Assessing feasibility and acceptability of web-based enhanced relapse prevention for bipolar disorder (ERPonline): a randomized controlled trial

Background: Interventions that teach people with Bipolar Disorder (BD) to recognise and respond to early warning signs of relapse are NICE recommended but implementation in clinical practice is poor. Objective: This study tests the feasibility and acceptability of a randomised controlled trial to evaluate an online enhanced relapse prevention intervention (ERPonline), and reports preliminary evidence of effectiveness. Methods: Single blind, parallel primarily online randomised controlled trial (n=96) over 48 weeks comparing ERPonline plus usual treatment to waitlist (WL) control plus usual treatment for people with BD recruited through National Health Services, voluntary organisations, and media. Randomisation was independent, minimised on number of previous episodes (<8,8-20,21+). Primary outcomes were feasibility and acceptability assessed by rates of study recruitment and retention, levels of intervention use, adverse events and participant feedback. Process and clinical outcomes were assessed by telephone and online and compared using linear models with intention-to-treat analysis. Results: Two hundred and eighty people registered interest online, from which ninety-six met inclusion criteria, consented and were randomised (49 to WL, 47 to ERPonline) over seventeen months, with 80% retention in telephone and online follow up, except week 48 online (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 users have with online interventions and how this is created as an extension of the relationship with the humans perceived as offering and supporting its use. Differences between the group means suggested that access to ERPonline was associated with: a more positive model of bipolar disorder at 24 (10.70 (0.90-20.5 95%CIs)) and 48 weeks (13.1 (2.44-23.93 95%CIs)); increased monitoring of early warning signs of depression at 48 weeks (-1.39 (-2.61, -.163 95%CIs)) and of (hypo)mania at 24 (-1.72 (-2.98, -0.47 95%CIs)) and 48 weeks (-1.61 (-2.92, -0.30 95%CIs)), compared to WL. There was no evidence of impact of ERPonline on clinical outcomes or medication adherence, but relapse rates across both arms were very low (15%) and the sample remained high functioning throughout. One person died by suicide prior to randomisation. Five people in ERPonline and six in WL control reported ideas of suicide or self-harm during the study. None were deemed study related by an independent Trial Steering Committee. Conclusions: ERPonline 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 considere

Statistical analysis protocol - ERPonline.docx

Statistics protocol for <p><b>Randomised controlled trial to assess feasibility and acceptability of web-based enhanced relapse prevention for bipolar disorder (ERPonline) 2016</b></p

First-order marginalised transition random effects models with probit link function

Marginalised models, also known as marginally specified models, have recently become a popular tool for analysis of discrete longitudinal data. Despite being a novel statistical methodology, these models introduce complex constraint equations and model fitting algorithms. On the other hand, there is a lack of publicly available software to fit these models. In this paper, we propose a three-level marginalised model for analysis of multivariate longitudinal binary outcome. The implicit function theorem is introduced to approximately solve the marginal constraint equations explicitly. probit link enables direct solutions to the convolution equations. Parameters are estimated by maximum likelihood via a Fisher-Scoring algorithm. A simulation study is conducted to examine the finite-sample properties of the estimator. We illustrate the model with an application to the data set from the Iowa Youth and Families Project. The R package pnmtrem is prepared to fit the model

Forecasting multivariate longitudinal binary data with marginal and marginally specified models

Forecasting with longitudinal data has been rarely studied. Most of the available studies are for continuous response and all of them are for univariate response. In this study, we consider forecasting multivariate longitudinal binary data. Five different models including simple ones, univariate and multivariate marginal models, and complex ones, marginally specified models, are studied to forecast such data. Model forecasting abilities are illustrated via a real-life data set and a simulation study. The simulation study includes a model independent data generation to provide a fair environment for model competitions. Independent variables are forecast as well as the dependent ones to mimic the real-life cases best. Several accuracy measures are considered to compare model forecasting abilities. Results show that complex models yield better forecasts