Incorporation of genuine prior information in cost-effectiveness analysis of clinical trial data

The Bayesian approach to statistics has been growing rapidly in popularity as an alternative to the frequentist approach in the appraisal of heathcare technologies in clinical trials. Bayesian methods have significant advantages over classical frequentist statistical methods and the presentation of evidence to decision makers. A fundamental feature of a Bayesian analysis is the use of prior information as well as the clinical trial data in the final analysis. However, the incorporation of prior information remains a controversial subject that provides a potential barrier to the acceptance of practical uses of Bayesian methods. The pur pose of this paper is to stimulate a debate on the use of prior information in evidence submitted to decision makers. We discuss the advantages of incorporating genuine prior information in cost-effectiveness analyses of clinical trial data and explore mechanisms to safeguard scientific rigor in the use of such prior information

When future change matters: modelling future price and diffusion in health technology assessments of medical devices

Objectives: Whilst health technology assessments (HTAs) that take account of future price change have been examined in the literature, the important issue of price reductions that are generated by the reimbursement decision has been ignored. Our objective is to explore the impact of future price reductions caused by increasing uptake on HTAs and decision making for medical devices. Methods: We demonstrate the use of a two-stage modelling approach to derive estimates of technology price as a consequence of changes in technology uptake over future periods based on existing theory and supported by empirical studies. We explore the impact on cost-effectiveness and expected value of information analysis in an illustrative example based on a technology used in pre-term birth screening that is in development. Results: The application of our approach to the case study technology generates smaller incremental cost-effectiveness ratios (ICERs) compared to the commonly used single cohort approach. The extent of this reduction of the ICER depends on the magnitude of the modelled price reduction, the speed of diffusion and the length of the assumed technology-life horizon. Results of value of information analysis are affected through changes in the expected net benefit calculation, the addition of uncertain parameters and the diffusion-adjusted estimate of the affected patient population. Conclusions: Since modelling future changes in price and uptake has the potential to affect HTA outcomes, modelling techniques that can address such changes should be considered for medical devices that may otherwise be rejected

Using evidence from randomised controlled trials in economic models: What information is relevant and is there a minimum amount of sample data required to make decisions?

Evidence from randomised controlled trials (RCTs) is used to support regulatory approval and reimbursement decisions. I discuss how these decisions are typically made and argue that the amount of sample data and regulatory authorities concerns over multiplicity are irrelevant when making reimbursement decisions. Decision analytic models (DAMs) are usually necessary to meet the requirements of an economic evaluation. DAMs involve inputs relating to health benefits and resource use that represent unknown true population parameters. Evidence about parameters may come from a variety of sources including RCTs, and uncertainty about parameters is represented by their joint posterior distribution. Any impact of multiplicity is mitigated through the prior distribution. I illustrate our perspective with three examples: the estimation of a treatment effect on a rare event; the number of RCTs available in a meta-analysis; and the estimation of population mean overall survival. I conclude by recommending that reimbursement decisions should be followed by an assessment of the value of sample information and the DAM revised as necessary to include any new sample data that may be generated

A study of lead-acid battery efficiency near top-of-charge and the impact on PV system design

Knowledge of the charge efficiency of lead-acid batteries near top-of-charge is important to the design of small photovoltaic systems. In order to know how much energy is required from the photovoltaic array in order to accomplish the task of meeting load, including periodic full battery charge, a detailed knowledge of the battery charging efficiency as a function of state of charge is required, particularly in the high state-of-charge regime, as photovoltaic systems are typically designed to operate in the upper 20 to 30% of battery state-of-charge. This paper presents the results of a process for determining battery charging efficiency near top-of-charge and discusses the impact of these findings on the design of small PV systems

Incorporating genuine prior information about between-study heterogeneity in random effects pairwise and network meta-analyses

Background: Pairwise and network meta-analyses using fixed effect and random effects models are commonly applied to synthesise evidence from randomised controlled trials. The models differ in their assumptions and the interpretation of the results. The model choice depends on the objective of the analysis and knowledge of the included studies. Fixed effect models are often used because there are too few studies with which to estimate the between-study standard deviation from the data alone. Objectives: The aim is to propose a framework for eliciting an informative prior distribution for the between-study standard deviation in a Bayesian random effects meta-analysis model to genuinely represent heterogeneity when data are sparse. Methods: We developed an elicitation method using external information such as empirical evidence and experts' beliefs on the 'range' of treatment effects in order to infer the prior distribution for the between-study standard deviation. We also developed the method to be implemented in R. Results: The three-stage elicitation approach allows uncertainty to be represented by a genuine prior distribution to avoid making misleading inferences. It is flexible to what judgments an expert can provide, and is applicable to all types of outcome measure for which a treatment effect can be constructed on an additive scale. Conclusions: The choice between using a fixed effect or random effects meta-analysis model depends on the inferences required and not on the number of available studies. Our elicitation framework captures external evidence about heterogeneity and overcomes the often implausible assumption that studies are estimating the same treatment effect, thereby improving the quality of inferences in decision making

