The progression trajectory of Bipolar Disorder: results from the application of a staging model over a ten-year observation

BACKGROUND

Trying to better define Bipolar Disorder (BD) progression, different staging models have been conceptualized, each one emphasizing different aspects of illness. In a previous article we retrospectively applied the main staging models to a sample of 100 bipolar patients at four time points over a ten-year observation. In the present study, focusing on Kupka & Hillegers's model, we aimed to assess the transition of the same sample through the different stages of illness and to explore the potential role of clinical variables on the risk of progression.

METHODS

Multistate Model using the mstate package in R and Markov model with stratified hazards were used for statistical analysis.

RESULTS

A high hazard of transition from stage 2 to 3 emerged, with a probability of staying in stage 2 decreasing to 14 % after 3 years. BD II was significantly associated with transition from stage 1 to 2, whereas the number of lifetime episodes >3 and the elevated predominant polarity with transition from stage 3 to 4.

CONCLUSION

Our results corroborated the evidence on BD progression and contributed to outline its trajectory over time. Further effort may help to define a standardized staging approach towards ever increasing tailored interventions.

Comparison of nonparametric estimators of the expected number of recurrent events

We compare the performance of nonparametric estimators for the mean number of recurrent events and provide a systematic overview for different recurrent event settings. The mean number of recurrent events is an easily interpreted marginal feature often used for treatment comparisons in clinical trials. Incomplete observations, dependencies between successive events, terminating events acting as competing risk, or gaps between at risk periods complicate the estimation. We use survival multistate models to represent different complex recurrent event situations, profiting from recent advances in nonparametric estimation for non-Markov multistate models, and explain several estimators by using multistate intensity processes, including the common Nelson-Aalen-type estimators with and without competing mortality. In addition to building on estimation of state occupation probabilities in non-Markov models, we consider a simple extension of the Nelson-Aalen estimator by allowing for dependence on the number of prior recurrent events. We pay particular attention to the assumptions required for the censoring mechanism, one issue being that some settings require the censoring process to be entirely unrelated while others allow for state-dependent or event-driven censoring. We conducted extensive simulation studies to compare the estimators in various complex situations with recurrent events. Our practical example deals with recurrent chronic obstructive pulmonary disease exacerbations in a clinical study, which will also be used to illustrate two-sample-inference using resampling.

The ROCK trial-a multidisciplinary Rehabilitation intervention for sudden Out-of-hospital Cardiac arrest survivors focusing on return-to-worK: a pragmatic randomized controlled trial protocol

INTRODUCTION

Most cardiac arrest survivors are classified with mild to moderate cognitive impairment; roughly, 50% experience long-term neurocognitive impairment. Postarrest challenges complicate participation in society and are associated with social issues such as failure to resume social activities and impaired return to work. The effectiveness of rehabilitation interventions for out-of-hospital cardiac arrest survivors are sparsely described, but the body of evidence describes high probabilities of survivors not returning to work, returning to jobs with modified job descriptions, returning to part-time employment, and often in combination with extensive unmet rehabilitation needs. Hence, there is a need to develop and test a pragmatic individual targeted intervention to facilitate return to work (RTW) in survivors of OHCA. The overall aim of the ROCK trial is to evaluate the effectiveness of a comprehensive individually tailored multidisciplinary rehabilitation intervention for survivors of OHCA on RTW compared to usual care.

METHODS AND ANALYSIS

The ROCK trial is a two-arm parallel group multicentre investigator-initiated pragmatic randomized controlled superiority trial with primary endpoint measured 12 months after the cardiac arrest. Adult survivors who were part of the labour force prior to the OCHA and had at least 2 years until they are qualified to receive retirement state pensions are eligible for inclusion. Survivors will be randomized 1:1 to usual care group or usual care plus a comprehensive tailored rehabilitation intervention focusing on supporting RTW. After comprehensive assessment of individual rehabilitation needs, the intervention is ongoingly coordinated within a multidisciplinary rehabilitation team, and the intervention can be delivered for up until 12 months. Data for the primary outcome will be obtained from the national register on social transfer payments. The primary outcome will be analysed using logistic regression assessing RTW status at 12 months adjusting for the intervention and age at OHCA, sex, marital status, and occupation prior to OHCA.

DISCUSSION

The ROCK trial is the first RCT to investigate the effectiveness of a rehabilitation intervention focusing on return to work after cardiac arrest.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05173740. Registered on May 2018.

Inference for transition probabilities in non-Markov multi-state models

Multi-state models are frequently used when data come from subjects observed over time and where focus is on the occurrence of events that the subjects may experience. A convenient modeling assumption is that the multi-state stochastic process is Markovian, in which case a number of methods are available when doing inference for both transition intensities and transition probabilities. The Markov assumption, however, is quite strict and may not fit actual data in a satisfactory way. Therefore, inference methods for non-Markov models are needed. In this paper, we review methods for estimating transition probabilities in such models and suggest ways of doing regression analysis based on pseudo observations. In particular, we will compare methods using land-marking with methods using plug-in. The methods are illustrated using simulations and practical examples from medical research.

A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models

Multi-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen estimator. Since landmarking is a stratification method a disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for "less traveled" transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Introducing the concept of partially non-Markov multi-state models, we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. We also show how non-Markov transitions can be identified using a testing procedure. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. We show that the proposed estimator is consistent, but that the traditional variance estimator can underestimate the variance of both the hybrid and landmark estimator. Bootstrapping is therefore recommended. The methods are compared in a simulation study and in a real data application using registry data to model individual transitions for a birth cohort of 184 951 Norwegian men between states of sick leave, disability, education, work and unemployment.