High Percentage Of Missing Data Research Articles

Interrater reliability of a national acute myocardial infarction register.

BackgroundDisease-specific registers may be used for measuring and improving healthcare and patient outcomes, and for disease surveillance and research, provided they contain valid and reliable data. The aim of this study was to assess the interrater reliability of all variables in a national myocardial infarction register.MethodsWe randomly selected 280 patients who had been enrolled from 14 hospitals to the Norwegian Myocardial Infarction Register during the year 2013. Experienced audit nurses, who were blinded to the data about the 280 patients already in the register, completed the Norwegian Myocardial Infarction paper forms for 240 patients by review of medical records. We then extracted all registered data on the same patients from the Norwegian Myocardial Infarction Register. To compare the interrater reliability between the register and the audit nurses, we calculated intraclass correlations coefficient for continuous variables, Cohen’s kappa and Gwet’s first agreement coefficient (AC1) for nominal variables, and quadratic weighted Cohen’s kappa and Gwet’s second AC for ordinal variables.ResultsWe found excellent (AC1 >0.80) or good (AC1 0.61–0.80) agreement for most variables, including date and time variables, medical history, investigations and treatments during hospitalization, medication at discharge, and ST-segment elevation or non-ST-segment elevation acute myocardial infarction. However, only moderate agreement (AC1 0.41–0.60) was found for family history of coronary heart disease, diagnostic electrocardiography, and complications during hospitalization, whereas fair agreement (AC1 0.21–0.40) was found for acute myocardial infarction location. A high percentage of missing data was found for symptom onset, family history, body mass index, infarction location, and new Q-wave.ConclusionMost variables in Norwegian Myocardial Infarction Register had excellent or good reliability. However, some important variables had lower reliability than expected or had missing data. Precise definitions of data elements and proper training of data abstractors are necessary to ensure that clinical registries contain valid and reliable data.

Reconstruction and analysis of long-term satellite-derived sea surface temperature for the South China Sea

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. Unfortunately, the SST data sources in the South China Sea (SCS) are not abundant due to sparse measurements of in situ SST and a high percentage of missing data in the satellite-derived SST. Therefore, SST data sets with low resolution and/or a short-term period have often been used in previous researches. Here we used Data INterpolating Empirical Orthogonal Functions, a self-consistent and parameter-free method for filling in missing data, to reconstruct the daily nighttime 4-km AVHRR Pathfinder SST for the long-term period spanning from 1989 to 2009. In addition to the reconstructed field, we also estimated the local error map for each reconstructed image. Comparisons between the reconstructed and other data sets (satellite-derived microwave and in situ SSTs) show that the results are reliable for use in many different researches, such as validating numerical models, or identifying and tracking meso-scale oceanic features. Moreover, the Empirical Orthogonal Function (EOF) analysis of the reconstructed SST and the reconstructed SST anomalies clearly shows the subseasonal, seasonal, and interannual variability of SST under the influence of monsoon and El Nino-Southern Oscillation (ENSO), as well as reveals some oceanic features that could not be captured well in previous EOF analyses. The SCS SST often lags ENSO by about half a year. However, in this study, we see that the time lag changes with the frequencies of the SST variability, from 1 to 6 months.

Nonrigid Structure From Motion via Sparse Representation.

This paper proposes a new approach for nonrigid structure from motion with occlusion, based on sparse representation. We address the occlusion problem based on the latest developments on sparse representation: matrix completion, which can recover the observation matrix that has high percentages of missing data and can also reduce the noises and outliers in the known elements. We introduce sparse transform to the joint estimation of 3-D shapes and motions. 3-D shape trajectory space is fit by wavelet basis to achieve better modeling of complex motion. Experimental results on datasets without and with occlusion show that our method can better estimate the 3-D shapes and motions, compared with state-of-the-art algorithms.

A morphological supermatrix-based phylogeny for the Neotropical fish superfamily Anostomoidea (Ostariophysi: Characiformes): phylogeny, missing data and homoplasy.

