A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment.

BACKGROUND: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare. PURPOSE: This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA. DATA SOURCES: Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking. STUDY SELECTION: Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation. DATA EXTRACTION: Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods. DATA SYNTHESIS: Across 18 methods identified from 22 studies, themes included use of informative prior(s) (n = 5), piecewise (n = 7), and general population adjustment (n = 9), plus a variety of "other" (n = 8) approaches. Most methods were applied in cancer populations (n = 13). No studies compared or validated their method against another method that also incorporated external evidence. LIMITATIONS: As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review. CONCLUSIONS: Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research.HighlightsThis review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment.We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of "other" approaches.No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.

Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment.

BACKGROUND: Survival extrapolation is essential in cost-effectiveness analysis to quantify the lifetime survival benefit associated with a new intervention, due to the restricted duration of randomized controlled trials (RCTs). Current approaches of extrapolation often assume that the treatment effect observed in the trial can continue indefinitely, which is unrealistic and may have a huge impact on decisions for resource allocation. OBJECTIVE: We introduce a novel methodology as a possible solution to alleviate the problem of survival extrapolation with heavily censored data from clinical trials. METHOD: The main idea is to mix a flexible model (e.g., Cox semiparametric) to fit as well as possible the observed data and a parametric model encoding assumptions on the expected behavior of underlying long-term survival. The two are "blended" into a single survival curve that is identical with the Cox model over the range of observed times and gradually approaching the parametric model over the extrapolation period based on a weight function. The weight function regulates the way two survival curves are blended, determining how the internal and external sources contribute to the estimated survival over time. RESULTS: A 4-y follow-up RCT of rituximab in combination with fludarabine and cyclophosphamide versus fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia is used to illustrate the method. CONCLUSION: Long-term extrapolation from immature trial data may lead to significantly different estimates with various modelling assumptions. The blending approach provides sufficient flexibility, allowing a wide range of plausible scenarios to be considered as well as the inclusion of external information, based, for example, on hard data or expert opinion. Both internal and external validity can be carefully examined. HIGHLIGHTS: Interim analyses of trials with limited follow-up are often subject to high degrees of administrative censoring, which may result in implausible long-term extrapolations using standard approaches.In this article, we present an innovative methodology based on "blending" survival curves to relax the traditional proportional hazard assumption and simultaneously incorporate external information to guide the extrapolation.The blended method provides a simple and powerful framework to allow a careful consideration of a wide range of plausible scenarios, accounting for model fit to the short-term data as well as the plausibility of long-term extrapolations.

A Scoping Review of Item-Level Missing Data in Within-Trial Cost-Effectiveness Analysis.

OBJECTIVES: Cost-effectiveness analysis (CEA) alongside randomized controlled trials often relies on self-reported multi-item questionnaires that are invariably prone to missing item-level data. The purpose of this study is to review how missing multi-item questionnaire data are handled in trial-based CEAs. METHODS: We searched the National Institute for Health Research journals to identify within-trial CEAs published between January 2016 and April 2021 using multi-item instruments to collect costs and quality of life (QOL) data. Information on missing data handling and methods, with a focus on the level and type of imputation, was extracted. RESULTS: A total of 87 trial-based CEAs were included in the review. Complete case analysis or available case analysis and multiple imputation (MI) were the most popular methods, selected by similar numbers of studies, to handle missing costs and QOL in base-case analysis. Nevertheless, complete case analysis or available case analysis dominated sensitivity analysis. Once imputation was chosen, missing costs were widely imputed at item-level via MI, whereas missing QOL was usually imputed at the more aggregated time point level during the follow-up via MI. CONCLUSIONS: Missing costs and QOL tend to be imputed at different levels of missingness in current CEAs alongside randomized controlled trials. Given the limited information provided by included studies, the impact of applying different imputation methods at different levels of aggregation on CEA decision making remains unclear.

Communication of personalised disease risk by general practitioners to motivate smoking cessation in England: a cost-effectiveness and research prioritisation study.

