How do pharmaceutical companies model survival of cancer patients? A review of NICE Single Technology Appraisals in 2017

Objectives Before an intervention is publicly funded within the United Kingdom, the cost-effectiveness is assessed by the National Institute of Health and Care Excellence (NICE). The efficacy of an intervention across the patients’ lifetime is often influential of the cost-effectiveness analyses, but is associated with large uncertainties. We reviewed committee documents containing company submissions and evidence review group (ERG) reports to establish the methods used when extrapolating survival data, whether these adhered to NICE Technical Support Document (TSD) 14, and how uncertainty was addressed. Methods A systematic search was completed on the NHS Evidence Search webpage limited to single technology appraisals of cancer interventions published in 2017, with information obtained from the NICE Web site. Results Twenty-eight appraisals were identified, covering twenty-two interventions across eighteen diseases. Every economic model used parametric curves to model survival. All submissions used goodness-of-fit statistics and plausibility of extrapolations when selecting a parametric curve. Twenty-five submissions considered alternate parametric curves in scenario analyses. Six submissions reported including the parameters of the survival curves in the probabilistic sensitivity analysis. ERGs agreed with the company's choice of parametric curve in nine appraisals, and agreed with all major survival-related assumptions in two appraisals. Conclusions TSD 14 on survival extrapolation was followed in all appraisals. Despite this, the choice of parametric curve remains subjective. Recent developments in Bayesian approaches to extrapolation are not implemented. More precise guidance on the selection of curves and modelling of uncertainty may reduce subjectivity, accelerating the appraisal process

Extrapolating parametric survival models in health technology assessment : a simulation study

Extrapolations of parametric survival models fitted to censored data are routinely used in the assessment of health technologies to estimate mean survival, particularly in diseases that potentially reduce the life expectancy of patients. Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) are commonly used in health technology assessment alongside an assessment of plausibility to determine which statistical model best fits the data and should be used for prediction of long-term treatment effects. We compare fit and estimates of restricted mean survival time (RMST) from 8 parametric models and contrast models preferred in terms of AIC, BIC, and log-likelihood, without considering model plausibility. We assess the methods’ suitability for selecting a parametric model through simulation of data replicating the follow-up of intervention arms for various time-to-event outcomes from 4 clinical trials. Follow-up was replicated through the consideration of recruitment duration and minimum and maximum follow-up times. Ten thousand simulations of each scenario were performed. We demonstrate that the different methods can result in disagreement over the best model and that it is inappropriate to base model selection solely on goodness-of-fit statistics without consideration of hazard behavior and plausibility of extrapolations. We show that typical trial follow-up can be unsuitable for extrapolation, resulting in unreliable estimation of multiple parameter models, and infer that selecting survival models based only on goodness-of-fit statistics is unsuitable due to the high level of uncertainty in a cost-effectiveness analysis. This article demonstrates the potential problems of overreliance on goodness-of-fit statistics when selecting a model for extrapolation. When follow-up is more mature, BIC appears superior to the other selection methods, selecting models with the most accurate and least biased estimates of RMST

Multimorbidity and co-morbidity in atrial fibrillation and effects on survival: findings from UK Biobank cohort

Aims: To examine the number and type of co-morbid long-term health conditions (LTCs) and their associations with all-cause mortality in an atrial fibrillation (AF) population. Methods and results: Community cohort participants (UK Biobank n = 502 637) aged 37–73 years were recruited between 2006 and 2010. Self-reported LTCs (n = 42) identified in people with AF at baseline. All-cause mortality was available for a median follow-up of 7 years (interquartile range 76–93 months). Hazard ratios (HRs) examined associations between number and type of co-morbid LTC and all-cause mortality, adjusting for age, sex, socio-economic status, smoking, and anticoagulation status. Three thousand six hundred fifty-one participants (0.7% of the study population) reported AF; mean age was 61.9 years. The all-cause mortality rate was 6.7% (248 participants) at 7 years. Atrial fibrillation participants with ≥4 co-morbidities had a six-fold higher risk of mortality compared to participants without any LTC. Co-morbid heart failure was associated with higher risk of mortality [HR 2.96, 95% confidence interval (CI) 1.83–4.80], whereas the presence of co-morbid stroke did not have a significant association. Among non-cardiometabolic conditions, presence of chronic obstructive pulmonary disease (HR 3.31, 95% CI 2.14–5.11) and osteoporosis (HR 3.13, 95% CI 1.63–6.01) was associated with a higher risk of mortality. Conclusion: Survival in middle-aged to older individuals with self-reported AF is strongly correlated with level of multimorbidity. This group should be targeted for interventions to optimize their management, which in turn may potentially reduce the impact of their co-morbidities on survival. Future AF clinical guidelines need to place greater emphasis on the issue of co-morbidity

