Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

OBJECTIVES: Partitioned survival models (PSMs) are routinely used to inform reimbursement decisions for oncology drugs. We discuss the appropriateness of PSMs compared to the most common alternative, state transition models (STMs). METHODS: In 2017, we published a National Institute for Health and Care Excellence (NICE) Technical Support Document (TSD 19) describing and critically reviewing PSMs. This article summarizes findings from TSD 19, reviews new evidence comparing PSMs and STMs, and reviews recent NICE appraisals to understand current practice. RESULTS: PSMs evaluate state membership differently from STMs and do not include a structural link between intermediate clinical endpoints (eg, disease progression) and survival. PSMs directly consider clinical trial endpoints and can be developed without access to individual patient data, but limit the scope for sensitivity analyses to explore clinical uncertainties in the extrapolation period. STMs facilitate these sensitivity analyses but require development of robust survival models for individual health-state transitions. Recent work has shown PSMs and STMs can produce substantively different survival extrapolations and that extrapolations from STMs are heavily influenced by specification of the underlying survival models. Recent NICE appraisals have not generally included both model types, reviewed individual clinical event data, or scrutinized life-years accrued in individual health states. CONCLUSIONS: The credibility of survival predictions from PSMs and STMs, including life-years accrued in individual health states, should be assessed using trial data on individual clinical events, external data, and expert opinion. STMs should be used alongside PSMs to support assessment of clinical uncertainties in the extrapolation period, such as uncertainty in post-progression survival.

Addition of Docetaxel to First-line Long-term Hormone Therapy in Prostate Cancer (STAMPEDE): Modelling to Estimate Long-term Survival, Quality-adjusted Survival, and Cost-effectiveness.

BACKGROUND: Results from large randomised controlled trials have shown that adding docetaxel to the standard of care (SOC) for men initiating hormone therapy for prostate cancer (PC) prolongs survival for those with metastatic disease and prolongs failure-free survival for those without. To date there has been no formal assessment of whether funding docetaxel in this setting represents an appropriate use of UK National Health Service (NHS) resources. OBJECTIVE: To assess whether administering docetaxel to men with PC starting long-term hormone therapy is cost-effective in a UK setting. DESIGN, SETTING, AND PARTICIPANTS: We modelled health outcomes and costs in the UK NHS using data collected within the STAMPEDE trial, which enrolled men with high-risk, locally advanced metastatic or recurrent PC starting first-line hormone therapy. INTERVENTION: SOC was hormone therapy for ≥2 yr and radiotherapy in some patients. Docetaxel (75mg/m2) was administered alongside SOC for six three-weekly cycles. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The model generated lifetime predictions of costs, changes in survival duration, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs). RESULTS AND LIMITATIONS: The model predicted that docetaxel would extend survival (discounted quality-adjusted survival) by 0.89 yr (0.51) for metastatic PC and 0.78 yr (0.39) for nonmetastatic PC, and would be cost-effective in metastatic PC (ICER £5514/QALY vs SOC) and nonmetastatic PC (higher QALYs, lower costs vs SOC). Docetaxel remained cost-effective in nonmetastatic PC when the assumption of no survival advantage was modelled. CONCLUSIONS: Docetaxel is cost-effective among patients with nonmetastatic and metastatic PC in a UK setting. Clinicians should consider whether the evidence is now sufficiently compelling to support docetaxel use in patients with nonmetastatic PC, as the opportunity to offer docetaxel at hormone therapy initiation will be missed for some patients by the time more mature survival data are available. PATIENT SUMMARY: Starting docetaxel chemotherapy alongside hormone therapy represents a good use of UK National Health Service resources for patients with prostate cancer that is high risk or has spread to other parts of the body.

Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: a tutorial.

BACKGROUND: Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. METHODS: In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards). RESULTS: A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. CONCLUSIONS: By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics.

No study left behind: a network meta-analysis in non-small-cell lung cancer demonstrating the importance of considering all relevant data.

OBJECTIVE: To demonstrate the importance of considering all relevant indirect data in a network meta-analysis of treatments for non-small-cell lung cancer (NSCLC). METHODS: A recent National Institute for Health and Clinical Excellence appraisal focussed on the indirect comparison of docetaxel with erlotinib in second-line treatment of NSCLC based on trials including a common comparator. We compared the results of this analysis to a network meta-analysis including other trials that formed a network of evidence. We also examined the importance of allowing for the correlations between the estimated treatment effects that can arise when analysing such networks. RESULTS: The analysis of the restricted network including only trials of docetaxel and erlotinib linked via the common placebo comparator produced an estimated mean hazard ratio (HR) for erlotinib compared with docetaxel of 1.55 (95% confidence interval [CI] 0.72-2.97). In contrast, the network meta-analysis produced an estimated HR for erlotinib compared with docetaxel of 0.83 (95% CI 0.65-1.06). Analyzing the wider network improved the precision of estimated treatment effects, altered their rankings and also allowed further treatments to be compared. Some of the estimated treatment effects from the wider network were highly correlated. CONCLUSIONS: This empirical example shows the importance of considering all potentially relevant data when comparing treatments. Care should therefore be taken to consider all relevant information, including correlations induced by the network of trial data, when comparing treatments.