Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Spatial portability of numerical models of leaf wetness duration based on empirical approaches

Leaf wetness duration (LWD) models based on empirical approaches offer practical advantages over physically based models in agricultural applications, but their spatial portability is questionable because they may be biased to the climatic conditions under which they were developed. In our study, spatial portability of three LWD models with empirical characteristics - a RH threshold model, a decision tree model with wind speed correction, and a fuzzy logic model - was evaluated using weather data collected in Brazil, Canada, Costa Rica, Italy and the USA. The fuzzy logic model was more accurate than the other models in estimating LWD measured by painted leaf wetness sensors. The fraction of correct estimates for the fuzzy logic model was greater (0.87) than for the other models (0.85-0.86) across 28 sites where painted sensors were installed, and the degree of agreement k statistic between the model and painted sensors was greater for the fuzzy logic model (0.71) than that for the other models (0.64-0.66). Values of the k statistic for the fuzzy logic model were also less variable across sites than those of the other models. When model estimates were compared with measurements from unpainted leaf wetness sensors, the fuzzy logic model had less mean absolute error (2.5 h day(-1)) than other models (2.6-2.7 h day(-1)) after the model was calibrated for the unpainted sensors. The results suggest that the fuzzy logic model has greater spatial portability than the other models evaluated and merits further validation in comparison with physical models under a wider range of climate conditions. (C) 2010 Elsevier B.V. All rights reserved.

Does using stepwise variable selection to build sequential path analysis models make sense?

Causal inference methods - mainly path analysis and structural equation modeling - offer plant physiologists information about cause-and-effect relationships among plant traits. Recently, an unusual approach to causal inference through stepwise variable selection has been proposed and used in various works on plant physiology. The approach should not be considered correct from a biological point of view. Here, it is explained why stepwise variable selection should not be used for causal inference, and shown what strange conclusions can be drawn based upon the former analysis when one aims to interpret cause-and-effect relationships among plant traits.

Asymmetry and duration dependence in Australian GDP and unemployment

The per iodic structure of business cycles suggests that significant asymmetries are present over different phases of the cycle. This paper uses markov regime-switching models with fixed and duration dependent transition probabilities to directly model expansions, contractions and durations in Australian GDP growth and unemployment growth. Evidence is found of significant asymmetry in growth rates across expansions and contractions for both series. GDP contractions exhibit duration dependence implying that as output recessions age the likelihood of switching into an expansion phase increases. Unemployment growth does not exhibit duration dependence in either phase. Evidence is also presented that non-linearities in unemployment growth are well explained by the asymmetries in the GDP growth cycle. The analysis suggests that recessions are periods of rapid and intense job destruction, that Australian unemployment tends to ratchet up in recessionary periods and, in contrast to US and UK studies, that shocks to Australian unemployment growth are more persistent in recessions than expansions. [E37 C5 C41].

Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd

Analysis of dynamic models for structural insight and model reduction .2. A multi-stage compressor shutdown case-study

In this second paper, the three structural measures which have been developed are used in the modelling of a three stage centrifugal synthesis gas compressor. The goal of this case study is to determine the essential mathematical structure which must be incorporated into the compressor model to accurately model the shutdown of this system. A simple, accurate and functional model of the system is created via three structural measures. It was found that the model can be correctly reduced into its basic modes and that the order of the differential system can be reduced from 51(st) to 20(th). Of the 31 differential equational 21 reduce to algebraic relations, 8 become constants and 2 can be deleted thereby increasing the algebraic set from 70 to 91 equations. An interpretation is also obtained as to which physical phenomena are dominating the dynamics of the compressor add whether the compressor will enter surge during the shutdown. Comparisons of the reduced model performance against the full model are given, showing the accuracy and applicability of the approach. Copyright (C) 1996 Elsevier Science Ltd

Benefit of antiretroviral therapy on survival of human immunodeficiency virus-infected patients admitted to an intensive care unit