BACKGROUND AND AIMS: Communication of personalised disease risk can motivate smoking cessation. We assessed whether routine implementation of this intervention by general practitioners (GPs) in England is cost-effective or whether we need further research to better establish its effectiveness. DESIGN: Cost-effectiveness analysis (CEA) with value of information (VoI) analysis from the UK National Health Service perspective, using GP communication of personalised disease risk on smoking cessation versus usual care. SETTING: GP practices in England. STUDY POPULATION: Healthy smokers aged 35-60 years attending the GP practice. MEASUREMENTS: Effectiveness of GP communication of personalised disease risk on smoking cessation was estimated through systematic review and meta-analysis. A Bayesian CEA was then performed using a lifetime Markov model on smokers aged 35-60 years that measured lifetime costs and quality-adjusted life-years (QALYs) assigned to the four diseases contributing the most to smoking-related morbidity, mortality and costs: chronic obstructive pulmonary disease, lung cancer, myocardial infarction and stroke. Costs and QALYs for each disease state were obtained from the literature. VoI analysis identified sources of uncertainty in the CEA and assessed how much would be worth investing in further research to reduce this uncertainty. FINDINGS: The meta-analysis odds ratio for the effectiveness estimate of GP communication of personalised disease risk was 1.48 (95% credibility interval, 0.91-2.26), an absolute increase in smoking cessation rates of 3.84%. The probability of cost-effectiveness ranged 89-94% depending on sex and age. VoI analysis indicated that: (i) uncertainty in the effectiveness of the intervention was the driver of the overall uncertainty in the CEA; and (ii) a research investment to reduce this uncertainty is justified if lower than £27.6 million (£7 per smoker). CONCLUSIONS: Evidence to date shows that, in England, incorporating disease risk communication into general practitioners' practices to motivate smoking cessation is likely to be cost-effective compared with usual care.

Comparison of Parametric Survival Extrapolation Approaches Incorporating General Population Mortality for Adequate Health Technology Assessment of New Oncology Drugs.

OBJECTIVES: Survival extrapolation of trial outcomes is required for health economic evaluation. Generally, all-cause mortality (ACM) is modeled using standard parametric distributions, often without distinguishing disease-specific/excess mortality and general population background mortality (GPM). Recent National Institute for Health and Care Excellence guidance (Technical Support Document 21) recommends adding GPM hazards to disease-specific/excess mortality hazards in the log-likelihood function ("internal additive hazards"). This article compares alternative extrapolation approaches with and without GPM adjustment. METHODS: Survival extrapolations using the internal additive hazards approach (1) are compared to no GPM adjustment (2), applying GPM hazards once ACM hazards drop below GPM hazards (3), adding GPM hazards to ACM hazards (4), and proportional hazards for ACM versus GPM hazards (5). The fit, face validity, mean predicted life-years, and corresponding uncertainty measures are assessed for the active versus control arms of immature and mature (30- and 75-month follow-up) multiple myeloma data and mature (64-month follow-up) breast cancer data. RESULTS: The 5 approaches yielded considerably different outcomes. Incremental mean predicted life-years vary most in the immature multiple myeloma data set. The lognormal distribution (best statistical fit for approaches 1-4) produces survival increments of 3.5 (95% credible interval: 1.4-5.3), 8.5 (3.1-13.0), 3.5 (1.3-5.4), 2.9 (1.1-4.5), and 1.6 (0.4-2.8) years for approaches 1 to 5, respectively. Approach 1 had the highest face validity for all data sets. Uncertainty over parametric distributions was comparable for GPM-adjusted approaches 1, 3, and 4, and much larger for approach 2. CONCLUSION: This study highlights the importance of GPM adjustment, and particularly of incorporating GPM hazards in the log-likelihood function of standard parametric distributions.

Methods for population adjustment with limited access to individual patient data: A review and simulation study.