A systematic review of economic evaluations assessing the cost-effectiveness of licensed drugs used for previously treated epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) negative advanced/metastatic non-small cell lung cancer

Background Non-small cell lung cancer (NSCLC) is one of the most commonly diagnosed cancers. There are many published studies of cost-effectiveness analyses of licensed treatments, but no study has compared these studies or their approaches simultaneously. Objective To investigate the methodology used in published economic analyses of licensed interventions for previously treated advanced/metastatic NSCLC in patients without anaplastic lymphoma kinase or epidermal growth factor receptor expression. Methods A systematic review was performed, including a systematic search of key databases (e.g. MEDLINE, EMBASE, Web of Knowledge, Cost-effectiveness Registry) limited to the period from 01 January 2001 to 26 July 2019. Two reviewers independently screened, extracted data and quality appraised identified studies. The reporting quality of the studies was assessed by using the Consolidated Health Economic Evaluation Reporting Standards and the Philips’ checklists. Results Thirty-one published records met the inclusion criteria, which corresponded to 30 individual cost-effectiveness analyses. Analytical approaches included partitioned survival models (n = 14), state-transition models (n = 7) and retrospective analyses of new or published data (n = 8). Model structure was generally consistent, with pre-progression, post-progression and death health states used most commonly. Other characteristics varied more widely, including the perspective of analysis, discounting, time horizon, usually to align with the country that the analysis was set in. Conclusions There are a wide range of approaches in the modelling of treatments for advanced NSCLC; however, the model structures are consistent. There is variation in the exploration of sensitivity analyses, with considerable uncertainty remaining in most evaluations. Improved reporting is necessary to ensure transparency in future analyses

Extrapolating parametric survival models in Health Technology Assessment using model averaging : a simulation study

Previous work examined the suitability of relying on routine methods of model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability of restricted mean survival time (RMST) estimates from trial data by assessing model plausibility and implementing model averaging. We compare our previous methods of selecting a model for extrapolation using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Our methods of model averaging include using equal weighting across models falling within established threshold ranges for AIC and BIC and using BIC-based weighted averages. We apply our plausibility assessment and implement model averaging to the output of our previous simulations, where 10,000 runs of 12 trial-based scenarios were examined. We demonstrate that removing implausible models from consideration reduces the mean squared error associated with the restricted mean survival time (RMST) estimate from each selection method and increases the percentage of RMST estimates that were within 10% of the RMST from the parameters of the sampling distribution. The methods of averaging were superior to selecting a single optimal extrapolation, aside from some of the exponential scenarios where BIC already selected the exponential model. The averaging methods with wide criterion-based thresholds outperformed BIC-weighted averaging in the majority of scenarios. We conclude that model averaging approaches should feature more widely in the appraisal of health technologies where extrapolation is influential and considerable uncertainty is present. Where data demonstrate complicated underlying hazard rates, funders should account for the additional uncertainty associated with these extrapolations in their decision making. Extended follow-up from trials should be encouraged and used to review prices of therapies to ensure a fair price is paid

Multimorbidity in stroke

No abstract available

Global health inequalities of chronic kidney disease:a meta-analysis

Background and hypothesis: Chronic kidney disease (CKD) is a significant contributor to global morbidity and mortality. This study investigated disparities in age, sex and socio-economic status in CKD and updated global prevalence estimates through systematic review and meta-analysis.Methods: Five databases were searched from 2014 to 2022, with 14,871 articles screened, 119 papers included and data analysed on 29,159,948 participants. Random effects meta-analyses were conducted to determine overall prevalence, prevalence of stages 3 – 5 and prevalence in males/females. Influences of age, sex and socio-economic status were assessed in subgroup analyses, and risk of bias assessment and meta-regressions were conducted to explore heterogeneity.Results: Overall prevalence of CKD was 13.0% (11.3 – 14.8%) and 6.6% (5.6 – 7.8%) for stages 3 – 5. Prevalence was higher in studies of older populations (19.3% for stages 1 – 5, 15.0% for stages 3 – 5) and meta-regression demonstrated association of age, body mass index, diabetes and hypertension with prevalence of stages 3 – 5. The prevalence of CKD stages 1 – 5 was similar in males and females (13.1% versus 13.2%) but prevalence of stages 3 – 5 was higher in females (6.4% versus 7.5%). Overall prevalence was 11.4%, 15.0% and 10.8% in low, middle and high-income countries respectively; for stages 3 – 5 prevalence was 4.0%, 6.7% and 6.8%, respectively. Included studies were at moderate-high risk of bias in the majority of cases (92%), and heterogeneity was high.Conclusion: This study provides a comprehensive assessment of CKD prevalence, highlighting important disparities related to age, sex and socio-economic status. Future research should focus on targeted screening and treatment approaches, improving access to care and more effective data monitoring, particularly in low or middle income countries