Objective: To evaluate the impact of antiretroviral therapy (ART) and the prognostic factors for in-intensive care unit (ICU) and 6-month mortality in human immunodeficiency virus (HIV)-infected patients. Design: A retrospective cohort study was conducted in patients admitted to the ICU from 1996 through 2006. The follow-up period extended for 6 months after ICU admission. Setting: The ICU of a tertiary-care teaching hospital at the Universidade de Sao Paulo, Brazil. Participants: A total of 278 HIV-infected patients admitted to the ICU were selected. We excluded ICU readmissions (37), ICU admissions who stayed less than 24 hours (44), and patients with unavailable medical charts (36). Outcome Measure: In-ICU and 6-month mortality. Main Results: Multivariate logistic regression analysis and Cox proportional hazards models demonstrated that the variables associated with in-ICU and 6-month mortality were sepsis as the cause of admission (odds ratio [OR] = 3.16 [95% confidence interval [CI] 1.65-6.06]); hazards ratio [HR] = 1.37 [95% Cl 1.01-1.88)), an Acute Physiology and Chronic Health Evaluation 11 score >19 [OR = 2.81 (95% CI 1.57-5.04); HR = 2.18 (95% CI 1.62-2.94)], mechanical ventilation during the first 24 hours [OR = 3.92 (95% CI 2.20-6.96); HR = 2.25 (95% CI 1.65-3.07)], and year of ICU admission [OR = 0.90 (95% CI 0.81-0.99); HR = 0.92 [95% CI 0.87-0.97)]. CD4 T-cell count <50 cells/mm(3) Was only associated with ICU mortality [OR = 2.10 (95% Cl 1.17-3.76)]. The use of ART in the ICU was negatively predictive of 6-month mortality in the Cox model [HR = 0.50 (95% CI 0.35-0.71)], especially if this therapy was introduced during the first 4 days of admission to the ICU [HR = 0.58 (95% CI 0.41-0.83)]. Regarding HIV-infected patients admitted to ICU without using ART, those who have started this treatment during ICU, stay presented a better prognosis when time and potential confounding factors were adjusted for [HR 0.55 (95% CI 0.31-0.98)]. Conclusions: The ICU outcome of HIV-infected patients seems to be dependent not only on acute illness severity, but also on the administration of antiretroviral treatment. (Crit Care Med 2009; 37: 1605-1611)

Squamous Cell Carcinoma of the Tongue and Floor of the Mouth: Analysis of Survival Rate and Independent Prognostic Factors in the Amazon Region

Oral squamous cell carcinoma (OSCC) accounts for more than 95% of all malignant neoplasms in the oral cavity. Although several studies have shown the epidemiology of this cancer in Brazil, there do not seem to be any studies that describe the prognostic factors related to OSCC in the Amazon region. Therefore, the aim of this study was to determine the survival rate and prognostic significance of different factors in patients from this region affected by OSCC. Data from 85 patients with histologically confirmed squamous cell carcinoma of the tongue and floor of the mouth identified from the Ofir Loyola Hospital archives were collected and analyzed using univariate (log-rank test) and multivariate (Cox proportional hazard model) tests. The overall 5-year survival rate was found to be 27%. Univariate analysis showed that the 5-year survival rate was significantly higher for younger (<= 45 y) female patients, patients with T1-2 tumors and clinically clear neck nodes (N0), patients with early stage cancers (AJCC stage I-II), and patients treated with surgical procedures. However, multivariate analysis showed that the 5-year survival rate was significantly higher only in the younger patients and those who underwent surgical treatment. The age of the patient at the moment of diagnosis and treatment with surgical procedures were the only independent prognostic factors that affected the 5-year survival rate of the patients in this region.

Assessing alternative models of individualism and collectivism: A confirmatory factor analysis

Six alternative structural models of individualism-collectivism are reviewed and empirically compared in a confirmatory factor analysis of questionnaire data from an Australian student sample (N=340). Central to the debate about the structure of this broad social attitude are the issues of (I) polarity (are individualism and collectivism bipolar opposites, or orthogonal factors?) and (2) dimensionality (are individualism and collectivism themselves higher-order constructs subsuming several more specific factors and, if so, what are they?). The data from this Australian sample support a model that represents individualism and collectivism as a higher-order bipolar factor hierarchically subsuming several bipolar reference-group-specific individualisms and collectivisms. Copyright (C) 2001 John Wiley & Sons, Ltd.

Power of the joint segregation analysis method for testing mixed major-gene and polygene inheritance models of quantitative traits

Understanding the genetic architecture of quantitative traits can greatly assist the design of strategies for their manipulation in plant-breeding programs. For a number of traits, genetic variation can be the result of segregation of a few major genes and many polygenes (minor genes). The joint segregation analysis (JSA) is a maximum-likelihood approach for fitting segregation models through the simultaneous use of phenotypic information from multiple generations. Our objective in this paper was to use computer simulation to quantify the power of the JSA method for testing the mixed-inheritance model for quantitative traits when it was applied to the six basic generations: both parents (P-1 and P-2), F-1, F-2, and both backcross generations (B-1 and B-2) derived from crossing the F-1 to each parent. A total of 1968 genetic model-experiment scenarios were considered in the simulation study to quantify the power of the method. Factors that interacted to influence the power of the JSA method to correctly detect genetic models were: (1) whether there were one or two major genes in combination with polygenes, (2) the heritability of the major genes and polygenes, (3) the level of dispersion of the major genes and polygenes between the two parents, and (4) the number of individuals examined in each generation (population size). The greatest levels of power were observed for the genetic models defined with simple inheritance; e.g., the power was greater than 90% for the one major gene model, regardless of the population size and major-gene heritability. Lower levels of power were observed for the genetic models with complex inheritance (major genes and polygenes), low heritability, small population sizes and a large dispersion of favourable genes among the two parents; e.g., the power was less than 5% for the two major-gene model with a heritability value of 0.3 and population sizes of 100 individuals. The JSA methodology was then applied to a previously studied sorghum data-set to investigate the genetic control of the putative drought resistance-trait osmotic adjustment in three crosses. The previous study concluded that there were two major genes segregating for osmotic adjustment in the three crosses. Application of the JSA method resulted in a change in the proposed genetic model. The presence of the two major genes was confirmed with the addition of an unspecified number of polygenes.

