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Background: The timing of efficacy-related clinical events recorded at scheduled study visits in clinical trials are interval censored, with the interval duration pre-determined by the study protocol. Events may happen any time during that interval but can only be detected during a planned or unplanned visit. Disease progression in oncology is a notable example where the time to an event is affected by the schedule of visits within a study. This can become a source of bias when studies with varying assessment schedules are used in unanchored comparisons using methods such as matching-adjusted indirect comparisons.Objective: We illustrate assessment-time bias (ATB) in a simulation study based on data from a recent study in second-line treatment for locally advanced or metastatic urothelial carcinoma, and present a method to adjust for differences in assessment schedule when comparing progression-free survival (PFS) against a competing treatment.Methods: A multi-state model for death and progression was used to generate simulated death and progression times, from which PFS times were derived. PFS data were also generated for a hypothetical comparator treatment by applying a constant hazard ratio (HR) to the baseline treatment. Simulated PFS times for the two treatments were then aligned to different assessment schedules so that progression events were only observed at set visit times, and the data were analysed to assess the bias and standard error of estimates of HRs between two treatments with and without assessment-schedule matching (ASM).Results: ATB is highly affected by the rate of the event at the first assessment time; in our examples, the bias ranged from 3 to 11% as the event rate increased. The proposed method relies on individual-level data from a study and attempts to adjust the timing of progression events to the comparator's schedule by shifting them forward or backward without altering the patients' actual follow-up time. The method removed the bias almost completely in all scenarios without affecting the precision of estimates of comparative effectiveness.Conclusions: Considering the increasing use of unanchored comparative analyses for novel cancer treatments based on single-arm studies, the proposed method offers a relatively simple means of improving the accuracy of relative benefits of treatments on progression times. [ABSTRACT FROM AUTHOR]

Background: In the absence of head-to-head trials comparing axitinib with cabozantinib or everolimus, the aim of this study was to conduct an indirect comparison of their relative efficacy in patients with metastatic renal cell carcinoma (mRCC), using data from the AXIS and METEOR trials.Methods: Progression-free survival (PFS) and overall survival (OS) in prior sunitinib-treated patients with mRCC were compared by conducting matching-adjusted indirect comparison (MAIC) analyses, including base-case and sensitivity analyses. Individual patient-level data from prior sunitinib-treated patients who received axitinib in AXIS were weighted to match published baseline characteristics of prior sunitinib-treated patients who received either cabozantinib or everolimus in METEOR.Results: There was no statistically significant difference in PFS (aHR [adjusted hazard ratio] = 1.15 [CI: 0.82-1.63]) and OS (aHR = 1.00 [CI: 0.69-1.46]) between axitinib versus cabozantinib in the base-case analysis. In the sensitivity analysis, PFS (aHR = 1.39 [CI: 1.00-1.92]) and OS (aHR = 1.35 [CI: 0.95-1.92]) were shorter for axitinib compared with cabozantinib; however, the OS difference was not statistically significant. Axitinib was associated with significantly longer PFS compared with everolimus in the base-case (aHR = 0.53 [CI: 0.36-0.80]) and sensitivity analyses (aHR = 0.63 [CI: 0.45-0.88]), respectively. Results suggested an OS benefit for axitinib versus everolimus in base-case analyses (aHR = 0.63 [CI: 0.42-0.96]); however, the difference in OS in the sensitivity analysis was not statistically significant (aHR = 0.84 [CI: 0.59-1.18]).Conclusions: MAIC analyses suggest PFS and OS for axitinib and cabozantinib are dependent on the Memorial Sloan Kettering Cancer Center definition used; in the base-case analysis, there was no significant difference in PFS and OS between axitinib and cabozantinib. In the sensitivity analysis, PFS in favour of cabozantinib was significant; however, the trend for prolonged OS with cabozantinib was not significant. For axitinib and everolimus, MAIC analyses indicate patients treated with axitinib may have an improved PFS and OS benefit when compared to everolimus. Disparities between the base-case and sensitivity analyses in this study underscore the importance of adjusting for the differences in baseline characteristics and that naïve indirect comparisons are not appropriate. [ABSTRACT FROM AUTHOR]

