Biomedical subjects

Gareth M James

Publications and source records attributed to Gareth M James.

3 recordsLinked to original sources

PubMed2006

A comparison of outcomes among patients with schizophrenia in two mental health systems: a health state approach.

This paper introduces a health state modeling approach using clustering and Markov analysis to compare short- and long-term outcomes among health care populations. We provide a comparison to more conventional mixed effects regression methods and show that discrete state modeling offers a richer portrait of patient outcomes than the standard univariate techniques. We demonstrate our approach using primary data from a three year observational study of patients treated for schizophrenia at a VA Medical Center (VA) and in a Community Mental Health Center (CMHC) in the same urban community. Randomly selected samples of outpatients treated for schizophrenia or schizoaffective disorder were interviewed every six months using standardized psychiatric assessments such as the Positive and Negative Syndrome Scale (PANSS). Items from the PANSS were used to define 7 discrete health states representing different levels of severity and diverse mixtures of psychiatric symptoms. Conventional analysis showed that VA patients exhibited increasingly severe symptoms, while CMHC patients remained more stable over the study period. Health state analysis reinforced these results but also identified which subpopulations of VA patients were deteriorating. In particular they showed that there was little change over time among VA patients in the best and worst health states. Instead the deterioration was caused by VA patients with: a) mild symptoms and hallucinations and b) serious positive and negative symptoms, being more likely to enter a state with severe positive and negative symptoms accompanied by moderate general distress.

Adult↗

PubMed2005

Bayesian sparse hidden components analysis for transcription regulation networks.

MOTIVATION: In systems like Escherichia Coli, the abundance of sequence information, gene expression array studies and small scale experiments allows one to reconstruct the regulatory network and to quantify the effects of transcription factors on gene expression. However, this goal can only be achieved if all information sources are used in concert. RESULTS: Our method integrates literature information, DNA sequences and expression arrays. A set of relevant transcription factors is defined on the basis of literature. Sequence data are used to identify potential target genes and the results are used to define a prior distribution on the topology of the regulatory network. A Bayesian hidden component model for the expression array data allows us to identify which of the potential binding sites are actually used by the regulatory proteins in the studied cell conditions, the strength of their control, and their activation profile in a series of experiments. We apply our methodology to 35 expression studies in E.Coli with convincing results. AVAILABILITY: www.genetics.ucla.edu/labs/sabatti/software.html SUPPLEMENTARY INFORMATION: The supplementary material are available at Bioinformatics online.

Algorithms↗

PubMed2004

Discrete state analysis for interpretation of data from clinical trials.

OBJECTIVE: The objective of this study was to demonstrate a multivariate health state approach to analyzing complex disease data that allows projection of long-term outcomes using clustering, Markov modeling, and preference weights. SUBJECTS: We studied patients hospitalized 30 to 364 days with refractory schizophrenia at 15 Veterans Affairs medical centers. STUDY DESIGN: We conducted a randomized clinical trial comparing clozapine, an atypical antipsychotic, and haloperidol, a conventional antipsychotic. METHODS: Health status instruments measuring disease-related symptoms and drug side effects were administered in face-to-face interviews at baseline, 6 weeks, and quarterly follow-up intervals for 1 year. Cost data were derived from Veterans Affairs records supplemented by interviews. K-means clustering was used to identify a small number of health states for each instrument. Markov modeling was used to estimate long-term outcomes. RESULTS: Multivariate models with 7 and 6 states, respectively, were required to describe patterns of psychiatric symptoms and side effects (movement disorders). Clozapine increased the proportion of clients in states characterized by mild psychiatric symptoms and decreased the proportion with severe positive symptoms but showed no long-term benefit for negative symptoms. Clozapine dramatically increased the proportion of patients with no movement side effects and decreased incidences of mild akathisia. Effects on extrapyramidal symptoms and tardive dyskinesia were far less pronounced and slower to develop. Markov modeling confirms the consistency of these findings. CONCLUSIONS: Analyzing complex disease data using multivariate health state models allows a richer understanding of trial effects and projection of long-term outcomes. Although clozapine generates substantially fewer side effects than haloperidol, its impact on psychiatric aspects of schizophrenia is less robust and primarily involves positive symptoms.

Antipsychotic Agents↗