Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

Cognitive performance is related to cortical grey matter volumes in early stages of schizophrenia: A population-based study of first-episode psychosis

Background: Neuropsychological deficits have been reported in association with first-episode psychosis (FEP). Reductions in grey matter (GM) volumes have been documented in FEP subjects compared to healthy controls. However, the possible inter-relationship between the findings of those two lines of research has been scarcely investigated. Objective: To investigate the relationship between neuropsychological deficits and GM volume abnormalities in a population-based sample of FEP patients compared to healthy controls from the same geographical area. Methods: FEP patients (n = 88) and control subjects (n = 86) were evaluated by neuropsychological assessment (Controlled Oral Word Association Test, forward and backward digit span tests) and magnetic resonance imaging using voxel-based morphometry. Results: Single-group analyses showed that prefrontal and temporo-parietal GM volumes correlated significantly (p < 0.05, corrected) with cognitive performance in FEP patients. A similar pattern of direct correlations between neocortical GM volumes and cognitive impairment was seen in the schizophrenia subgroup (n = 48). In the control group, cognitive performance was directly correlated with GM volume in the right dorsal anterior cingulate cortex and inversely correlated with parahippocampal gyral volumes bilaterally. Interaction analyses with ""group status"" as a predictor variable showed significantly greater positive correlation within the left inferior prefrontal cortex (BA46) in the FEP group relative to controls, and significantly greater negative correlation within the left parahippocampal gyrus in the control group relative to FEP patients. Conclusion: Our results indicate that cognitive deficits are directly related to brain volume abnormalities in frontal and temporo-parietal cortices in FEP subjects, most specifically in inferior portions of the dorsolateral prefrontal cortex. (C) 2009 Elsevier B.V. All rights reserved.

Indicadores de prognóstico em utentes com disfunção do ombro submetidos a uma intervenção terapêutica direcionada para a estabilidade dinâmica da escápulo-torácica