Population-adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross-trial differences in effect modifiers. Popular methods include matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). There is limited formal evaluation of these methods and whether they can be used to accurately compare treatments. Thus, we undertake a comprehensive simulation study to compare standard unadjusted indirect comparisons, MAIC and STC across 162 scenarios. This simulation study assumes that the trials are investigating survival outcomes and measure continuous covariates, with the log hazard ratio as the measure of effect. MAIC yields unbiased treatment effect estimates under no failures of assumptions. The typical usage of STC produces bias because it targets a conditional treatment effect where the target estimand should be a marginal treatment effect. The incompatibility of estimates in the indirect comparison leads to bias as the measure of effect is non-collapsible. Standard indirect comparisons are systematically biased, particularly under stronger covariate imbalance and interaction effects. Standard errors and coverage rates are often valid in MAIC but the robust sandwich variance estimator underestimates variability where effective sample sizes are small. Interval estimates for the standard indirect comparison are too narrow and STC suffers from bias-induced undercoverage. MAIC provides the most accurate estimates and, with lower degrees of covariate overlap, its bias reduction outweighs the loss in precision under no failures of assumptions. An important future objective is the development of an alternative formulation to STC that targets a marginal treatment effect.

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies.

BACKGROUND: While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions. METHODS: We discuss the original 'Pocock' criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer. RESULTS: A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be. CONCLUSION: Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.

Cannabidiol for the treatment of cannabis use disorder: a phase 2a, double-blind, placebo-controlled, randomised, adaptive Bayesian trial.

Background A substantial and unmet clinical need exists for pharmacological treatment of cannabis use disorders. Cannabidiol could offer a novel treatment, but it is unclear which doses might be efficacious or safe. Therefore, we aimed to identify efficacious doses and eliminate inefficacious doses in a phase 2a trial using an adaptive Bayesian design. METHODS: We did a phase 2a, double-blind, placebo-controlled, randomised, adaptive Bayesian trial at the Clinical Psychopharmacology Unit (University College London, London, UK). We used an adaptive Bayesian dose-finding design to identify efficacious or inefficacious doses at a-priori interim and final analysis stages. Participants meeting cannabis use disorder criteria from DSM-5 were randomly assigned (1:1:1:1) in the first stage of the trial to 4-week treatment with three different doses of oral cannabidiol (200 mg, 400 mg, or 800 mg) or with matched placebo during a cessation attempt by use of a double-blinded block randomisation sequence. All participants received a brief psychological intervention of motivational interviewing. For the second stage of the trial, new participants were randomly assigned to placebo or doses deemed efficacious in the interim analysis. The primary objective was to identify the most efficacious dose of cannabidiol for reducing cannabis use. The primary endpoints were lower urinary 11-nor-9-carboxy-δ-9-tetrahydrocannabinol (THC-COOH):creatinine ratio, increased days per week with abstinence from cannabis during treatment, or both, evidenced by posterior probabilities that cannabidiol is better than placebo exceeding 0·9. All analyses were done on an intention-to-treat basis. This trial is registered with ClinicalTrials.gov (NCT02044809) and the EU Clinical Trials Register (2013-000361-36). FINDINGS: Between May 28, 2014, and Aug 12, 2015 (first stage), 48 participants were randomly assigned to placebo (n=12) and to cannabidiol 200 mg (n=12), 400 mg (n=12), and 800 mg (n=12). At interim analysis, cannabidiol 200 mg was eliminated from the trial as an inefficacious dose. Between May 24, 2016, and Jan 12, 2017 (second stage), randomisation continued and an additional 34 participants were allocated (1:1:1) to cannabidiol 400 mg (n=12), cannabidiol 800 mg (n=11), and placebo (n=11). At final analysis, cannabidiol 400 mg and 800 mg exceeded primary endpoint criteria (0·9) for both primary outcomes. For urinary THC-COOH:creatinine ratio, the probability of being the most efficacious dose compared with placebo given the observed data was 0·9995 for cannabidiol 400 mg and 0·9965 for cannabidiol 800 mg. For days with abstinence from cannabis, the probability of being the most efficacious dose compared with placebo given the observed data was 0·9966 for cannabidiol 400 mg and 0·9247 for cannabidiol 800 mg. Compared with placebo, cannabidiol 400 mg decreased THC-COOH:creatinine ratio by -94·21 ng/mL (95% interval estimate -161·83 to -35·56) and increased abstinence from cannabis by 0·48 days per week (0·15 to 0·82). Compared with placebo, cannabidiol 800 mg decreased THC-COOH:creatinine ratio by -72·02 ng/mL (-135·47 to -19·52) and increased abstinence from cannabis by 0·27 days per week (-0·09 to 0·64). Cannabidiol was well tolerated, with no severe adverse events recorded, and 77 (94%) of 82 participants completed treatment. INTERPRETATION: In the first randomised clinical trial of cannabidiol for cannabis use disorder, cannabidiol 400 mg and 800 mg were safe and more efficacious than placebo at reducing cannabis use. FUNDING: Medical Research Council.

