Missing Data

Background: Palliative care has been proposed for progressive non-cancer conditions but there have been few evaluations of service developments. We analysed recruitment, compliance and follow-up data of a fast track (or wait list control) randomised controlled trial of a new palliative care service-a design not previously used to assess palliative care. Methods/Design: An innovative palliative care service (comprising a consultant in palliative medicine, a clinical nurse specialist, an administrator and a psychosocial worker) was delivered to people severely affected by multiple sclerosis (MS), and their carers, in southeast London. Our design followed the MRC Framework for the Evaluation of Complex Interventions. In phase II we conducted randomised controlled trial, of immediate referral to the service (fast-track) versus a 12-week wait (standard best practice). Main outcome measures were: compliance (the extent the trial protocol was adhered to), recruitment (target 50 patients), attrition and missing data rates; trial outcomes were Palliative Care Outcome Scale and MS Impact Scale. Results: 69 patients were referred, 52 entered the trial (26 randomised to each arm), 5 refused consent and 12 were excluded from the trial for other reasons, usually illness or urgent needs, achieving our target numbers. 25/ 26 fast track and 21/26 standard best practice patients completed the trial, resulting in 217/225 (96%) of possible interviews completed, 87% of which took place in the patient's home. Main reasons for failure to interview and/ or attrition were death or illness. There were three deaths in the standard best practice group and one in the fast-track group during the trial. At baseline there were no differences between groups. Missing data for individual questionnaire items were small (median 0, mean 1-5 items out of 56+ items per interview), not associated with any patient or carer characteristics or with individual questionnaires, but were associated with interviewer. Conclusion: This is the first time a fast track (or wait list) randomised trial has been reported in palliative care. We found it achieved good recruitment and is a feasible method to evaluate palliative care services when patients are expected to live longer than 3-6 months. Home interviews are needed for a trial of this kind; interviewers need careful recruitment, training and supervision; and there should be careful separation from the clinical service of the control patients to prevent accidental contamination.

Objective: To determine the interrater reliability of the United States Agency for Health Care Policy and Research (AHCPR) Clinical Practice Guideline Number 16 for rehabilitative placement of poststroke patients. Design: Pairs of rehabilitation professionals, highly trained in the Guideline, rated the appropriateness of rehabilitative placements. Setting: Acute care hospitals in three regions of the country. Patients: Sixty patients with moderate-to-severe stroke. Measures: Numerous factors affecting appropriate placement according to the Guideline were abstracted from medical records or obtained by direct evaluation of patients. Results: Good reliability was attained for home and nursing facility placement with rehabilitation services but with no multidisciplinary rehabilitation program (intraclass correlation coefficient = .73 and .60, respectively). Serious reliability problems were found for placements in low-intensity outpatient rehabilitation and high-intensity inpatient rehabilitation programs. Chief sources of unreliability were ambiguous or missing data in hospital medical records, complexities in the Guideline, and raters' tendencies to follow their own clinical judgments. More than one type of placement was appropriate for 65% of patients. Conclusions: Reliable placement guidelines are possible, but aspects of the Guideline require additional development. Evidence of demonstrated reliability and validity will be required to resolve disputes between rehabilitation professionals and payers regarding appropriate levels and types of rehabilitation and to guide patients and their families.

MODELLING STUDY FOR CHARACTERIZING AND PREDICTING URBAN AIR POLLUTION Gregorio Andria, Giuseppe Cavone, Anna ML Lanzolla, Alessandro Rubino Department of Electrics and Electronics (DEE) – Politecnico di Bari Viale del Turismo 8, 74100 Taranto ...

A new quasi-imputation strategy for correlated ordinal responses is proposed by borrowing ideas from random number generation. The essential idea is collapsing ordinal levels to binary ones, and converting correlated binary outcomes to multivariate normal outcomes in a sensible way so that re-conversion to the binary and then ordinal scale, after conducting multiple imputation, yields the original marginal distributions and correlations. This conversion process ensures that the correlations are transformed reasonably which in turn allows us to take advantage of well-developed imputation techniques for Gaussian outcomes. We use the phrase "quasi" because the original observations are not guaranteed to be preserved. We present an application using a data set from psychiatric research. We conclude that the proposed method may be a promising tool for handling incomplete longitudinal or clustered ordinal outcomes.