Although 11 studies have addressed the systematics of the four families and 281 fish species of the ecomorphologically diverse Anostomoidea, none has proposed a global hypothesis of relationships. We synthesized these studies to yield a supermatrix with 463 morphological characters among 174 ingroup species, and inferred phylogeny with parsimony and Bayesian optimization. We evaluated the applicability of the supermatrix approach to morphological datasets, tested its sensitivity to missing data, determined the impact of homoplastic characters on phylogenetic resolution, and determined the distribution of homologies and homoplasies on the topology. Despite more than 60% missing data, analyses supported the monophyly of all families, and phylogenetic structure degraded only with inclusion of species with high percentages of missing data and in analyses limited to homoplasies. The latter differs modestly from the full matrix indicating phylogenetic signal in homoplastic characters. Character distributions differ across the phylogeny, with a greater prevalence of homologies at deeper nodes and homoplasies nearer the tips than expected by chance. This may suggest early diversification into distinct bauplans with subsequent diversification of faster evolving character systems. The morphological supermatrix approach is powerful and allows integration of classical data with modern methods to examine the evolution of multiple character systems.

Treatment of childhood anxiety disorder in the context of maternal anxiety disorder: a randomised controlled trial and economic analysis.

Cognitive-behavioural therapy (CBT) for childhood anxiety disorders is associated with modest outcomes in the context of parental anxiety disorder. This study evaluated whether or not the outcome of CBT for children with anxiety disorders in the context of maternal anxiety disorders is improved by the addition of (i) treatment of maternal anxiety disorders, or (ii) treatment focused on maternal responses. The incremental cost-effectiveness of the additional treatments was also evaluated. Participants were randomised to receive (i) child cognitive-behavioural therapy (CCBT); (ii) CCBT with CBT to target maternal anxiety disorders [CCBT + maternal cognitive-behavioural therapy (MCBT)]; or (iii) CCBT with an intervention to target mother-child interactions (MCIs) (CCBT + MCI). A NHS university clinic in Berkshire, UK. Two hundred and eleven children with a primary anxiety disorder, whose mothers also had an anxiety disorder. All families received eight sessions of individual CCBT. Mothers in the CCBT + MCBT arm also received eight sessions of CBT targeting their own anxiety disorders. Mothers in the MCI arm received 10 sessions targeting maternal parenting cognitions and behaviours. Non-specific interventions were delivered to balance groups for therapist contact. Primary clinical outcomes were the child's primary anxiety disorder status and degree of improvement at the end of treatment. Follow-up assessments were conducted at 6 and 12 months. Outcomes in the economic analyses were identified and measured using estimated quality-adjusted life-years (QALYs). QALYS were combined with treatment, health and social care costs and presented within an incremental cost-utility analysis framework with associated uncertainty. MCBT was associated with significant short-term improvement in maternal anxiety; however, after children had received CCBT, group differences were no longer apparent. CCBT + MCI was associated with a reduction in maternal overinvolvement and more confident expectations of the child. However, neither CCBT + MCBT nor CCBT + MCI conferred a significant post-treatment benefit over CCBT in terms of child anxiety disorder diagnoses [adjusted risk ratio (RR) 1.18, 95% confidence interval (CI) 0.87 to 1.62, p = 0.29; adjusted RR CCBT + MCI vs. control: adjusted RR 1.22, 95% CI 0.90 to 1.67, p = 0.20, respectively] or global improvement ratings (adjusted RR 1.25, 95% CI 1.00 to 1.59, p = 0.05; adjusted RR 1.20, 95% CI 0.95 to 1.53, p = 0.13). CCBT + MCI outperformed CCBT on some secondary outcome measures. Furthermore, primary economic analyses suggested that, at commonly accepted thresholds of cost-effectiveness, the probability that CCBT + MCI will be cost-effective in comparison with CCBT (plus non-specific interventions) is about 75%. Good outcomes were achieved for children and their mothers across treatment conditions. There was no evidence of a benefit to child outcome of supplementing CCBT with either intervention focusing on maternal anxiety disorder or maternal cognitions and behaviours. However, supplementing CCBT with treatment that targeted maternal cognitions and behaviours represented a cost-effective use of resources, although the high percentage of missing data on some economic variables is a shortcoming. Future work should consider whether or not effects of the adjunct interventions are enhanced in particular contexts. The economic findings highlight the utility of considering the use of a broad range of services when evaluating interventions with this client group. Current Controlled Trials ISRCTN19762288. This trial was funded by the Medical Research Council (MRC) and Berkshire Healthcare Foundation Trust and managed by the National Institute for Health Research (NIHR) on behalf of the MRC-NIHR partnership (09/800/17) and will be published in full in Health Technology Assessment; Vol. 19, No. 38.