The Effects of Model Misspecification in Unanchored Matching-Adjusted Indirect Comparison: Results of a Simulation Study.

OBJECTIVES: To assess the performance of unanchored matching-adjusted indirect comparison (MAIC) by matching on first moments or higher moments in a cross-study comparisons under a variety of conditions. A secondary objective was to gauge the performance of the method relative to propensity score weighting (PSW). METHODS: A simulation study was designed based on an oncology example, where MAIC was used to account for differences between a contemporary trial in which patients had more favorable characteristics and a historical control. A variety of scenarios were then tested varying the setup of the simulation study, including violating the implicit or explicit assumptions of MAIC. RESULTS: Under ideal conditions and under a variety of scenarios, MAIC performed well (shown by a low mean absolute error [MAE]) and was unbiased (shown by a mean error [ME] of about zero). The performance of the method deteriorated where the matched characteristics had low explanatory power or there was poor overlap between studies. Only when important characteristics are not included in the matching did the method become biased (nonzero ME). Where the method showed poor performance, this was exaggerated if matching was also performed on the variance (ie, higher moments). Relative to PSW, MAIC provided similar results in most circumstances, although it exhibited slightly higher MAE and a higher chance of exaggerating bias. CONCLUSIONS: MAIC appears well suited to adjust for cross-trial comparisons provided the assumptions underpinning the model are met, with relatively little efficiency loss compared with PSW.

Frequentist and Bayesian meta-regression of health state utilities for multiple myeloma incorporating systematic review and analysis of individual patient data.

This analysis presents the results of a systematic review for health state utilities in multiple myeloma, as well as analysis of over 9,000 observations taken from registry and trial data. The 27 values identified from 13 papers are then synthesised in a frequentist nonparametric bootstrap model and a Bayesian meta-regression. Results were similar between the frequentist and Bayesian models with low utility on disease diagnosis (approximately 0.55), raising to approximately 0.65 on first line treatment and declining slightly with each subsequent line. Stem cell transplant was also found to be a significant predictor of health-related quality of life in both individual patient data and meta-regression, with an increased utility of approximately 0.06 across different models. The work presented demonstrates the feasibility of Bayesian methods for utility meta-regression, whilst also presenting an internally consistent set of data from the analysis of registry data. To facilitate easy updating of the data and model, data extraction tables and model code are provided as Data S1. The main limitations of the model relate to the low number of studies available, particularly in highly pretreated patients.

Uptake of the English Bowel (Colorectal) Cancer Screening Programme: an update 5 years after the full roll-out.

