Joint models should be preferred for simultaneous analyses of repeated measurement and survival data, especially when the former is measured with error and the association between the underlying error-free measurement process and the hazard for survival is of scientific interest.

BackgroundInterventions that teach people with bipolar disorder (BD) to recognize and respond to early warning signs (EWS) of relapse are recommended but implementation in clinical practice is poor.ObjectivesThe objective of this study was to test the feasibility and acceptability of a randomized controlled trial (RCT) to evaluate a Web-based enhanced relapse prevention intervention (ERPonline) and to report preliminary evidence of effectiveness.MethodsA single-blind, parallel, primarily online RCT (n=96) over 48 weeks comparing ERPonline plus usual treatment with “waitlist (WL) control” plus usual treatment for people with BD recruited through National Health Services (NHSs), voluntary organizations, and media. Randomization was independent, minimized on number of previous episodes (<8, 8-20, 21+). Primary outcomes were recruitment and retention rates, levels of intervention use, adverse events, and participant feedback. Process and clinical outcomes were assessed by telephone and Web and compared using linear models with intention-to-treat analysis.ResultsA total of 280 people registered interest online, from which 96 met inclusion criteria, consented, and were randomized (49 to WL, 47 to ERPonline) over 17 months, with 80% retention in telephone and online follow-up at all time points, except at week 48 (76%). Acceptability was high for both ERPonline and trial methods. ERPonline cost approximately £19,340 to create, and £2176 per year to host and maintain the site. Qualitative data highlighted the importance of the relationship that the users have with Web-based interventions. Differences between the group means suggested that access to ERPonline was associated with: a more positive model of BD at 24 weeks (10.70, 95% CI 0.90 to 20.5) and 48 weeks (13.1, 95% CI 2.44 to 23.93); increased monitoring of EWS of depression at 48 weeks (−1.39, 95% CI −2.61 to −0.163) and of hypomania at 24 weeks (−1.72, 95% CI −2.98 to −0.47) and 48 weeks (−1.61, 95% CI −2.92 to −0.30), compared with WL. There was no evidence of impact of ERPonline on clinical outcomes or medication adherence, but relapse rates across both arms were low (15%) and the sample remained high functioning throughout. One person died by suicide before randomization and 5 people in ERPonline and 6 in WL reported ideas of suicide or self-harm. None were deemed study related by an independent Trial Steering Committee (TSC).ConclusionsERPonline offers a cheap accessible option for people seeking ongoing support following successful treatment. However, given high functioning and low relapse rates in this study, testing clinical effectiveness for this population would require very large sample sizes. Building in human support to use ERPonline should be considered.Trial registrationInternational Standard Randomized Controlled Trial Number (ISRCTN): 56908625; http://www.isrctn.com/ISRCTN56908625 (Archived by WebCite at http://www.webcitation.org/6of1ON2S0)

BackgroundPeople with bipolar disorder (BD) experience additional parenting challenges associated with mood driven fluctuations in communication, impulse control and motivation. This paper describes a novel web‐based self‐management approach (Integrated Bipolar Parenting Intervention; IBPI) to support parents with BD.MethodParents with BD with children aged 3–10 years randomised to IBPI plus treatment as usual (TAU) or waitlist control (WL). IBPI offered 16 weeks access to interactive self‐management information concerning BD and parenting issues. Feasibility was through recruitment, retention and web usage. Clinical outcomes were assessed at baseline, 16, 24, 36 and 48 weeks. Trial Registration Number: ISRCTN75279027.ResultsNinety seven participants were recruited with 98% retention to end of intervention and 90% to final follow‐up (56%–94% data analysed of retained participants; higher rates for observer measures). 77% of IBPI participants accessed the website (53% accessed parenting modules). Child behaviour, parenting sense of competence and parenting stress improved significantly in IBPI compared to WL to end of intervention, sustained to 48 weeks. Impacts of IBPI on family functioning, parent mood and time to mood relapse were not significant.ConclusionsOnline self‐management support for parents with BD is feasible, with promising improvements in parenting and child behaviour outcomes. A definitive clinical and cost‐effectiveness trial is required to confirm and extend these findings.

Chronic renal failure is a progressive condition that, typically, is asymptomatic for many years. Early detection of incipient kidney failure enables ameliorative treatment that can slow the rate of progression to end-stage renal failure, at which point expensive and invasive renal replacement therapy (dialysis or transplantation) is required. We use routinely collected clinical data from a large sample of primary care patients to develop a system for real-time monitoring of the progression of undiagnosed incipient renal failure. Progression is characterized as the rate of change in a person's kidney function as measured by the estimated glomerular filtration rate, an adjusted version of serum creatinine level in a blood sample. Clinical guidelines in the UK suggest that a person who is losing kidney function at a relative rate of at least 5% per year should be referred to specialist secondary care. We model the time-course of a person's underlying kidney function through a combination of explanatory variables, a random intercept and a continuous-time, non-stationary stochastic process. We then use the model to calculate for each person the predictive probability that they meet the clinical guideline for referral to secondary care. We suggest that probabilistic predictive inference linked to clinical criteria can be a useful component of a real-time surveillance system to guide, but not dictate, clinical decision-making.

Box-Cox power transformation is a commonly used methodology to transform the distribution of a non-normal data into a normal one. Estimation of the transformation parameter is crucial in this methodology. In this study, the estimation process is hold via a searching algorithm and is integrated into wellknown seven goodness of fit tests for normal distribution. An artificial covariate method is also included for comparative purposes. Simulation studies are implemented to compare the effectiveness of the proposed methods. The methods are also illustrated on two different real life data applications. Moreover, an R package AID is proposed for implementation.

No abstract

We consider the analysis of continuous repeated measurement outcomes that are collected longitudinally. A standard framework for analysing data of this kind is a linear Gaussian mixed effects model within which the outcome variable can be decomposed into fixed effects, time invariant and time-varying random effects, and measurement noise. We develop methodology that, for the first time, allows any combination of these stochastic components to be non-Gaussian, using multivariate normal variance-mean mixtures. To meet the computational challenges that are presented by large data sets, i.e. in the current context, data sets with many subjects and/or many repeated measurements per subject, we propose a novel implementation of maximum likelihood estimation using a computationally efficient subsampling-based stochastic gradient algorithm. We obtain standard error estimates by inverting the observed Fisher information matrix and obtain the predictive distributions for the random effects in both filtering (conditioning on past and current data) and smoothing (conditioning on all data) contexts. To implement these procedures, we introduce an R package: ngme. We reanalyse two data sets, from cystic fibrosis and nephrology research, that were previously analysed by using Gaussian linear mixed effects models.

No abstract