RE-EM Trees: A New Data Mining Approach for Longitudinal Data

Longitudinal data refer to the situation where repeated observations are available for each sampled individual. Methodologies that take this structure into account allow for systematic differences between individuals that are not related to covariates. A standard methodology in the statistics literature for this type of data is the random effects model, where these differences between individuals are represented by so-called “effects” that are estimated from the data. This paper presents a methodology that combines the flexibility of tree-based estimation methods with the structure of random effects models for longitudinal data. We apply the resulting estimation method, called the RE-EM tree, to pricing in online transactions, showing that the RE-EM tree is less sensitive to parametric assumptions and provides improved predictive power compared to linear models with random effects and regression trees without random effects. We also perform extensive simulation experiments to show that the estimator improves predictive performance relative to regression trees without random effects and is comparable or superior to using linear models with random effects in more general situations.Statistics Group, Information, Operations, and Management Science Department, Stern School of Business, New York UniversityStatistics Working Papers Serie

Efficiency and Consistency for Regularization Parameter Selection in Penalized Regression: Asymptotics and Finite-Sample Corrections

This paper studies the asymptotic and nite-sample performance of penalized regression methods when different selectors of the regularization parameter are used under the assumption that the true model is, or is not, included among the candidate model. In the latter setting, we relax assumptions in the existing theory to show that several classical information criteria are asymptotically efficient selectors of the regularization parameter. In both settings, we assess the nite-sample performance of these as well as other common selectors and demonstrate that their performance can suffer due to sensitivity to the number of variables that are included in the full model. As alternatives, we propose two corrected information criteria which are shown to outperform the existing procedures while still maintaining the desired asymptotic properties. In the non-true model world, we relax the assumption made in the literature that the true error variance is known or that a consistent estimator is available to prove that Akaike's information criterion (AIC), Cp and Generalized cross-validation (GCV) themselves are asymptotically efficient selectors of the regularization parameter and we study their performance in nite samples. In classical regression, AIC tends to select overly complex models when the dimension of the maximum candidate model is large relative to the sample size. Simulation studies suggest that AIC suffers from the same shortcomings when used in penalized regression. We therefore propose the use of the classical AICc as an alternative. In the true model world, a similar investigation into the nite sample properties of BIC reveals analogous overfitting tendencies and leads us to further propose the use of a corrected BIC (BICc). In their respective settings (whether the true model is, or is not, among the candidate models), BICc and AICc have the desired asymptotic properties and we use simulations to assess their performance, as well as that of other selectors, in nite samples for penalized regressions fit using the Smoothly clipped absolute deviation (SCAD) and Least absolute shrinkage and selection operator (Lasso) penalty functions. We nd that AICc and 10-fold cross-validation outperform the other selectors in terms of squared error loss, and BICc avoids the tendency of BIC to select overly complex models when the dimension of the maximum candidate model is large relative to the sample size.NYU Stern School of BusinessStatistics Working Papers Serie

Efficiency and Consistency for Regularization Parameter Selection in Penalized Regression: Asymptotics and Finite-Sample Corrections

This paper studies the asymptotic and nite-sample performance of penalized regression methods when different selectors of the regularization parameter are used under the assumption that the true model is, or is not, included among the candidate model. In the latter setting, we relax assumptions in the existing theory to show that several classical information criteria are asymptotically efficient selectors of the regularization parameter. In both settings, we assess the nite-sample performance of these as well as other common selectors and demonstrate that their performance can suffer due to sensitivity to the number of variables that are included in the full model. As alternatives, we propose two corrected information criteria which are shown to outperform the existing procedures while still maintaining the desired asymptotic properties. In the non-true model world, we relax the assumption made in the literature that the true error variance is known or that a consistent estimator is available to prove that Akaike's information criterion (AIC), Cp and Generalized cross-validation (GCV) themselves are asymptotically efficient selectors of the regularization parameter and we study their performance in nite samples. In classical regression, AIC tends to select overly complex models when the dimension of the maximum candidate model is large relative to the sample size. Simulation studies suggest that AIC suffers from the same shortcomings when used in penalized regression. We therefore propose the use of the classical AICc as an alternative. In the true model world, a similar investigation into the nite sample properties of BIC reveals analogous overfitting tendencies and leads us to further propose the use of a corrected BIC (BICc). In their respective settings (whether the true model is, or is not, among the candidate models), BICc and AICc have the desired asymptotic properties and we use simulations to assess their performance, as well as that of other selectors, in nite samples for penalized regressions fit using the Smoothly clipped absolute deviation (SCAD) and Least absolute shrinkage and selection operator (Lasso) penalty functions. We nd that AICc and 10-fold cross-validation outperform the other selectors in terms of squared error loss, and BICc avoids the tendency of BIC to select overly complex models when the dimension of the maximum candidate model is large relative to the sample size.NYU Stern School of BusinessStatistics Working Papers Serie

Moving from development to implementation of digital innovations within the NHS: myHealthE, a remote monitoring system for tracking patient outcomes in child and adolescent mental health services

