A dynamic social relations model for clustered longitudinal dyadic data with continuous or ordinal responses.

Social relations models allow the identification of cluster, actor, partner, and relationship effects when analysing clustered dyadic data on interactions between individuals or other units of analysis. We propose an extension of this model which handles longitudinal data and incorporates dynamic structure, where the response may be continuous, binary, or ordinal. This allows the disentangling of the relationship effects from temporal fluctuation and measurement error and the investigation of whether individuals respond to their partner's behaviour at the previous observation.

Association between surgical volume and failure of primary total hip replacement in England and Wales: findings from a prospective national joint replacement register.

Objective: To investigate the association of volume of total hip arthroplasty (THA) between consultants and within the same consultant in the previous year and the hazard of revision using multilevel survival models.

Design: Prospective cohort study using data from a national joint replacement register.

Setting: Elective THA across all private and public centres in England and Wales between April 2003 and February 2017.

Intention, beliefs and mood assessed using electronic diaries predicts attendance at cardiac rehabilitation: An observational study.

Background: Cardiac rehabilitationis effective in promoting physical/psychological recovery following acute coronary syndrome. Yet, rates of attendance at outpatient cardiac rehabilitation by eligible patients are low.

Objectives: This study examined the determinants of attendance at outpatient cardiac rehabilitation in acute coronary syndrome patients following discharge until cardiac rehabilitation commencement.

Probabilistic record linkage.

Studies involving the use of probabilistic record linkage are becoming increasingly common. However, the methods underpinning probabilistic record linkage are not widely taught or understood, and therefore these studies can appear to be a 'black box' research tool. In this article, we aim to describe the process of probabilistic record linkage through a simple exemplar.

Capturing the temporal sequence of interaction in young siblings.

We explored whether young children exhibit subtypes of behavioral sequences during sibling interaction. Ten-minute, free-play observations of over 300 sibling dyads were coded for positivity, negativity and disengagement. The data were analyzed using growth mixture modeling (GMM).

Adjusting for selection bias in longitudinal analyses using simultaneous equations modeling: the relationship between employment transitions and mental health.

Background: Effects of labor force participation on mental health can be difficult to discern due to the possibility of selection bias. Previous research typically adjusts for direct selection (reverse causality) but ignores indirect selection (unmeasured confounders).

Methods: We investigate the relationship between men's employment transitions and mental health using a dynamic simultaneous equations model applied to data from the British Household Panel Survey (1991-2009).

Interrelationships between childbearing and housing transitions in the family life course.

Research has examined the effect of family changes on housing transitions and childbearing patterns within various housing types. Although most research has investigated how an event in one domain of family life depends on the current state in another domain, the interplay between them has been little studied. This study examines the interrelationships between childbearing decisions and housing transitions.

A multilevel simultaneous equations model for within-cluster dynamic effects, with an application to reciprocal parent-child and sibling effects.

There has been substantial interest in the social and health sciences in the reciprocal causal influences that people in close relationships have on one another. Most research has considered reciprocal processes involving only 2 units, although many social relationships of interest occur within a larger group (e.g.

Consequences of family disruption on children's educational outcomes in Norway.

Using high-quality data from Norwegian population registers, we examine the relationship between family disruption and children's educational outcomes. We distinguish between disruptions caused by parental divorce and paternal death and, using a simultaneous equation model, pay particular attention to selection bias in the effect of divorce. We also allow for the possibility that disruption may have different effects at different stages of a child's educational career.

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models.

We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model.

Changing compatibility of cohabitation and childbearing between young British women born in 1958 and 1970.

We investigate the effect of parenthood on whether non-marital unions led to marriage or parting for two cohorts of British women when they were aged between 16 and 29. We compare the effect of conceptions leading to births and the presence and characteristics of children on the odds that a cohabitation was dissolved, or that it was converted to marriage, for women born in 1958 and 1970. A multilevel, multiprocess, competing-risks model allows for multiple cohabitation per woman and endogeneity of fertility status.

The relationship between childbearing and transitions from marriage and cohabitation in Britain.

In this article, we describe a general framework for the analysis of correlated event histories, with an application to a study of partnership transitions and fertility among a cohort of British women. Using a multilevel, multistate competing-risks model, we examine the relationship between prior fertility outcomes (the presence and characteristics of children and current pregnancy) and the dissolution of marital and cohabiting unions and movements from cohabitation to marriage. Using a simultaneous-equations model, we model these partnership transitions jointly with fertility, allowing for correlation between the unobserved woman-level characteristics that affect each process.

A discrete-time multilevel mixture model for event history data with long-term survivors, with an application to an analysis of contraceptive sterilization in Bangladesh.

Event history models typically assume that the entire population is at risk of experiencing the event of interest throughout the observation period. However, there will often be individuals, referred to as long-term survivors, who may be considered a priori to have a zero hazard throughout the study period. In this paper, a discrete-time mixture model is proposed in which the probability of long-term survivorship and the timing of event occurrence are modelled jointly.

Appropriate methods for analyzing the effect of method choice on contraceptive discontinuation.

The contraceptive method chosen is an important determinant of contraceptive discontinuation. However, method choice is endogenous to contraceptive discontinuation. Using data from the 1997 Indonesia Demographic and Health Survey, we apply a multilevel multi-process model to examine the impact of method choice on three types of contraceptive discontinuation.

The effects of kin on child mortality in rural Gambia.

We analyzed data that were collected continuously between 1950 and 1974 from a rural area of the Gambia to determine the effects of kin on child mortality. Multilevel event-history models were used to demonstrate that having a living mother, maternal grandmother, or elder sisters had a significant positive effect on the survival probabilities of children, whereas having a living father, paternal grandmother, grandfather, or elder brothers had no effect. The mother's remarriage to a new husband had a detrimental effect on child survival, but there was little difference in the mortality rates of children who were born to monogamous or polygynous fathers.