Increasing prevalence of HIV infection among first time clients in Italian drug treatment services - is it sexual transmission?

BackgroundOver the last two decades, the proportion of people who inject drugs among newly reported HIV cases in Italy has been continuously declining. This trend is reflected in the prevalence of HIV infection among problem drug users followed in drug treatment services. We report nationwide trends in the prevalence of HIV and HCV among tested clients in charge to drug addiction services from 2005 to 2011.MethodsData on the prevalence of HIV and HCV among drug users from public drug treatment services across Italy were collected and analyzed for the period from 2005 to 2011. Prevalence of HIV and HCV were compared between clients returning to treatment and those entering treatment for the first time, and by gender. Due to the high percentage of missing data, the “inverse probability weight” method was used. Trends in testing uptake were also analysed.ResultsA significant decrease of HIV and HCV prevalence is observed among all PDUs entering treatment (from 14.7% to 11.1% and from 61.6% to 50%, respectively, in 2005–2011). By contrast, among those entering the services for the first time, after an initial decline the prevalence of HIV infection steadily increased in both sexes, from 2.2% in 2009 to 5.3% in 2011. Self-reported injecting rates in this group decreased over time, and in 2011 the proportion reporting drug injecting was lower among new clients than in people returning to services (14.5 vs. 34.4%). We also observed a progressive and significant reduction in HIV and HCV testing in drug treatment services.ConclusionsChanges in injection practice and type of drugs used, coupled with a concurrent reduction in HCV prevalence, do not support drug injection as the main explanation for an increased HIV transmission in people entering drug treatment services for the first time. While reductions in testing rates raise concerns over data quality, the possibility of increased sexual transmission needs to be considered.

Evaluation of the quality of data in the Live Birth Information System and the Information System on Mortality during the neonatal period in the state of Espírito Santo, Brazil, between 2007 and 2009

Health information systems make it possible to be aware of health problems and often represent the sole source of information, thereby making it essential to assess their quality. The scope of this paper was to evaluate the quality of data about live births and deaths in the neonatal period in the Live Births Information System (SINASC) and Mortality Information System (SIM) in the state of Espírito Santo during the period from 2007 to 2009. Descriptive methodology with secondary data obtained from the electronic address of the Informatics Department of the Unified Health System was used to analyze the dimensions of quality of information, namely accessibility, timeliness and completeness. The results indicate that the information is accessible and timely, with a lag of three years compared to the beginning of the study. The completeness of the variables analyzed in SINASC was predominantly classified as excellent. SIM revealed a high percentage of missing data on the number of the Live Birth Declaration, mother's education, age and reproductive history. The conclusion reached is that SINASC is an excellent source of information about births, though SIM reveals a marked need for improvements in the completeness of its information.

Creating multimodal predictors using missing data: classifying and subtyping autism spectrum disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by wide range of symptoms and severity including domains such as language impairment (LI). This study aims to create a quantifiable marker of ASD and a stratification marker for LI using multimodality imaging data that can handle missing data by including subjects that fail to complete all the aspects of a multimodality imaging study, obviating the need to remove subjects with incomplete data, as is done by conventional methods. An ensemble of classifiers with several subsets of complete data is employed. The outputs from such subset classifiers are fused using a weighted aggregation giving an aggregate probabilistic score for each subject. Such fusion classifiers are created to obtain a marker for ASD and to stratify LI using three categories of features, two extracted from separate auditory tasks using magnetoencephalography (MEG) and the third extracted from diffusion tensor imaging (DTI). A clear distinction between ASD and neurotypical controls (5-fold accuracy of 83.3% and testing accuracy of 87%) and between ASD/+LI and ASD/-LI (5-fold accuracy of 70.1% and testing accuracy of 61.1%) was obtained. One of the MEG features, mismatch field (MMF) latency contributed the most to group discrimination, followed by DTI features from superior temporal white matter and superior longitudinal fasciculus as determined by feature ranking. Higher classification accuracy was achieved in comparison with single modality classifiers. This methodology can be readily applied in large studies where high percentage of missing data is expected.

