SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale

Yang, Shu; Zhang, Yilong; Liu, Guanghan Frank; Guan, Qian

doi:10.1111/biom.13555

Cited by 12 publications

(13 citation statements)

References 32 publications

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“…Although we present the framework for sensitivity analyses using continuous longitudinal outcomes, it is possible to extend the framework to the cases of categorical or survival outcomes under some prespecified imputation mechanisms. For example, Tang 37 modifies the control-based imputation model for binary and ordinal responses based on the generalized linear mixed model; Yang et al 38 adopt the δ-adjusted and control-based imputation models for survival outcomes in sensitivity analyses. With motivated treatment effect estimands and suitable prespecified sensitivity assumptions, our framework can handle sensitivity analyses for different types of outcomes in clinical trials.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity analyses in longitudinal clinical trials via distributional imputation

Liu

Yang

Zhang

et al. 2022

Stat Methods Med Res

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Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analyses are critically important to assess the robustness of the study conclusions against untestable assumptions. Toward this end, regulatory agencies and the pharmaceutical industry use sensitivity models such as return-to-baseline, control-based, and washout imputation, following the ICH E9(R1) guidance. Multiple imputation is popular in sensitivity analyses; however, it may be inefficient and result in an unsatisfying interval estimation by Rubin’s combining rule. We propose distributional imputation in sensitivity analysis, which imputes each missing value by samples from its target imputation model given the observed data. Drawn on the idea of Monte Carlo integration, the distributional imputation estimator solves the mean estimating equations of the imputed dataset. It is fully efficient with theoretical guarantees. Moreover, we propose weighted bootstrap to obtain a consistent variance estimator, taking into account the variabilities due to model parameter estimation and target parameter estimation. The superiority of the distributional imputation framework is validated in the simulation study and an antidepressant longitudinal clinical trial.

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Section: Discussionmentioning

confidence: 99%

Sensitivity analyses in longitudinal clinical trials via distributional imputation

Liu

Yang

Zhang

et al. 2022

Stat Methods Med Res

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“…One alternative would be to investigate the corrected Rubin’s pooled variance of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$AC{E}^{I_E=1}$\end{document} suggested in Giganti and Shepherd ( 8 ). However, obtaining accurate confidence interval estimates in this way for the ACE using MI requires complex methods ( 32 – 34 ).…”

Section: Discussionmentioning

confidence: 99%

Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization Due to Infant Respiratory Illness

Tompsett

Zylbersztejn

Hardelid

et al. 2022

American Journal of Epidemiology

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Target trial emulation (TTE) applies the principles of randomised controlled trials to the causal analysis of observational datasets. On challenge that is rarely considered in TTE is the sources of bias that may arise if the variables involved in the definition of eligibility into the trial are missing. We highlight patterns of bias that might arise when estimating the causal effect of a point exposure when restricting the target trial (TT) to individuals with complete eligibility data. Simulations consider realistic scenarios where the variables affecting eligibility modify the causal effect of the exposure and are Missing at Random (MAR) or Missing Not at Random (MNAR). We discuss multiple means to address these patterns of bias, namely, (i) controlling for the collider bias induced by the missing dataon eligibility, and (ii) imputing the missing values of the eligibility variables prior to selection into the TT. Results are compared to when TTE is performed ignoring the impact of missing eligibility. A study of Palivizumab, a monoclonal antibody recommended for the prevention of respiratory hospital admissions due to Respiratory Synctial Virus in high risk infants, is used for illustrations.

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“…The variance estimation using Rubin's combining rule may produce an inconsistent variance estimate even when the imputation and analysis models are the same correctly specified models (Wang and Robins, 1998;Robins and Wang, 2000). Under the MNAR assumption, the overestimation issue raised from Rubin's variance estimator is more pronounced (e.g., Lu, 2014;Liu and Pang, 2016;Yang and Kim, 2016;Guan and Yang, 2019;Yang et al, 2021;Liu et al, 2022). One can resort to the bootstrap variance estimation to obtain a consistent variance estimator, which however exaggerates the computational cost.…”

Section: Jump-to-reference Imputation Modelmentioning

confidence: 99%

Robust analyses for longitudinal clinical trials with missing and non-normal continuous outcomes

Liu¹,

Zhang²,

Golm³

et al. 2022

Preprint

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Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group. We develop a general robust framework to handle non-normal outcomes under CBI without imposing any parametric modeling assumptions. Under the proposed framework, sequential weighted robust regressions are applied to protect the constructed imputation model against non-normality in both the covariates and the response variables. Accompanied by the subsequent mean imputation and robust model analysis, the resulting ATE estimator has good theoretical properties in terms of consistency and asymptotic normality. Moreover, our proposed method guarantees the analysis model robustness of the ATE estimation, in the sense that its asymptotic results remain intact even when the analysis model is misspecified. The superiority of the proposed robust method is demonstrated by comprehensive simulation studies and an AIDS clinical trial data application.

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SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale

Cited by 12 publications

References 32 publications

Sensitivity analyses in longitudinal clinical trials via distributional imputation

Sensitivity analyses in longitudinal clinical trials via distributional imputation

Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization Due to Infant Respiratory Illness

Robust analyses for longitudinal clinical trials with missing and non-normal continuous outcomes

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