RESUMO: Introdução/ Objetivo: Segundo a revisão sistemática de Chester e colaboradores (2013b)apenas dois fatores de prognóstico demonstraram uma associação consistente com o resultado que foram a duração dos sintomas e a funcionalidade na avaliação inicial. O objetivo do estudo é identificar indicadores de bom e mau prognóstico em utentes com disfunção do complexo articular do ombro (DCAO), tendo por base, aspetos da avaliação inicial do utente e critérios de alta de abolição da dor, aumento da funcionalidade e da estabilidade dinâmica considerando uma intervenção terapêutica direcionada para o aumento da estabilidade dinâmica da escápulo-torácica. Metodologia: Efetuou-se um estudo de coorte clínico retrospetivo. Para tal, aplicou-se um protocolo de intervenção terapêutica e analisou-se os resultados. A amostra foi constituída por 82 indivíduos com DCAO [53 com síndrome do conflito subacromial (SCSA) e 29 com instabilidade da glenoumeral (IGU)], residentes nos distritos de Lisboa, Setúbal e Santarém com o intuito de iniciar tratamento de fisioterapia. A análise dos dados foi efetuada tendo em consideração dois procedimentos: análise univariada (através do método de Kaplan-Meier para cada CVP) e análise multifatorial (pela análise de regressão de Cox e regressão logística nos grupos de utentes com SCSA, IGU e DCAO). Resultados: O tempo mediano de continuação no tratamento em fisioterapia foi de 7 semanas para os utentes com SCSA e 6 semanas para utentes com IGU. Segundo o teste de Logrank, na análise univariada, existem sete e oito covariáveis preditoras (CVP) com associação estatisticamente significativa (p<0,05) para o subgrupo SCSA e IGU, respectivamente. De acordo com estes resultados, a primeira parte da DASH e a SPADI são as únicas CVP com associação comuns às duas disfunções. Pela análise multifatorial e, em congruência com o teste de Wald, nenhuma das CVP contribui estatisticamente para o modelo preditivo de continuidade do tratamento de fisioterapia em qualquer um dos três modelos estudados: subgrupo SCSA, subgrupo IGU e utentes com DCAO. Conclusão: Por uma análise univariada verificou-se que existem CVP associadas à alta dos tratamentos em fisioterapia e estas não são as mesmas em ambas as DCAO. Contudo, a magnitude do efeito de cada CVP nos modelos multifatoriais definidos para os grupos de utentes com SCSA, IGU e DCAO não demonstraram valor estatisticamente significativo pelo que não foi possível determinar modelos de prognóstico em utentes com DCAO.-------------ABSTRACT: Background/ Purpose: According with the systematic review from Chester and collaborators (2013b) just two prognostic factors demonstrated a consistent association with the outcome: the duration of symptoms and functionality in the initial assessment. The purpose of the study is to identify indicators of good and poor prognosis in patients with shoulder’s dysfunctions, based on aspects of the initial assessment and discharge criteria of absence of pain, increased functionality and dynamic stability considering a therapeutic intervention used to increase the dynamic stability of scapulo-thoracic. Methodology: It was conducted a retrospective study of clinical cohort. For this purpose it was applied a protocol with therapeutic intervention and the results were analyzed. The sample consisted of 82 individuals with shoulder’s dysfunction (53 with subacromial impingement (SIMP) and 29 with shoulder instability (SINS) residing in the districts of Lisbon, Setúbal and Santarém in order to start physiotherapy. Data analysis was performed taking into account two procedures: univariate analysis [using the Kaplan-Meier method for each co-variant predictor variable (CVP)] and multifactorial analysis [analysis by Cox regression and logistic regression on groups of patients with SIMP, SINS and shoulder’s dysfunction (SD)]. Results: The median time of follow-up treatment at physical therapy was 7 weeks for patients with SIMP and 6 weeks for patients with SINS. According to the Logrank test in the univariate analysis, there are seven and eight CVP with a statistically significant association (p<0.05) for the patients with SIMP and SINS, respectively. According to these results, the first part of the DASH and SPADI are the only CVP common to both disorders association. By multifatorial analyses, and in agreement with the Wald test, none of the CVP contributes statistically to the predictive model of continuity of physiotherapy treatment in any of the three studied models: patients with SIMP, patients with SINS and patients with SD. Conclusion: In an univariate analysis, it was verified that there are CVP associated with discharge from treatments of physical therapy and these are not the same in both SD. However, the magnitude of effect of each CVP in multifactorial models for defined patients groups with SIMP, SINS and SD showed no statistically significant. Therefore, it was not possible to determine prognostic models for patients with SD.

Postoperative self-efficacy and psychological morbidity in radical prostatectomy

Objective: evaluate the general and perceived self-efficacy, psychological morbidity, and knowledge about postoperative care of patients submitted to radical prostatectomy. Identify the relationships between the variables and know the predictors of self-efficacy. Method: descriptive, cross-sectional study, conducted with 76 hospitalized men. The scales used were the General and Perceived Self-efficacy Scale and the Hospital Anxiety and Depression Scale, in addition to sociodemographic, clinical and knowledge questionnaires. Results: a negative relationship was found for self-efficacy in relation to anxiety and depression. Psychological morbidity was a significant predictor variable for self-efficacy. An active professional situation and the waiting time for surgery also proved to be relevant variables for anxiety and knowledge, respectively. Conclusion: participants had a good level of general and perceived self-efficacy and small percentage of depression. With these findings, it is possible to produce the profile of patients about their psychological needs after radical prostatectomy and, thus, allow the nursing professionals to act holistically, considering not only the need for care of physical nature, but also of psychosocial nature.

Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R (adj) (2) > 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change.

Acetylcholinesterase inhibitors and healing of hip fracture in Alzheimer's disease patients: a retrospective cohort study.

