Adjusting for treatment switching in the METRIC study shows further improved overall survival with trametinib compared with chemotherapy

Trametinib, a selective inhibitor of mitogen-activated protein kinase kinase 1 (MEK1) and MEK2, significantly improves progression-free survival compared with chemotherapy in patients with BRAF V600E/K mutation–positive advanced or metastatic melanoma (MM). However, the pivotal clinical trial permitted randomized chemotherapy control group patients to switch to trametinib after disease progression, which confounded estimates of the overall survival (OS) advantage of trametinib. Our purpose was to estimate the switching-adjusted treatment effect of trametinib for OS and assess the suitability of each adjustment method in the primary efficacy population. Of the patients randomized to chemotherapy, 67.4% switched to trametinib. We applied the rank-preserving structural failure time model, inverse probability of censoring weights, and a two-stage accelerated failure time model to obtain estimates of the relative treatment effect adjusted for switching. The intent-to-treat (ITT) analysis estimated a 28% reduction in the hazard of death with trametinib treatment (hazard ratio [HR], 0.72; 95% CI, 0.52–0.98) for patients in the primary efficacy population (data cut May 20, 2013). Adjustment analyses deemed plausible provided OS HR point estimates ranging from 0.48 to 0.53. Similar reductions in the HR were estimated for the first-line metastatic subgroup. Treatment with trametinib, compared with chemotherapy, significantly reduced the risk of death and risk of disease progression in patients with BRAF V600E/K mutation–positive advanced melanoma or MM. Adjusting for switching resulted in lower HRs than those obtained from standard ITT analyses. However, CI are wide and results are sensitive to the assumptions associated with each adjustment method

Efficacy of “therapist-selected” versus “randomly selected” mobilisation techniques for the treatment of low back pain: A randomised controlled trial

The aim of this study was to establish whether the mobilisation technique selected by the treating physiotherapist is more effective in relieving low back pain than a randomly selected mobilisation technique. Two manipulative physiotherapists and 140 subjects suffering non-specific low back pain participated. Baseline measurements were taken before treatment allocation; the therapist then assessed subjects and nominated the preferred treatment grade, spinal level to be treated and mobilisation technique to be used. The subjects were then randomly allocated to one of two groups. One group received the preferred mobilisation technique as selected by the therapist; the other group received a randomly assigned mobilisation technique. All mobilisation treatments were applied to the nominated spinal level using the nominated treatment grade. Follow-up measures were taken immediately after intervention. Two-way ANOVA was used to analyse the data; the first factor was the treatment group and the second factor was the direction of the patient's most painful movement. The choice of mobilisation treatment had no effect on any outcome measure investigated in this study; however, post hoc tests revealed that mobilisation treatment applied to the lower lumbar levels had a greater analgesic effect than when applied to upper lumbar levels. The results of this study confirm that lumbar mobilisation treatment has an immediate effect in relieving low back pain, however the specific technique used seems unimportant

Improved two-stage estimation to adjust for treatment switching in randomised trials:g-estimation to address time-dependent confounding

In oncology trials, control group patients often switch onto the experimental treatment during follow-up, usually after disease progression. In this case, an intention-to-treat analysis will not address the policy question of interest – that of whether the new treatment represents an effective and cost-effective use of health care resources, compared to the standard treatment. Rank preserving structural failure time models (RPSFTM), inverse probability of censoring weights (IPCW) and two-stage estimation (TSE) have often been used to adjust for switching to inform treatment reimbursement policy decisions. TSE has been applied using a simple approach (TSEsimp), assuming no time-dependent confounding between the time of disease progression and the time of switch. This is problematic if there is a delay between progression and switch. In this paper we introduce TSEgest, which uses structural nested models and g-estimation to account for time-dependent confounding, and compare it to TSEsimp, RPSFTM and IPCW. We simulated scenarios where control group patients could switch onto the experimental treatment with and without time-dependent confounding being present. We varied switching proportions, treatment effects and censoring proportions. We assessed adjustment methods according to their estimation of control group restricted mean survival times that would have been observed in the absence of switching. All methods performed well in scenarios with no time-dependent confounding. TSEgest and RPSFTM continued to perform well in scenarios with time-dependent confounding, but TSEsimp resulted in substantial bias. IPCW also performed well in scenarios with time-dependent confounding, except when inverse probability weights were high in relation to the size of the group being subjected to weighting, which occurred when there was a combination of modest sample size and high switching proportions. TSEgest represents a useful addition to the collection of methods that may be used to adjust for treatment switching in trials in order to address policy-relevant questions

Treatment Switching: statistical and decision making challenges and approaches

Objectives: Treatment switching refers to the situation in a randomised 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 non-statisticians, and to summarise 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

Treatment switching in cancer trials: Issues and proposals

Objectives: Treatment switching occurs when patients in a randomized clinical trial switch from the treatment initially assigned to them to another treatment, typically from the control to experimental treatment. This study discusses the issues this raises and possible approaches to addressing them in trials of cancer drugs. Methods: Stakeholders from around the world were invited to a 1.5-day Workshop in Adelaide, Australia. This study attempts to capture the key points from the discussion and the perspectives of the various stakeholder groups, but is not a formal consensus statement. Results: Treatment switching raises challenging ethical issues with arguments for and against allowing it. It is increasingly common in cancer drug trials and presents challenges for the interpretation of results by regulators, clinicians, patients, and payers. Proposals are offered for good practice in the design, management, and analysis of trials and wider development programs for cancer drugs in which treatment switching has occurred or is likely to. Recommendations are also offered for further action to improve understanding of the importance and challenges of treatment switching and to promote agreement between key stakeholders on guidelines and other steps to address these challenges. Conclusions: The handling of treatment switching in trials is of concern to all stakeholders. On the basis of the discussions at the Adelaide International Workshop, there would appear to be common ground on approaches to addressing treatment switching in cancer trials and scope for the development of formal guidelines to inform the work of regulators, payers, industry, trial designers and other stakeholders

Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study

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 intentionto-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

Modeling the multi‐state natural history of rare diseases with heterogeneous individual patient data : a simulation study

Multi‐state survival models are used to represent the natural history of a disease, forming the basis of a health technology assessment comparing a novel treatment to current practice. Constructing such models for rare diseases is problematic, since evidence sources are typically much sparser and more heterogeneous. This simulation study investigated different one‐stage and two‐stage approaches to meta‐analyzing individual patient data (IPD) in a multi‐state survival setting when the number and size of studies being meta‐analyzed are small. The objective was to assess methods of different complexity to see when they are accurate, when they are inaccurate and when they struggle to converge due to the sparsity of data. Biologically plausible multi‐state IPD were simulated from study‐ and transition‐specific hazard functions. One‐stage frailty and two‐stage stratified models were estimated, and compared to a base case model that did not account for study heterogeneity. Convergence and the bias/coverage of population‐level transition probabilities to, and lengths of stay in, each state were used to assess model performance. A real‐world application to Duchenne Muscular Dystrophy, a neuromuscular rare disease, was conducted, and a software demonstration is provided. Models not accounting for study heterogeneity were consistently out‐performed by two‐stage models. Frailty models struggled to converge, particularly in scenarios of low heterogeneity, and predictions from models that did converge were also subject to bias. Stratified models may be better suited to meta‐analyzing disparate sources of IPD in rare disease natural history/economic modeling, as they converge more consistently and produce less biased predictions of lengths of stay

Statistical methods for adjusting estimates of treatment effectiveness for patient nonadherence in the context of time-to-event outcomes and health technology assessment: A systematic review of methodological papers

Introduction. Medication nonadherence can have a significant negative impact on treatment effectiveness. Standard intention-to-treat analyses conducted alongside clinical trials do not make adjustments for nonadherence. Several methods have been developed that attempt to estimate what treatment effectiveness would have been in the absence of nonadherence. However, health technology assessment (HTA) needs to consider effectiveness under real-world conditions, where nonadherence levels typically differ from those observed in trials. With this analytical requirement in mind, we conducted a review to identify methods for adjusting estimates of treatment effectiveness in the presence of patient nonadherence to assess their suitability for use in HTA. Methods. A “Comprehensive Pearl Growing” technique, with citation searching and reference checking, was applied across 7 electronic databases to identify methodological papers for adjusting time-to-event outcomes for nonadherence using individual patient data. A narrative synthesis of identified methods was conducted. Methods were assessed in terms of their ability to reestimate effectiveness based on alternative, suboptimal adherence levels. Results. Twenty relevant methodological papers covering 12 methods and 8 extensions to those methods were identified. Methods are broadly classified into 4 groups: 1) simple methods, 2) principal stratification methods, 3) generalized methods (g-methods), and 4) pharmacometrics-based methods using pharmacokinetics and pharmacodynamics (PKPD) analysis. Each method makes specific assumptions and has associated limitations. Five of the 12 methods are capable of adjusting for real-world nonadherence, with only g-methods and PKPD considered appropriate for HTA. Conclusion. A range of statistical methods is available for adjusting estimates of treatment effectiveness for nonadherence, but most are not suitable for use in HTA. G-methods and PKPD appear to be more appropriate to estimate effectiveness in the presence of real-world adherence

Clinical and cost effectiveness of computer treatment for aphasia post stroke (Big CACTUS): study protocol for a randomised controlled trial

Background Aphasia affects the ability to speak, comprehend spoken language, read and write. One third of stroke survivors experience aphasia. Evidence suggests that aphasia can continue to improve after the first few months with intensive speech and language therapy, which is frequently beyond what resources allow. The development of computer software for language practice provides an opportunity for self-managed therapy. This pragmatic randomised controlled trial will investigate the clinical and cost effectiveness of a computerised approach to long-term aphasia therapy post stroke. Methods/Design A total of 285 adults with aphasia at least four months post stroke will be randomly allocated to either usual care, computerised intervention in addition to usual care or attention and activity control in addition to usual care. Those in the intervention group will receive six months of self-managed word finding practice on their home computer with monthly face-to-face support from a volunteer/assistant. Those in the attention control group will receive puzzle activities, supplemented by monthly telephone calls. Study delivery will be coordinated by 20 speech and language therapy departments across the United Kingdom. Outcome measures will be made at baseline, six, nine and 12 months after randomisation by blinded speech and language therapist assessors. Primary outcomes are the change in number of words (of personal relevance) named correctly at six months and improvement in functional conversation. Primary outcomes will be analysed using a Hochberg testing procedure. Significance will be declared if differences in both word retrieval and functional conversation at six months are significant at the 5% level, or if either comparison is significant at 2.5%. A cost utility analysis will be undertaken from the NHS and personal social service perspective. Differences between costs and quality-adjusted life years in the three groups will be described and the incremental cost effectiveness ratio will be calculated. Treatment fidelity will be monitored. Discussion This is the first fully powered trial of the clinical and cost effectiveness of computerised aphasia therapy. Specific challenges in designing the protocol are considered. Trial registration Registered with Current Controlled Trials ISRCTN68798818 webcite on 18 February 2014