Statistical model leaves Peru counting the cost of civil war.
Explore the source record for details and available documents.
Cause of Death↗
SEARCH · Search PubMed
Results for “Models, Statistical”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Statistical modelling of breast cancer incidence and mortality rates in Scotland.
The interpretation of time trends in disease rates can be facilitated using estimable contrasts from age-period-cohort models. Cohort and period trends in breast cancer incidence and mortality rates in Scotland were investigated using contrasts that measure the changes in the linear trends. These contrasts were compared with estimates obtained from mortality rates in the USA and Japan. A significant moderation of both breast cancer incidence and mortality rates was observed in Scotland, associated with cohorts of women born after the Second World War compared with women born between the two world wars. The moderation of breast cancer mortality among cohorts born after 1925 compared with cohorts born before 1925 that was observed in the USA and Japan was also observed in this study. This moderation is not present in the incidence rates. The relative decline in the risk of breast cancer seen in younger cohorts seems to be contradictory to the temporal pattern present among breast cancer risk factors. It may well be that the alteration of eating patterns as a result of rationing in the wartime and immediate post-war period, and the subsequent influence on certain breast cancer risk factors probably produced by such changes, may have had some influence on the development of healthier girls and women. Such speculation could be addressed in a well-designed epidemiological study. There have been no changes in the mortality rate trends with period in Scotland, although the changes in the incidence rate trends with period are consistent with an increase in registration coverage.
Adult↗
A statistical model to predict nonsurvival in congenital diaphragmatic hernia.
OBJECTIVE: To develop a predictive model using echocardiographic indices to identify nonsurvivors from survivors in preoperative patients with congenital diaphragmatic hernia (CDH). STUDY DESIGN: Eight cases of CDH, with a mortality rate of 38%, underwent echocardiographic study before surgical repair. Left ventricular mass (LVMASS) using the area-length method of Wyatt et al. and fractional shortening (FS) by M-mode measurements were determined. RESULTS: We identified a nonlinear nonoverlapping distribution that predicted nonsurvivors from survivors, p=0.04. Multiple regression analysis demonstrated the quantity (LVMASS x FS)(1/2) to be correlated with nonsurvival with a coefficient of determination r(2)=0.55. Comparison of the means of the quantity (LVMASS x FS)(1/2) for the two groups suggested two distinct populations, p=0.04. CONCLUSION: The mathematical quantity (LVMASS x FS)(1/2) calculated from echocardiographic measurements obtained preoperatively in babies with CDH may predict nonsurvival despite maximal intervention.
Female↗
Statistical model for assessing the quality and cost of inspection procedures in forensic and environmental analysis.
Legal regulations on the composition and safety of food products require inspection programmes to maintain the standards required or to obtain an insight in the real situation. Such programmes consist of a sampling procedure followed by analysis of the samples. When monitoring environmental contamination similar programmes are required. When large numbers of samples have to be analysed in order to monitor contaminants in either environmental or food-chain samples, and the majority fulfil the legal requirements, a two-stage control system is desirable. Firstly, samples are analysed by a simple 'screening method', to sift out the large number of samples that fulfil the requirements (negative results). The minority not fulfilling the requirements (positive results) are investigated further by a more sophisticated confirmatory method, specific for the analyte(s) of interest, for an ultimate judgement. For this type of inspection a simple model is used in this study, in order to calculate the fraction of false negative results in the final decision. It is assumed, that, when the quality criteria for the confirmatory method are appropriate, the fraction of false positive results is negligibly small. The quality of the inspection is expressed in terms of the fraction f of false negative results with respect to the real positive samples and is related to labour and costs. In an example given, the costs of analysing 40,000 samples with f = 6.6% are ecu 7,800,000. Improving f down to 3.2% creates additional costs of ecu 16,000,000. The proposed model might not be sufficiently realistic, but more realistic models can be implemented.(ABSTRACT TRUNCATED AT 250 WORDS)
Costs and Cost Analysis↗
Deriving statistical models for predicting peptide tandem MS product ion intensities.
