Pedro Saramago

Mixed treatment comparisons using aggregate and individual participant level data

Mixed treatment comparisons (MTC) extend the traditional pair-wise meta-analytic framework to synthesize information on more than two interventions. Although most MTCs use aggregate data (AD), a proportion of the evidence base might be available at the individual level (IPD). We develop a series of novel Bayesian statistical MTC models to allow for the simultaneous synthesis of IPD and AD, potentially incorporating study and individual level covariates. The effectiveness of different interventions to increase the provision of functioning smoke alarms in households with children was used as a motivating dataset. This included 20 studies (11 AD and 9 IPD), including 11 500 participants. Incorporating the IPD into the network allowed the inclusion of information on subject level covariates, which produced markedly more accurate treatment-covariate interaction estimates than an analysis solely on the AD from all studies. Including evidence at the IPD level in the MTC is desirable when exploring participant level covariates; even when IPD is available only for a fraction of the studies. Such modelling may not only reduce inconsistencies within networks of trials but also assist the estimation of intervention subgroup effects to guide more individualised treatment decisions.

Point prevalence of complex wounds in a defined United Kingdom population

Complex wounds (superficial‐, partial‐, or full‐thickness skin loss wounds healing by secondary intention) are common; however, there is a lack of high‐quality, contemporary epidemiological data. This paper presents point prevalence estimates for complex wounds overall as well as for individual types. A multiservice, cross‐sectional survey was undertaken across a United Kingdom city (Leeds, population 751,485) during 2 weeks in spring of 2011. The mean age of people with complex wounds was approximately 70 years, standard deviation 19.41. The point prevalence of complex wounds was 1.47 per 1,000 of the population, 95% confidence interval 1.38 to 1.56. While pressure ulcers and leg ulcers were the most frequent, one in five people in the sample population had a less common wound type. Surveys confined to people with specific types of wound would underestimate the overall impact of complex wounds on the population and health care resources.

Deriving input parameters for cost-effectiveness modeling: taxonomy of data types and approaches to their statistical synthesis.

BACKGROUND The evidence base informing economic evaluation models is rarely derived from a single source. Researchers are typically expected to identify and combine available data to inform the estimation of model parameters for a particular decision problem. The absence of clear guidelines on what data can be used and how to effectively synthesize this evidence base under different scenarios inevitably leads to different approaches being used by different modelers. OBJECTIVES The aim of this article is to produce a taxonomy that can help modelers identify the most appropriate methods to use when synthesizing the available data for a given model parameter. METHODS This article developed a taxonomy based on possible scenarios faced by the analyst when dealing with the available evidence. While mainly focusing on clinical effectiveness parameters, this article also discusses strategies relevant to other key input parameters in any economic model (i.e., disease natural history, resource use/costs, and preferences). RESULTS The taxonomy categorizes the evidence base for health economic modeling according to whether 1) single or multiple data sources are available, 2) individual or aggregate data are available (or both), or 3) individual or multiple decision model parameters are to be estimated from the data. References to examples of the key methodological developments for each entry in the taxonomy together with citations to where such methods have been used in practice are provided throughout. CONCLUSIONS The use of the taxonomy developed in this article hopes to improve the quality of the synthesis of evidence informing decision models by bringing to the attention of health economics modelers recent methodological developments in this field.

Cost-effectiveness of adjunct non-pharmacological interventions for osteoarthritis of the knee

Background There is limited information on the costs and benefits of alternative adjunct non-pharmacological treatments for knee osteoarthritis and little guidance on which should be prioritised for commissioning within the NHS. This study estimates the costs and benefits of acupuncture, braces, heat treatment, insoles, interferential therapy, laser/light therapy, manual therapy, neuromuscular electrical stimulation, pulsed electrical stimulation, pulsed electromagnetic fields, static magnets and transcutaneous electrical nerve Stimulation (TENS), based on all relevant data, to facilitate a more complete assessment of value. Methods Data from 88 randomised controlled trials including 7,507 patients were obtained from a systematic review. The studies reported a wide range of outcomes. These were converted into EQ-5D index values using prediction models, and synthesised using network meta-analysis. Analyses were conducted including firstly all trials and secondly only trials with low risk of selection bias. Resource use was estimated from trials, expert opinion and the literature. A decision analytic model synthesised all evidence to assess interventions over a typical treatment period (constant benefit over eight weeks or linear increase in effect over weeks zero to eight and dissipation over weeks eight to 16). Results When all trials are considered, TENS is cost-effective at thresholds of £20–30,000 per QALY with an incremental cost-effectiveness ratio of £2,690 per QALY vs. usual care. When trials with a low risk of selection bias are considered, acupuncture is cost-effective with an incremental cost-effectiveness ratio of £13,502 per QALY vs. TENS. The results of the analysis were sensitive to varying the intensity, with which interventions were delivered, and the magnitude and duration of intervention effects on EQ-5D. Conclusions Using the £20,000 per QALY NICE threshold results in TENS being cost-effective if all trials are considered. If only higher quality trials are considered, acupuncture is cost-effective at this threshold, and thresholds down to £14,000 per QALY.

