Summary Background Post-stroke aphasia might improve over many years with speech and language therapy; however speech and language therapy is often less readily available beyond a few months after stroke. We assessed self-managed computerised speech and language therapy (CSLT) as a means of providing more therapy than patients can access through usual care alone. Methods In this pragmatic, superiority, three-arm, individually randomised, single-blind, parallel group trial, patients were recruited from 21 speech and language therapy departments in the UK. Participants were aged 18 years or older and had been diagnosed with aphasia post-stroke at least 4 months before randomisation; they were excluded if they had another premorbid speech and language disorder caused by a neurological deficit other than stroke, required treatment in a language other than English, or if they were currently using computer-based word-finding speech therapy. Participants were randomly assigned (1:1:1) to either 6 months of usual care (usual care group), daily self-managed CSLT plus usual care (CSLT group), or attention control plus usual care (attention control group) with the use of computer-generated stratified blocked randomisation (randomly ordered blocks of sizes three and six, stratified by site and severity of word finding at baseline based on CAT Naming Objects test scores). Only the outcome assessors and trial statistician were masked to the treatment allocation. The speech and language therapists who were doing the outcome assessments were different from those informing participants about which group they were assigned to and from those delivering all interventions. The statistician responsible for generating the randomisation schedule was separate from those doing the analysis. Co-primary outcomes were the change in ability to retrieve personally relevant words in a picture naming test (with 10% mean difference in change considered a priori as clinically meaningful) and the change in functional communication ability measured by masked ratings of video-recorded conversations, with the use of Therapy Outcome Measures (TOMs), between baseline and 6 months after randomisation (with a standardised mean difference in change of 0·45 considered a priori as clinically meaningful). Primary analysis was based on the modified intention-to-treat (mITT) population, which included randomly assigned patients who gave informed consent and excluded those without 6-month outcome measures. Safety analysis included all participants. This trial has been completed and was registered with the ISRCTN, number ISRCTN68798818. Findings From Oct 20, 2014, to Aug 18, 2016, 818 patients were assessed for eligibility, of which 278 (34%) participants were randomly assigned (101 [36%] to the usual care group; 97 [35%] to the CSLT group; 80 [29%] to the attention control group). 86 patients in the usual care group, 83 in the CSLT group, and 71 in the attention control group...

BackgroundStandard parametric survival models are commonly used to estimate long-term survival in oncology health technology assessments; however, they can inadequately represent the complex pattern of hazard functions or underlying mechanism of action (MoA) of immuno-oncology (IO) treatments.ObjectiveThe aim of this study was to explore methods for extrapolating overall survival (OS) and provide insights on model selection in the context of the underlying MoA of IO treatments.MethodsStandard parametric, flexible parametric, cure, parametric mixture and landmark models were applied to data from ATLANTIC (NCT02087423; data cut-off [DCO] 3 June 2016). The goodness of fit of each model was compared using the observed survival and hazard functions, together with the plausibility of corresponding model extrapolation beyond the trial period. Extrapolations were compared with updated data from ATLANTIC (DCO 7 November 2017) for validation.ResultsA close fit to the observed OS was seen with all models; however, projections beyond the trial period differed. Estimated mean OS differed substantially across models. The cure models provided the best fit for the new DCO.ConclusionsStandard parametric models fitted to the initial ATLANTIC DCO generally underestimated longer-term OS, compared with the later DCO. Cure, parametric mixture and response-based landmark models predicted that larger proportions of patients with metastatic non-small cell lung cancer receiving IO treatments may experience long-term survival, which was more in keeping with the observed data. Further research using more mature OS data for IO treatments is needed.Electronic supplementary materialThe online version of this article (10.1007/s40273-019-00806-4) contains supplementary material, which is available to authorized users.

When patients randomised to the control group of a randomised controlled trial are allowed to switch onto the experimental treatment, intention-to-treat analyses of the treatment effect are confounded because the separation of randomised groups is lost. Previous research has investigated statistical methods that aim to estimate the treatment effect that would have been observed had this treatment switching not occurred and has demonstrated their performance in a limited set of scenarios. Here, we investigate these methods in a new range of realistic scenarios, allowing conclusions to be made based upon a broader evidence base. We simulated randomised controlled trials incorporating prognosis-related treatment switching and investigated the impact of sample size, reduced switching proportions, disease severity, and alternative data-generating models on the performance of adjustment methods, assessed through a comparison of bias, mean squared error, and coverage, related to the estimation of true restricted mean survival in the absence of switching in the control group. Rank preserving structural failure time models, inverse probability of censoring weights, and two-stage methods consistently produced less bias than the intention-to-treat analysis. The switching proportion was confirmed to be a key determinant of bias: sample size and censoring proportion were relatively less important. It is critical to determine the size of the treatment effect in terms of an acceleration factor (rather than a hazard ratio) to provide information on the likely bias associated with rank-preserving structural failure time model adjustments. In general, inverse probability of censoring weight methods are more volatile than other adjustment methods.

Background: Immune-checkpoint inhibitors may provide long-term survival benefits via a cured proportion, yet data are usually insufficient to prove this upon submission to health technology assessment bodies. Objective: We revisited the National Institute for Health and Care Excellence assessment of ipilimumab in melanoma (TA319). We used updated data from the pivotal trial to assess the accuracy of the extrapolation methods used and compared these to previously unused techniques to establish whether an alternative extrapolation may have provided more accurate survival projections. Methods: We compared projections from the piecewise survival model used in TA319 and those produced by alternative models (fit to trial data with minimum follow-up of 3 years) to a longer-term data cut (5-year follow-up). We also compared projections to external data to help assess validity. Alternative approaches considered were parametric, spline-based, mixture, and mixture-cure models. Results: Only the survival model used in TA319 and a mixture-cure model provided 5-year survival predictions close to those observed in the 5-year follow-up data set. Standard parametric, spline, and nonecurativemixture models substantially underestimated 5-year survival. Survival estimates from the TA319 model and the mixture-cure model diverge considerably after 5 years and remain unvalidated. Conclusions: In our case study, only models that incorporated an element of external information (through a cure fraction combined with background mortality rates or using registry data) provided accurate estimates of 5-year survival. Flexible models that were able to capture the complex hazard functions observed during the trial, but which did not incorporate external information, extrapolated poorly.

Objectives: Treatment switching refers to the situation in a randomized controlled trial where patients switch from their randomly assigned treatment onto an alternative. Often, switching is from the control group onto the experimental treatment. In this instance, a standard intention-to-treat analysis does not identify the true comparative effectiveness of the treatments under investigation. We aim to describe statistical methods for adjusting for treatment switching in a comprehensible way for nonstatisticians, and to summarize views on these methods expressed by stakeholders at the 2014 Adelaide International Workshop on Treatment Switching in Clinical Trials. Methods: We describe three statistical methods used to adjust for treatment switching: marginal structural models, two-stage adjustment, and rank preserving structural failure time models. We draw upon discussion heard at the Adelaide International Workshop to explore the views of stakeholders on the acceptability of these methods. Results: Stakeholders noted that adjustment methods are based on assumptions, the validity of which may often be questionable. There was disagreement on the acceptability of adjustment methods, but consensus that when these are used, they should be justified rigorously. The utility of adjustment methods depends upon the decision being made and the processes used by the decision-maker. Conclusions: Treatment switching makes estimating the true comparative effect of a new treatment challenging. However, many decision-makers have reservations with adjustment methods. These, and how they affect the utility of adjustment methods, require further exploration. Further technical work is required to develop adjustment methods to meet real world needs, to enhance their acceptability to decision-makers.