OBJECTIVE: This paper aims to report our experience of developing, implementing, and evaluating myHealthE (MHE), a digital innovation for Child and Adolescents Mental Health Services (CAMHS), which automates the remote collection and reporting of Patient-Reported Outcome Measures (PROMs) into National Health Services (NHS) electronic healthcare records. METHODS: We describe the logistical and governance issues encountered in developing the MHE interface with patient-identifiable information, and the steps taken to overcome these development barriers. We describe the application's architecture and hosting environment to enable its operability within the NHS, as well as the capabilities needed within the technical team to bridge the gap between academic development and NHS operational teams. RESULTS: We present evidence on the feasibility and acceptability of this system within clinical services and the process of iterative development, highlighting additional functions that were incorporated to increase system utility. CONCLUSION: This article provides a framework with which to plan, develop, and implement automated PROM collection from remote devices back to NHS infrastructure. The challenges and solutions described in this paper will be pertinent to other digital health innovation researchers aspiring to deploy interoperable systems within NHS clinical systems

Regression, developmental trajectory and associated problems in disorders in the autism spectrum: the SNAP study

We report rates of regression and associated findings in a population derived group of 255 children aged 9-14 years, participating in a prevalence study of autism spectrum disorders (ASD); 53 with narrowly defined autism, 105 with broader ASD and 97 with non-ASD neurodevelopmental problems, drawn from those with special educational needs within a population of 56,946 children. Language regression was reported in 30% with narrowly defined autism, 8% with broader ASD and less than 3% with developmental problems without ASD. A smaller group of children were identified who underwent a less clear setback. Regression was associated with higher rates of autistic symptoms and a deviation in developmental trajectory. Regression was not associated with epilepsy or gastrointestinal problems

Bounds on Integrals of the Wigner Function

The integral of the Wigner function over a subregion of the phase-space of a quantum system may be less than zero or greater than one. It is shown that for systems with one degree of freedom, the problem of determining the best possible upper and lower bounds on such an integral, over all possible states, reduces to the problem of finding the greatest and least eigenvalues of an hermitian operator corresponding to the subregion. The problem is solved exactly in the case of an arbitrary elliptical region. These bounds provide checks on experimentally measured quasiprobability distributions.Comment: 10 pages, 1 PostScript figure, Latex file; revised following referees' comments; to appear in Physical Review Letter

'Everyday memory' impairments in autism spectrum disorders

‘Everyday memory’ is conceptualised as memory within the context of day-to-day life and, despite its functional relevance, has been little studied in individuals with autism spectrum disorders (ASDs). In the first study of its kind, 94 adolescents with an ASD and 55 without an ASD completed measures of everyday memory from the Rivermead Behavioural Memory Test (RBMT) and a standard word recall task (Children’s Auditory Verbal Learning Test-2: CAVLT-2). The ASD group showed significant impairments on the RBMT, including in prospective memory, alongside impaired performance on the CAVLT-2. Social and communication ability was significantly associated with prospective remembering in an everyday memory context but not with the CAVLT-2. The complex nature of everyday memory and its relevance to ASD is discussed

Local linear density estimation for filtered survival data, with bias correction

A class of local linear kernel density estimators based on weighted least-squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition to accommodating unusual patterns of exposure as well as occurrence. It is shown that the local linear estimators corresponding to all different weightings have the same pointwise asymptotic properties. However, the weighting previously used in the literature in the i.i.d. case is seen to be far from optimal when it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a 'pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias-correction methods within our framework. The multiplicative bias-correction method proves to be the best in a simulation study comparing the performance of the considered estimators. An example concerning old-age mortality demonstrates the importance of the improvements provided

Remote Recruitment Strategy and Structured E-Parenting Support (STEPS) App: Feasibility and Usability Study.

BACKGROUND: The Structured E-Parenting Support (STEPS) app provides support for parents of children with elevated hyperactivity, impulsivity, inattention, and conduct problems who are awaiting clinical assessment. STEPS will be evaluated in a randomized controlled trial (RCT) within the Online Parent Training for the Initial Management of ADHD Referrals (OPTIMA) research program in the United Kingdom. Phase 1 of the OPTIMA tested the feasibility of participants' recruitment and the app's usability. OBJECTIVE: This study aimed to adapt a digital routine clinical monitoring system, myHealthE, for research purposes to facilitate waitlist recruitment; test using remote methods to screen and identify participants quickly and systematically; pilot the acceptability of the recruitment and assessment protocol; and explore the usability of STEPS. METHODS: myHealthE was adapted to screen patients' data. Parents' and clinicians' feedback on myHealthE was collected, and information governance reviews were conducted in clinical services planning to host the RCT. Potential participants for the observational feasibility study were identified from new referrals using myHealthE and non-myHealthE methods. Descriptive statistics were used to summarize the demographic and outcome variables. We estimated whether the recruitment rate would meet the planned RCT sample size requirement (n=352). In addition to the feasibility study participants, another group of parents was recruited to assess the STEPS usability. They completed the adapted System Usability Scale and responded to open-ended questions about the app, which were coded using the Enlight quality construct template. RESULTS: Overall, 124 potential participants were identified as eligible: 121 (97.6%) via myHealthE and 3 (2.4%) via non-myHealthE methods. In total, 107 parents were contacted, and 48 (44.9%) consented and were asked if, hypothetically, they would be willing to participate in the OPTIMA RCT. Of the 28 feasibility study participants who provided demographic data, 21 (75%) identified as White. Their children had an average age of 8.4 (SD 1.7) years and 65% (31/48) were male. During the primary recruitment period (June to July 2021) when 45 participants had consented, 38 (84%) participants agreed hypothetically to take part in the RCT (rate of 19/mo, 95% CI 13.5-26.1), meeting the stop-go criterion of 18 participants per month to proceed with the RCT. All parents were satisfied or very satisfied with the study procedures. Parents (n=12) recruited to assess STEPS' usability described it as easy to navigate and use and as having an attractive combination of colors and visual design. They described the content as useful, pitched at the right level, and sensitively presented. Suggested improvements included adding captions to videos or making the recorded reflections editable. CONCLUSIONS: Remote recruitment and study procedures for testing a parenting intervention app are feasible and acceptable for parents. The parents felt that STEPS was a useful and easy-to-use digital parenting support tool. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s40814-021-00959-0