A. Abraham el al.(Eels.) 251 Soft Computing Systems: Design, Management and Applications IOS Press. 2002 A Study of i^-Nearest Neighbour as an Imputation Method Gustavo EAPA Batista and Maria Carolina Monard University of Sao Paulo-USP Institute of Mathematics and ...

Power spectral analyses of the plasma density measured by the Voyager 2 spacecraft are used to investigate the spectral characteristics and fluctuation level of density turbulence from 1 to 60 AU, corresponding to the period 1977 to 1999. Only periods without clear discontinuities, with less than 10% missing data, and at least 256 samples are selected, strongly limiting the number and coverage of intervals analyzed. Typically, the power spectra have a dual power law form, with a closely Kolmogorov component at higher frequencies and a steeper component (/ f À2) at lower frequencies, consistent with observations inside 1 AU. The spectral indices remain approximately constant with heliocentric distance R ] 30 AU, with weak evidence for a decrease toward the Kraichnan index at larger R for the h component. For R ] 20 AU the fluctuation level (dn) 2 (R) decreases approximately as R À3±1 but then flattens out in the region 20-30 AU before increasing slowly for R^30 AU. This injection of energy into density turbulence beyond 20-30 AU is not a heliolatitude effect and is contrary to the R À4 behavior for (dn) 2 (R) predicted on the basis of observations inside 1 AU. It is interpreted as the result of charge-exchange and pickup of interstellar neutral atoms for R > 1 AU driving enhanced MHD turbulence and associated density variations. The results provide independent support that observations of proton heating and enhanced magnetic turbulence levels beyond about 20 AU are both real and associated with pickup ions. They also suggest that existing calculations of angular scattering of the 2-3 kHz radiation observed by the Voyagers in the outer heliosphere must be redone and provide opportunities to test turbulence theories by comparing density and magnetic turbulence as a function of R in the outer heliosphere. Owing to limitations on the coverage and quality of data, confirmation of these results using nonspectral methods is desirable.

This paper shows how Bayesian Networks can be used to create models for discrete data from contingency tables. The advantage is that the models are created relatively automatically using existing software. The models provide representations that approximately preserve the joint relationships of variables and are easy to apply. The models allow imputation for missing data in contingency tables and for the creation of discrete, synthetic microdata satisfying analytic constraints.

Recent developments in ecological statistics have reached behavioral ecology, and an increasing number of studies now apply analytical tools that incorporate alternatives to the conventional null hypothesis testing based on significance levels. However, these approaches continue to receive mixed support in our field. Because our statistical choices can influence research design and the interpretation of data, there is a compelling

The objective of this research is to implement a method for estimating the real missing data in heart disease datasets and to show how it affects the resulting knowledge. Missing data is common problem in knowledge discovery from database (KDD) processes that can lead significant error in extracted knowledge. We use hybridization of artificial neural network and rough set theory (ANNRST) to estimate the real missing data on heart disease from UCI (University of California, Irvine) datasets. ANN with reduced input ...

Existing commercial database management systems offer little or no functionality to handle the complexity of geoscience data-and other environmental science data-particularly in respect of missing and partially missing (incomplete or imprecise) data items. The emphasis of both the relational theorists (Codd, Date, and others) and the developers of database systems is on commercial applications where only rudimentary treatment of missing data is required, in the form of NULLs, and even these are not handled properly by the SQL language.

Longitudinal observational studies provide rich opportunities to examine treatment effectiveness during the course of a chronic illness. However, there are threats to the validity of observational inferences. For instance, clinician judgment and self-selection play key roles in treatment assignment. To account for this, an adjustment such as the propensity score can be used if certain assumptions are fulfilled. Here, we consider a problem that could surface in a longitudinal observational study and has been largely overlooked. It can occur when subjects have a varying number of distinct periods of therapeutic intervention. We evaluate the implications of baseline variables in the propensity model being associated with the number of post baseline observations per subject and refer to it as 'covariate-dependent representation'. An observational study of antidepressant treatment effectiveness serves as a motivating example. The analyses examine the first 20 years of follow-up data from the National Institute of Mental Health Collaborative Depression Study, a longitudinal, observational study. A simulation study evaluates the consequences of covariate-dependent representation in longitudinal observational studies of treatment effectiveness under a range of data specifications.The simulations found that estimates were adversely affected by underrepresentation when there was lower ICC among repeated doses and among repeated outcomes.