BACKGROUND: The initial roll-out of the English Bowel (Colorectal) Cancer Screening programme, during 2006 and 2009, found uptake to be low (54%) and socially graded. The current analysis used data from 2010 to 2015 to test whether uptake is increasing and becoming less socially graded over time. METHODS: Postcode-derived area-level uptake of 4.4 million first-time invitees, stratified by gender and the year of the first invitation (2010-2015), was generated using the National Bowel Cancer Screening System. Data were limited to people aged 60-64 years. Binomial regression tested for variations in uptake by the year of invitation, gender, region, area-based socio-economic deprivation and area-based ethnic diversity. RESULTS: Overall, the first-time colorectal cancer (CRC) screening uptake across 6 years was 52% (n = 2,285,996/4,423,734) with a decline between 2010 and 2015 (53%, 54%, 52%, 50%, 49%, 49% respectively). Uptake continued to be socially graded between the most and the least deprived area-level socio-economic deprivation quintiles (43% vs 57%), the most and the least area-based ethnic diversity quintiles (41% vs 56%) and men and women (47% vs 56%). Multivariate analysis demonstrated the effects of year, deprivation, ethnicity and gender on uptake. The effect of deprivation was more pronounced in the most deprived area quintile between men and women (40% vs 47%) than the least deprived area quintile (52% vs 62% respectively). CONCLUSION: We did not find evidence of change in uptake patterns in CRC screening since its initial launch 10 years ago. The programme is unlikely to realise its full public health benefits and is en route to widening inequalities in CRC outcomes.

A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease.

BACKGROUND: Health economic evaluations of interventions in infectious disease are commonly based on the predictions of ordinary differential equation (ODE) systems or Markov models (MMs). Standard MMs are static, whereas ODE systems are usually dynamic and account for herd immunity which is crucial to prevent overestimation of infection prevalence. Complex ODE systems including distributions on model parameters are computationally intensive. Thus, mainly ODE-based models including fixed parameter values are presented in the literature. These do not account for parameter uncertainty. As a consequence, probabilistic sensitivity analysis (PSA), a crucial component of health economic evaluations, cannot be conducted straightforwardly. METHODS: We present a dynamic MM under a Bayesian framework. We extend a static MM by incorporating the force of infection into the state allocation algorithm. The corresponding output is based on dynamic changes in prevalence and thus accounts for herd immunity. In contrast to deterministic ODE-based models, PSA can be conducted straightforwardly. We introduce a case study of a fictional sexually transmitted infection and compare our dynamic Bayesian MM to a deterministic and a Bayesian ODE system. The models are calibrated to simulated time series data. RESULTS: By means of the case study, we show that our methodology produces outcome which is comparable to the "gold standard" of the Bayesian ODE system. CONCLUSIONS: In contrast to ODE systems in the literature, the dynamic MM includes distributions on all model parameters at manageable computational effort (including calibration). The run time of the Bayesian ODE system is 15 times longer.

Incidence and prevalence of diabetes and cost of illness analysis of polycystic ovary syndrome: a Bayesian modelling study.