Comparative efficacy and tolerability of medications for attention-deficit hyperactivity disorder in children, adolescents, and adults: a systematic review and network meta-analysis

Background: The benefits and safety of medications for attention-deficit hyperactivity disorder (ADHD) remain controversial, and guidelines are inconsistent on which medications are preferred across different age groups. We aimed to estimate the comparative efficacy and tolerability of oral medications for ADHD in children, adolescents, and adults. Methods: We did a literature search for published and unpublished double-blind randomised controlled trials comparing amphetamines (including lisdexamfetamine), atomoxetine, bupropion, clonidine, guanfacine, methylphenidate, and modafinil with each other or placebo. We systematically contacted study authors and drug manufacturers for additional information. Primary outcomes were efficacy (change in severity of ADHD core symptoms based on teachers' and clinicians' ratings) and tolerability (proportion of patients who dropped out of studies because of side-effects) at timepoints closest to 12 weeks, 26 weeks, and 52 weeks. We estimated summary odds ratios (ORs) and standardised mean differences (SMDs) using pairwise and network meta-analysis with random effects. We assessed the risk of bias of individual studies with the Cochrane risk of bias tool and confidence of estimates with the Grading of Recommendations Assessment, Development, and Evaluation approach for network meta-analyses. This study is registered with PROSPERO, number CRD42014008976. Findings: 133 double-blind randomised controlled trials (81 in children and adolescents, 51 in adults, and one in both) were included. The analysis of efficacy closest to 12 weeks was based on 10 068 children and adolescents and 8131 adults; the analysis of tolerability was based on 11 018 children and adolescents and 5362 adults. The confidence of estimates varied from high or moderate (for some comparisons) to low or very low (for most indirect comparisons). For ADHD core symptoms rated by clinicians in children and adolescents closest to 12 weeks, all included drugs were superior to placebo (eg, SMD −1·02, 95% CI −1·19 to −0·85 for amphetamines, −0·78, −0·93 to −0·62 for methylphenidate, −0·56, −0·66 to −0·45 for atomoxetine). By contrast, for available comparisons based on teachers' ratings, only methylphenidate (SMD −0·82, 95% CI −1·16 to −0·48) and modafinil (−0·76, −1·15 to −0·37) were more efficacious than placebo. In adults (clinicians' ratings), amphetamines (SMD −0·79, 95% CI −0·99 to −0·58), methylphenidate (−0·49, −0·64 to −0·35), bupropion (−0·46, −0·85 to −0·07), and atomoxetine (−0·45, −0·58 to −0·32), but not modafinil (0·16, −0·28 to 0·59), were better than placebo. With respect to tolerability, amphetamines were inferior to placebo in both children and adolescents (odds ratio [OR] 2·30, 95% CI 1·36–3·89) and adults (3·26, 1·54–6·92); guanfacine was inferior to placebo in children and adolescents only (2·64, 1·20–5·81); and atomoxetine (2·33, 1·28–4·25), methylphenidate (2·39, 1·40–4·08), and modafinil (4·01, 1·42–11·33) were less well tolerated than placebo in adults only. In head-to-head comparisons, only differences in efficacy (clinicians' ratings) were found, favouring amphetamines over modafinil, atomoxetine, and methylphenidate in both children and adolescents (SMDs −0·46 to −0·24) and adults (−0·94 to −0·29). We did not find sufficient data for the 26-week and 52-week timepoints. Interpretation: Our findings represent the most comprehensive available evidence base to inform patients, families, clinicians, guideline developers, and policymakers on the choice of ADHD medications across age groups. Taking into account both efficacy and safety, evidence from this meta-analysis supports methylphenidate in children and adolescents, and amphetamines in adults, as preferred first-choice medications for the short-term treatment of ADHD. New research should be funded urgently to assess long-term effects of these drugs. Funding: Stichting Eunethydis (European Network for Hyperkinetic Disorders), and the UK National Institute for Health Research Oxford Health Biomedical Research Centre