Intention-to-treat (ITT) in randomized controlled trials involves keeping participants in the treatment groups to which they were randomized regardless of whether they withdraw following randomization. Intention-totreat is a strategy for maintaining the integrity of randomization and strengthening the trial's internal validity. Although ITT is advocated by the Consolidated Standards of Reporting Trials (CONSORT) guidelines, there is confusion about what ITT means and little specific advice on how to achieve it. The purpose of this article is to present definitions of ITT and to suggest strategies for implementing ITT as a total design strategy in nursing clinical trials. Recommendations are offered regarding study planning, study design, subject retention, sampling, data collection, data analysis, and reporting within the context of ITT. ß

Incomplete information in a soft set restricts the usage of the soft set. To make the incomplete soft set more useful, in this paper, we propose a data filling approach for incomplete soft set in which missing data is filled in terms of the association degree between the parameters when stronger association exists between the parameters or in terms of the probability of objects appearing in the mapping sets of parameters when no stronger association exists between the parameters. An illustrative example is employed to show the feasibility and validity of our approach in practical applications.

Background Patients receiving intensive chemotherapy can experience increased distressed related to both the cancer diagnosis and treatment isolation. If not addressed, distress can lead to anxiety, depression, and post-traumatic stress disorder. The purpose of this study was to determine the feasibility and possible benefits of a music imagery intervention for patients hospitalized in a protective environment for the treatment of acute leukemia or highgrade non-Hodgkin's lymphoma. Materials and methods Adults receiving intensive myelosuppressive chemotherapy in a protective environment were randomized to standard care or standard care plus music imagery. The music imagery sessions occurred twice weekly for up to eight sessions. Patients were encouraged to use the music imagery daily. Results The principal criteria of feasibility were rate of consent, rate of completion of scheduled sessions, and rate of questionnaire completion. Forty-nine out of 78 patients consented, a 63% consent rate. Seventy-two percent of all scheduled music imagery sessions were completed. The rate of questionnaire completion was 60% with missing data because of illness severity and early discharge. The principal outcomes of benefit (e.g., efficacy) were positive and negative affects, fatigue, and anxiety. Both groups improved over time on all outcomes (all p < 0.001). However, a subgroup of individuals with low baseline negative affect who received the intervention reported significantly less anxiety at discharge than individuals with low baseline negative affect who did not receive the intervention. Conclusions Music imagery is feasible for adults with acute leukemia in protected environments. Patients with lower initial distress may benefit from a music imagery program in terms of reduced anxiety at discharge.

Accuracy and robustness with respect to missing or corrupt input data are two key characteristics for any travel time prediction model that is to be applied in a real-time environment (e.g. for display on variable message signs on freeways). This article proposes a freeway travel time prediction framework that exhibits both qualities. The framework exploits a recurrent neural network topology, the so-called statespace neural network (SSNN), with preprocessing strategies based on imputation. Although the SSNN model is a neural network, its design (in terms of input-and model selection) is not ''black box'' nor location-specific. Instead, it is based on the lay-out of the freeway stretch of interest. In this sense, the SSNN model combines the generality of neural network approaches, with traffic related (''white-box'') design. Robustness to missing data is tackled by means of simple imputation (data replacement) schemes, such as exponential forecasts and spatial interpolation. Although there are clear theoretical shortcomings to ''simple'' imputation schemes to remedy input failure, our results indicate that their use is justified in this particular application. The SSNN model appears to be robust to the ''damage'' done by these imputation schemes. This is true for both incidental (random) and structural input failure. We demonstrate that the SSNN travel time prediction framework yields good accurate and robust travel time predictions on both synthetic and real data.