OBJECTIVES This study was designed to assess effects of cholinergic stimulation using acetylcholinesterase inhibitors (AChEIs), a group of drugs that stimulate cholinergic receptors and are used to treat Alzheimer's disease (AD), on healing of hip fractures. METHODS A retrospective cohort study was performed using 46-female AD patients, aged above 75 years, who sustained hip fractures. Study analyses included the first 6-months after hip fracture fixation procedure. Presence of AChEIs was used as predictor variable. Other variables that could affect study outcomes: age, body mass index (BMI), mental state or type of hip fracture, were also included. Radiographic union at fracture site (Hammer index), bone quality (Singh index) and fracture healing complications were recorded as study outcomes. The collected data was analyzed by student's-t, Mann-Whitney-U and chi-square tests. RESULTS No significant differences in age, BMI, mental state or type of hip fracture were observed between AChEIs-users and nonusers. However, AChEIs-users had better radiographic union at the fracture site (relative risk (RR),2.7; 95%confidence interval (CI),0.9-7.8), better bone quality (RR,2.0; 95%CI,1.2-3.3) and fewer healing complications (RR,0.8; 95%CI,0.7-1.0) than nonusers. CONCLUSION In elderly female patients with AD, the use of AChEIs might be associated with an enhanced fracture healing and minimized complications.

Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.

Recidiva de câncer labial em pacientes atendidos no hospital DR. Luiz Antônio (Natal-RN) entre 1997-2004

Introduction: Cancer of the lip is very common in tropical countries, being noticeable the squamous cell carcinoma as the main histological type. Objective: Evaluate the socialdemographic profile, habits, occupation, clinical characteristics of the cancer lesions and the aftermath of treatment of the patients treated on the Luiz Antônio Hospital (Natal-RN). Design: Retrospective cohort. Methods: We analyzed 181 medical records of patients from the Luiz Antônio Cancer Hospital (Natal-RN) in the period between 1997 and 2004. The statistic evaluation of time between the diagnosis and the relapse or the cure of the patient were done through the Kaplan-Meier method and the comparison of survivor functions were done through the Log-rank test. Later, was estimated the proportional risk model of Cox. Results: The study population were composed by 69,1% males, 95,2% unlettered, the mean age of 66,5 years, 89,0% of smokers and 64,1% had an occupation involving sun exposure. In regard to the clinical characteristics, most lesions were in the lower lip (77,9%), the size of the tumor was smaller than 2 cm (51,8%), 92,6% had localized lesions. Were verified 16,3% of local relapse and 13% of regional. Almost the totally of the cases corresponded to squamous cell carcinoma (97,2%). We observed smaller accumulated probability of not occurrence of local relapse when the base and borders were free of lesions (p=0,041), as well as a smaller probability of regional relapse when the sort of treatment was surgery, associated with other therapeutics modalities (p=0,001). The patients with advanced pathologic stage (p=0,016), treated with surgery associated with other therapeutics modalities (p=0,001) and diameter above 4cm (p=0,019) presented a bigger possibility of any kind of relapse. The multivariable analysis pointed the complex treatments (surgery plus other therapeutics modalities) as a predictor variable for occurrence of new local lesions (p=0,001) and total (p=0,046), besides the age above 70 years to the regional relapse (p=0,050). Conclusion: Cancer of the lip occur in the lower lip, in males, smokers and individuals exposed to Sun light. The relapse was frequent, even being localized and without great consequences to the patient s health. The probability of relapse is related to the size and borders of the lesion and to the histological exam, as well as to the patient s age and complexity of the treatment chosen

Early impairment of cognitive functions in Parkinson's disease

Background: Impairment in non-motor functions such as disturbances of some executive functions are also common events in Parkinson's disease patients. Objective: To verify the performance of Parkinson's disease patients in activities requiring visuoconstructive and visuospatial skills. Method: Thirty elderly patients with mild or moderate stages of Parkinson's disease were studied. The assessment of the clinical condition was based on the unified Parkinson's disease rating scale (56.28; SD=33.48), Hoehn and Yahr (2.2; SD=0.83), Schwab and England (78.93%), clock drawing test (7.36; SD=2.51), and mini-mental state examination (26.48; SD=10.11). Pearson's correlation and stepwise multiple regression were used for statistical analyses. Results: The patients presented deterioration in visuospatial and visuoconstructive skills. Conclusion: The clock drawing test proved to be a useful predictive tool for identifying early cognitive impairment in these individuals.