Estimates of the relative effects of competing treatments are rarely available from head-to-head trials. These effects must therefore be derived from indirect comparisons of results from different studies. The feasibility of comparisons relies on the network linking treatments through common comparators; the reliability of these may also be impacted when the studies are heterogeneous or when multiple intermediate comparisons are needed to link two specific treatments of interest. Simulated treatment comparison and matching-adjusted indirect comparison have been developed to address these challenges. These focus on comparisons of outcomes for two specific treatments of interest by using patient-level data for one treatment (the index) and published results for the other treatment (the comparator) from compatible studies, taking into account possible confounding due to population differences. This paper provides an overview of how and when these approaches can be used as an alternative or to complement standard MTC approaches. [ABSTRACT FROM AUTHOR]

Assessing the economic value of treatments for chronic myeloid leukaemia (CML) is important but poses a number of challenges. This paper reviews economic models of CML treatment to learn lessons from this experience and support ongoing efforts to model CML. A search of databases and submissions to key health technology assessment agencies identified 12 studies that reported 22 models. Common practice included the use of cohort Markov models-most models used health states organised around the key stages in CML: chronic phase, accelerated phase and blast phase-and the use of utility estimates in the literature that correspond with the National Institute for Health and Care Excellence reference case. Two key areas of uncertainty were the extrapolation of survival outcomes beyond the period observed by the trial; and the effectiveness of second-line therapies. Further work is required to overcome these uncertainties in existing models, such as longer-term trial data collection, including trials of second-line therapies; validation of health-related quality-of-life instruments; and the testing of alternative modelling approaches. In the meantime, it is important that the impact of uncertainties is tested through the use of sensitivity and scenario analysis. [ABSTRACT FROM AUTHOR]

Health economic models rely on data from trials to project the risk of events (e.g., death) over time beyond the span of the available data. Parametric survival analysis methods can be applied to identify an appropriate statistical model for the observed data, which can then be extrapolated to derive a complete time-to-event curve. This paper describes the properties of the most commonly used statistical distributions as a basis for these models and describes an objective process of identifying the most suitable parametric distribution in a given dataset. The approach can be applied with both individual-patient data as well as with survival probabilities derived from published Kaplan-Meier curves. Both are illustrated with analyses of overall survival from the Sorafenib Hepatocellular Carcinoma Assessment Randomised Protocol trial. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to assess the quality of life and drug costs associated with switching from any ongoing antipsychotic treatment to once-daily extended release quetiapine fumarate (quetiapine XR) in patients with schizophrenia.Methods: This assessment was based on data collected during a 12-week study in patients with schizophrenia (n = 477) who switched from their current antipsychotic due to insufficient efficacy or poor tolerability to a flexible dose of quetiapine XR. Patients were assigned utilities based on their Positive and Negative Syndrome Scale (PANSS) scores and the presence of adverse events by applying the methods of Lenert et al.1. Quality adjusted life year (QALY) gains were calculated assuming a linear change of utility between two consecutive visits. Incremental costs were calculated by comparing the hypothetical mean drug cost (assuming patients stay on previous treatment) with the actual mean cost of quetiapine XR based on European prices.Results: Patients who completed the study (n = 279) increased their average utility by 0.116, corresponding to a QALY gain of 0.0207. For the total sample, the mean utility increased by 0.09, reflecting a QALY gain of 0.0170. The additional costs for quetiapine XR per QALY gained varied from approximately 16,000 euro to 24,000 euro. Notably, this is a non-comparative study; therefore, no conclusions can be reached regarding the relative impact of switching to quetiapine XR compared with other antipsychotics. Further limitations included the short trial duration on which the utility estimates are based, and the restriction of cost data to drug costs alone. Furthermore, in a 'real world' scenario, it is to be expected that other drug regimens might be introduced during periods of treatment failure.Conclusion: This analysis demonstrates that patients with schizophrenia who switch their antipsychotic medication to quetiapine XR because of insufficient efficacy or poor tolerability benefit from significant QALY gains at a reasonable increase in drug cost. [ABSTRACT FROM AUTHOR]