A Review of Survival Analysis Methods Used in NICE Technology Appraisals of Cancer Treatments: Consistency, Limitations, and Areas for Improvement.

Objectives. In June 2011, the National Institute for Health and Care Excellence (NICE) Decision Support Unit published a Technical Support Document (TSD) providing recommendations on survival analysis for NICE technology appraisals (TAs). Survival analysis outputs are influential inputs into economic models estimating the cost-effectiveness of new cancer treatments. Hence, it is important that systematic and justifiable model selection approaches are used. This study investigates the extent to which the TSD recommendations have been followed since its publication. Methods. We reviewed NICE cancer TAs completed between July 2011 and July 2017. Information on survival analyses undertaken and associated critiques for overall survival (OS) and progression-free survival were extracted from the company submissions, Evidence Review Group (ERG) reports, and final appraisal determination documents. Results. Information was extracted from 58 TAs. Only 4 (7%) followed all TSD recommendations for OS outcomes. The vast majority (91%) compared a range of common parametric models and assessed their fit to the data (86%). Only a minority of TAs included an assessment of the shape of the hazard function (38%) or proportional hazards assumption (40%). Validation of the extrapolated portion of the survival function using external data was attempted in a minority of TAs (40%). Extrapolated survival functions were frequently criticized by ERGs (71%). Conclusions. Survival analysis within NICE TAs remains suboptimal, despite publication of the TSD. Model selection is not undertaken in a systematic way, resulting in inconsistencies between TAs. More attention needs to be given to assessing hazard functions and validation of extrapolated survival functions. Novel methods not described in the TSD have been used, particularly in the context of immuno-oncology, suggesting that an updated TSD may be of value.

Varying results of early benefit assessment of newly approved pharmaceutical drugs in Germany from 2011 to 2017: A study based on federal joint committee data.

BACKGROUND: Since January 2011, the Federal Joint Committee (FJC) conducts early benefit assessments (EBA) of newly approved pharmaceutical drugs compared to appropriate standard therapies. The FJC commissions the Institute for Quality and Efficiency in Healthcare (IQEH) to prepare preliminary reports. We aimed to evaluate the extent, impact, and reason for different judgments on added benefit of both institutions. METHODS: We searched EBA data on the FJC website and included completed procedures from 2011 to 2017. We conducted a quantitative analysis of the difference between FJC and IQEH on divergent judgments, a quantitative analysis of the impact of EBA on market withdrawal, and a qualitative analysis to identify potential factors contributing to divergent judgments. RESULTS: FJC rated an added benefit in 30% (139 of 457) and IQEH in 22% (101 of 457) matching research questions (P = 0.004). In the aftermath of EBA, 28 pharmaceutical drugs were withdrawn from the German market. We identified three potential factors that might have contributed to the divergent judgments. IQEH used a unique threshold concept to define the rating, FJC conducted additional public hearings, and FJC showed more flexibility with adherence to stringent criteria and interpretation of results. CONCLUSIONS: FJC and IQEH differed significantly in their early benefit assessment. In response to negative EBA decisions, pharmaceutical companies withdrew a considerable number of medicines from the German market. The present work uncovers the subjectivity and possible variance inherent in benefit assessment, as the two institutions observe the same rules of procedure.

Estimating a Preference-Based Single Index Measuring the Quality-of-Life Impact of Self-Management for Diabetes.

OBJECTIVE: Self-management is becoming increasingly important in diabetes but is neglected in conventional preference-based measures. The objective of this paper was to generate health state utility values for a novel classification system measuring the quality-of-life impact of self-management for diabetes, which can be used to generate quality-adjusted life years (QALYs). METHODS: A large online survey was conducted using a discrete choice experiment (DCE), with duration as an additional attribute, on members of the UK general population ( n = 1,493) to elicit values for health (social limitations, mood, vitality, hypoglycaemia) and non-health (stress, hassle, control, support) aspects of self-management in diabetes. The data were modelled using a conditional fixed-effects logit model and utility estimates were anchored on the one to zero (full health to dead) scale. RESULTS: The model produced significant and consistent coefficients, with one logical inconsistency and 3 insignificant coefficients for the milder levels of some attributes. The anchored utilities ranged from 1 for the best state to -0.029 for the worst state (meaning worse than dead) defined by the classification system. CONCLUSION: The results presented here can potentially be used to generate utility values capturing the day to day impact of interventions in diabetes on both health and self-management. These utility values can potentially be used to generate QALYs for economic models of the cost-effectiveness of interventions in diabetes.