STUDY QUESTION: What is the incidence/prevalence of type 2 diabetes in women with polycystic ovary syndrome (PCOS) and the economic burden associated with PCOS in the UK? SUMMARY ANSWER: The incidence and prevalence of type 2 diabetes in women with PCOS are 3-33 per 1000 person years and 26.5%, respectively, with an associated annual healthcare burden of at least £237 million in the UK. WHAT IS KNOWN ALREADY: Although observational studies have been designed to assess the incidence of diabetes in women with PCOS, these have been open to criticism because of short periods of follow-up, small sample sizes or invalidated diagnosis of PCOS. Only one study has estimated the healthcare-related economic burden of PCOS, reporting a cost of $4.36 billion per year in the USA. STUDY DESIGN, SIZE, DURATION: This was a modelling study using individual patient data from a UK primary care database between 2004 and 2014 and aggregate data from the literature to obtain conversion rates through disease progression of PCOS. A simulation approach was applied to model the population dynamics of PCOS over a follow-up period of 25 years. PARTICIPANTS/MATERIALS, SETTING, METHODS: A total of 14 135 women with PCOS or symptoms indicative of PCOS were selected from the primary care database to estimate the incidence of confirmed diagnosis of PCOS and diagnosis of type 2 diabetes. A 'virtual' cohort including the entire PCOS population (size estimated from the UK census data) was simulated to model the population dynamics of PCOS. The economic and utility analyses were further conducted from a healthcare perspective. MAIN RESULTS AND THE ROLE OF CHANCE: The peak conversion rate from possible to diagnosed PCOS was 121 per 1000 person-year (PY). The maximal incidence of type 2 diabetes was 33 per 1000 PY. The estimated prevalence of diabetes in the PCOS population was 26.5% (95% interval: 25.4-27.8%) during a 25-year follow-up. The annual healthcare burden of PCOS based on our conservative estimate is at least £237 million for the follow-up period examined. LIMITATIONS, REASONS FOR CAUTION: Due to lack of data, a full economic evaluation including healthcare costs of all the comorbidities associated with PCOS was not possible. Simplification of the real-world situation represented by the model may be a concern. WIDER IMPLICATIONS OF THE FINDINGS: This study suggests that a large number of women with symptoms indicative of PCOS never receive a definitive diagnosis yet can suffer from a rapid conversion to diabetes. This significantly reduces the quality of life for individual patients and incurs high costs for healthcare providers. As the risk of diabetes in women with PCOS is similar to that seen in populations at high risks of diabetes, it is possible that including them in national screening programmes may be cost effective. STUDY FUNDING/COMPETING INTEREST(S): There was no funding for the current study. There are no conflicts of interest. TRIAL REGISTRATION NUMBER: Not applicable.

Cost-effectiveness analysis of the nine-valent HPV vaccine in Italy.

BACKGROUND: In Italy HPV vaccination with the quadrivalent vaccine (Gardasil®) is offered actively and free of charge to girls aged 12 since 2007. A nine-valent vaccine (Gardasil 9®) received the European market authorization in 2015 to protect, with only 2 doses, against around 90% of all HPV positive cancers, over 80% of high-grade precancerous lesions and 90% of genital warts caused by HPV types 6/11. METHODS: A dynamic transmission model simulating the natural history of HPV-infections was calibrated to the Italian setting and used to estimate costs and QALYs associated with vaccination strategies. The analyses compared two strategies with the nine-valent vaccine (cervical cancer screening and vaccination in girls only or vaccination in boys and girls) to four alternative strategies (cervical cancer screening and vaccination with quadrialent vaccine in girls only, in both boys and girls, with bivalent vaccine in girls and screening strategy only). The National Health Service perspective was considered. CONCLUSION: The switch to the nine-valent vaccine in Italy can further reduce the burden associated to cervical cancer and HPV-related diseases and is highly cost-effective. RESULTS: Compared to the current vaccination program with quadrivalent vaccine, the nine-valent vaccine in a programme including girls and boys shows further reductions of 17% in the incidence of cervical cancer, 35 and 14% in anal cancer for males and females, as well as over a million cases of genital warts avoided after 100 years. The new technology is associated with an ICER of 10,463€ per QALY gained in universal vaccination, decreasing to 4483€ when considering the vaccine switch for girls-only.

Regulatory approval of pharmaceuticals without a randomised controlled study: analysis of EMA and FDA approvals 1999-2014.