Improved search algorithms and scoring functions are required before the identification of peptide tandem MS data can be considered to be fully reliable and automatable. The development of models that can accurately predict product ion spectra from a peptide sequence would certainly help achieve this goal, but this firstly requires a better understanding of the process of fragmentation of peptides in the gas-phase. We summarize recent developments in this area and show that the prediction of product ion spectra is feasible and should improve the identification of peptide tandem MS data, especially for peptides that currently give low or insignificant scores with current search algorithms.
Algorithms↗
Statistical models for predicting response to interferon-alpha and spontaneous seroconversion in children with chronic hepatitis B.
To develop prognostic models for identifying children with hepatitis B who are likely to respond to interferon-alpha (IFN-alpha) or to spontaneously seroconvert, we evaluated results of a multinational controlled trial comprising 70 children with chronic hepatitis B who received IFN-alpha and 74 children who did not receive therapy. Prognostic models were developed using SMILES (similarity of least squares), which is a data analysis network that incorporates multidimensional relationships in the clinical data of complex diseases. Commonly collected clinical data included age, gender, serum aminotransferase (aspartate aminotransferase [AST] and alanine aminotransferase [ALT]) and hepatitis B virus (HBV) DNA levels, and IFN-alpha dose. Additional data included pretreatment directional information (e.g. increases or decreases in serum aminotransferase and HBV DNA levels), liver biopsy results, race and transmission mode. Using data available prior to initiation of treatment, the SMILES models achieved prospective predictions of 89% for responders, 96% for non-responders, 100% for seroconverters and 93% for non-seroconverters. Although not predictive by themselves, the variables that had the greatest impact on predictions for IFN-alpha response were HBV DNA pretreatment direction, baseline HBV DNA, IFN-alpha dose and gender. The variables that had the greatest impact on predictions for spontaneous seroconversion were ALT pretreatment direction, baseline HBV DNA level, age and AST pretreatment direction. Therefore, these models may be useful in determining, in children with hepatitis B, the likelihood of response to IFN-alpha and spontaneous seroconversion.
Adolescent↗
Assessing quality and outcomes for percutaneous coronary intervention: choosing statistical models, outcomes, time periods, and patient populations.
Explore the source record for details and available documents.
Angioplasty, Balloon, Coronary↗
STATISTICAL MODELS VERSUS EMPIRICAL INTUITION: INTERACTIONS IN ANALYSIS-OF-VARIANCE TECHNIQUES.
Explore the source record for details and available documents.
Biometry↗
A statistical model for FEV1 response to arbitrary dynamic ozone exposure conditions.
Lung function response to inhaled ozone at ambient air pollution levels is known to be a function of ozone concentration, exposure duration, and minute ventilation. Most data-driven exposure-response models address exposures under static condition (i.e., with a constant ozone concentration and exercise pattern). Such models are simplifications, as both ambient ozone concentrations and normal human activity patterns change with time. The purpose of this study was to develop a dynamic model of response with the advantages of a statistical model (a relatively simple structure with few parameters). A previously proposed mechanistic model for changes in specific airways resistance was adapted to describe the percent change in forced expiratory volume in one second (FEV1). This model was then reduced using the fit to three existing exposure-response data sets as criterion. The resulting model consists of a single linear differential equation together with an algebraic logistic equation. Under restricted static conditions the model reduces to a logistic model presented earlier by the authors.
Computer Simulation↗
Statistical modeling of dose-response relationships in clinical trials--a case study.
This paper presents a model-based assessment of the relationship between the dose of a test treatment and response. The data set used in the analysis describes the results of two clinical trials that were designed to assess the dose-response relationship of a test treatment. The models described in the paper are fit using weighted regression as implemented by the SAS procedure CATMOD. Relationships between weighted regression and other similar procedures are discussed.
Anticholesteremic Agents↗
A task-based statistical model of a worker's exposure distribution: Part I--Description of the model.