The impact of positive peritoneal washings and serosal and adnexal involvement on survival in patients with stage IIIA uterine cancer

Objective. The aim of this study was to determine the prognostic significance of serosal involvement (SER), adnexal involvement (ADN), and positive peritoneal washings (PPW) in patients with Stage IIIA uterine cancer. We also sought to determine patterns of recurrence in patients with this disease. Methods. The records of 136 patients with Stage IIIA uterine cancer treated at the Queensland Centre for Gynecological Cancer between March 1983 and August 2001 were reviewed. One hundred thirty-six patients underwent surgery and 58 (42.6%) had full surgical staging. Seventy-five patients (55.2%) had external beam radiotherapy and/or brachytherapy postoperatively. Overall survival was the primary statistical endpoint. Statistical analysis included univariate and multivariate Cox models. Results. Forty-six patients (33.8%) had adnexal involvement, 23 (16.9%) had serosal involvement, and 40 (29.4%) had positive peritoneal washings. Median follow-up was 55.1 months (95% confidence interval, 36.9 to 73.4 months) after which time 71 patients (52.2%) remained alive. For patients with endometrioid adenocarcinoma, ADN and SER were associated with impaired survival on multivariate analysis (odds ratio 2.8 and 3.2, respectively). In the subgroup of patients with high-risk tumors (including papillary serous carcinomas, clear cell carcinomas, and uterine sarcomas), neither ADN, nor SER, nor PPW influenced survival. Conclusion. Patients with Stage IIIA uterine cancer constitute a heterogeneous group. For patients with endometrioid adenocarcinoma, both ADN and SER, but not PPW, were associated with impaired prognosis. For patients with high-risk histological types, prognosis is poor for all three factors. (C) 2002 Elsevier Science (USA).

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.

An approach to verifying and debugging simulation models governed by ordinary differential equations: Part 2. Residuals analysis and a case study

In Part 1 of this paper a methodology for back-to-back testing of simulation software was described. Residuals with error-dependent geometric properties were generated. A set of potential coding errors was enumerated, along with a corresponding set of feature matrices, which describe the geometric properties imposed on the residuals by each of the errors. In this part of the paper, an algorithm is developed to isolate the coding errors present by analysing the residuals. A set of errors is isolated when the subspace spanned by their combined feature matrices corresponds to that of the residuals. Individual feature matrices are compared to the residuals and classified as 'definite', 'possible' or 'impossible'. The status of 'possible' errors is resolved using a dynamic subset testing algorithm. To demonstrate and validate the testing methodology presented in Part 1 and the isolation algorithm presented in Part 2, a case study is presented using a model for biological wastewater treatment. Both single and simultaneous errors that are deliberately introduced into the simulation code are correctly detected and isolated. Copyright (C) 2003 John Wiley Sons, Ltd.

Dietary influences on survival after ovarian cancer

We evaluated the effects of various food groups and micronutrients in the diet on survival among women who originally participated in a population-based case-control study of ovarian cancer conducted across 3 Australian states between 1990 and 1993. This analysis included 609 women with invasive epithelial ovarian cancer, primarily because there was negligible mortality in women with borderline tumors. The women's usual diet was assessed using a validated food frequency questionnaire. Deaths in the cohort were identified using state-based cancer registries and the Australian National Death Index (NDI). Crude 5-year survival probabilities were estimated using the Kaplan-Meier technique, and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were obtained from Cox regression models. After adjusting for important confounding factors, a survival advantage was observed for those who reported higher intake of vegetables in general (HR = 0.75, 95% CI = 0.57-0.99, p-value trend 0.01 for the highest third, compared to the lowest third), and cruciferous vegetables in particular (HR = 0.75, 95% CI = 0.57-0.98, p-value trend 0.03), and among women in the upper third of intake of vitamin E (HR = 0.76, 95% CI = 0.58-1.01, p-value trend 0.04). Inverse associations were also seen with protein (p-value trend 0.09), red meat (p-value trend 0.06) and white meat (p-value trend 0.07), and modest positive trends (maximum 30% excess) with lactose (p-value trend 0.04), calcium and dairy products. Although much remains to be learned about the influence of nutritional factors after a diagnosis of ovarian cancer, our study suggests the possibility that a diet high in vegetable intake may help improve survival. (C) 2003 Wiley-Liss, Inc.