Ophthalmol Ther

There is little understanding of long-term treatment persistence in patients receiving anti-vascular endothelial growth factor (anti-VEGF) injections for diabetic macular edema (DME), particularly relating to treatment intervals. The aim of this study was to investigate the association between treatment interval and discontinuation rate after 24 months of unilateral anti-VEGF treatment in patients with DME under routine clinical care in the USA. This was a non-interventional, retrospective cohort study to review the health insurance claims of adults with DME linked with the IBM MarketScan Commercial and Medicare Supplemental databases, who were continuously enrolled in a health plan for at least 6 months prior to their first anti-VEGF treatment and for a duration of at least 24 months between July 2011 and June 2017. Patients were grouped on the basis of the injection interval they achieved at 24 months of treatment. Discontinuation rate beyond 24 months and its association with treatment intervals at 24 months was estimated using the Kaplan-Meier method and Cox proportional hazards models. The overall discontinuation rate among the 1702 eligible patients from 24 to 60 months after treatment initiation was 30%. At 60 months, patients were more likely to remain on treatment in shorter (75.3% [4-week interval group]) versus longer treatment interval groups (62.1% [> 12-week interval group], difference = 13.2%, [95% confidence interval (CI) 1.06, 2.06], p = 0.01). Patients on a > 12-week interval were twice as likely to discontinue treatment compared with those on an 8-week interval (hazard ratio = 2.01 [95% CI 1.43, 2.82], p < 0.001). Patients with DME on longer anti-VEGF treatment intervals at 24 months consistently had higher discontinuation rates in the following years than those on shorter treatment intervals

second line cabozantinib versus nivolumab in advanced renal cell carcinoma systematic review and indirect treatment comparison

Abstract Background Nivolumab and cabozantinib, two new treatment options for previously-treated advanced/metastatic renal cell carcinoma (aRCC), have recently been approved. Methods Two independent reviewers performed study selection, data extraction, and risk of bias assessment. Indirect treatment comparisons were carried out by directly assessing HR differences and statistical modeling of Kaplan-Meier curves from these two trials. Results Publications identified showed that no head-to-head comparisons had been carried out. Two indirect treatment comparisons used agreed that there was no significant difference in OS between cabozantinib and nivolumab and that cabozantinib significantly improved PFS compared to nivolumab. Conclusions The field of aRCC treatments is evolving rapidly, creating opportunities for individualized treatments and challenges for clinicians to keep up with the evidence. In lieu of availability of direct comparisons of all options, advanced modeling results presented herein can help to inform and improve personalized treatments

Ethical and social implications of using predictive modeling for Alzheimer´s disease prevention:a systematic literature review

BACKGROUND: The therapeutic paradigm in Alzheimer's disease (AD) is shifting from symptoms management toward prevention goals. Secondary prevention requires the identification of individuals without clinical symptoms, yet "at-risk" of developing AD dementia in the future, and thus, the use of predictive modeling. OBJECTIVE: The objective of this study was to review the ethical concerns and social implications generated by this new approach. METHODS: We conducted a systematic literature review in Medline, Embase, PsycInfo, and Scopus, and complemented it with a gray literature search between March and July 2018. Then we analyzed data qualitatively using a thematic analysis technique. RESULTS: We identified thirty-one ethical issues and social concerns corresponding to eight ethical principles: (i) respect for autonomy, (ii) beneficence, (iii) non-maleficence, (iv) equality, justice, and diversity, (v) identity and stigma, (vi) privacy, (vii) accountability, transparency, and professionalism, and (viii) uncertainty avoidance. Much of the literature sees the discovery of disease-modifying treatment as a necessary and sufficient condition to justify AD risk assessment, overlooking future challenges in providing equitable access to it, establishing long-term treatment outcomes and social consequences of this approach, e.g., medicalization. The ethical/social issues associated specifically with predictive models, such as the adequate predictive power and reliability, infrastructural requirements, data privacy, potential for personalized medicine in AD, and limiting access to future AD treatment based on risk stratification, were covered scarcely. CONCLUSION: The ethical discussion needs to advance to reflect recent scientific developments and guide clinical practice now and in the future, so that necessary safeguards are implemented for large-scale AD secondary prevention.</p

The 'RCT augmentation': a novel simulation method to add patient heterogeneity into phase III trials.

BACKGROUND: Phase III randomized controlled trials (RCT) typically exclude certain patient subgroups, thereby potentially jeopardizing estimation of a drug's effects when prescribed to wider populations and under routine care ('effectiveness'). Conversely, enrolling heterogeneous populations in RCTs can increase endpoint variability and compromise detection of a drug's effect. We developed the 'RCT augmentation' method to quantitatively support RCT design in the identification of exclusion criteria to relax to address both of these considerations. In the present manuscript, we describe the method and a case study in schizophrenia. METHODS: We applied typical RCT exclusion criteria in a real-world dataset (cohort) of schizophrenia patients to define the 'RCT population' subgroup, and assessed the impact of re-including each of the following patient subgroups: (1) illness duration 1-3 years; (2) suicide attempt; (3) alcohol abuse; (4) substance abuse; and (5) private practice management. Predictive models were built using data from different 'augmented RCT populations' (i.e., subgroups where patients with one or two of such characteristics were re-included) to estimate the absolute effectiveness of the two most prevalent antipsychotics against real-world results from the entire cohort. Concurrently, the impact on RCT results of relaxing exclusion criteria was evaluated by calculating the comparative efficacy of those two antipsychotics in virtual RCTs drawing on different 'augmented RCT populations'. RESULTS: Data from the 'RCT population', which was defined with typical exclusion criteria, allowed for a prediction of effectiveness with a bias < 2% and mean squared error (MSE) = 5.8-6.8%. Compared to this typical RCT, RCTs using augmented populations provided improved effectiveness predictions (bias < 2%, MSE = 5.3-6.7%), while returning more variable comparative effects. The impact of augmentation depended on the exclusion criterion relaxed. Furthermore, half of the benefit of relaxing each criterion was gained from re-including the first 10-20% of patients with the corresponding real-world characteristic. CONCLUSIONS: Simulating the inclusion of real-world subpopulations into an RCT before running it allows for quantification of the impact of each re-inclusion upon effect detection (statistical power) and generalizability of trial results, thereby explicating this trade-off and enabling a controlled increase in population heterogeneity in the RCT design