The authors present a task-based model to describe a single worker's exposures to a single airborne chemical toxicant. The model accounts for variability in short-term time-weighted average (TWA) exposure values within a task, and for variability in arithmetic mean exposure levels between tasks. For a given workday, the 8-hour TWA value is equated with the sample mean of an appropriate number of short-term TWAs arising from stratified random sampling of short-term TWAs with proportional allocation by task. The model accounts for autocorrelation in the stochastic process that generates successive short-term TWA values. Due to the underlying random process, a given type of workday with regard to the set of task times has an associated distribution of 8-hour TWA values; the variance of this distribution increases with increasing autocorrelation in the time series of short-term TWAs. A worker's total distribution of 8-hour TWAs is a mixture of these day-specific distributions weighted by the relative frequency of each type of workday; the variance of the total distribution increases with greater day-to-day variability in the array of task times.
Air Pollutants, Occupational↗
Statistical modelling of formaldehyde occupational exposure levels in French industries, 1986-2003.
Occupational exposure databanks (OEDBs) have been cited as sources of exposure data for exposure surveillance and exposure assessment in epidemiology. In 2003, an extract was made from COLCHIC, the French national OEDB, of all concentrations of formaldehyde. The data were analysed with extended linear mixed-effects models in order to identify influent variables and elaborate a multi-sector picture of formaldehyde exposures. Respectively, 1401 and 1448 personal and area concentrations were available for the analysis. The fixed effects of the personal and area models explained, respectively, 57 and 53% of the total variance. Personal concentrations were related to the sampling duration (short-term higher than TWA levels), decreased with the year of sampling (-9% per year) and were higher when local exhaust ventilation was present. Personal levels taken during planned visits and for occupational illness notification purpose were consistently lower than those taken during ventilation modification programmes or because the hygienist suspected the presence of significant risk or exposure. Area concentrations were related to the sampling duration (short-term higher than TWA levels), and decreased with the year of sampling (-7% per year) and when the measurement sampling flow increased. Significant within-facility (correlation coefficient 0.4-0.5) and within-sampling campaign correlation (correlation coefficient 0.8) was found for both area and personal data. The industry/task classification appeared to have the greatest influence on exposure variability while the sample duration and the sampling flow were significant in some cases. Estimates made from the models for year 2002 showed elevated formaldehyde exposure in the fields of anatomopathological and biological analyses, operation of gluing machinery in the wood industry, operation and monitoring of mixers in the pharmaceutical industry, and garages and warehouses in urban transit authorities.
Air Pollutants, Occupational↗
Variations on probabilistic suffix trees: statistical modeling and prediction of protein families.
MOTIVATION: We present a method for modeling protein families by means of probabilistic suffix trees (PSTs). The method is based on identifying significant patterns in a set of related protein sequences. The patterns can be of arbitrary length, and the input sequences do not need to be aligned, nor is delineation of domain boundaries required. The method is automatic, and can be applied, without assuming any preliminary biological information, with surprising success. Basic biological considerations such as amino acid background probabilities, and amino acids substitution probabilities can be incorporated to improve performance. RESULTS: The PST can serve as a predictive tool for protein sequence classification, and for detecting conserved patterns (possibly functionally or structurally important) within protein sequences. The method was tested on the Pfam database of protein families with more than satisfactory performance. Exhaustive evaluations show that the PST model detects much more related sequences than pairwise methods such as Gapped-BLAST, and is almost as sensitive as a hidden Markov model that is trained from a multiple alignment of the input sequences, while being much faster.
Algorithms↗
Statistical models for estimating the genetic basis of repeated measures and other function-valued traits.
The genetic analysis of characters that are best considered as functions of some independent and continuous variable, such as age, can be a complicated matter, and a simple and efficient procedure is desirable. Three methods are common in the literature: random regression, orthogonal polynomial approximation, and character process models. The goals of this article are (i) to clarify the relationships between these methods; (ii) to develop a general extension of the character process model that relaxes correlation stationarity, its most stringent assumption; and (iii) to compare and contrast the techniques and evaluate their performance across a range of actual and simulated data. We find that the character process model, as described in 1999 by Pletcher and Geyer, is the most successful method of analysis for the range of data examined in this study. It provides a reasonable description of a wide range of different covariance structures, and it results in the best models for actual data. Our analysis suggests genetic variance for Drosophila mortality declines with age, while genetic variance is constant at all ages for reproductive output. For growth in beef cattle, however, genetic variance increases linearly from birth, and genetic correlations are high across all observed ages.