Development of the Functional Vision Questionnaire for Children and Young People with Visual Impairment: The FVQ_CYP

To develop a novel age-appropriate measure of functional vision (FV) for self-reporting by visually impaired (VI) children and young people. Questionnaire development. A representative patient sample of VI children and young people aged 10 to 15 years, visual acuity of the logarithm of the minimum angle of resolution (logMAR) worse than 0.48, and a school-based (nonrandom) expert group sample of VI students aged 12 to 17 years. A total of 32 qualitative semistructured interviews supplemented by narrative feedback from 15 eligible VI children and young people were used to generate draft instrument items. Seventeen VI students were consulted individually on item relevance and comprehensibility, instrument instructions, format, and administration methods. The resulting draft instrument was piloted with 101 VI children and young people comprising a nationally representative sample, drawn from 21 hospitals in the United Kingdom. Initial item reduction was informed by presence of missing data and individual item response pattern. Exploratory factor analysis (FA) and parallel analysis (PA), and Rasch analysis (RA) were applied to test the instrument's psychometric properties. Psychometric indices and validity assessment of the Functional Vision Questionnaire for Children and Young People (FVQ_CYP). A total of 712 qualitative statements became a 56-item draft scale, capturing the level of difficulty in performing vision-dependent activities. After piloting, items were removed iteratively as follows: 11 for high percentage of missing data, 4 for skewness, and 1 for inadequate item infit and outfit values in RA, 3 having shown differential item functioning across age groups and 1 across gender in RA. The remaining 36 items showed item fit values within acceptable limits, good measurement precision and targeting, and ordered response categories. The reduced scale has a clear unidimensional structure, with all items having a high factor loading on the single factor in FA and PA. The summary scores correlated significantly with visual acuity. We have developed a novel, psychometrically robust self-report questionnaire for children and young people-the FVQ_CYP-that captures the functional impact of visual disability from their perspective. The 36-item, 4-point unidimensional scale has potential as a complementary adjunct to objective clinical assessments in routine pediatric ophthalmology practice and in research.

Spatio-temporal MODIS EVI gap filling under cloud cover: An example in Scotland

Time series of satellite data have an important role in the monitoring of regional and global ecosystem properties. Satellite images often present missing data due to atmospheric aerosol, clouds or other atmospheric conditions. Most methods proposed to minimise the effects of degradation and to restore signal values do not take into account the spatial and temporal correlation of the values in the pixels. The aim of this study was to propose and test a spatio-temporal interpolation method to reconstruct pixel values in MODIS data time series that are missing due to cloud cover or other image noise. The method presented and tested is an example of a hybrid Generalised Additive Model (GAM)-geostatistical space-time model, including the fitting of a smoother spatio-temporal trend and a spatial component to account for local details supported by information in covariates. The method is not limited by the type of noise or degradation of pixels values, latitude, vegetation dynamics and land uses. The application of cloud masks on the target image provided the data for a quantitative validation through the comparison between the modelled EVI values and those from the MODIS product. The method was able to restore data providing very good to adequate responses in series of simulations of missing data. The comparison of distributions showed good agreement and predictive capabilities. The spatio-temporal method always performed better and the use of kriged residuals was helpful for situations with high percentages of missing data. The spatial pattern and the local features were well preserved for cloud coverage ⩽20%. For higher percentages of missing data, the results were smoother with less local detail retained, but still showing the general spatial pattern of the variable. The method has proved to be flexible and able to provide reconstructed images reproducing spatial patterns and local features of the measured product, even with substantial amounts of missing pixels.

Machine Learning Analysis of the Relationship Between Changes in Immunological Parameters and Changes in Resistance to Listeria monocytogenes: A New Approach for Risk Assessment and Systems Immunology

