Coherent forecasting of multiple-decrement life tables: a test using Japanese cause of death data

Planners in public and private institutions would like coherent forecasts of the components of age-specic mortality, such as causes of death. This has been di cult toachieve because the relative values of the forecast components often fail to behave ina way that is coherent with historical experience. In addition, when the group forecasts are combined the result is often incompatible with an all-groups forecast. It hasbeen shown that cause-specic mortality forecasts are pessimistic when compared withall-cause forecasts (Wilmoth, 1995). This paper abandons the conventional approachof using log mortality rates and forecasts the density of deaths in the life table. Sincethese values obey a unit sum constraint for both conventional single-decrement life tables (only one absorbing state) and multiple-decrement tables (more than one absorbingstate), they are intrinsically relative rather than absolute values across decrements aswell as ages. Using the methods of Compositional Data Analysis pioneered by Aitchison(1986), death densities are transformed into the real space so that the full range of multivariate statistics can be applied, then back-transformed to positive values so that theunit sum constraint is honoured. The structure of the best-known, single-decrementmortality-rate forecasting model, devised by Lee and Carter (1992), is expressed incompositional form and the results from the two models are compared. The compositional model is extended to a multiple-decrement form and used to forecast mortalityby cause of death for Japan

Ki-67 MIB1 labelling index and the prognosis of primary TaT1 urothelial cell carcinoma of the bladder.

Aims: To evaluate whether ki-67 labelling index (LI) has independent prognostic value for survival of patients with bladder urothelial tumours graded according to the 2004 World Health Organisation classification. Methods: Ki-67 LI was evaluated in 164 cases using the grid counting method. Non-invasive (stage Ta) tumours were: papilloma (n = 5), papillary urothelial neoplasia of low malignant potential (PUNLMP; n = 26), and low (LG; n = 34) or high grade (HG; n = 15) papillary urothelial carcinoma. Early invasive (stage T1) tumours were: LG (n = 58) and HG (n = 26) carcinoma. Statistical analysis included Fisher and x2 tests, and mean comparisons by ANOVA and t test. Univariate and multivariate survival analyses were performed according to the Kaplan–Meier method with log rank test and Cox’s proportional hazard method. Results: Mean ki-67 LI increased from papilloma to PUNLMP, LG, and HG in stage Ta (p,0.0001) and from LG to HG in stage T1 (p = 0.013) tumours. High tumour proliferation (.13%) was related to greater tumour size (p = 0.036), recurrence (p = 0.036), progression (p = 0.035), survival (p = 0.054), and high p53 accumulation (p = 0.015). Ki-67 LI and tumour size were independent predictors of disease free survival (DFS), but only ki-67 LI was related to progression free survival (PFS). Cancer specific overall survival (OS) was related to ki-67 LI, tumour size, and p27kip1 downregulation. Ki-67 LI was the main independent predictor of DFS (p = 0.0005), PFS (p = 0.0162), and cancer specific OS (p = 00195). Conclusion: Tumour proliferation measured by Ki-67 LI is related to tumour recurrence, stage progression, and is an independent predictor of DFS, PFS, and cancer specific OS in TaT1 bladder urothelial cell carcinoma.

Use of high-resolution geophysical data to characterize heterogeneous aquifers: influence of data integration method on hydrological predictions

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. The investigation of exactly how much benefit can be brought by geophysical data in terms of its effect on hydrological predictions, however, has received considerably less attention in the literature. Here, we examine the potential hydrological benefits brought by a recently introduced simulated annealing (SA) conditional stochastic simulation method designed for the assimilation of diverse hydrogeophysical data sets. We consider the specific case of integrating crosshole ground-penetrating radar (GPR) and borehole porosity log data to characterize the porosity distribution in saturated heterogeneous aquifers. In many cases, porosity is linked to hydraulic conductivity and thus to flow and transport behavior. To perform our evaluation, we first generate a number of synthetic porosity fields exhibiting varying degrees of spatial continuity and structural complexity. Next, we simulate the collection of crosshole GPR data between several boreholes in these fields, and the collection of porosity log data at the borehole locations. The inverted GPR data, together with the porosity logs, are then used to reconstruct the porosity field using the SA-based method, along with a number of other more elementary approaches. Assuming that the grid-cell-scale relationship between porosity and hydraulic conductivity is unique and known, the porosity realizations are then used in groundwater flow and contaminant transport simulations to assess the benefits and limitations of the different approaches.

