295 research outputs found

    A product of independent beta probabilities dose escalation design for dual-agent phase I trials.

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    Dual-agent trials are now increasingly common in oncology research, and many proposed dose-escalation designs are available in the statistical literature. Despite this, the translation from statistical design to practical application is slow, as has been highlighted in single-agent phase I trials, where a 3 + 3 rule-based design is often still used. To expedite this process, new dose-escalation designs need to be not only scientifically beneficial but also easy to understand and implement by clinicians. In this paper, we propose a curve-free (nonparametric) design for a dual-agent trial in which the model parameters are the probabilities of toxicity at each of the dose combinations. We show that it is relatively trivial for a clinician's prior beliefs or historical information to be incorporated in the model and updating is fast and computationally simple through the use of conjugate Bayesian inference. Monotonicity is ensured by considering only a set of monotonic contours for the distribution of the maximum tolerated contour, which defines the dose-escalation decision process. Varied experimentation around the contour is achievable, and multiple dose combinations can be recommended to take forward to phase II. Code for R, Stata and Excel are available for implementation.We would like to acknowledge funding from the UK Medical Research Council (grant code U1052.00.014) for this work. We would also like to thank the reviewers for providing some excellent suggestions to help improve the manuscript.This is the final published version. It first appeared at http://onlinelibrary.wiley.com/doi/10.1002/sim.6434/abstract

    A scoping methodological review of simulation studies comparing statistical and machine learning approaches to risk prediction for time-to-event data

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    BACKGROUND: There is substantial interest in the adaptation and application of so-called machine learning approaches to prognostic modelling of censored time-to-event data. These methods must be compared and evaluated against existing methods in a variety of scenarios to determine their predictive performance. A scoping review of how machine learning methods have been compared to traditional survival models is important to identify the comparisons that have been made and issues where they are lacking, biased towards one approach or misleading. METHODS: We conducted a scoping review of research articles published between 1 January 2000 and 2 December 2020 using PubMed. Eligible articles were those that used simulation studies to compare statistical and machine learning methods for risk prediction with a time-to-event outcome in a medical/healthcare setting. We focus on data-generating mechanisms (DGMs), the methods that have been compared, the estimands of the simulation studies, and the performance measures used to evaluate them. RESULTS: A total of ten articles were identified as eligible for the review. Six of the articles evaluated a method that was developed by the authors, four of which were machine learning methods, and the results almost always stated that this developed method's performance was equivalent to or better than the other methods compared. Comparisons were often biased towards the novel approach, with the majority only comparing against a basic Cox proportional hazards model, and in scenarios where it is clear it would not perform well. In many of the articles reviewed, key information was unclear, such as the number of simulation repetitions and how performance measures were calculated. CONCLUSION: It is vital that method comparisons are unbiased and comprehensive, and this should be the goal even if realising it is difficult. Fully assessing how newly developed methods perform and how they compare to a variety of traditional statistical methods for prognostic modelling is imperative as these methods are already being applied in clinical contexts. Evaluations of the performance and usefulness of recently developed methods for risk prediction should be continued and reporting standards improved as these methods become increasingly popular

    Youth vaping and smoking and parental vaping: a panel survey

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    Background: Concerns remain about potential negative impacts of e-cigarettes including possibilities that: youth e-cigarette use (vaping) increases risk of youth smoking; and vaping by parents may have impacts on their children’s vaping and smoking behaviour. Methods: With panel data from 3291 youth aged 10–15 years from the 7th wave of the UK Understanding Society Survey (2015–2017), we estimated effects of youth vaping on youth smoking (ever, current and past year initiation), and of parental vaping on youth smoking and vaping, and examined whether the latter differed by parental smoking status. Propensity weighting was used to adjust for measured confounders and estimate average effects of vaping for all youth, and among youth who vaped. E-values were calculated to assess the strength of unmeasured confounding influences needed to negate our estimates. Results: Associations between youth vaping and youth smoking were attenuated considerably by adjustment for measured confounders. Estimated average effects of youth vaping on youth smoking were stronger for all youth (e.g. OR for smoking initiation: 32.5; 95% CI: 9.8–107.1) than among youth who vaped (OR: 4.4; 0.6–30.9). Relatively strong unmeasured confounding would be needed to explain these effects. Associations between parental vaping and youth vaping were explained by measured confounders. Estimates indicated effects of parental vaping on youth smoking, especially for youth with ex-smoking parents (e.g. OR for smoking initiation: 11.3; 2.7–46.4) rather than youth with currently smoking parents (OR: 1.0; 0.2–6.4), but these could be explained by relatively weak unmeasured confounding. Conclusions While measured confounding accounted for much of the associations between youth vaping and youth smoking, indicating support for underlying propensities, our estimates suggested residual effects that could only be explained away by considerable unmeasured confounding or by smoking leading to vaping. Estimated effects of youth vaping on youth smoking were stronger among the general youth population than among the small group of youth who actually vaped. Associations of parental vaping with youth smoking and vaping were either explained by measured confounding or could be relatively easily explained by unmeasured confounding

