Estimating and evaluating the statistics of gapped local-alignment scores

We present a novel maximum-likelihood-based algorithm for estimating the distribution of alignment scores from the scores of unrelated sequences in a database search. Using a new method for measuring the accuracy of p-values, we show that our maximum-likelihood-based algorithm is more accurate than existing regression-based and lookup table methods. We explore a more sophisticated way of modeling and estimating the score distributions (using a two-component mixture model and expectation maximization), but conclude that this does not improve significantly over simply ignoring scores with small E-values during estimation. Finally, we measure the classification accuracy of p-values estimated in different ways and observe that inaccurate p-values can, somewhat paradoxically, lead to higher classification accuracy. We explain this paradox and argue that statistical accuracy, not classification accuracy, should be the primary criterion in comparisons of similarity search methods that return p-values that adjust for target sequence length.

Aggregated personality, climate and demographic factors as predictors of departmental shrinkage

The present study investigated how demographic, personality, and climate variables act to predict departmental theft. Participants in the current field survey were 153 employees from 17 departments across two stores. The results of confirmatory factor analyses supported the construct validity of the Big Five Inventory (John, Donahue, & Kentle, 1991) and the Occupational Climate Questionnaire (Furnham & Gunter, 1997) in UK work settings. The results of regression analysis indicate that the variability in departmental theft is accountable in terms of a linear combination of demographic, personality, and climate factors. We concluded that an expanded theoretical perspective (utilizing demographic, personality, and climate variables) explained more variance than might otherwise be expected from any single perspective. Indeed, climate, personality, and demographic variables operated legitimately at the departmental level. Finally, we explained aggregated personality as a form of social interaction which is the by-product of individual differences.

Prospective independent validation of APACHE III models in an Australian tertiary adult intensive care unit

Evaluation of the performance of the APACHE III (Acute Physiology and Chronic Health Evaluation) ICU (intensive care unit) and hospital mortality models at the Princess Alexandra Hospital, Brisbane is reported. Prospective collection of demographic, diagnostic, physiological, laboratory, admission and discharge data of 5681 consecutive eligible admissions (1 January 1995 to 1 January 2000) was conducted at the Princess Alexandra Hospital, a metropolitan Australian tertiary referral medical/surgical adult ICU. ROC (receiver operating characteristic) curve areas for the APACHE III ICU mortality and hospital mortality models demonstrated excellent discrimination. Observed ICU mortality (9.1%) was significantly overestimated by the APACHE III model adjusted for hospital characteristics (10.1%), but did not significantly differ from the prediction of the generic APACHE III model (8.6%). In contrast, observed hospital mortality (14.8%) agreed well with the prediction of the APACHE III model adjusted for hospital characteristics (14.6%), but was significantly underestimated by the unadjusted APACHE III model (13.2%). Calibration curves and goodness-of-fit analysis using Hosmer-Lemeshow statistics, demonstrated that calibration was good with the unadjusted APACHE III ICU mortality model, and the APACHE III hospital mortality model adjusted for hospital characteristics. Post hoc analysis revealed a declining annual SMR (standardized mortality rate) during the study period. This trend was present in each of the non-surgical, emergency and elective surgical diagnostic groups, and the change was temporally related to increased specialist staffing levels. This study demonstrates that the APACHE III model performs well on independent assessment in an Australian hospital. Changes observed in annual SMR using such a validated model support an hypothesis of improved survival outcomes 1995-1999.

Health and demographic characteristics of respondents in an Australian national sexuality survey: comparison with population norms

Study objective: To assess the representativeness of survey participants by systematically comparing volunteers in a national health and sexuality survey with the Australian population in terms of self reported health status (including the SF-36) and a wide range of demographic characteristics. Design: A cross sectional sample of Australian residents were compared with demographic data from the 1996 Australian census and health data from the 1995 National Health Survey. Setting: The Australian population. Participants: A stratified random sample of adults aged 18-59 years drawn from the Australian electoral roll, a compulsory register of voters. Interviews were completed with 1784 people, representing 40% of those initially selected (58% of those for whom a valid telephone number could be located). Main results: Participants were of similar age and sex to the national population. Consistent with prior research, respondents had higher socioeconomic status, more education, were more likely to be employed, and less likely to be immigrants. The prevalence estimates, means, and variances of self reported mental and physical health measures (for example, SF-36 subscales, women's health indicators, current smoking status) were similar to population norms. Conclusions: These findings considerably strengthen inferences about the representativeness of data on health status from volunteer samples used in health and sexuality surveys.

Bridging the gap between different statistical approaches: An integrated framework for modelling

This paper proposes a template for modelling complex datasets that integrates traditional statistical modelling approaches with more recent advances in statistics and modelling through an exploratory framework. Our approach builds on the well-known and long standing traditional idea of 'good practice in statistics' by establishing a comprehensive framework for modelling that focuses on exploration, prediction, interpretation and reliability assessment, a relatively new idea that allows individual assessment of predictions. The integrated framework we present comprises two stages. The first involves the use of exploratory methods to help visually understand the data and identify a parsimonious set of explanatory variables. The second encompasses a two step modelling process, where the use of non-parametric methods such as decision trees and generalized additive models are promoted to identify important variables and their modelling relationship with the response before a final predictive model is considered. We focus on fitting the predictive model using parametric, non-parametric and Bayesian approaches. This paper is motivated by a medical problem where interest focuses on developing a risk stratification system for morbidity of 1,710 cardiac patients given a suite of demographic, clinical and preoperative variables. Although the methods we use are applied specifically to this case study, these methods can be applied across any field, irrespective of the type of response.

At-risk children: A demographic analysis of the children of clients attending mental health community clinics

Children of parents with mental illness have an increased risk of psychological problems. The aim of this study was to identify the demographic characteristics of dependent children of adults presenting at mental health clinics in Western Australia. A survey of clients who attended the clinics indicated that half reported having had children. Of these, 21% had a primary diagnosis of schizophrenia. Although schizophrenia was the most common illness, there were almost seven times more children living with a parent with a primary diagnosis of depression than schizophrenia. Recommendations include that children of clients with mental illness be included as part of a wider client focus.