No method has been reported to predict, even approximately, the impact of mild-to-moderate changes in several immunological parameters on resistance to infection. The ability to make such predictions would be useful in risk assessment. In addition, equations that predict host resistance on the basis of changes in components of a complex biological system (the immune system) would fulfill one of the major goals of systems biology. In this study, multiple machine learning classification methods were used to predict the effects of a series of drugs and chemicals on host resistance to Listeria monocytogenes in mice on the basis of changes in several holistic immunological parameters. A data set produced under the sponsorship of the National Toxicology Program (NTP) was used in this study. The NTP data set was found to have a high percentage of missing data and to be noisy (probably due to the intrinsically stochastic nature of immune responses). Data preprocessing steps were used to mitigate these problems. In evaluating the machine learning classifiers, we first randomly partitioned the NTP data set into 10 subsets. Each time, we used nine subsets of the data to train the machine learning classifiers, and the remaining single subset to predict outcomes with regard to host resistance. This process was repeated until all 10 combinations of the 9-1 split of the subsets have been tested. The best of the classifiers predicted host resistance outcome correctly for 94.7% of cases, a result which indicates it is possible to identify mathematical expressions that will be useful for risk assessment and to establish a basis for systems immunology.

Abstract 50: A Novel Method for Quantification of Brain Edema in Ischemic Stroke

Background: Measurement of final infarct volumes are typically done on MRI or CT scans obtained at 30 or 90 days after symptom onset. At these delayed time points, true infarct volume is often underestimated because of atrophy. Moreover, late imaging leads to a high percentage of missing data due to early mortality and poor compliance with late imaging. Obtaining an early assessment of infarct volume avoids these limitations; however, it overestimates the true infarct volume because of vasogenic edema. The aim of this study was to develop a novel approach to quantify brain edema so that final infarct volumes can be approximated on day 5 scans. Method: We analyzed MRI scans from 15 acute stroke patients enrolled in the DEFUSE 2 study. The edema volume was determined by comparing spatially-coregistered ADC maps obtained at baseline and at day 5. Edema displaces CSF from the sulci and the ventricles. We therefore estimated the edema volume as the change in CSF volume between baseline and follow-up. CSF displacement was determined by analyzing ADC maps; a voxel by voxel comparison was performed and voxels with ADC values that shifted from the CSF range (2.0-4.4 x 10 -3 mm 2 /s) to the parenchymal range (0.60-1.05 x 10 -3 mm 2 /s) were considered to represent edema (yellow region in figure ). The analysis was performed on 5 control patients with no or very small lesions (2.4±2.0 mL on FLAIR) and 10 patients with large lesions (240±115 mL on FLAIR). Results: The mean volume of CSF displacement in the control group was 1.6±2.4 mL. CSF displacement (edema volume) in the large infarct group ranged from 33 to 152 mL. The estimated edema volume, as a percentage of the overall day 5 FLAIR lesion volume, ranged from 15% to 52%. Linear regression analysis performed between day 5 FLAIR lesion volumes and the estimated edema volumes produced a correlation coefficient (r)of 0.78 (P< 0.01) with a slope of 0.31. Conclusion: Vasogenic edema can be measured based on assessment of CSF displacement. Using this novel technique we determined that the day 5 FLAIR lesion volume, on average, overestimates the final infarct volume by 31%. These findings suggest that the final infarct volume can be accurately estimated from an MRI scan obtained during the vasogenic edema phase. Validation of this technique, using data sets that have infarct volume determinations at multiple time points, is in progress and results will be presented.

Influence of Dietary Habits on the Risk of Inflammatory Bowel Disease: A Case Control Study

