical Trials

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In longitudinal clinical trials, missing data are inevitable due to intercurrent events (ICEs) such as treatment interruption or premature discontinuation for different reasons. The COVID-19 pandemic has had substantial impact on clinical trials since early 2020 as it may result in missing data due to missed visits and premature discontinuations. The missing data due to COVID-19 can reasonably be assumed as missing at random (MAR). We propose a combined hypothetical strategy for sensitivity analyses to handle missing data due to both COVID-19 and non-COVID reasons. We modify the commonly used missing not at random (MNAR) methods, reference based imputation (RBI) and tipping point analysis, under this strategy. We propose the standard multiple imputation approach and derive an analytic likelihood based approach to implement the proposed methods to improve efficiency in applications. The proposed strategy and methods are applicable to a more general scenario when there are missing data due to both MAR and MNAR reasons.

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A monotone data augmentation algorithm for multivariate nonnormal data: With applications to controlled imputations for longitudinal trials

Cited by 5 publications

References 57 publications

A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal ortdistributions

A monotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal ortdistributions

Sequential modeling for a class of reference‐based imputation methods in clinical trials with quantitative or binary outcomes

Modified reference based imputation and tipping point analysis in the presence of missing data due to COVID-19

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