    AplusB: A Web Application for Investigating A + B Designs for Phase I Cancer Clinical Trials.

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    In phase I cancer clinical trials, the maximum tolerated dose of a new drug is often found by a dose-escalation method known as the A + B design. We have developed an interactive web application, AplusB, which computes and returns exact operating characteristics of A + B trial designs. The application has a graphical user interface (GUI), requires no programming knowledge and is free to access and use on any device that can open an internet browser. A customised report is available for download for each design that contains tabulated operating characteristics and informative plots, which can then be compared with other dose-escalation methods. We present a step-by-step guide on how to use this application and provide several illustrative examples of its capabilities.GMW and APM are supported by the UK Medical Research Council (www.mrc.ac.uk; grant number G0800860). MJS is supported by a European Research Council Advanced Investigator Award: EPIC-Heart (https://erc.europa.eu; grant number 268834), the UK Medical Research Council (grant number MR/L003120/1), the British Heart Foundation (www.bhf.org.uk), and the Cambridge National Institute for Health Research Biomedical Research Centre (http://www.cambridge-brc.org.uk). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLOS at http://dx.doi.org/10.1371/journal.pone.0159026

    Landmark models for optimizing the use of repeated measurements of risk factors in electronic health records to predict future disease risk

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    The benefits of using electronic health records for disease risk screening and personalized heathcare decisions are becoming increasingly recognized. We present a computationally feasible statistical approach to address the methodological challenges in utilizing historical repeat measures of multiple risk factors recorded in electronic health records to systematically identify patients at high risk of future disease. The approach is principally based on a two-stage dynamic landmark model. The first stage estimates current risk factor values from all available historical repeat risk factor measurements by landmark-age-specific multivariate linear mixed-effects models with correlated random-intercepts, which account for sporadically recorded repeat measures, unobserved data and measurements errors. The second stage predicts future disease risk from a sex-stratified Cox proportional hazards model, with estimated current risk factor values from the first stage. Methods are exemplified by developing and validating a dynamic 10-year cardiovascular disease risk prediction model using electronic primary care records for age, diabetes status, hypertension treatment, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol from 41,373 individuals in 10 primary care practices in England and Wales contributing to The Health Improvement Network (1997-2016). Using cross-validation, the model was well-calibrated (Brier score = 0.041 [95%CI: 0.039, 0.042]) and had good discrimination (C-index = 0.768 [95%CI: 0.759, 0.777]).This work was funded by the Medical Research Council (MRC) (grant MR/K014811/1). J.B. was supported by an MRC fellowship (grant G0902100) and the MRC Unit Program (grant MC_UU_00002/5). R.H.K. was supported by an MRC Methodology Fellowship (grant MR/M014827/1)

    Causal effects of transitions to adult roles on early adult smoking and drinking: Evidence from three cohorts