Introduction: At the Los Angeles County University of Southern California Medical Center (LAC+USC) we service an inflammatory bowel disease (IBD) clinic for a low economic population. We evaluated the frequency of the types of IBD and identified risk factors associated with ulcerative colitis (UC) versus Crohns disease (CD). Methods: This is a retrospective, cross-sectional review of patients with the diagnosis of IBD including UC, CD or indeterminate colitis that were seen at the Roybal Comprehensive Health Clinic between 1/01/2004 through 12/31/2009. Ulcerative colitis was defined as a continuous inflammation of the colon involving the distal rectum and with architectural distortion on histology. Crohn's disease was defined by characteristic small and large bowel changes on imaging studies and/or segmental colitis in untreated patients on colonoscopy. Using an IBD database, ICD-9 diagnosis codes, electronic medical records, and the Clinical Outcomes Research Initiative (CORI) system, the type of colitis, extent of disease, and demographic information were obtained. Univariate and stepwise multivariate analysis were performed. Results: A total of 287 patients with IBD were identified. The frequency of UC in this population was 68% (196/287); (95% Confidence Interval (CI): 63%-74%); CD 24% (70/287); (95% CI; 19%-29%); and indeterminate colitis (IC) 7% (21/287); (95% CI: 4-10). In the 266 patients with UC or CD, 55% were male, 65% over 30 years of age, 69% Hispanic, 12% African American (AA), 9% Asian and 9% non-Hispanic American. Sixty-one percent were immigrants and 52% were from Latin America. We did not include in the analysis education, history of smoking and family history of IBD because of the high percentage of missing data 37, 30 and 47% respectively. Table 1 provides the association of each risk factor with UC vs CD. Stepwise logistic regression yielded two significant independent risk factors: AA ethnicity [adjusted OR: 0.34; 95% CI: 0.15, 0.77; p=0.01] and immigrant [adjusted OR: 2.16; 95% CI: 1.19, 3.94, p=0.01). The R-squared for AA was 7% and that for immigrant was 3%. These two factors explained 10% of the variation between UC and CD. Conclusion: In the LAC+USC IBD clinic which serves low socioeconomic patients, there is a 3 to 1 ratio of UC to CD. Immigrant and non-African American ethnicity are predictive of UC whereas non-immigrant and African American ethnicity are predictive of CD.

Inflammatory Bowel Disease in a Low Socioeconomic Population

Introduction: At the Los Angeles County University of Southern California Medical Center (LAC+USC) we service an inflammatory bowel disease (IBD) clinic for a low economic population. We evaluated the frequency of the types of IBD and identified risk factors associated with ulcerative colitis (UC) versus Crohns disease (CD). Methods: This is a retrospective, cross-sectional review of patients with the diagnosis of IBD including UC, CD or indeterminate colitis that were seen at the Roybal Comprehensive Health Clinic between 1/01/2004 through 12/31/2009. Ulcerative colitis was defined as a continuous inflammation of the colon involving the distal rectum and with architectural distortion on histology. Crohn's disease was defined by characteristic small and large bowel changes on imaging studies and/or segmental colitis in untreated patients on colonoscopy. Using an IBD database, ICD-9 diagnosis codes, electronic medical records, and the Clinical Outcomes Research Initiative (CORI) system, the type of colitis, extent of disease, and demographic information were obtained. Univariate and stepwise multivariate analysis were performed. Results: A total of 287 patients with IBD were identified. The frequency of UC in this population was 68% (196/287); (95% Confidence Interval (CI): 63%-74%); CD 24% (70/287); (95% CI; 19%-29%); and indeterminate colitis (IC) 7% (21/287); (95% CI: 4-10). In the 266 patients with UC or CD, 55% were male, 65% over 30 years of age, 69% Hispanic, 12% African American (AA), 9% Asian and 9% non-Hispanic American. Sixty-one percent were immigrants and 52% were from Latin America. We did not include in the analysis education, history of smoking and family history of IBD because of the high percentage of missing data 37, 30 and 47% respectively. Table 1 provides the association of each risk factor with UC vs CD. Stepwise logistic regression yielded two significant independent risk factors: AA ethnicity [adjusted OR: 0.34; 95% CI: 0.15, 0.77; p=0.01] and immigrant [adjusted OR: 2.16; 95% CI: 1.19, 3.94, p=0.01). The R-squared for AA was 7% and that for immigrant was 3%. These two factors explained 10% of the variation between UC and CD. Conclusion: In the LAC+USC IBD clinic which serves low socioeconomic patients, there is a 3 to 1 ratio of UC to CD. Immigrant and non-African American ethnicity are predictive of UC whereas non-immigrant and African American ethnicity are predictive of CD.

