Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: Methodology and application in a clinical trial with drop-outs

Moreno‐Betancur, Margarita; Chavance, Michel

doi:10.1177/0962280213490014

Cited by 26 publications

(28 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…by using the SAS software from https://www.missingdata.org.uk or Stata software by Cro et al . () or R code implementing related approaches by Moreno‐Betancur and Chavance (). This is generally called controlled multiple imputation , because the form of the imputation for the missing data is controlled by the analyst.…”

Section: Class 1 Sensitivity Analysis and Theory For Information Anchmentioning

confidence: 99%

Information-Anchored Sensitivity Analysis: Theory and Application

Cro

Carpenter

Kenward³

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

Summary Analysis of longitudinal randomized clinical trials is frequently complicated because patients deviate from the protocol. Where such deviations are relevant for the estimand, we are typically required to make an untestable assumption about post‐deviation behaviour to perform our primary analysis and to estimate the treatment effect. In such settings, it is now widely recognized that we should follow this with sensitivity analyses to explore the robustness of our inferences to alternative assumptions about post‐deviation behaviour. Although there has been much work on how to conduct such sensitivity analyses, little attention has been given to the appropriate loss of information due to missing data within sensitivity analysis. We argue that more attention needs to be given to this issue, showing that it is quite possible for sensitivity analysis to decrease and increase the information about the treatment effect. To address this critical issue, we introduce the concept of information‐anchored sensitivity analysis. By this we mean sensitivity analyses in which the proportion of information about the treatment estimate lost because of missing data is the same as the proportion of information about the treatment estimate lost because of missing data in the primary analysis. We argue that this forms a transparent, practical starting point for interpretation of sensitivity analysis. We then derive results showing that, for longitudinal continuous data, a broad class of controlled and reference‐based sensitivity analyses performed by multiple imputation are information anchored. We illustrate the theory with simulations and an analysis of a peer review trial and then discuss our work in the context of other recent work in this area. Our results give a theoretical basis for the use of controlled multiple‐imputation procedures for sensitivity analysis.

show abstract

Section: Class 1 Sensitivity Analysis and Theory For Information Anchmentioning

confidence: 99%

Information-Anchored Sensitivity Analysis: Theory and Application

Cro

Carpenter

Kenward³

2018

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

show abstract

“…Third, if it cannot be decided whether missing data are MCAR, and/or if it can be judged from existing studies or experience that MNAR data might be present at a high level, and then it is necessary to perform sensitivity analysis (Graham et al, 1997; Carpenter et al, 2007; Jamshidian and Yuan, 2012; Morenobetancur and Chavance, 2016). Many methodologists recommend that different models or methods should be used to analyze data that may contain MNAR-values in order to examine the differences between the results yielded by these different methods (Enders, 2011a; Muthén et al, 2011).…”

Section: Discussion and Suggestionsmentioning

confidence: 99%

Comparison of Different LGM-Based Methods with MAR and MNAR Dropout Data

Chen

Cui

et al. 2017

Front. Psychol.

View full text Add to dashboard Cite

The missing not at random (MNAR) mechanism may bias parameter estimates and even distort study results. This study compared the maximum likelihood (ML) selection model based on missing at random (MAR) mechanism and the Diggle–Kenward selection model based on MNAR mechanism for handling missing data through a Monte Carlo simulation study. Four factors were considered, including the missingness mechanism, the dropout rate, the distribution shape (i.e., skewness and kurtosis), and the sample size. The results indicated that: (1) Under the MAR mechanism, the Diggle–Kenward selection model yielded similar estimation results with the ML approach; Under the MNAR mechanism, the results of ML approach were underestimated, especially for the intercept mean and intercept slope (μi and μs). (2) Under the MAR mechanism, the 95% CP of the Diggle–Kenward selection model was lower than that of the ML method; Under the MNAR mechanism, the 95% CP for the two methods were both under the desired level of 95%, but the Diggle–Kenward selection model yielded much higher coverage probabilities than the ML method. (3) The Diggle–Kenward selection model was easier to be influenced by the non-normal degree of target variable's distribution than the ML approach. The level of dropout rate was the major factor affecting the parameter estimation precision, and the dropout rate-induced difference of two methods can be ignored only when the dropout rate falls below 10%.

show abstract

“…The broad principle of the proposed methodology is to posit a family of MNAR models indexed by a parameter whose value is associated with an assumption about missingness. This approach has been advocated in the incomplete multivariate data context(e.g., Little, ; Molenberghs, Kenward, and Goetghebeur, ; Moreno‐Betancur and Chavance, ).…”

Section: Sensitivity Analysis Methodologymentioning

confidence: 99%

Direct Likelihood Inference and Sensitivity Analysis for Competing Risks Regression with Missing Causes of Failure

2015

View full text Add to dashboard Cite

Competing risks arise in the analysis of failure times when there is a distinction between different causes of failure. In many studies, it is difficult to obtain complete cause of failure information for all individuals. Thus, several authors have proposed strategies for semi-parametric modeling of competing risks when some causes of failure are missing under the missing at random (MAR) assumption. As many authors have stressed, while semi-parametric models are convenient, fully-parametric regression modeling of the cause-specific hazards (CSH) and cumulative incidence functions (CIF) may be of interest for prediction and is likely to contribute towards a fuller understanding of the time-dynamics of the competing risks mechanism. We propose a so-called "direct likelihood" approach for fitting fully-parametric regression models for these two functionals under MAR. The MAR assumption not being verifiable from the observed data, we propose an approach for performing sensitivity analyses to assess the robustness of inferences to departures from this assumption. The method relies on so-called "pattern-mixture models" from the missing data literature and was evaluated in a simulation study. This sensitivity analysis approach is applicable to various competing risks regression models (fully-parametric or semi-parametric, for the CSH or the CIF). We illustrate the proposed methods with the analysis of a breast cancer clinical trial, including suggestions for ad hoc graphical goodness-of-fit assessments under MAR.

show abstract

Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: Methodology and application in a clinical trial with drop-outs

Cited by 26 publications

References 33 publications

Information-Anchored Sensitivity Analysis: Theory and Application

Information-Anchored Sensitivity Analysis: Theory and Application

Comparison of Different LGM-Based Methods with MAR and MNAR Dropout Data

Direct Likelihood Inference and Sensitivity Analysis for Competing Risks Regression with Missing Causes of Failure

Contact Info

Product

Resources

About