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    Transitions into work and family roles have become increasingly delayed as participation in tertiary education widens. Such transitions may have adverse or beneficial effects on health behaviours such as smoking and drinking (alcohol). Role socialisation effects may reduce smoking or drinking, but clustering of transitions may lead to role overload, weakening or reversing any role socialisation effects. Effects of transitions were examined in three UK cohorts: the 1958 National Child Development Study, the 1970 British Birth Cohort Study, and the West of Scotland: Twenty-07 Youth Cohort (from around Glasgow, growing up in the same time period as the 1970 cohort). Latent class analysis was employed to identify heterogeneous patterns of transition timing for leaving education, entering employment, starting cohabitation, having a first child, and leaving the parental home. Propensity weighting was then used to estimate causal effects of transition patterns (relative to tertiary education) on smoking and heavy drinking in early adulthood (ages 22–26), adjusting for background confounders (gender, parental socioeconomic position, family structure, parental and adolescent health behaviours, adolescent distress and school performance). Three groups made early (age 16) transitions from education to employment and then either delayed other transitions, made other transitions quickly, or staggered transitions with cohabitation beginning around ages 19–21; a fourth group transitioned from education to employment around ages 17–18. Compared to those in tertiary education with similar background characteristics, those in these groups generally had higher levels of smoking, especially where transitions were more clustered, but less heavy drinking (except those who delayed other transitions after moving into employment). Results partially supported role socialisation effects for drinking, and role overload effects for smoking. Wider participation in tertiary education could have helped reduce smoking levels in these cohorts, but might also have increased risk for heavy drinking

    Patterns of initiation in the poetry of Ted Hughes from 1970 to 1980

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    This study seeks to give a close reading of the poems in the major sequences by Ted Hughes published since 1970. The consideration of the poems centres upon the influence of initiatory religious patterns and their mythologies upon the poet's work. A key to such initiatory patterns is the primitive religious phenomenon known as shamanism. The first chapter of the thesis charts evidence of Hughes's fascination with shamanic practice and shows its pervasive and central influence from early poems such as 'Jaguar' right up to the pivotal work of Caudate. In chapters on each of the three volumes preceding Gaudete Hughes is shown as being preoccupied with shamanic questions rather than with the answers that a fully initiated shaman is able to give. Crow takes its protagonist to the very threshold of initiation, as does Prometheus on his Crag, but the need for the contextualization of the abstract experiences in the poems becomes very clear. Cave Birds is an attempt to introduce a coda of the realities of common human experience, but it is not until Gaudete that the strong mythological element in Hughes's work is disciplined into a greater, though flawed, whole. More and more Hughes seeks to transform the profane experience of our present life into a perception of the sacred. In Gaudete Lumb fails to do this with the rituals of his personal fertility cult and those around him are killed or emotionally damaged. But he himself is radically changed. The Epilogue Poems of Gaudete reflect this change and point the way away from a poetic reliance on the mechanisms of mythology to a living out of ritual in the poetry itself. Both 'Moortown' and Remains of Elmet exemplify this development in their perception of the sacredness of Nature. By Adam and the Sacred Nine the mythological element can now be presented yoked in a balanced way to the sensitivity of the poetry

    Socioeconomic position and early adolescent smoking development: evidence from the British Youth Panel Survey (1994-2008).

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    Objective Smoking usually develops in adolescence and is patterned by socioeconomic position (SEP). We examined whether early adolescent smoking development and associations with SEP have changed over time in a population with well-developed tobacco control policies. We additionally investigated the relative importance of socioeconomic inequalities at different stages of smoking development. Methods An annual UK rotating panel survey including data from 5122 adolescents (51% male) aged 11-15 years between 1994 and 2008. Rates of smoking initiation, progression to occasional smoking (experimentation), progression to daily smoking (escalation), and quitting were examined using discrete-time event history analysis. Results Initiation, experimentation and escalation rates declined over the study period while quitting rates increased. Decreases in initiation were concentrated among older adolescents and decreases in escalation among those who spent a year or two as occasional smokers. Socioeconomic disadvantage was associated with higher rates of initiation and escalation, with similar findings across SEP measures. Inequalities in initiation were stronger at younger ages. There was less evidence of associations between SEP and quitting or experimentation. Inequalities in escalation remained constant over time, while inequalities in initiation widened before narrowing. Further modelling suggested that differential initiation rates contributed more to inequalities in daily smoking at age 15 than did differential escalation. Conclusions Increasing tobacco control in the UK is associated with reduced uptake and more quitting in early adolescence, but socioeconomic inequalities remain. Interventions should focus on reducing inequalities in initiation among early adolescents
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