INTRODUCTION: The efficacy of pharmaceuticals is most often demonstrated by randomised controlled trials (RCTs); however, in some cases, regulatory applications lack RCT evidence. OBJECTIVE: To investigate the number and type of these approvals over the past 15 years by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA). METHODS: Drug approval data were downloaded from the EMA website and the 'Drugs@FDA' database for all decisions on pharmaceuticals published from 1 January 1999 to 8 May 2014. The details of eligible applications were extracted, including the therapeutic area, type of approval and review period. RESULTS: Over the period of the study, 76 unique indications were granted without RCT results (44 by the EMA and 60 by the FDA), demonstrating that a substantial number of treatments reach the market without undergoing an RCT. The majority was for haematological malignancies (34), with the next most common areas being oncology (15) and metabolic conditions (15). Of the applications made to both agencies with a comparable data package, the FDA granted more approvals (43/44 vs 35/44) and took less time to review products (8.7 vs 15.5 months). Products reached the market first in the USA in 30 of 34 cases (mean 13.1 months) due to companies making FDA submission before EMA submissions and faster FDA review time. DISCUSSION: Despite the frequency with which approvals are granted without RCT results, there is no systematic monitoring of such treatments to confirm their effectiveness or consistency regarding when this form of evidence is appropriate. We recommend a more open debate on the role of marketing authorisations granted without RCT results, and the development of guidelines on what constitutes an acceptable data package for regulators.

Diagnosis and management of polycystic ovary syndrome in the UK (2004-2014): a retrospective cohort study.

OBJECTIVE: To estimate the incidence and prevalence of polycystic ovary syndrome (PCOS) in UK primary care and investigate prescribing patterns before and after a PCOS diagnosis. DESIGN: Retrospective cohort study. SETTING: UK primary care (2004-2014). PARTICIPANTS: Women aged 15-45 years. PRIMARY AND SECONDARY OUTCOME MEASURES: The incidence and prevalence of diagnosed PCOS and probable PCOS (ie, those without a confirmed diagnosis but with at least 2 PCOS features recorded within 3 years). Among women with diagnosed or probable PCOS, the prevalence of prescribing of drugs typically used to treat PCOS was calculated prior to and in the 24 months after the diagnosis of PCOS. RESULTS: We identified 7233 women with PCOS diagnoses and 7057 women with records suggestive of probable PCOS, corresponding to incidence rates of 0.93 and 0.91 per 1000 person-years at risk (PYAR) and an overall rate of 1.84 per 1000 PYAR. Women aged 20-24 years and women living in deprived areas had the highest incidence of PCOS. The prevalence of PCOS in 2014 was ∼2%. The proportion of women with a prescription in the 24 months after their PCOS index date varied by drug type: 10.2% metformin, 15.2% combined oral contraceptives, 18.8% acne-related treatments, 1.93% clomiphene, 1.0% spironolactone, 0.28% cyproterone and 3.11% eflornithine. Acne-related treatments were more commonly used to treat probable (28.3%) than diagnosed (12.3%) cases, while metformin was prescribed much more commonly in diagnosed cases. CONCLUSIONS: In conclusion, compared to rates estimated in community samples, the incidence and prevalence of women presenting in primary care with PCOS diagnoses and features are low, indicating that PCOS is an under-recognised condition. Although considerable variation is observed in treatments prescribed to women with PCOS, the treatments initiated following a confirmed diagnosis generally reflect the long-term prognostic concerns raised in PCOS consensuses.

Uptake of Bowel Scope (Flexible Sigmoidoscopy) Screening in the English National Programme: the first 14 months.

OBJECTIVE: To examine uptake in the first six pilot centres of the English Bowel Scope Screening (BSS) programme, which began in early 2013 and invites adults aged 55 for a one off Flexible Sigmoidoscopy. METHODS: Between March 2013 and May 2014 the six pilot centres sent 21,187 invitations. Using multivariate logistic regression analysis, we examined variation in uptake by gender, socioeconomic deprivation (using the Index of Multiple Deprivation), area-based ethnic diversity (proportion of non-white residents), screening centre, and appointment time (routine: daytime vs out-of-hours: evening/weekend). RESULTS: Uptake was 43.1%. Men were more likely to attend than women (45% vs 42%; OR 1.136, 95% CI 1.076, 1.199, p < 0.001). Combining data across centres, there was a socioeconomic gradient in uptake, ranging from 33% in the most deprived to 53% in the least deprived quintile. Areas with the highest level of ethnic diversity also had lower uptake (39%) than other areas (41-47%) (all p < 0.02), but there was no gradient. Individuals offered a routine appointment were less likely to attend than those offered an out-of-hours appointment (42% vs. 44%; OR 0.931, 95% CI 0.882, 0.983, p = 0.01). Multivariate analyses confirmed independent effects of deprivation, gender, and centre, but not of ethnic diversity or appointment time. CONCLUSION: Early indications of uptake are encouraging. Future efforts should focus on increasing public awareness of the programme and reducing socioeconomic inequalities.

Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy.

OBJECT The objective of this study was to evaluate the clinical utility of multitrajectory computer-assisted planning software (CAP) to plan stereoelectroencephalography (SEEG) electrode arrangements. METHODS A cohort of 18 patients underwent SEEG for evaluation of epilepsy at a single center between August 2013 and August 2014. Planning of electrodes was performed manually and stored using EpiNav software. CAP was developed as a planning tool in EpiNav. The user preselects a set of cerebral targets and optimized trajectory constraints, and then runs an automated search of potential scalp entry points and associated trajectories. Each trajectory is associated with metrics for a safety profile, derived from the minimal distance to vascular structures, and an efficacy profile, derived from the proportion of depth electrodes that are within or adjacent to gray matter. CAP was applied to the cerebral targets used in the cohort of 18 previous manually planned implantations to generate new multitrajectory implantation plans. A comparison was then undertaken for trajectory safety and efficacy. RESULTS CAP was applied to 166 electrode targets in 18 patients. There were significant improvements in both the safety profile and efficacy profile of trajectories generated by CAP compared with manual planning (p < 0.05). Three independent neurosurgeons assessed the feasibility of the trajectories generated by CAP, with 131 (78.9%) of 166 trajectories deemed suitable for implementation in clinical practice. CAP was performed in real time, with a median duration of 8 minutes for each patient, although this does not include the time taken for data preparation. CONCLUSIONS CAP is a promising tool to plan SEEG implantations. CAP provides feasible depth electrode arrangements, with quantitatively greater safety and efficacy profiles, and with a substantial reduction in duration of planning within the 3D multimodality framework.

Cost-Effectiveness Analysis of Universal Human Papillomavirus Vaccination Using a Dynamic Bayesian Methodology: The BEST II Study.

BACKGROUND: Human papillomavirus (HPV) plays a role in the development of benign and malign neoplasms in both sexes. The Italian recommendations for HPV vaccines consider only females. The BEST II study (Bayesian modelling to assess the Effectiveness of a vaccination Strategy to prevent HPV-related diseases) evaluates 1) the cost-effectiveness of immunization strategies targeting universal vaccination compared with cervical cancer screening and female-only vaccination and 2) the economic impact of immunization on various HPV-induced diseases. OBJECTIVE: The objective of this study was to evaluate whether female-only vaccination or universal vaccination is the most cost-effective intervention against HPV. METHODS: We present a dynamic Bayesian Markov model to investigate transmission dynamics in cohorts of females and males in a follow-up period of 55 years. We assumed that quadrivalent vaccination (against HPV 16, 18, 6, and 11) is available for 12-year-old individuals. The model accounts for the progression of subjects across HPV-induced health states (cervical, vaginal, vulvar, anal, penile, and head/neck cancer as well as anogenital warts). The sexual mixing is modeled on the basis of age-, sex-, and sexual behavioral-specific matrices to obtain the dynamic force of infection. RESULTS: In comparison to cervical cancer screening, universal vaccination results in an incremental cost-effectiveness ratio of €1,500. When universal immunization is compared with female-only vaccination, it is cost-effective with an incremental cost-effectiveness ratio of €11,600. Probabilistic sensitivity analysis shows a relatively large amount of parameter uncertainty, which interestingly has, however, no substantial impact on the decision-making process. The intervention being assessed seems to be associated with an attractive cost-effectiveness profile. CONCLUSIONS: Universal HPV vaccination is found to be a cost-effective choice when compared with either cervical cancer screening or female-only vaccination within the Italian context.