Linezolid alone or combined with rifampin against methicillin-resistant Staphylococcus aureus in experimental foreign-body infection.

We investigated the activity of linezolid, alone and in combination with rifampin (rifampicin), against a methicillin-resistant Staphylococcus aureus (MRSA) strain in vitro and in a guinea pig model of foreign-body infection. The MIC, minimal bactericidal concentration (MBC) in logarithmic phase, and MBC in stationary growth phase were 2.5, >20, and >20 microg/ml, respectively, for linezolid; 0.01, 0.08, and 2.5 microg/ml, respectively, for rifampin; and 0.16, 0.63, >20 microg/ml, respectively, for levofloxacin. In time-kill studies, bacterial regrowth and the development of rifampin resistance were observed after 24 h with rifampin alone at 1x or 4x the MIC and were prevented by the addition of linezolid. After the administration of single intraperitoneal doses of 25, 50, and 75 mg/kg of body weight, linezolid peak concentrations of 6.8, 12.7, and 18.1 microg/ml, respectively, were achieved in sterile cage fluid at approximately 3 h. The linezolid concentration remained above the MIC of the test organism for 12 h with all doses. Antimicrobial treatments of animals with cage implant infections were given twice daily for 4 days. Linezolid alone at 25, 50, and 75 mg/kg reduced the planktonic bacteria in cage fluid during treatment by 1.2 to 1.7 log(10) CFU/ml; only linezolid at 75 mg/kg prevented bacterial regrowth 5 days after the end of treatment. Linezolid used in combination with rifampin (12.5 mg/kg) was more effective than linezolid used as monotherapy, reducing the planktonic bacteria by >or=3 log(10) CFU (P < 0.05). Efficacy in the eradication of cage-associated infection was achieved only when linezolid was combined with rifampin, with cure rates being between 50% and 60%, whereas the levofloxacin-rifampin combination demonstrated the highest cure rate (91%) against the strain tested. The linezolid-rifampin combination is a treatment option for implant-associated infections caused by quinolone-resistant MRSA.

Mandibular osteoradionecrosis in squamous cell carcinoma of the oral cavity and oropharynx: incidence and risk factors.

Objective. Mandibular osteoradionecrosis (ORN) is a serious complication of radiotherapy (RT) in head and neck cancer patients. The aim of this study was to analyze the incidence of and risk factors for mandibular ORN in squamous cell carcinoma (SCC) of the oral cavity and oropharynx.Study Design. Case series with chart review.Setting. University tertiary care center for head and neck oncology.Subjects and Methods. Seventy-three patients treated for stage I to IV SCC of the oral cavity and oropharynx between 2000 and 2007, with a minimum follow-up of 2 years, were included in the study. Treatment modalities included both RT with curative intent and adjuvant RT following tumor surgery. The log-rank test and Cox model were used for univariate and multivariate analyses.Results. The incidence of mandibular ORN was 40% at 5 years. Using univariate analysis, the following risk factors were identified: oral cavity tumors (P < .01), bone invasion (P < .02), any surgery prior to RT (P < .04), and bone surgery (P < .0001). By multivariate analysis, mandibular surgery proved to be the most important risk factor and the only one reaching statistical significance (P < .0002).Conclusion. Mandibular ORN is a frequent long-term complication of RT for oral cavity and oropharynx cancers. Mandibular surgery before irradiation is the only independent risk factor. These aspects must be considered when planning treatment for these tumors.

A tale of two logits, compositional data analysis and zero observations

The application of compositional data analysis through log ratio trans-formations corresponds to a multinomial logit model for the shares themselves.This model is characterized by the property of Independence of Irrelevant Alter-natives (IIA). IIA states that the odds ratio in this case the ratio of shares is invariant to the addition or deletion of outcomes to the problem. It is exactlythis invariance of the ratio that underlies the commonly used zero replacementprocedure in compositional data analysis. In this paper we investigate using thenested logit model that does not embody IIA and an associated zero replacementprocedure and compare its performance with that of the more usual approach ofusing the multinomial logit model. Our comparisons exploit a data set that com-bines voting data by electoral division with corresponding census data for eachdivision for the 2001 Federal election in Australia