Infidelity is an often cited problem for couples seeking therapy, but the research literature to date is very limited on couple therapy outcomes when infidelity is a problem. The current study is a secondary analysis of a community-based sample of couple therapy in Germany and Austria. Outcomes for 145 couples who reported infidelity as a problem in their relationship were compared to 385 couples who sought therapy for other reasons. Analyses based on hierarchical linear modeling revealed that infidelity couples were significantly more distressed and reported more depressive symptoms at the start of therapy but continued improving through the end of therapy and to six months post-therapy. At the follow-up assessment infidelity couples were not statistically distinguishable from non-infidelity couples, replicating previous research. Sexual dissatisfaction did not depend on infidelity status. Although there was substantial missing data, sensitivity analyses suggested that the primary findings were not due to missing data. The current findings based on a large community sample replicated previous work from an efficacy trial and show generally optimistic results for couples in which there has been an affair.

Prostaglandin analogues are effective ocular hypotensive agents and are being used increasingly in the treatment of elevated intraocular pressure (IOP). These agents are typically dosed once daily. A pilot study was conducted to evaluate the duration of travoprost&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s IOP-lowering efficacy up to 84 hours after the final dose in patients with open-angle glaucoma. A follow-up study was conducted to compare diurnal IOP control with travoprost and latanoprost over a 44-hour period. In the open label pilot study, patients received 0.004% travoprost in both eyes at 8 pm daily for 2 weeks. After 2 weeks, IOP was measured before administration of the last daily dose, every 4 hours thereafter for 36 hours, and 60 and 84 hours after the last dose, with no additional ocular hypotensive medication given. In the controlled, double-masked, parallel-group, follow-up study, patients were randomized to self-administer 1 drop of the marketed doses of 0.004% travoprost or 0.005% latanoprost in both eyes at 8 pm daily for 2 weeks. At the end of this period, patients returned to the facility at approximately 8 pm for IOP measurement and administration of the final dose of study medication. IOP was then measured at 4-hour intervals for 44 hours after the last dose, with no additional ocular hypotensive medication given. The pilot study included 21 patients (67% female, 33% male; age range, 35-81 years) with open-angle glaucoma. IOP values were significantly below baseline at all time points up to 84 hours after the final dose of travoprost ( P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001). The follow-up study enrolled 35 patients, 1 of whom was excluded for missing data; thus, the intent-to-treat analysis included 34 patients (68% female, 32% male; age range, 36-72 years). At the unmedicated eligibility visit, mean IOP over 24 hours ranged from 21 to 26 mm Hg in each treatment group. After 2 weeks of treatment and 24 hours after the last dose, mean (SD) IOP was 13.1 (2.1) mm Hg (change from eligibility visit, -10.4 [2.7] mm Hg) in the travoprost group and 16.0 (3.1) mm Hg (change from eligibility visit, -7.1 [2.4] mm Hg) in the latanoprost group. The difference in change from baseline was statistically significant between treatment groups (P=0.006). Travoprost lowered IOP significantly at all time points throughout the 44-hour period after the last dose (mean IOP, &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;or=18 mm Hg; [P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001) and was statistically superior to latanoprost at 8 pm before the last dose (P=0.041) and 24 hours after the last dose (P=0.006). Latanoprost showed greater IOP-lowering efficacy compared with travoprost 4 hours after the last dose (P=0.040). IOP reductions were significantly different from zero at all time points with both treatments (P&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;0.001). The results of the pilot study suggest that travoprost produces reductions in IOP that may be sustained for up to 84 hours after dosing. The results of the follow-up study suggest that both prostaglandin analogues significantly lower IOP from baseline in patients with open-angle glaucoma and provide excellent diurnal IOP control throughout a 24-hour period.