Cabozantinib versus everolimus, nivolumab, axitinib, sorafenib and best supportive care: A network meta-analysis of progression-free survival and overall survival in second line treatment of advanced renal cell carcinoma

Background Relative effect of therapies indicated for the treatment of advanced renal cell carcinoma (aRCC) after failure of first line treatment is currently not known. The objective of the present study is to evaluate progression-free survival (PFS) and overall survival (OS) of cabozantinib compared to everolimus, nivolumab, axitinib, sorafenib, and best supportive care (BSC) in aRCC patients who progressed after previous VEGFR tyrosine-kinase inhibitor (TKI) treatment. Methodology & findings Systematic literature search identified 5 studies for inclusion in this analysis. The assessment of the proportional hazard (PH) assumption between the survival curves for different treatment arms in the identified studies showed that survival curves in two of the studies did not fulfil the PH assumption, making comparisons of constant hazard ratios (HRs) inappropriate. Consequently, a parametric survival network meta-analysis model was implemented with five families of functions being jointly fitted in a Bayesian framework to PFS, then OS, data on all treatments. The comparison relied on data digitized from the Kaplan-Meier curves of published studies, except for cabozantinib and its comparator everolimus where patient level data were available. This analysis applied a Bayesian fixed-effects network meta-analysis model to compare PFS and OS of cabozantinib versus its comparators. The log-normal fixed-effects model displayed the best fit of data for both PFS and OS, and showed that patients on cabozantinib had a higher probability of longer PFS and OS than patients exposed to comparators. The survival advantage of cabozantinib increased over time for OS. For PFS the survival advantage reached its maximum at the end of the first year’s treatment and then decreased over time to zero. Conclusion With all five families of distributions, cabozantinib was superior to all its comparators with a higher probability of longer PFS and OS during the analyzed 3 years, except with the Gompertz model, where nivolumab was preferred after 24 months

Nivolumab versus Cabozantinib: Comparing Overall Survival in Metastatic Renal Cell Carcinoma.

Renal-cell carcinoma (RCC) affects over 330,000 new patients every year, of whom 1/3 present with metastatic RCC (mRCC) at diagnosis. Most mRCC patients treated with a first-line agent relapse within 1 year and need second-line therapy. The present study aims to compare overall survival (OS) between nivolumab and cabozantinib from two recent pivotal studies comparing, respectively, each one of the two emerging treatments against everolimus in patients who relapse following first-line treatment. Comparison is traditionally carried out using the Bucher method, which assumes proportional hazard. Since OS curves intersected in one of the pivotal studies, models not assuming proportional hazards were also considered to refine the comparison. Four Bayesian parametric survival network meta-analysis models were implemented on overall survival (OS) data digitized from the Kaplan-Meier curves reported in the studies. Three models allowing hazard ratios (HR) to vary over time were assessed against a fixed-HR model. The Bucher method favored cabozantinib, with a fixed HR for OS vs. nivolumab of 1.09 (95% confidence interval: [0.77, 1.54]). However, all models with time-varying HR showed better fits than the fixed-HR model. The log-logistic model fitted the data best, exhibiting a HR for OS initially favoring cabozantinib, the trend inverting to favor nivolumab after month 5 (95% credible interval <1 from 10 months). The initial probability of cabozantinib conferring superior OS was 54%, falling to 41.5% by month 24. Numerical differences in study-adjusted OS estimates between the two treatments remained small. This study evidences that HR for OS of nivolumab vs. cabozantinib varies over time, favoring cabozantinib in the first months of treatment but nivolumab afterwards, a possible indication that patients with poor prognosis benefit more from cabozantinib in terms of survival, nivolumab benefiting patients with better prognosis. More evidence, including real-world observational data, is needed to compare effectiveness between cabozantinib and nivolumab

Overall survival curves over time derived from the Bayesian log-logistic model after adjustment of reference parameters to match those in the Motzer et al study [6], Shaded areas represent 95% credible intervals.

<p>Overall survival curves over time derived from the Bayesian log-logistic model after adjustment of reference parameters to match those in the Motzer et al study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155389#pone.0155389.ref006" target="_blank">6</a>], Shaded areas represent 95% credible intervals.</p

Probability of being the best treatment in terms of overall survival according to the four Bayesian models, as a function of time since the beginning of therapy.

<p>Probability of being the best treatment in terms of overall survival according to the four Bayesian models, as a function of time since the beginning of therapy.</p

Estimated hazard ratios over time for nivolumab vs. cabozantinib.

<p>Estimated hazard ratios over time for nivolumab vs. cabozantinib.</p