Estimating future health technology diffusion using expert beliefs calibrated to an established diffusion model

Objectives: Estimates of future health technology diffusion, or future uptake over time, are a requirement for different analyses performed within health technology assessments. Methods for obtaining such estimates include constant uptake estimates based on expert opinion or analogous technologies, and extrapolation from initial data points using parametric curves – but remain divorced from established diffusion theory and modelling. We propose an approach to obtaining diffusion estimates using experts’ beliefs calibrated to an established diffusion model to address this methodological gap. Methods: We performed an elicitation of experts’ beliefs on future diffusion of a new preterm birth screening illustrative case study technology. The elicited quantities were chosen such that they could be calibrated to yield the parameters of the Bass model of new product growth, which was chosen based on a review of the diffusion literature. Results: With the elicitation of only three quantities per diffusion curve, our approach enabled us to quantify uncertainty about diffusion of the new technology in different scenarios. Pooled results showed that the attainable number of adoptions was predicted to be relatively low compared with what was thought possible. Further research evidence improved the attainable number of adoptions only slightly but resulted in greater speed of diffusion. Conclusions: The proposed approach of eliciting experts’ beliefs about diffusion and informing the Bass model has the potential to fill the methodological gap evident in value of implementation and research, as well as budget impact and some cost-effectiveness analyses

Pharmacological thromboprophylaxis to prevent venous thromboembolism in patients with temporary lower limb immobilization after injury : systematic review and network meta‐analysis

Background Thromboprophylaxis has the potential to reduce venous thromboembolism (VTE) following lower limb immobilization due to injury. Objectives We aimed to estimate the effectiveness of thromboprophylaxis, compare different agents and identify any factors associated with effectiveness. Methods We undertook a systematic review and network meta‐analysis (NMA) of randomised trials reporting VTE or bleeding outcomes that compared thromboprophylactic agents to each other or to no pharmacological prophylaxis, for this indication. An NMA was undertaken for each outcome or agent used, and a series of study level network meta‐regressions examined whether population characteristics, type of injury, treatment of injury or duration of thromboprophylaxis were associated with treatment effect. Results Data from 6857 participants across 13 randomised trials showed that, compared to no treatment, low molecular weight heparin (LMWH) reduced the risk of any VTE (OR 0.52; 95% CrI 0.37, 0.71), clinically detected deep vein thrombosis (DVT) (OR 0.39; 95% CrI 0.12, 0.94) and pulmonary embolism (PE) (OR 0.16; 95% CrI 0.01, 0.74), while fondaparinux reduced the risk of any VTE (OR 0.13; 95% CrI 0.05, 0.30) and clinically detected DVT (OR 0.10; 95% CrI 0.01, 0.86), with inconclusive results for PE (OR 0.40; 95% CrI 0.01, 7.53). Conclusions Thromboprophylaxis with either fondaparinux or LMWH appears to reduce the odds of both asymptomatic and clinically detected VTE in people with temporary lower limb immobilization following an injury. Treatment effects vary by outcome and are not always conclusive. We were unable to identify any treatment effect modifiers other than thromboprophylactic agent used

A User-centric Framework for Accessing Biological Sources and Tools

Biologists face two problems in interpreting their experiments: the integration of their data with information from multiple heterogeneous sources and data analysis with bioinformatics tools. It is difficult for scientists to choose between the numerous sources and tools without assistance. Following a thorough analysis of scientists’ needs during the querying process, we found that biologists express preferences concerning the sources to be queried and the tools to be used. Interviews also showed that the querying process itself – the strategy followed – differs between scientists. In response to these findings, we have introduced a user-centric framework allowing to specify various querying processes. Then we have developed the BioGuide system which helps the scientists to choose suitable sources and tools, find complementary information in sources, and deal with divergent data. It is generic in that it can be adapted by each user to provide answers respecting his/her preferences, and obtained following his/her strategies

Assessment-schedule matching in unanchored indirect treatment comparisons of progression-free survival in cancer studies

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