There is concern about environmental impacts of cropping in catchments of Australia's Great Barrier Reef, especially losses of nitrogen (N) from cropping systems. Sugarcane production in the Burdekin region in the dry tropics stands out from other crops/regions because it is grown with the highest applications of irrigation water and N fertiliser rates of any sugarcane producing region in Australia, attributes which may enhance losses of N. Little is known about N losses from sugarcane production systems, especially irrigated systems. We measured parts of the water and N balance over three sugarcane crops at three contrasting sites in different parts of the Burdekin region, covering a range of soil types/textures and irrigation managements. The experimental data were used to parameterise the APSIM-Sugarcane cropping systems model, and the model then used to 'infill' missing data and develop more complete water and N balances for each of the crops at the three sites. The model was also used to simulate long-term yields and N losses through runoff and leaching below the root zone at the sites under a range of N fertiliser and irrigation management practices. Unlike the experience in other cropping systems, N losses through runoff and leaching below the root zone were not higher at our sites than measured in rainfed sugarcane production systems. The long-term simulations showed there were clear opportunities for reducing N losses while maintaining yields through reducing N fertiliser application rates. Simulations results suggested that long-term N surpluses of 50 kg ha −1 yr −1 , considerably less than those during the experiment or common in the study region, were sufficient to maintain yields but reduce N losses by 50-57%. So, N fertiliser management should aim to keep surpluses to that level. Improved irrigation management could also help reduce N losses but generally to a much lesser extent than reduced N fertiliser applications. Research is required to confirm these predicted benefits, and investigate potential interaction between N fertiliser and irrigation management practices, and impacts of other management practices.

In this paper, the results obtained by inter-comparing several statistical techniques for modelling SO 2 concentration at a point such as neural networks, fuzzy logic, generalised additive techniques and other recently proposed statistical approaches are reported. The results of the inter-comparison are the fruits of collaboration between some of the partners of the APPETISE project funded under the Framework V Information Societies and Technologies (IST) programme. Two different cases for study were selected: the Siracusa industrial area, in Italy, where the pollution is dominated by industrial emissions and the Belfast urban area, in the UK, where domestic heating makes an important contribution. The different kinds of pollution (industrial/ urban) and different locations of the areas considered make the results more general and interesting. In order to make the intercomparison more objective, all the modellers considered the same datasets. Missing data in the original time series was filled by using appropriate techniques. The inter-comparison work was carried out on a rigorous basis according to the performance indices recommended by the European Topic Centre on Air and Climate Change (ETC/ACC). The targets for the implemented prediction models were defined according to the EC normative relating to limit values for sulphur dioxide. According to this normative, three different kinds of targets were considered namely daily mean values, daily maximum values and hourly mean values. The inter-compared models were tested on real cases of poor air quality. In the paper, the inter-compared techniques are ranked in terms of their capability to predict critical episodes. A ranking in terms of their predictability of the three different targets considered is also proposed. Several key issues are illustrated and discussed such as the role of input variable selection, the use of meteorological data, and the use of interpolated time series. Moreover, a novel approach referred to as the technique of balancing the training pattern set, which was successfully applied to improve the capability of ANN models to predict exceedences is introduced. The results show that there is no single modelling approach, which generates optimum results in terms of the full range of performance indices considered. In view of the implementation of a warning system for air quality control, approaches that are able to work better in the prediction of critical episodes must be preferred. Therefore, the artificial neural network prediction models can be recommended for this purpose. The best forecasts were achieved for daily averages of SO 2 while daily maximum and hourly mean values are difficult to predict with acceptable accuracy. #

The main purpose of this paper is to study the problem created by the lack of information about the credit history of some debtors in the databases used to develop credit scoring models and the use of information about behavior compiled by a credit risk register as a potential solution to the problem. The paper analyzes two problems: (i) the need to provide a credit risk estimation of debtors whose behavior is unknown (because they are deleted from the databases without indication of the reason), and (ii) the assessment of the impact of ignoring these data when evaluating the credit risk of entities’ portfolios. The fundamental strategy will be to allow the use of debtors’ credit history in other entities, recorded in the credit risk register.

It is shown that the classical taxonomy of missing data models, namely missing completely at random, missing at random and informative missingness, which has been developed almost exclusively within a selection modelling framework, can also be applied to pattern-mixture models. In particular, intuitively appealing identifying restrictions are proposed for a pattern-mixture MAR mechanism.

