Search PubMed⌕ Search

Biomedical subjects

J Rasbash

Publications and source records attributed to J Rasbash.

9 recordsLinked to original sources
PubMed2001

Family settings and children's adjustment: differential adjustment within and across families.

BACKGROUND: Children in stepfamilies and single-parent families exhibit elevated levels of behavioural and emotional problems compared with children in intact (biological) families, but there is variation within and across these family types. AIMS: To examine the sources of variation in children's behavioural and emotional problems across diverse family settings. METHOD: Levels of behavioural and emotional problems in children from diverse stepfamilies and single-parent families were compared with children living with both biological parents. Psychosocial risks were measured at the individual child and family levels. RESULTS: Behavioural and emotional problems were elevated in children in stepmother/complex stepfamilies and single-parent families, but not in simple stepfather families, relative to 'biological' families. Psychopathology associated with family type was explained by compromised quality of the parent-child relationship, parental depression and socio-economic adversity. Sibling similarity in behavioural and emotional problems was most pronounced in high-risk family settings. CONCLUSIONS: Family type is a proxy for exposure to psychosocial risks; the extent of family-wide influence on children's development may be strongest in high-stress settings.

Adaptation, Psychological↗
PubMed1999

Simultaneous analysis of individual and aggregate responses in psychometric data using multilevel modeling.

Psychometric data on risk perceptions are often collected using the method developed by Slovic, Fischhoff, and Lichtenstein, where an array of risk issues are evaluated with respect to a number of risk characteristics, such as how dreadful, catastrophic or involuntary exposure to each risk is. The analysis of these data has often been carried out at an aggregate level, where mean scores for all respondents are compared between risk issues. However, this approach may conceal important variation between individuals, and individual analyses have also been performed for single risk issues. This paper presents a new methodological approach using a technique called multilevel modelling for analysing individual and aggregated responses simultaneously, to produce unconditional and unbiased results at both individual and aggregate levels of the data. Two examples are given using previously published data sets on risk perceptions collected by the authors, and results between the traditional and new approaches compared. The discussion focuses on the implications of and possibilities provided by the new methodology.

Humans↗
PubMed1999

Multilevel modelling of the geographical distributions of diseases.

"Multilevel modelling is used on problems arising from the analysis of spatially distributed health data. We use three applications to demonstrate the use of multilevel modelling in this area. The first concerns small area all-cause mortality rates from Glasgow where spatial autocorrelation between residuals is examined. The second analysis is of prostate cancer cases in Scottish counties where we use a range of models to examine whether the incidence is higher in more rural areas. The third develops a multiple-cause model in which deaths from cancer and cardiovascular disease in Glasgow are examined simultaneously in a spatial model. We discuss some of the issues surrounding the use of complex spatial models and the potential for future developments."

Cause of Death↗
PubMed1994

Multilevel time series models with applications to repeated measures data.

The analysis of repeated measures data can be conducted efficiently using a two-level random coefficients model. A standard assumption is that the within-individual (level 1) residuals are uncorrelated. In some cases, especially where measurements are made close together in time, this may not be reasonable and this additional correlation structure should also be modelled. A time series model for such data is proposed which consists of a standard multilevel model for repeated measures data augmented by an autocorrelation model for the level 1 residuals. First- and second-order autoregressive models are considered in detail, together with a seasonal component. Both discrete and continuous time are considered and it is shown how the autocorrelation parameters can themselves be structured in terms of further explanatory variables. The models are fitted to a data set consisting of repeated height measurements on children.

Adolescent↗
PubMedDate not supplied

Multivariate spatial models for event data.

This paper describes how estimates made for event rates in small areas may be enhanced through spatial modelling of the data - taking the geographical location of each area into account - and through the addition of further information from each area. In particular we consider the use of spatial models to predict more than one outcome simultaneously. This is done by writing the spatial model as a multi-level model and subsequently enhancing this to encompass a multivariate data structure; estimates are obtained using iterative generalized least squares in the software package MLwiN. The example given considers mortality due to two causes--neoplasms and circulatory disease--in 143 postcode sectors in Greater Glasgow Health Board, Scotland. In addition, a measure of socio-economic deprivation is available for each area. Correlations between causes within areas, between areas within causes and between areas and causes are quantified, as are the relative contributions of the heterogeneous and spatial parts of the model. The results suggest a tendency for there to be pockets with high mortality rates due to neoplasms, whilst mortality due to circulatory disease follows a much smoother pattern. After taking deprivation into account, the spatial component accounts for just 19 per cent of the variation in the mortality due to neoplasms in Greater Glasgow but 66 per cent of the mortality due to circulatory disease.

Age Factors↗
PubMedDate not supplied

Data management for growth studies.

This paper outlines the requirements for a computerized data management package designed specifically to handle growth data. Features include: screen design for data entry, data checking on distances and velocities, extraction of selected subsets of records or variables.

Database Management Systems↗