Níveis de mercúrio, prolactina e interleucina 10 em mulheres em idade reprodutiva e puérperas dos municípios de Itaituba e Ananindeua, Pará

Ao mercúrio tem sido atribuída a capacidade de interferir nos sistemas orgânicos imunológico e hormonal, além dos sistemas nervoso e renal frequentemente atingidos por esse agente tóxico. Mulheres em idade fértil ou grávidas constituem um grupo vulnerável a esses efeitos, em relação a si mesmas e seus conceptos. Foi avaliada a exposição ao mercúrio (Hg) e os níveis de prolactina (PRL) e interleucina-10 (IL-10) em 144 mulheres (no pós-parto e cerca de um ano depois) de Itaituba, área sob impacto ambiental do mercúrio e em mulheres de municípios da área metropolitana de Belém, sobretudo Ananindeua, área sem impacto conhecido do mercúrio (156 puérperas e 156 não puérperas). As análises de mercúrio total (Hg-t) em sangue foram feitas por Espectrometria de Absorção Atômica por Vapor Frio. As análises séricas de PRL foram feitas por Ensaio Imunoenzimático com detecção final em fluorescência e as determinações de IL-10 foram realizadas por Ensaio Imunoenzimático de Fase Sólida. Dados demográficos e epidemiológicos foram obtidos através de questionário semi-estruturado. As puérperas de Itaituba apresentaram média de Hg-t, PRL e IL-10 de 13,93 μg/l, 276,20 ng/ml e 39,54 pg/ml, respectivamente. Nas puérperas de Ananindeua as respectivas médias foram 3,67 μg/l, 337,70 ng/ml e 4,90 pg/ml. As mulheres não puérperas de Itaituba apresentaram média de Hg-t de 12,68 μg/l, média de PRL de 30,75 ng/ml e média de IL-10 de 14,20 pg/ml. As médias de Hg-t, PRL e IL-10 das mulheres de Ananindeua foram 2,73 μg/l, 17,07 ng/ml e 1,49 pg/ml, respectivamente. Os níveis de Hg-t, PRL e IL-10 foram maiores em Itaituba (p<0,0001), exceto em relação à PRL das puérperas, maior em Ananindeua. Os níveis semelhantes de Hg-t nas duas avaliações das mulheres de Itaituba (p=0,7056) e a correlação moderada sugerem continuidade da exposição (r=0,4736, p<0,0001). A principal variável preditora dos níveis de mercúrio foi o consumo de peixe nos modelos de regressão múltipla linear e logística. A paridade e os níveis de IL-10 apresentaram associação positiva com a PRL nas puérperas de Itaituba e o peso do recém-nascido e a IL-10, associação positiva com a PRL em puérperas de Ananindeua. A IL-10 apresentou associação negativa com a PRL nas mulheres não puérperas de Itaituba (p=0,0270) e positiva nas mulheres de Ananindeua (p=0,0266). Os níveis de Hg-t estavam associados negativamente com a PRL nas puérperas (p=0,0460) e positivamente com o trabalho em garimpo (p=0,0173) (este também importante para as não puérperas) em Itaituba, segundo os modelos logísticos. A IL-10 esteve associada positivamente à morbidade recente nas puérperas de Itaituba (p=0,0210), negativamente ao consumo de bebida alcoólica (p=0,0178) e positivamente ao trabalho em garimpo nas mulheres não puérperas (p=0,0199). A exposição crônica ao Hg das mulheres de Itaituba, a diferença nos níveis dos fatores imunoendócrinos avaliados em relação às mulheres não expostas e a associação com variáveis epidemiológicas relevantes, sugerem a possibilidade de impactos da exposição no perfil imunoendócrino das mulheres de Itaituba, chamando atenção para a importância da vigilância da saúde dessa população e o possível uso de bioindicadores como a PRL em sua avaliação.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