To present and evaluate a measurement tool for assessing characteristics of people with drug and/or alcohol problems for triage and evaluation in treatment. Measurements in the Addictions for Triage and Evaluation (MATE) is composed of 10 modules, selected on the basis of a detailed set of specifications. Conceptually, the MATE was constructed according to the ICD and International Classification of Functioning (ICF) in the World Health Organization (WHO) classification system. Two of the ICF-related modules were newly designed. Design Monitoring feasibility and field-testing in a treatment-seeking population with researcher and clinician-administered test-retest interviews, construct validation with related instruments and evaluation of the dimensional structure of the ICF-related modules. Setting The research was conducted in a large, regional substance abuse treatment centre in the Netherlands and at the Municipal Health Service of Amsterdam. Participants A total of 945 treatment-seeking patients were recruited during routine intakes, 159 of whom were interviewed twice; 32 problem drug users were also recruited from the Amsterdam cohort studies among problem drug users. Findings Completion time was reasonably short, and there were relatively few missing data. The factor structure of the ICF-related modules revealed a three-factor model with an acceptable fit. Inter-rater reliability ranged between 0.75 and 0.92 and was satisfactory, but interviewer reliability ranged between 0.34 and 0.73, indicating that some of subscales need to be improved. Concurrent validity was indicated by significant correlations (>0.50) between the ICF-related modules and the WHO Disability Assessment Schedule II (WHODAS II) and WHO Quality of Life brief version (WHOQOL-BREF). Conclusions The MATE can be used to allocate patients to substance abuse treatment. Because it is a comprehensive but flexible measurement tool that is also practical to use, the MATE is well suited for use in a heterogeneous population.

In some cases, in data analysis, missingness happens in the observation for different reasons and ways. How to deal with these observations in the data analysis process is very important, especially in the high stack decisions, the usual way to dealing with missing data issues was eliminating missing values. This method leads to low quality in data and consequently leads to bias in results. Today, regarding to the advances in various fields of science and powerful statistical methods, imputation methods are applied is possible in the case of incomplete data. In this paper, the various types of missing data, imputation methods, the assumptions, their advantages and disadvantages were discussed. In this setting, we shall try to provide applied examples using statistical software. Especially an applied example due to 91th TOLIMO test, which was provided by Iranian National Organization of Educational Testing, has been presented (NOET). Comparison of results according to MSE using three methods of multiple imputation, the EM algorithm and the DA algorithm, has showed that the EM algorithm had the best performance for analyzing this data set.

The extended family as a potential cause of and protection against intimate partner violence (IPV) remains relatively unstudied. This mixed-methods study used focus group discussions (FGDs) and a clinic-based survey to investigate several family-based risk and protective factors associated with women's risk of IPV in Jordan. Seventeen FGDs (total number of participants ¼ 105) were conducted with women in Amman. Each transcript was coded for categories using open coding methodology and mapping. Relevant categories and subcategories were family support, family interference, family abuse, exposure to violence in childhood, and place of residence. For the survey, systematic probability proportionate to size methodology was used to select a sample of 517 literate, ever married, women from seven reproductive health clinics located throughout the country (response rate ¼ 70%); due to missing data, the analytic sample was restricted to 418 women. Measures assessed the categories mentioned above. Bivariate and multivariate logistic regression models were constructed to examine the relationship between IPV and the main predictors of interest (residence, family interference, family violence, exposure to violence as a child, and family support). The combined results of the FGDs and the survey demonstrated that the respondent's husband's exposure to violence in childhood and violence perpetrated by other family members were risk factors for IPV. Family interference was also significantly related to IPV but only when the respondent identified the interference as harmful to her relationship. Residence with the respondent's in-laws demonstrated mixed effects. A supportive family was protective against IPV, although the FGDs revealed that families were not always an effective source of assistance. Findings demonstrate the continued role of the wife's and husband's kin in women's risk of IPV in Jordan, highlighting the importance of a broader view of the context of IPV.