An R Library for Compositional Data Analysis in Archaeometry

Compositional data naturally arises from the scientific analysis of the chemicalcomposition of archaeological material such as ceramic and glass artefacts. Data of thistype can be explored using a variety of techniques, from standard multivariate methodssuch as principal components analysis and cluster analysis, to methods based upon theuse of log-ratios. The general aim is to identify groups of chemically similar artefactsthat could potentially be used to answer questions of provenance.This paper will demonstrate work in progress on the development of a documentedlibrary of methods, implemented using the statistical package R, for the analysis ofcompositional data. R is an open source package that makes available very powerfulstatistical facilities at no cost. We aim to show how, with the aid of statistical softwaresuch as R, traditional exploratory multivariate analysis can easily be used alongside, orin combination with, specialist techniques of compositional data analysis.The library has been developed from a core of basic R functionality, together withpurpose-written routines arising from our own research (for example that reported atCoDaWork'03). In addition, we have included other appropriate publicly availabletechniques and libraries that have been implemented in R by other authors. Availablefunctions range from standard multivariate techniques through to various approaches tolog-ratio analysis and zero replacement. We also discuss and demonstrate a smallselection of relatively new techniques that have hitherto been little-used inarchaeometric applications involving compositional data. The application of the libraryto the analysis of data arising in archaeometry will be demonstrated; results fromdifferent analyses will be compared; and the utility of the various methods discussed

Compositional analysis of archaeological glasses

At CoDaWork'03 we presented work on the analysis of archaeological glass composi-tional data. Such data typically consist of geochemical compositions involving 10-12variables and approximates completely compositional data if the main component, sil-ica, is included. We suggested that what has been termed `crude' principal componentanalysis (PCA) of standardized data often identi ed interpretable pattern in the datamore readily than analyses based on log-ratio transformed data (LRA). The funda-mental problem is that, in LRA, minor oxides with high relative variation, that maynot be structure carrying, can dominate an analysis and obscure pattern associatedwith variables present at higher absolute levels. We investigate this further using sub-compositional data relating to archaeological glasses found on Israeli sites. A simplemodel for glass-making is that it is based on a `recipe' consisting of two `ingredients',sand and a source of soda. Our analysis focuses on the sub-composition of componentsassociated with the sand source. A `crude' PCA of standardized data shows two clearcompositional groups that can be interpreted in terms of di erent recipes being used atdi erent periods, reected in absolute di erences in the composition. LRA analysis canbe undertaken either by normalizing the data or de ning a `residual'. In either case,after some `tuning', these groups are recovered. The results from the normalized LRAare di erently interpreted as showing that the source of sand used to make the glassdi ered. These results are complementary. One relates to the recipe used. The otherrelates to the composition (and presumed sources) of one of the ingredients. It seemsto be axiomatic in some expositions of LRA that statistical analysis of compositionaldata should focus on relative variation via the use of ratios. Our analysis suggests thatabsolute di erences can also be informative

Probabilistic sonar scan matching for an AUV

This paper proposes MSISpIC, a probabilistic sonar scan matching algorithm for the localization of an autonomous underwater vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), the robot displacement estimated through dead-reckoning using a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method is an extension of the pIC algorithm. An extended Kalman filter (EKF) is used to estimate the robot-path during the scan in order to reference all the range and bearing measurements as well as their uncertainty to a scan fixed frame before registering. The major contribution consists of experimentally proving that probabilistic sonar scan matching techniques have the potential to improve the DVL-based navigation. The algorithm has been tested on an AUV guided along a 600 m path within an abandoned marina underwater environment with satisfactory results

Adiponectin and C-reactive protein are positively associated with microalbuminuria in an adult Caucasian population

Objective: Microalbuminuria (MAU) is a marker of early kidney injury and cardiovascular risk. We assessed the association of MAU with plasma adiponectin, leptin and hsCRP, as inflammatory markers, accounting for hypertension, diabetes and obesity. Design and methods: Population based, cross-sectional study in Caucasian subjects aged 35 to 75 years in Lausanne, Switzerland. MAU, measured on spot morning urine, was used either as a continuous (MAU) or dichotomized variable (MA defined as MAU >2.5 and >3.5 mg/mmol creatinine in men and women, respectively). Results: The 2955 women (age 53.3 ± 10.7, mean ± SD years) had mean body mass index (BMI) 24.9 ± 4.5 kg/m. The 2479 men (age 53.1 ± 10.8 years) had mean BMI 27.0 ± 3.9 kg/m². Median hsCRP was 1.3 and 1.3 mg/L, median adiponectin 6.2 and 10.6 mg/mL in men and women, respectively. MA prevalence was 4.9% in women and 9.8% in men. In multivariate regression analysis adjusting for potential confounders (age, sex, hypertension, diabetes, eGFR, BMI, percent fat mass, insulin and smoking), log-transformed MAU was positively associated with hsCRP (P <0.001) and adiponectin (P = 0.002), but not with leptin. The association of adiponectin with MAU was stronger in subjects with low hsCRP, and vice versa (P interaction <0.001). Conclusion: Adiponectin and hsCRP are significant positive determinants of MAU, independently of diabetes, hypertension and fat mass. A negative interaction between hsCRP and adiponectin was found for their effect on MAU. Whether hyperadiponectinemia represents an adequate protective response to vascular stress or has negative causal impact on the development of MAU should be assessed in further studies.