Cost-effectiveness of interventions for increasing the possession of functioning smoke alarms in households with pre-school children: a modelling study.

BackgroundThe UK has one of the highest rates for deaths from fire and flames in children aged 0–14 years compared to other high income countries. Evidence shows that smoke alarms can reduce the risk of fire-related injury but little exists on their cost-effectiveness. We aimed to compare the cost effectiveness of different interventions for the uptake of ‘functioning’ smoke alarms and consequently for the prevention of fire-related injuries in children in the UK.MethodsWe carried out a decision model-based probabilistic cost-effectiveness analysis. We used a hypothetical population of newborns and evaluated the impact of living in a household with or without a functioning smoke alarm during the first 5 years of their life on overall lifetime costs and quality of life from a public health perspective. We compared seven interventions, ranging from usual care to more complex interventions comprising of education, free/low cost equipment giveaway, equipment fitting and/or home safety inspection.ResultsEducation and free/low cost equipment was the most cost-effective intervention with an estimated incremental cost-effectiveness ratio of £34,200 per QALY gained compared to usual care. This was reduced to approximately £4,500 per QALY gained when 1.8 children under the age of 5 were assumed per household.ConclusionsAssessing cost-effectiveness, as well as effectiveness, is important in a public sector system operating under a fixed budget restraint. As highlighted in this study, the more effective interventions (in this case the more complex interventions) may not necessarily be the ones considered the most cost-effective.

Network meta-analysis of (individual patient) time to event data alongside (aggregate) count data

BackgroundNetwork meta-analysis methods extend the standard pair-wise framework to allow simultaneous comparison of multiple interventions in a single statistical model. Despite published work on network meta-analysis mainly focussing on the synthesis of aggregate data, methods have been developed that allow the use of individual patient-level data specifically when outcomes are dichotomous or continuous. This paper focuses on the synthesis of individual patient-level and summary time to event data, motivated by a real data example looking at the effectiveness of high compression treatments on the healing of venous leg ulcers.MethodsThis paper introduces a novel network meta-analysis modelling approach that allows individual patient-level (time to event with censoring) and summary-level data (event count for a given follow-up time) to be synthesised jointly by assuming an underlying, common, distribution of time to healing. Alternative model assumptions were tested within the motivating example. Model fit and adequacy measures were used to compare and select models.ResultsDue to the availability of individual patient-level data in our example we were able to use a Weibull distribution to describe time to healing; otherwise, we would have been limited to specifying a uniparametric distribution. Absolute effectiveness estimates were more sensitive than relative effectiveness estimates to a range of alternative specifications for the model.ConclusionsThe synthesis of time to event data considering individual patient-level data provides modelling flexibility, and can be particularly important when absolute effectiveness estimates, and not just relative effect estimates, are of interest.

Methods for network meta-analysis of continuous outcomes using individual patient data : a case study in acupuncture for chronic pain

BackgroundNetwork meta-analysis methods, which are an extension of the standard pair-wise synthesis framework, allow for the simultaneous comparison of multiple interventions and consideration of the entire body of evidence in a single statistical model. There are well-established advantages to using individual patient data to perform network meta-analysis and methods for network meta-analysis of individual patient data have already been developed for dichotomous and time-to-event data. This paper describes appropriate methods for the network meta-analysis of individual patient data on continuous outcomes.MethodsThis paper introduces and describes network meta-analysis of individual patient data models for continuous outcomes using the analysis of covariance framework. Comparisons are made between this approach and change score and final score only approaches, which are frequently used and have been proposed in the methodological literature. A motivating example on the effectiveness of acupuncture for chronic pain is used to demonstrate the methods. Individual patient data on 28 randomised controlled trials were synthesised. Consistency of endpoints across the evidence base was obtained through standardisation and mapping exercises.ResultsIndividual patient data availability avoided the use of non-baseline-adjusted models, allowing instead for analysis of covariance models to be applied and thus improving the precision of treatment effect estimates while adjusting for baseline imbalance.ConclusionsThe network meta-analysis of individual patient data using the analysis of covariance approach is advocated to be the most appropriate modelling approach for network meta-analysis of continuous outcomes, particularly in the presence of baseline imbalance. Further methods developments are required to address the challenge of analysing aggregate level data in the presence of baseline imbalance.

High‐throughput, non‐invasive prenatal testing for fetal Rhesus D genotype to guide antenatal prophylaxis with anti‐D immunoglobulin: a cost‐effectiveness analysis

To evaluate the cost‐effectiveness of high‐throughput, non‐invasive prenatal testing (HT‐NIPT) for fetal Rhesus D (RhD) genotype to guide antenatal prophylaxis with anti‐D immunoglobulin compared with routine antenatal anti‐D immunoglobulin prophylaxis (RAADP).

MO3 INTEGRATING INDIVIDUAL PATIENT LEVEL RCT DATA WITH A COMPREHENSIVE DECISION ANALYTIC COST EFFECTIVENESS MODEL

INTEGRATING INDIVIDUAL PATIENT LEVEL RCT DATA WITH A COMPREHENSIVE DECISION ANALYTIC COST EFFECTIVENESS MODEL