Missing data constitute a common but widely underappreciated problem in both cross-sectional and longitudinal research. Furthermore, both the gravity of the problems associated with missing data and the availability of the applicable solutions are greatly increased by the use of multivariate analysis. The most common approaches to dealing with missing data are reviewed, such as data deletion and data imputation, and their relative merits and limitations are discussed. One particular form of data imputation based on latent variable modeling, which we call Multivariate Imputation, is highlighted as holding great promise for dealing with missing data in the context of multivariate analysis. The recent theoretical extension of latent variable modeling to growth curve analysis also permitted us to extend the same kind of solution to the problem of missing data in longitudinal studies. Data simulations are used to compare the results of multivariate imputation to other common approaches to missing data.

Given the variety of palliative care settings within which symptom distress must be assessed, development of a valid and reliable clinical tool that can be simply applied in every day practice is needed. The Symptom Assessment Scale (SAS) uses a 0-10 numerical scale with zero being no symptom and 10 being the worst possible. The key symptoms included in the scale are breathing, bowel problems, appetite problems, pain, insomnia, nausea and fatigue. The instrument is structured to allow either the patient, family member or nurse to assess the symptoms. The scale was tested on 572 cancer patients recruited from five palliative care services in Western Australia. Results indicated that the instrument was brief, clinically useful and was administered with minimal missing data. Internal consistency reliability estimates of the scale ranged from 0.64-0.92 as measured by the Cronbach's alpha coefficient. Test-retest reliabilities of 0.84-0.92 were obtained using Pearson's correlation co-efficient. The instrument does not provide an in-depth assessment of individual symptoms, but serves as a screening tool to identify troublesome symptoms that warrant attentive and immediate investigation and comprehensive assessment.

In this paper, we present a comprehensive review of methods for spectral analysis of nonuniformly sampled data. For a given finite set of nonuniformly sampled data, a reasonable way to choose the Nyquist frequency and the resampling time are discussed. The various existing methods for spectral analysis of nonuniform data are grouped and described under four broad categories: methods based on least squares; methods based on interpolation techniques; methods based on slotted resampling; methods based on continuous time models. The performance of the methods under each category is evaluated on simulated data sets. The methods are then classified according to their capabilities to handle different types of spectrum, signal models and sampling patterns. Finally the performance of the different methods is evaluated on two real life nonuniform data sets. Apart from the spectral analysis methods, methods for exact signal reconstruction from nonuniform data are also reviewed.

Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment.

Web Real-Time Communication (WebRTC) is an open-source real-time interactive audio, video communication framework and potentially useful standard that allows to incorporate features such as voice calling, video chatting, messages features and peer to peer (P2P) file sharing directly into the browser. New Firefox Hello video and chat client that lets user talk securely to anyone else using an up-to-date Firefox, Chrome, or Opera browser, without the need to downloaded any add-on, or configure any new settings. In WebRTC, Communication is extremely sensitive to affected by errors. Research paper discusses some of the mechanisms utilized in WebRTC to handle data missing, Virtual Private Network (VPN) bugs, Reporting errors and WebRTC traditional error in the communication path while WebRTC processing. In this case tried to solve the problems on the WebRTC communication. This Paper's reviews the techniques that have been developed for error detect, locate and correct in the WebRTC. More importantly, this system design can correct an error at a small modification of the basic structure.

Objective To evaluate the psychometric properties of the Sinhala version of the breast cancer-specific health-related quality of life (HRQL) module of the European Organization for Research and Treatment of Cancer (QLQ-BR23). Methods Psychometric testing assessed the hypothesized scale structure, internal consistency, construct validity and acceptability of the Sinhala version of the QLQ-BR23 in a consecutive series of 356 newly diagnosed breast cancer patients recruited from tertiary care oncology treatment centres in Sri Lanka. Results Compliance and self-completion rates were high (98% and 88%, respectively), and missing data low (0.06%). Multitrait scaling confirmed the scale structure of the QLQ-BR23 with excellent item convergence (95%), item discrimination (99%), and scaling success (99%) rates. The Cronbach's alpha coefficients of the scales for internal consistency reliability ranged from 0.68 to 0.93. Construct validity was confirmed with satisfactory results for interscale correlations and known-groups comparisons. QLQ-BR23 item-scale correlations met or exceeded the convergent validity criterion of 0.40 for all but one item. QLQ-BR23 interscale correlations met this criterion for three comparisons and for five comparisons with conceptually related QLQ-C30 scales. Correlations between QLQ-BR23 scales and QLQ-C30 functional scales were lower as expected. As expected, most dimensions of the QLQ-BR23 were able to discriminate clearly between pretreatment and current treatment patients. Conclusion Overall psychometric results for the Sinhala version of the QLQ-BR23 confirmed it as a reliable and valid questionnaire for assessing breast cancer-specific HRQL in Sri Lanka.