Discrete and continuous compositions

This paper examines a dataset which is modeled well by thePoisson-Log Normal process and by this process mixed with LogNormal data, which are both turned into compositions. Thisgenerates compositional data that has zeros without any need forconditional models or assuming that there is missing or censoreddata that needs adjustment. It also enables us to model dependenceon covariates and within the composition

Multivariate ARIMA Compositional Time Series Analysis

A compositional time series is obtained when a compositional data vector is observed atdifferent points in time. Inherently, then, a compositional time series is a multivariatetime series with important constraints on the variables observed at any instance in time.Although this type of data frequently occurs in situations of real practical interest, atrawl through the statistical literature reveals that research in the field is very much in itsinfancy and that many theoretical and empirical issues still remain to be addressed. Anyappropriate statistical methodology for the analysis of compositional time series musttake into account the constraints which are not allowed for by the usual statisticaltechniques available for analysing multivariate time series. One general approach toanalyzing compositional time series consists in the application of an initial transform tobreak the positive and unit sum constraints, followed by the analysis of the transformedtime series using multivariate ARIMA models. In this paper we discuss the use of theadditive log-ratio, centred log-ratio and isometric log-ratio transforms. We also presentresults from an empirical study designed to explore how the selection of the initialtransform affects subsequent multivariate ARIMA modelling as well as the quality ofthe forecasts

Compositional analysis of bivariate discrete probabilities

A joint distribution of two discrete random variables with finite support can be displayed as a two way table of probabilities adding to one. Assume that this table hasn rows and m columns and all probabilities are non-null. This kind of table can beseen as an element in the simplex of n · m parts. In this context, the marginals areidentified as compositional amalgams, conditionals (rows or columns) as subcompositions. Also, simplicial perturbation appears as Bayes theorem. However, the Euclideanelements of the Aitchison geometry of the simplex can also be translated into the tableof probabilities: subspaces, orthogonal projections, distances.Two important questions are addressed: a) given a table of probabilities, which isthe nearest independent table to the initial one? b) which is the largest orthogonalprojection of a row onto a column? or, equivalently, which is the information in arow explained by a column, thus explaining the interaction? To answer these questionsthree orthogonal decompositions are presented: (1) by columns and a row-wise geometric marginal, (2) by rows and a columnwise geometric marginal, (3) by independenttwo-way tables and fully dependent tables representing row-column interaction. Animportant result is that the nearest independent table is the product of the two (rowand column)-wise geometric marginal tables. A corollary is that, in an independenttable, the geometric marginals conform with the traditional (arithmetic) marginals.These decompositions can be compared with standard log-linear models.Key words: balance, compositional data, simplex, Aitchison geometry, composition,orthonormal basis, arithmetic and geometric marginals, amalgam, dependence measure,contingency table

A unified approach for representing rows and columns in contingency tables

By using suitable parameters, we present a uni¯ed aproach for describing four methods for representing categorical data in a contingency table. These methods include:correspondence analysis (CA), the alternative approach using Hellinger distance (HD),the log-ratio (LR) alternative, which is appropriate for compositional data, and theso-called non-symmetrical correspondence analysis (NSCA). We then make an appropriate comparison among these four methods and some illustrative examples are given.Some approaches based on cumulative frequencies are also linked and studied usingmatrices.Key words: Correspondence analysis, Hellinger distance, Non-symmetrical correspondence analysis, log-ratio analysis, Taguchi inertia

Error and complexity of random walk Monte Carlo radiosity

The author studies the error and complexity of the discrete random walk Monte Carlo technique for radiosity, using both the shooting and gathering methods. The author shows that the shooting method exhibits a lower complexity than the gathering one, and under some constraints, it has a linear complexity. This is an improvement over a previous result that pointed to an O(n log n) complexity. The author gives and compares three unbiased estimators for each method, and obtains closed forms and bounds for their variances. The author also bounds the expected value of the mean square error (MSE). Some of the results obtained are also shown