Developmental studies of mental disorders based on epidemiological data are often based on cross-sectional retrospective surveys. Under such designs, observations are right-censored, causing underestimation of lifetime prevalences and correlations, and inducing bias in latent trait models on the observations. In this paper we propose a Partial Likelihood (PL) method to estimate unbiased IRT models of lifetime predisposition to develop a certain outcome. A two-step estimation procedure corrects the IRT likelihood of outcome appearance with a function depending on (a) projected outcome frequencies at the end of the risk period, and (b) outcome censoring status at the time of the observation. Simulation results showed that the PL method yielded good recovery of true frequencies and intercepts. Slopes were best estimated when events were sufficiently correlated. When PL is applied to lifetime mental health disorders (assessed in the ESEMeD project surveys), estimated univariate prevalences were, on average, 1.4 times above raw estimates, and 2.06 higher in the case of bivariate prevalences.

Many large-scale standardized tests are intended to measure skills related to ability rather than the rate at which examinees can work. Time limits imposed on these tests make it difficult to distinguish between the effect of low proficiency and the effect of lack of time. This paper proposes a mixture cure-rate model approach to address this issue. Maximum likelihood estimation is proposed for parameter and variance estimation for three cases: when examinee parameters are to be estimated given precalibrated item parameters, when item parameters are to be calibrated given known examinee parameters, and when item parameters are to be estimated without assuming known examinee parameters. Large-sample properties are established for the cases under suitable regularity conditions. Simulation studies suggest that the proposed approach is appropriate for inferences concerning model parameters. In addition, not distinguishing between the effect of low proficiency and the effect of lack of time is shown to have considerable consequences for parameter estimation. A real data example is presented to demonstrate the new model. Choice of survival models for the latent power times is also discussed.

A version of the discrete proportional hazards model is developed for psychometrical applications. In such applications, a primary covariate that influences failure times is a latent variable representing a psychological construct. The Metropolis-Hastings algorithm is studied as a method for performing marginal likelihood inference on the item parameters. The model is illustrated with a real data example that relates the age at which teenagers first experience various substances to the latent ability to avoid the onset of such behaviors.

Classification and Regression Trees (CART), and their successors—bagging and random forests, are statistical learning tools that are receiving increasing attention. However, due to characteristics of censored data collection, standard CART algorithms are not immediately transferable to the context of survival analysis. Questions about the occurrence and timing of events arise throughout psychological and behavioral sciences, especially in longitudinal studies. The prediction power and other key features of tree-based methods are promising in studies where an event occurrence is the outcome of interest. This article reviews existing tree algorithms designed specifically for censored responses as well as recently developed survival ensemble methods, and introduces available computer software. Through simulations and a practical example, merits and limitations of these methods are discussed. Suggestions are provided for practical use.

The analysis of insurance and annuity products issued on multiple lives requires the use of statistical models which account for lifetime dependence. This paper presents a Dirichlet process mixture-based approach that allows to model dependent lifetimes within a group, such as married couples, accounting for individual as well as group-specific covariates. The model is analyzed in a fully Bayesian setting and illustrated to jointly model the lifetime of male–female couples in a portfolio of joint and last survivor annuities of a Canadian life insurer. The inferential approach allows to account for right censoring and left truncation, which are common features of data in survival analysis. The model shows improved in-sample and out-of-sample performance compared to traditional approaches assuming independent lifetimes and offers additional insights into the determinants of the dependence between lifetimes and their impact on joint and last survivor annuity prices.

Research on mobile-assisted language learning (MALL) has revealed that high rates of attrition among users can undermine the potential benefits of this learning method. To explore this issue, we surveyed 3,670 adult MALL users based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and also conducted an in-depth analysis of their historical app usage data. The results of hierarchical k-means cluster analysis and recurrent event survival analysis revealed three major findings. First, three distinct profiles of learners were characterized by different MALL acceptance and engagement experiences. Second, those with greater MALL acceptance displayed more intense, frequent, and durable app usage (behavioral engagement). Lastly, high levels of MALL acceptance were associated with more frequent pauses in app usage but also (a) longer active usage, (b) shorter breaks before returning to the app, and, ultimately, (c) fewer dropouts. We argue that persistence is a multidimensional process involving cyclical phases of engagement, disengagement, dormancy, and reengagement, with each aspect, like intensity, frequency, and duration, building up cumulatively over time. Implications for promoting persistent MALL engagement are discussed.

