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GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.

ABSTRACT: The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real-world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software. We identified 12 articles in which four approaches were used: multi-state models, discrete event simulation models, physiology-based models and survival and generalized linear models. Studies predicted outcomes over longer time periods in different patient populations, including patients with lower levels of adherence or persistence to treatment or examined doses not tested in trials. Eight studies included individual patient data. Seven examined cardiovascular and metabolic diseases and three neurological conditions. Most studies included sensitivity analyses, but external validation was performed in only three studies. We conclude that mathematical modelling to predict real-world effectiveness of drug interventions is not widely used at present and not well validated. © 2016 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

SUBMITTER: Panayidou K

PROVIDER: S-EPMC5129568 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.

Panayidou Klea K Gsteiger Sandro S Egger Matthias M Kilcher Gablu G Carreras Máximo M Efthimiou Orestis O Debray Thomas P A TP Trelle Sven S Hummel Noemi N

Research synthesis methods 20160816 3

The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real-world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model an ...[more]

PMID: 27529762

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Project description:BackgroundTo date, coronavirus disease 2019 (COVID-19) becomes increasingly fierce due to the emergence of variants. Rapid herd immunity through vaccination is needed to block the mutation and prevent the emergence of variants that can completely escape the immune surveillance. We aimed to systematically evaluate the effectiveness and safety of COVID-19 vaccines in the real world and to establish a reliable evidence-based basis for the actual protective effect of the COVID-19 vaccines, especially in the ensuing waves of infections dominated by variants.MethodsWe searched PubMed, Embase and Web of Science from inception to July 22, 2021. Observational studies that examined the effectiveness and safety of SARS-CoV-2 vaccines among people vaccinated were included. Random-effects or fixed-effects models were used to estimate the pooled vaccine effectiveness (VE) and incidence rate of adverse events after vaccination, and their 95% confidence intervals (CI).ResultsA total of 58 studies (32 studies for vaccine effectiveness and 26 studies for vaccine safety) were included. A single dose of vaccines was 41% (95% CI: 28-54%) effective at preventing SARS-CoV-2 infections, 52% (31-73%) for symptomatic COVID-19, 66% (50-81%) for hospitalization, 45% (42-49%) for Intensive Care Unit (ICU) admissions, and 53% (15-91%) for COVID-19-related death; and two doses were 85% (81-89%) effective at preventing SARS-CoV-2 infections, 97% (97-98%) for symptomatic COVID-19, 93% (89-96%) for hospitalization, 96% (93-98%) for ICU admissions, and 95% (92-98%) effective for COVID-19-related death, respectively. The pooled VE was 85% (80-91%) for the prevention of Alpha variant of SARS-CoV-2 infections, 75% (71-79%) for the Beta variant, 54% (35-74%) for the Gamma variant, and 74% (62-85%) for the Delta variant. The overall pooled incidence rate was 1.5% (1.4-1.6%) for adverse events, 0.4 (0.2-0.5) per 10 000 for severe adverse events, and 0.1 (0.1-0.2) per 10 000 for death after vaccination.ConclusionsSARS-CoV-2 vaccines have reassuring safety and could effectively reduce the death, severe cases, symptomatic cases, and infections resulting from SARS-CoV-2 across the world. In the context of global pandemic and the continuous emergence of SARS-CoV-2 variants, accelerating vaccination and improving vaccination coverage is still the most important and urgent matter, and it is also the final means to end the pandemic.

Project description:Background: Big data and real-world data (RWD) have been increasingly used to measure the effectiveness and costs in cost-effectiveness analysis (CEA). However, the characteristics and methodologies of CEA based on big data and RWD remain unknown. The objectives of this study were to review the characteristics and methodologies of the CEA studies based on big data and RWD and to compare the characteristics and methodologies between the CEA studies with or without decision-analytic models. Methods: The literature search was conducted in Medline (Pubmed), Embase, Web of Science, and Cochrane Library (as of June 2020). Full CEA studies with an incremental analysis that used big data and RWD for both effectiveness and costs written in English were included. There were no restrictions regarding publication date. Results: 70 studies on CEA using RWD (37 with decision-analytic models and 33 without) were included. The majority of the studies were published between 2011 and 2020, and the number of CEA based on RWD has been increasing over the years. Few CEA studies used big data. Pharmacological interventions were the most frequently studied intervention, and they were more frequently evaluated by the studies without decision-analytic models, while those with the model focused on treatment regimen. Compared to CEA studies using decision-analytic models, both effectiveness and costs of those using the model were more likely to be obtained from literature review. All the studies using decision-analytic models included sensitivity analyses, while four studies no using the model neither used sensitivity analysis nor controlled for confounders. Conclusion: The review shows that RWD has been increasingly applied in conducting the cost-effectiveness analysis. However, few CEA studies are based on big data. In future CEA studies using big data and RWD, it is encouraged to control confounders and to discount in long-term research when decision-analytic models are not used.

