Stressful life events increase the risk of major depressive episodes: A population-based twin study

Previous studies have found that stressful life events (SLEs) are associated with increased risk of adult depression. However, many studies are observational in nature and limited by methodological issues, such as potential confounding by genetic factors. Genetically informative studies, such as the co-twin control design, can strengthen causal inference in observational research. The co-twin control design involves comparing patterns of associations in the full sample and within dizygotic (DZ) and monozygotic twins (MZ). Discrete-time survival analysis has several benefits and multilevel survival analysis can incorporate frailty terms (random effects) to estimate the components of the biometric model. In the current study, we investigated associations between SLEs and depression risk in a population-based twin sample (N = 2299) with a co-twin control design. Associations were modelled using discrete-time survival analysis with biometric frailty terms. SLE occurrence was associated with increased depression risk. Co-twin control analyses indicated that this association was at least in part due to causal influence of SLE exposure on depression risk for event occurrence across all SLEs and of violent SLEs. Stronger within-pair estimates for economic SLEs compared with the full sample association could have resulted if the full sample association was suppressed or if within-pair estimates were inflated. If the former occurred, economic SLEs may represent particularly important risk factors for depression. A minor proportion of the total genetic risk of depression reflected genetic effects related to SLEs. Our findings have implications for future research on SLEs and depression.

Uncovering trends in training progression for a national cohort of psychiatry trainees: discrete-time survival analysis

Background The global rise in mental health issues calls for a strong psychiatry workforce. Yet, psychiatry training worldwide is facing recruitment challenges, causing unfilled consultant posts and possibly threatening the quality of patient care. An in-depth understanding of trainees’ progression through training is warranted to explore what happens to recruited trainees during training. Aims To uncover current trends in psychiatry trainees’ progression through training in the UK. Method This national retrospective cohort study with data from the UK Medical Education Database used discrete-time survival analysis to analyse training progression for those trainees who started their core psychiatry post in 2012–2017 (2820 trainees; 59.6% female, 67.6% UK graduates (UKGs)). The impact of sociodemographic characteristics on training progression were also investigated. Results The overall probability of completing training in 6 years (minimum years required to complete psychiatry training in the UK) was 17.2% (ranging from 4.8% for non-UKG females to 29% for UKG males). The probability to not progress was highest (57.1%) from core to specialty training. For UKGs, trainees from ethnicities other than White, trainees with a disability, and trainees who had experienced childhood social deprivation (measured as entitlement to free school meals) had a significantly (P ≤ 0.02) lower probability of completing training in 6 years. Conclusions Less than one in five psychiatry trainees are likely to complete training in 6 years and this probability varies across groups of doctors. Completing psychiatry training in 6 years is, therefore, the exception rather than the norm and this has important implications for trainees, those planning psychiatry workforces or responsible for psychiatry training.

Multiple imputation of partially observed covariates in discrete-time survival analysis

Many phenomena in the social or the medical sciences can be described as events, meaning that a qualitative change occurs at some particular point in time. Typical research questions focus on whether, when, and under which circumstances events occur. In the social sciences, discrete-time-to-event models are popular (Discrete-Time Survival Analysis Model, DTSAM). Data analyzed through DTSAMs is in the so-called person-period format. The model is a logistic regression model with the event indicator as the dependent variable. However, like many other statistical applications, the practical analysis of discrete-time survival data is challenged by missing data in one or more covariates. Negative consequences of such missing data range from efficiency losses to bias. A popular approach to circumvent these unwanted effects of missing data is multiple imputation (MI). With multiple imputation, it is crucial to include outcome information in the model for imputing partially observed covariates. Unfortunately, this is not straightforward in case of DTSAM, since we (a) usually have a partly observed (left- or right-censored) outcome, (b) do not have only one outcome variable, but two: the event indicator and the time-to-event and (c) have to decide whether to impute while the data set is still in person format or after transformation in person-period format, especially if we look at time-invariant information. Since there is little guidance on how to incorporate the observed outcome information in the imputation model of missing covariates in discrete-time survival analysis, we explore different approaches using fully conditional specification (FCS) (van Buuren 2006) and the newer substantial model compatible (SMC-) FCS MI (Bartlett et al., 2014). These approaches vary in their complexity with which we incorporate the outcome into the imputation model, the FCS algorithm used, and the data format used during the imputation. We compare the methods using Monte Carlo simulations and provide a practical example using data from the German Family Panel pairfam.We confirm the results by White and Royston (2009) and Beesley et al. (2016) that imputing conditional on the (partly imputed) uncensored time-to-event yields high bias. A compatible imputation model for SMC-FCS MI with data in person-period format proves to be the key to imputations with good performance results under different simulation conditions.