Effect of Idalopirdine as Adjunct to Cholinesterase Inhibitors on Change in Cognition in Patients With Alzheimer Disease

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.

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“…(), Atri et al . (), O’Kelly and Ratitch () and references therein). Our aim has been to provide a more formal underpinning.…”

Section: Discussionmentioning

confidence: 97%

Information-Anchored Sensitivity Analysis: Theory and Application

Cro

Carpenter

Kenward³

2018

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

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“…This difficulty has been widely acknowledged. For example, Daniels and Hogan quote from Scharfstein et al who comment: “…the biggest challenge in conducting sensitivity analyses is the choice of one or more sensitivity parameterized functions whose interpretation can be communicated to patient matter experts with sufficient clarity….” It is therefore encouraging that reference‐based sensitivity analysis via multiple imputation has increasingly been used (see, for example, Philipsen et al, Jans et al, Billings et al, and Atri et al) which motivated us to set out to systematically extend it to the time‐to‐event setting. In Section , we present a number of possibilities, many derived from the setting with continuous outcomes (Carpenter et al).…”

Section: Discussionmentioning

confidence: 99%

Reference‐based sensitivity analysis for time‐to‐event data

Atkinson

Kenward²,

Clayton

et al. 2019

Pharmaceutical Statistics

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The analysis of time‐to‐event data typically makes the censoring at random assumption, ie, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved (ie, right censored). When patients who remain in follow‐up stay on their assigned treatment, then analysis under this assumption broadly addresses the de jure, or “while on treatment strategy” estimand. In such cases, we may well wish to explore the robustness of our inference to more pragmatic, de facto or “treatment policy strategy,” assumptions about the behaviour of patients post‐censoring.This is particularly the case when censoring occurs because patients change, or revert, to the usual (ie, reference) standard of care. Recent work has shown how such questions can be addressed for trials with continuous outcome data and longitudinal follow‐up, using reference‐based multiple imputation. For example, patients in the active arm may have their missing data imputed assuming they reverted to the control (ie, reference) intervention on withdrawal. Reference‐based imputation has two advantages: (a) it avoids the user specifying numerous parameters describing the distribution of patients' postwithdrawal data and (b) it is, to a good approximation, information anchored, so that the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. In this article, we build on recent work in the survival context, proposing a class of reference‐based assumptions appropriate for time‐to‐event data. We report a simulation study exploring the extent to which the multiple imputation estimator (using Rubin's variance formula) is information anchored in this setting and then illustrate the approach by reanalysing data from a randomized trial, which compared medical therapy with angioplasty for patients presenting with angina.

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“…Phase III clinical trials were undertaken to determine whether idalopirdine can improve cognition in people with Alzheimer’s disease who are currently taking cholinesterase inhibitors, mostly donepezil 1. These multicentre, multinational studies—STARSHINE, STARBEAM, and STARBRIGHT—enrolled 2525 participants with mild to moderate Alzheimer’s dementia and compared three doses of idalopirdine and placebo over 24 weeks.…”

Section: Negative Drug Trialsmentioning

confidence: 99%

“…The results of these high quality, definitive studies were recently published and were entirely negative 1. The authors noted that the positive results from the phase II subgroup analysis had been achieved with a higher dose but, even so, they concluded that idalopirdine was ineffective.…”

Section: Negative Drug Trialsmentioning

confidence: 99%

“…Negative results have been posted for phase III clinical trials of two 5-hydroxytryptamine-6 receptor (5-HT 6 ) antagonists for the symptomatic treatment of moderate dementia,12 as well as for a trial of an amyloid β antibody as a disease modifying treatment in mild dementia 3. Pfizer announced that it will end its neuroscience and related dementia investments in drug development, removing a major industry partner from the field 4…”

mentioning

confidence: 99%

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