Using a Discrete-Choice Experiment Involving Cost to Value a Classification System Measuring the Quality-of-Life Impact of Self-Management for Diabetes.

OBJECTIVES: To describe the use of a novel approach in health valuation of a discrete-choice experiment (DCE) including a cost attribute to value a recently developed classification system for measuring the quality-of-life impact (both health and treatment experience) of self-management for diabetes. METHODS: A large online survey was conducted using DCE with cost on UK respondents from the general population (n = 1497) and individuals with diabetes (n = 405). The data were modeled using a conditional logit model with robust standard errors. The marginal rate of substitution was used to generate willingness-to-pay (WTP) estimates for every state defined by the classification system. Robustness of results was assessed by including interaction effects for household income. RESULTS: There were some logical inconsistencies and insignificant coefficients for the milder levels of some attributes. There were some differences in the rank ordering of different attributes for the general population and diabetic patients. The WTP to avoid the most severe state was £1118.53 per month for the general population and £2356.02 per month for the diabetic patient population. The results were largely robust. CONCLUSIONS: Health and self-management can be valued in a single classification system using DCE with cost. The marginal rate of substitution for key attributes can be used to inform cost-benefit analysis of self-management interventions in diabetes using results from clinical studies in which this new classification system has been applied. The method shows promise, but found large WTP estimates exceeding the cost levels used in the survey.

Blood pressure and cholesterol level checks as dynamic interrelated screening examinations.

This study analysed the determinants of screening uptake for blood pressure and cholesterol level checks. Furthermore, it investigated the presence of possible spillover effects from one type of cardiovascular screening to another type of cardiovascular screening. A dynamic random effects bivariate panel probit model with initial conditions (Wooldridge-type estimator) was adopted for the estimation. The outcome variables were the participation in blood pressure and cholesterol level checks by individuals in a given year. The balanced panel sample of 21,138 observations was constructed from 1,626 individuals from the British Household Panel Survey (BHPS) between 1996 and 2008. The analysis showed the significance of past screening behaviour for both cardiovascular screening examinations. For both cardiovascular screening examinations state dependence exist. The study also shows a significant spillover effect of the cholesterol level check on the blood pressure check and vice versa. Also a poorer health status led to a higher uptake for both types of screening examinations. Changes in recommendations have to consider the fact that taking part in one type of cardiovascular screening examination can influence the decision to take part in the other type of cardiovascular screening examination.

Characteristics of general practices associated with numbers of elective admissions.

BACKGROUND: In England both emergency (unplanned) and non-emergency (elective) hospital admissions have been increasing. Some elective admissions are potentially avoidable. AIM: to identify the characteristics of general practices and patients associated with elective admissions. METHODS: A cross-sectional study, in Leicestershire, England, was conducted using admission data (2006-07 and 2007-08). Practice characteristics (list size, distance from principal hospital, quality and outcomes framework score and general practitioner (GP) patient access survey data) and patient characteristics (age, ethnicity and deprivation and gender) were used as predictors of elective hospital admissions in a negative binomial regression model. RESULTS: Practices with a higher proportion of patients aged 65 years or greater and of white ethnicity had higher rates of elective hospital admissions. Practices with more male patients and with more patients reporting being able to consult a particular GP had fewer elective hospital admissions. For 2007-08 practices with a larger list size were associated with higher elective hospital admissions. Quality and outcomes framework performance did not predict admission numbers. CONCLUSIONS: As for unplanned admissions, elective admissions increase as being able to consult a particular GP declines. Interventions to improve continuity should be investigated. Practices face major problems in managing the increased need for planned care as the population ages.