Previous work on emotion recognition from physiology has rarely addressed the problem of missing data. However, data loss due to artifacts is a frequent phenomenon in practical applications. Discarding the whole data instance if only a part is corrupted results in a substantial loss of data. To address this problem, two methods for handling missing data (imputation and reduced-feature models) in combination with two classifier fusion approaches (majority and confidence voting) are investigated in this work. The five emotions amusement, anger, contentment, neutral and sadness were elicited in 20 subjects by films while six physiological signals (ECG, EMG, EOG, EDA, respiration and finger temperature) were recorded. Results show that classifier fusion significantly increases the recognition accuracy in comparison to single classifiers by up to 16.3%. Regarding the methods for handling missing data, reduced-feature models are competitive or even slightly better than models which employ imputation. This is beneficial for practical applications where computational complexity is critical.

Jan. 8, 2016: Perhaps wherever I noted "the estimated standard error of the random factors of the estimated residuals," I should have said "the estimated standard deviation of the random factors of the estimated residuals." I saw some things by a couple of people that made me realize my language was sloppy here. But I also read where this may be fairly common. At any rate, this is a fixed value to be estimated. It is a standard deviation.
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Previous notes:
The classical ratio estimator (CRE) is very simple, has a long history, and has a stunningly broad range of application, especially with regard to econometrics, and to survey statistics, particularly establishment survey statistics. The CRE has a number of desirable properties, one of which is that the sum of its estimated residuals is always zero. It is easily extended to multiple regression, and a property shown in Sarndal, Swensson and Wretman (1992) may be used to indicate the desirability of this zero sum of estimated residuals feature when constructing regression weights for multiple regression. In the single regressor form, the zero sum of estimated residuals property is related to an interesting phenomenon expressed in Fox (1997). Finally, relationships of the CRE to some other statistics are also considered.
-- Note added November 2014: As noted in other works I have done, and elsewhere, for this model, only the individual values of x corresponding to individual y values need to be known, as long as the sum of the remaining x (for out-of-sample cases) is known, and then one can still estimate variance. If we do not know the sum of those remaining N-n x-values (where n is sample size selected minus cases to be imputed), but we know a range for that subtotal of x's, then we know a range of estimated variances for the estimated y-totals to go with that range. --

Customer satisfaction and retention are key issues for organizations in today’s competitive market place. As such, much research and revenue has been invested in developing accurate ways of assessing consumer satisfaction at both the macro (national) and micro (organizational) level, facilitating comparisons in performance both within and between industries. Since the instigation of the national customer satisfaction indices (CSI), partial

The Radon transform (RT) suffers from the typical problems of loss of resolution and aliasing that arise as a consequence of incomplete information, including limited aperture and discretization. Sparseness in the Radon domain is a valid and useful criterion for supplying this missing information, equivalent somehow to assuming smooth amplitude variation in the transition between known and unknown (missing) data. Applying this constraint while honoring the data can become a serious challenge for routine seismic processing because of the very limited processing time available, in general, per common midpoint. To develop methods that are robust, easy to use and flexible to adapt to different problems we have to pay attention to a variety of algorithms, operator design, and estimation of the hyperparame-ters that are responsible for the regularization of the solution. In this paper, we discuss fast implementations for several varieties of RT in the time and frequency domains. An iterative conjugate gradient algorithm with fast Fourier transform multiplication is used in all cases. To preserve the important property of iterative subspace methods of regularizing the solution by the number of iterations, the model weights are incorporated into the operators. This turns out to be of particular importance, and it can be understood in terms of the singular vectors of the weighted transform. The iterative algorithm is stopped according to a general cross validation criterion for subspaces. We apply this idea to several known implementations and compare results in order to better understand differences between, and merits of, these algorithms.