International Regulations and Recommendations for Utility Data for Health Technology Assessment

Recommendations and guidelines for the collection, generation, source and usage of utility data for health technology assessment (HTA) vary across different countries, with no international consensus. Many international agencies generate their own guidelines providing details on their preferred methods for HTA submissions, and there is variability in both what they recommend and the clarity and amount of detail provided in their guidelines. This article provides an overview of international regulations and recommendations for utility data in HTA for a selection of key HTA countries: Australia, Canada, France, Germany, the Netherlands, Spain (Catalonia), Sweden and the UK (England/Wales and Scotland). International guidelines are typically clear and detailed for the selection of countries assessed regarding the source description of health states (e.g. generic preference-based measure) and who should provide preference weights for these health states (e.g. general population for own country). Many guidelines specify the use of off-the-shelf generic preference-based measures, and some further specify a measure, such as EQ-5D. However, international guidelines are either unclear or lack detailed guidance regarding the collection (e.g. patients report own health), source (e.g. clinical trial) and usage (e.g. adjusting for comorbidities) of utility values. It is argued that there is a need for transparent and detailed international guidelines on utility data recommendations to provide decision makers with the best possible evidence. Where this is not possible it is recommended that best practice should be used to inform the collection, source and usage of utility values in HTA

The "efficacy-effectiveness gap" : Historical background and current conceptualization

Background The concept of the "efficacy-effectiveness gap" (EEG) has started to challenge confidence in decisions made for drugs when based on randomized controlled trials alone. Launched by the Innovative Medicines Initiative, the GetReal project aims to improve understanding of how to reconcile evidence to support efficacy and effectiveness and at proposing operational solutions. Objectives The objectives of the present narrative review were 1) to understand the historical background in which the concept of the EEG has emerged and 2) to describe the conceptualization of EEG. Methods A focused literature review was conducted across the gray literature and articles published in English reporting insights on the EEG concept. The identification of different "paradigms" was performed by simple inductive analysis of the documents' content. Results The literature on the EEG falls into three major paradigms, in which EEG is related to 1) real-life characteristics of the health care system; 2) the method used to measure the drug's effect; and 3) a complex interaction between the drug's biological effect and contextual factors. Conclusions The third paradigm provides an opportunity to look beyond any dichotomy between "standardized" versus "real-life" characteristics of the health care system and study designs. Namely, future research will determine whether the identification of these contextual factors can help to best design randomized controlled trials that provide better estimates of drugs' effectiveness

The "efficacy-effectiveness gap" : Historical background and current conceptualization

Background The concept of the "efficacy-effectiveness gap" (EEG) has started to challenge confidence in decisions made for drugs when based on randomized controlled trials alone. Launched by the Innovative Medicines Initiative, the GetReal project aims to improve understanding of how to reconcile evidence to support efficacy and effectiveness and at proposing operational solutions. Objectives The objectives of the present narrative review were 1) to understand the historical background in which the concept of the EEG has emerged and 2) to describe the conceptualization of EEG. Methods A focused literature review was conducted across the gray literature and articles published in English reporting insights on the EEG concept. The identification of different "paradigms" was performed by simple inductive analysis of the documents' content. Results The literature on the EEG falls into three major paradigms, in which EEG is related to 1) real-life characteristics of the health care system; 2) the method used to measure the drug's effect; and 3) a complex interaction between the drug's biological effect and contextual factors. Conclusions The third paradigm provides an opportunity to look beyond any dichotomy between "standardized" versus "real-life" characteristics of the health care system and study designs. Namely, future research will determine whether the identification of these contextual factors can help to best design randomized controlled trials that provide better estimates of drugs' effectiveness

Schizophr Res

OBJECTIVES: To explore the impact upon estimation of drug effect as a result of applying exclusion criteria in randomized-controlled trials (RCT) measuring the efficacy of antipsychotics (AP) in schizophrenia. METHODS: Three characteristics which may act as effect-modifiers of AP, while also common exclusion criteria in RCTs, were identified through literature review: schizophrenia duration, substance use disorder and poor adherence. The SOHO cohort was used to estimate the effect of initiating antipsychotic drugs "A", "B" or "C" (pooled) upon symptom evolution at 3months from baseline (CGI-S scale). "Estimated effectiveness" and "estimated efficacy" were drawn from the "SOHO" and "RCT-like" (patients with none of the above-listed exclusion criteria) samples, respectively. Effect-modification and impact of each exclusion criterion on AP effect estimates were explored using non-adjusted statistics. RESULTS: The "SOHO sample" included 8250 patients initiating drug A, B or C at baseline, whose AP "estimated effectiveness" was DeltaCGI-S=-0.78 (95% CI=-0.80, -0.76). The "RCT-like" sub-sample included 5348 (65%) patients whose AP "estimated efficacy" was DeltaCGI-S=-0.73 (95% CI=-0.75, -0.70). Patients with short illness duration (3years (mean DeltaCGI-S=-0.73; 95%CI=-0.76, -0.71). Excluding patients with short illness duration led to a change in AP effect estimates but this was not the case for substance use disorder or poor adherence. CONCLUSION: Using certain exclusion criteria in RCTs may impact the drug's effect estimate, particularly when exclusion criteria are AP effect-modifiers representing frequent characteristics among patients with schizophrenia