Low birth weight (BW) is consistently correlated with increased parental risk of subsequent cardiovascular disease, but the links with offspring placental weight (PW) are mostly unexplored. We have investigated the associations between parental coronary heart disease (CHD) and offspring BW and PW using the Walker cohort, a collection of 48,000 birth records from Dundee, Scotland, from the 1950s and 1960s. We linked the medical history of 13,866 mothers and 8,092 fathers to their offspring’s records and performed Cox survival analyses modelling maternal and paternal CHD risk by their offspring’s BW, PW, and the ratio between both measurements. We identified negative associations between offspring BW and both maternal (hazard ratio [HR]: 0.91, 95% confidence interval [CI]: 0.88–0.95) and paternal (HR: 0.96, 95% CI: 0.93–1.00) CHD risk, the stronger maternal correlation being consistent with previous reports. Offspring PW to BW ratio was positively associated with maternal CHD risk (HR: 1.14, 95% CI: 1.08–1.21), but the associations with paternal CHD were not significant. These analyses provide additional evidence for intergenerational associations between early growth and parental disease, identifying directionally opposed correlations of maternal CHD with offspring BW and PW, and highlight the importance of the placenta as a determinant of early development and adult disease.

This study considers data from 5 waves of the English Longitudinal Study of Ageing (ELSA). We aim to study the impact of demographic and self-rated health variables including disability and diseases on the survival of the population aged 50+. The disability variables that we consider are mobility impairment, difficulties in performing Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). One of the problems with the survey study is missing observations. This may happen due to different reasons, such as errors, nonresponse and temporary withdrawals. We address this problem by applying single and multiple imputation methods. We then fit a Generalized Linear model (GLM) and Generalized Linear Mixed model (GLMM) to our data and show that a GLMM performs better than a GLM in terms of information criteria. We also look at the predictability of our models in terms of the time-dependent receiver operating characteristic (ROC) and the area of ROC, i.e. AUC. We conclude that among the disability factors, IADL and among the diseases, cancer significantly affect the survival of the English population aged 50 and older.

In the UK, the incidence and prevalence of inflammatory bowel disease (IBD) is increasing in paediatric populations. Environmental factors including acute gastroenteritis episodes (AGE) may impact IBD development. Infant rotavirus vaccination has been shown to significantly reduce AGE. This study aims to explore the association between vaccination with live oral rotavirus vaccines and IBD development. A population-based cohort study was used, analysing primary care data from the Clinical Practice Research Datalink Aurum. Participants included children born in the UK from 2010 to 2015, followed from a minimum of 6 months old to a maximum of 7 years old. The primary outcome was IBD, and the primary exposure was rotavirus vaccination. Cox regression analysis with random intercepts for general practices was undertaken, with adjustment for potential confounding factors. In a cohort of 907,477 children, IBD was recorded for 96 participants with an incidence rate of 2.1 per 100,000 person-years at risk. The univariable analysis hazard ratio (HR) for rotavirus vaccination was 1.45 (95% confidence interval (CI) 0.93–2.28). Adjustment in the multivariable model attenuated the HR to 1.19 (95% CI 0.53–2.69). This study shows no statistically significant association between rotavirus vaccination and development of IBD. However, it provides further evidence for the safety of live rotavirus vaccination.

Using a corpus linguistic approach, this article aims to answer the question of which factors contribute to a better chance of survival for words in the early Middle English lexicon. Because of the cognitive benefits of rhyme that have been shown in modern studies, there is a particular interest in rhyming position as a potential factor; other factors include frequency, suffix and geographical spread. The data are analysed using survival analysis, random forests and conditional inference trees in R. The results show that geographical spread is the most important factor, usually in combination with particular suffixes. Rhyme is not generally a significant factor in the same vein, and its importance seems to be restricted to individual cases.

This study examines the influence of founding conditions and decisions on new companies' performance, analysing how both environmental context and organisational dynamics interact to determine their success. It distinguishes between two different success indicators: survival and profitable growth. An empirical study conducted using a sample of 3,722 new agri-food companies in two different periods, one of economic stability and the other of recession, showed that founding conditions had long-lasting effects on post-entry performance. The economic context acted as a moderator of the relationship between individual factors and success. Adverse environmental conditions were also a determinant of success, making surviving firms more competitive and resilient. The results reflect the survival of the fitter principle by showing that early profitability reduced the risk of failure and made firms more likely to become profitable in the medium term. Internationalisation strategies developed organisational capabilities that created an imprint for adaptability and growth.

From 1 January 2022 to 4 September 2022, a total of 53 996 mpox cases were confirmed globally. Cases are predominantly concentrated in Europe and the Americas, while other regions are also continuously observing imported cases. This study aimed to estimate the potential global risk of mpox importation and consider hypothetical scenarios of travel restrictions by varying passenger volumes (PVs) via airline travel network. PV data for the airline network, and the time of first confirmed mpox case for a total of 1680 airports in 176 countries (and territories) were extracted from publicly available data sources. A survival analysis technique in which the hazard function was a function of effective distance was utilised to estimate the importation risk. The arrival time ranged from 9 to 48 days since the first case was identified in the UK on 6 May 2022. The estimated risk of importation showed that regardless of the geographic region, most locations will have an intensified importation risk by 31 December 2022. Travel restrictions scenarios had a minor impact on the global airline importation risk against mpox, highlighting the importance to enhance local capacities for the identification of mpox and to be prepared to carry out contact tracing and isolation.