Computing Smooth Time Trajectories for Camera and Deformable Shape in Structure from Motion with Occlusion

We address the classical computer vision problems of rigid and nonrigid structure from motion (SFM) with occlusion. We assume that the columns of the input observation matrix W describe smooth 2D point trajectories over time. We then derive a family of efficient methods that estimate the column space of W using compact parameterizations in the Discrete Cosine Transform (DCT) domain. Our methods tolerate high percentages of missing data and incorporate new models for the smooth time trajectories of 2D-points, affine and weak-perspective cameras, and 3D deformable shape. We solve a rigid SFM problem by estimating the smooth time trajectory of a single camera moving around the structure of interest. By considering a weak-perspective camera model from the outset, we directly compute euclidean 3D shape reconstructions without requiring postprocessing steps such as euclidean upgrade and bundle adjustment. Our results on real SFM data sets with high percentages of missing data compared positively to those in the literature. In nonrigid SFM, we propose a novel 3D shape trajectory approach that solves for the deformable structure as the smooth time trajectory of a single point in a linear shape space. A key result shows that, compared to state-of-the-art algorithms, our nonrigid SFM method can better model complex articulated deformation with higher frequency DCT components while still maintaining the low-rank factorization constraint. Finally, we also offer an approach for nonrigid SFM when W is presented with missing data.

Psychological Assessments of Bariatric Surgery Patients. Development, Reliability, and Exploratory Factor Analysis of the PsyBari

Bariatric surgery patients are required to receive psychological clearance before they are eligible for surgery. In spite of this, there are no standard assessment practices or tests designed specifically for these evaluations. The objective of this study is to determine the reliability and construct validity of the PsyBari, a psychological test designed for bariatric surgery patients. The PsyBari was administered to 752 patients. Internal consistency reliability and exploratory factor analyses were conducted. Items with high percentages of missing data, low communalities, and low item loadings were identified and deleted. Cronbach's α = 0.930 (0.940 for males and 0.927 for females). Six factors were obtained for each gender: for females, awareness of eating habits, early life problems due to weight, dysphoric feelings about weight, weight-related impairment, surgical anxiety, and guilty feelings related to eating; for males, physical impairment with depression, awareness of eating habits, early life problems due to weight, interpersonal support with anxiety about weight, anger, and guilty feelings about eating habits. Results indicate that there are unique psychometric parameters when constructing tests for bariatric surgery patients. The PsyBari has good overall reliability, although two of the 11 subscales have poor reliability. Factor analyses revealed six factors for each gender. Some factors were common for both genders, some were unique for each gender, and some consisted of mixed constructs.

An iterative multiresolution scheme for SFM with missing data: Single and multiple object scenes

Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach.

Forecasting with missing data: application to coastal wave heights

This paper presents a comparative analysis of linear and mixed models for short-term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay. The series is interpolated with a linear predictor which minimizes the forecast mean square error. The linear models are seasonal ARIMA models and the mixed models have a linear component and a non-linear seasonal component. The non-linear component is estimated by a non-parametric regression of data versus time. Short-term forecasts, no more than two days ahead, are of interest because they can be used by the port authorities to notify the fleet. Several models are fitted and compared by their forecasting behaviour. Copyright © 1999 John Wiley & Sons, Ltd.

Outcome evaluation in a home palliative care service

The complexity of assessing the impact of palliative care is much greater than in other fields of medicine, due to the shortcomings of traditional outcome indicators. We conducted a prospective study to describe the patient's quality of life at the outset and during palliative care at home and to define some potential indicators of palliative care outcomes with the aim of assessing the quality of home care as provided by a palliative care unit. Seventy-three patients who received care at home were assessed. The median survival in palliative home care was 29 days. To evaluate the degree of symptom distress, we used an Italian version of the Symptom Distress Scale. Assessing the quality-of-life pattern over time, we observed that palliative care was effective in mitigating pain and, at least in part, in stimulating appetite, curbing nausea, and controlling psychological aspects. The subscales referring to social and functional aspects steadily worsened. The difficulties encountered (the high percentage of missing data, the considerable number of patients treated for less than 10 days, etc.) should be a warning against using only one assessment instrument. It is worthwhile defining the various potential outcomes of palliative care even though all results will not always be measurable in every patient. Only a global assessment, a “multiple outcomes approach,” based on different indicators, would allow for evaluation of the outcome of the care process.

Forecasting with Missing Data: Application to a Real Case

This paper presents a comparative analysis of linear and mixed models for short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay. The series is interpolated with a linear predictor which minimizes the forecast mean square error. The linear models are seasonal ARIMA models and the mixed models have a linear component and a non linear seasonal component. The non linear component is estimated by a non parametric regression of data versus time. Short term forecasts, no more than two days ahead, are of interest because they can be used by the port authorities to notify the fleet. Several models are fitted and compared by their forecasting behavior.