On analysis of longitudinal clinical trials with missing data using reference-based imputation

Liu, G Frank; Pang, Lei

doi:10.1080/10543406.2015.1094810

Cited by 51 publications

(66 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…If we denote the information‐anchored variance by

{\overset{σ}{true}}_{normalIA}^{2},

take a draw from

N (0, {\overset{σ}{true}}_{normalIA}^{2} - {\overset{σ}{true}}_{normalML}^{2})

and add this to the treatment estimate that is obtained from the reference‐based analysis by multiple imputation, this will result in an estimate with the information‐anchored variance in a long‐run sense. In practice

{\overset{false^}{σ}}_{ML}^{2}

could also be estimated by using one of the implementations of Lu (), Tang () or Liu and Pang (). In applications, however, we do not think that this step is typically worthwhile.…”

Section: Discussionmentioning

confidence: 99%

“…Our approach to determining the appropriate information in sensitivity analyses (which, as the simple example in Section 1 shows, is under the control of the analyst), contrasts with some recent work. Lu (2014), Tang (2017) and Liu and Pang (2016) each developed alternative implementations of the reference-based pattern mixture modelling approach. Lu (2014) introduced an analytical approach for placebo-based (copy reference) pattern mixture modelling which uses maximum likelihood and the delta method for treatment effect and variance estimation.…”

Section: Discussionmentioning

confidence: 99%

“…Although class 1 sensitivity analyses can be performed without using multiple imputation (Lu, ; Liu and Pang, ; Tang, ), multiple imputation is the most flexible approach and often the simplest to implement (e.g. by using the SAS software from https://www.missingdata.org.uk or Stata software by Cro et al .…”

Section: Class 1 Sensitivity Analysis and Theory For Information Anchmentioning

confidence: 99%

“…(), Ratitch et al . () and Liu and Pang (), is reference‐based multiple imputation. In this approach, the distribution of the missing data is specified by reference to other groups of patients.…”

Section: Class 1 Sensitivity Analysis and Theory For Information Anchmentioning

confidence: 99%

“…One approach is to obtain information about parameters that control the departure from MAR from experts (Mason et al, 2017), but this is controversial (Heitjan, 2017), and challenging for longitudinal data where multiple parameters are involved. An alternative, as introduced by Little and Yau (1996) and developed and discussed further more recently by, among others, , Ratitch et al (2013) and Liu and Pang (2016), is reference-based multiple imputation. In this approach, the distribution of the missing data is specified by reference to other groups of patients.…”

Section: Class 1 Sensitivity Analysis and Theory For Information Anchmentioning

confidence: 99%

See 4 more Smart Citations

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

“…If we denote the information‐anchored variance by

{\overset{σ}{true}}_{normalIA}^{2},

take a draw from

N (0, {\overset{σ}{true}}_{normalIA}^{2} - {\overset{σ}{true}}_{normalML}^{2})

{\overset{false^}{σ}}_{ML}^{2}

could also be estimated by using one of the implementations of Lu (), Tang () or Liu and Pang (). In applications, however, we do not think that this step is typically worthwhile.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Class 1 Sensitivity Analysis and Theory For Information Anchmentioning

confidence: 99%

Section: Class 1 Sensitivity Analysis and Theory For Information Anchmentioning

confidence: 99%

Section: Class 1 Sensitivity Analysis and Theory For Information Anchmentioning

confidence: 99%

See 3 more Smart Citations

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

Missing data in clinical trials: control‐based mean imputation and sensitivity analysis

Mehrotra

Liu

Permutt

2017

Pharmaceutical Statistics

View full text Add to dashboard Cite

In some randomized (drug versus placebo) clinical trials, the estimand of interest is the between-treatment difference in population means of a clinical endpoint that is free from the confounding effects of "rescue" medication (e.g., HbA1c change from baseline at 24 weeks that would be observed without rescue medication regardless of whether or when the assigned treatment was discontinued). In such settings, a missing data problem arises if some patients prematurely discontinue from the trial or initiate rescue medication while in the trial, the latter necessitating the discarding of post-rescue data. We caution that the commonly used mixed-effects model repeated measures analysis with the embedded missing at random assumption can deliver an exaggerated estimate of the aforementioned estimand of interest. This happens, in part, due to implicit imputation of an overly optimistic mean for "dropouts" (i.e., patients with missing endpoint data of interest) in the drug arm. We propose an alternative approach in which the missing mean for the drug arm dropouts is explicitly replaced with either the estimated mean of the entire endpoint distribution under placebo (primary analysis) or a sequence of increasingly more conservative means within a tipping point framework (sensitivity analysis); patient-level imputation is not required. A supplemental "dropout = failure" analysis is considered in which a common poor outcome is imputed for all dropouts followed by a between-treatment comparison using quantile regression. All analyses address the same estimand and can adjust for baseline covariates. Three examples and simulation results are used to support our recommendations.

show abstract

Estimation of treatment effects in short‐term depression studies. An evaluation based on the ICH E9(R1) estimands framework

Mitroiu

Teerenstra

Rengerink

et al. 2022

Pharmaceutical Statistics

View full text Add to dashboard Cite

Estimands aim to incorporate intercurrent events in design, data collection and estimation of treatment effects in clinical trials. Our aim was to understand what estimands may correspond to efficacy analyses commonly employed in clinical trials conducted before publication of ICH E9(R1). We reanalysed six clinical trials evaluating a new anti-depression treatment. We selected the following analysis methods-ANCOVA on complete cases, following last observation carried forward (LOCF) imputation and following multiple imputation; mixed-models for repeated measurements without imputation (MMRM), MMRM following LOCF imputation and following jump-toreference imputation; and pattern-mixture mixed models. We included a principal stratum analysis based on the predicted subset of the study population who would not discontinue due to adverse events or lack of efficacy. We translated each analysis into the implicitly targeted estimand, and formulated corresponding clinical questions. We could map six estimands to analysis methods. The same analysis method could be mapped to more than one estimand. The major difference between estimands was the strategy for intercurrent events, with other attributes mostly the same across mapped estimands. The quantitative differences in MADRS10 population-level summaries between the estimands were 4-8 points. Not all six estimands had a clini-

show abstract

On analysis of longitudinal clinical trials with missing data using reference-based imputation

Cited by 51 publications

References 10 publications

Information-Anchored Sensitivity Analysis: Theory and Application

Information-Anchored Sensitivity Analysis: Theory and Application

Missing data in clinical trials: control‐based mean imputation and sensitivity analysis

Estimation of treatment effects in short‐term depression studies. An evaluation based on the ICH E9(R1) estimands framework

Contact Info

Product

Resources

About