Project description:IntroductionBelimumab is a recombinant human monoclonal antibody that binds to soluble B-lymphocyte stimulator and inhibits its biological activity. Since receiving approvals for the treatment of systemic lupus erythematosus (SLE), several observational studies have investigated the effectiveness of belimumab in the real-world setting. This study reports a systematic review and meta-analysis of the literature to evaluate the real-world effectiveness of belimumab for the treatment of SLE.MethodsA literature search following PRISMA Guidelines and limited to studies in English was performed (2014-2020) to identify relevant studies reporting effectiveness outcomes of belimumab in patients with SLE. A modified version of the Newcastle-Ottawa Scale was used to assess study quality. Outcomes, including SLE Disease Activity Index (SLEDAI) score, prednisone-equivalent use, and SLE flare were pooled and analyzed using statistical aggregation methods.ResultsThe literature search identified 514 articles for initial review. Of these, 17 articles were suitable for data extraction and summary. Baseline characteristics of patients in real-world studies were generally similar to those of relevant clinical trials, including age, sex, disease duration, SLEDAI score, and prednisone-equivalent use. Real-world use of belimumab was associated with reductions in SLEDAI score (mean baseline score to month 6: 10.1-4.4; 57% reduction), prednisone-equivalent dosing (mean baseline dose to month 6: 12.1 mg/day to 6.9 mg/day; 43% reduction), and flare frequency (12 months prior to belimumab to 12 months after belimumab: 1.15-0.39 mean flares per patient per year; 66% reduction). Long-term data (up to 2 years post-treatment initiation) for SLEDAI score and prednisone-equivalent dose indicated that improvements in both outcomes continue over time among patients remaining on therapy.ConclusionsIn the real-world setting, observed outcomes with belimumab for the treatment of SLE are consistent with those reported from randomized clinical trials. Improvements persist long-term for SLEDAI activity and prednisone-equivalent use with belimumab.

Project description:BACKGROUND:The promise of real-world evidence (RWE) is especially relevant to pediatrics, where medicines prescribed for children are often used without evidence derived from randomized clinical trials. OBJECTIVES:The aim of this systematic review was to describe the state of RWE in pediatrics by identifying observational studies published during 2016 that used RWE to assess medication safety or effectiveness in children. METHODS:An electronic search of PubMed was combined with an extended search of references within systematic reviews and expert suggestions. Studies were included if they reported on an infant or child under 18 years with exposure to medications; assessed safety or effectiveness; specified a comparison or control group, and were published in English in 2016. Data extraction was conducted by one team member using a standardized form and reviewed by a second team member. Study quality was assessed using the GRACE checklist for rating the quality of observational studies. RESULTS:After removing duplicates, 915 citations were screened and 29 studies met the eligibility criteria. Most of the eligible studies relied on primary data collection or chart review at a single institution and did not use the growing number of administrative or electronic health record databases available. One-quarter of the studies did not use well-established statistical methods to control for confounders. No single disease group or medication predominated, and age groups ranged from infants to adolescents. CONCLUSIONS:A small body of observational studies published in 2016 were categorized by the study team as using real-world data to assess medication safety or effectiveness in children. Studies varied in age groups, diseases or conditions, and methods, and may not have fully met the FDA definition of RWE. Our review indicates that the use of RWE is not fully developed in pediatrics, and suggests an opportunity to further develop capabilities and more fully leverage administrative and electronic health record databases to study medication safety and effectiveness in children. Our systematic review appears generalizable to pediatrics broadly, and documents that the high level of activity in RWE in general has had less of an impact on pediatrics.

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GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.

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GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.

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