Critical evaluation of diameter increment modelling in the Great Lakes region

Simulations of forest stand dynamics in a modelling framework including Forest Vegetation Simulator (FVS) are diameter driven, thus the diameter or basal area increment model needs a special attention. This dissertation critically evaluates diameter or basal area increment models and modelling approaches in the context of the Great Lakes region of the United States and Canada. A set of related studies are presented that critically evaluate the sub-model for change in individual tree basal diameter used in the Forest Vegetation Simulator (FVS), a dominant forestry model in the Great Lakes region. Various historical implementations of the STEMS (Stand and Tree Evaluation and Modeling System) family of diameter increment models, including the current public release of the Lake States variant of FVS (LS-FVS), were tested for the 30 most common tree species using data from the Michigan Forest Inventory and Analysis (FIA) program. The results showed that current public release of the LS-FVS diameter increment model over-predicts 10-year diameter increment by 17% on average. Also the study affirms that a simple adjustment factor as a function of a single predictor, dbh (diameter at breast height) used in the past versions, provides an inadequate correction of model prediction bias. In order to re-engineer the basal diameter increment model, the historical, conceptual and philosophical differences among the individual tree increment model families and their modelling approaches were analyzed and discussed. Two underlying conceptual approaches toward diameter or basal area increment modelling have been often used: the potential-modifier (POTMOD) and composite (COMP) approaches, which are exemplified by the STEMS/TWIGS and Prognosis models, respectively. It is argued that both approaches essentially use a similar base function and neither is conceptually different from a biological perspective, even though they look different in their model forms. No matter what modelling approach is used, the base function is the foundation of an increment model. Two base functions – gamma and Box-Lucas – were identified as candidate base functions for forestry applications. The results of a comparative analysis of empirical fits showed that quality of fit is essentially similar, and both are sufficiently detailed and flexible for forestry applications. The choice of either base function in order to model diameter or basal area increment is dependent upon personal preference; however, the gamma base function may be preferred over the Box-Lucas, as it fits the periodic increment data in both a linear and nonlinear composite model form. Finally, the utility of site index as a predictor variable has been criticized, as it has been widely used in models for complex, mixed species forest stands though not well suited for this purpose. An alternative to site index in an increment model was explored, using site index and a combination of climate variables and Forest Ecosystem Classification (FEC) ecosites and data from the Province of Ontario, Canada. The results showed that a combination of climate and FEC ecosites variables can replace site index in the diameter increment model.

Surgical glove perforation and the risk of surgical site infection

HYPOTHESIS: Clinically apparent surgical glove perforation increases the risk of surgical site infection (SSI). DESIGN: Prospective observational cohort study. SETTING: University Hospital Basel, with an average of 28,000 surgical interventions per year. PARTICIPANTS: Consecutive series of 4147 surgical procedures performed in the Visceral Surgery, Vascular Surgery, and Traumatology divisions of the Department of General Surgery. MAIN OUTCOME MEASURES: The outcome of interest was SSI occurrence as assessed pursuant to the Centers of Disease Control and Prevention standards. The primary predictor variable was compromised asepsis due to glove perforation. RESULTS: The overall SSI rate was 4.5% (188 of 4147 procedures). Univariate logistic regression analysis showed a higher likelihood of SSI in procedures in which gloves were perforated compared with interventions with maintained asepsis (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.4-2.8; P < .001). However, multivariate logistic regression analyses showed that the increase in SSI risk with perforated gloves was different for procedures with vs those without surgical antimicrobial prophylaxis (test for effect modification, P = .005). Without antimicrobial prophylaxis, glove perforation entailed significantly higher odds of SSI compared with the reference group with no breach of asepsis (adjusted OR, 4.2; 95% CI, 1.7-10.8; P = .003). On the contrary, when surgical antimicrobial prophylaxis was applied, the likelihood of SSI was not significantly higher for operations in which gloves were punctured (adjusted OR, 1.3; 95% CI, 0.9-1.9; P = .26). CONCLUSION: Without surgical antimicrobial prophylaxis, glove perforation increases the risk of SSI.