Aging↗
Statistical modelling and prediction of lung cancer mortality in the Czech and Slovak Republics, 1960-1999.
BACKGROUND: The aim of the study was to analyse the pattern of lung cancer mortality from 1960 to 1989 and to predict lung cancer mortality for 1990-1999 for males and females aged > or = 30 years in the Czech and Slovak Republics. METHOD: The mortality pattern of lung cancer was examined and predicted using republic-age-period-cohort models. RESULTS: Trends in lung cancer mortality were upward for both sexes over the study period. In the early 1960s, lung cancer mortality in Slovak males was much lower than that in Czech males, but since the late 1960s lung cancer mortality in males increased more rapidly in Slovakia than in the Czech Republic. It was predicted that mortality due to lung cancer in Slovak males would exceed that in Czech males during the last 5 years of the 20th century. Slovak female lung cancer mortality was lower than that for Czech females throughout the study period, and the trends in both republics were similar.
Adult↗
A statistical model to identify the contaminated lots implicated in iatrogenic transmission of Creutzfeldt-Jakob disease among French human growth hormone recipients.
From 1970 to 1988, thousands of children have been treated with human growth hormone (hGH) to supply pituitary gland deficiency. In France, 51 of the 968 children treated by hGH lots produced between January 1984 and March 1985 had developed Creutzfeldt-Jakob disease (CJD) by the end of 1996. The authors' objective was to investigate which of the 13 hGH lots produced during this period might be implicated in the iatrogenic transmission of CJD. In this paper, the authors describe a model that was developed to compute the probability for each lot of being contaminated. The validity of the model was assessed by a simulation study that showed a good agreement between the estimated and the simulated number of contaminated lots. The model suggested that about half of the lots distributed during this period might have been contaminated by the infectious agent that causes CJD. The risk of iatrogenic CJD for patients exposed to contaminated hGH lots was estimated to be 0.06 (95% confidence interval 0.05-0.07).
Creutzfeldt-Jakob Syndrome↗
Statistical modelling reveals demography and time are the main contributing factors in global sperm count changes between 1938 and 1996.
Declining sperm count reports have caused enormous concern to both the scientific community and to society. We reproduced the linear regression analysis and the quadratic model analysis using the 50 year sperm count data published in Carlsen's report and found that neither model adequately described the data. The reported decline in sperm count could be due to observational bias and overinterpretation of linear regression. In fact only 36% of the total variability in sperm count was explained by the linear model and 42% by the quadratic model. The linear model was no longer valid when three new European reports on sperm counts were included in the analysis. The quadratic model, however, suggested an upward trend of sperm count after 1975 (R2 = 0.48, P < 0.0001). Factors other than the 'passage of time' may have contributed to the initial decline of sperm count. An immediate candidate was demography. Our analysis showed that sperm counts in USA were significantly higher in 1938-1956 compared with those in 1957-1974 and 1975-1988, but not in the European or Asian/African/South American countries. The variability of the USA sperm count (1938-1988) explained by the linear and quadratic models was found to be 71 and 70% respectively. The quadratic model importantly indicated that the sperm count in USA decreases asymptotically towards a limiting value and global sperm counts could be increasing since 1970. The non-uniform nature of the global sperm count change suggested that local variations in pollution, diet but not global warming were important determinants of reproductive health.
Demography↗
Statistical modeling to predict elective surgery time. Comparison with a computer scheduling system and surgeon-provided estimates.
BACKGROUND: Accurate estimation of operating times is a prerequisite for the efficient scheduling of the operating suite. The authors, in this study, sought to compare surgeons' time estimates for elective cases with those of commercial scheduling software, and to ascertain whether improvements could be made by regression modeling. METHODS: The study was conducted at the University of Washington Medical Center in three phases. Phase 1 retrospectively reviewed surgeons' time estimates and the scheduling system's estimates throughout 1 yr. In phase 2, data were collected prospectively from participating surgeons by means of a data entry form completed at the time of scheduling elective cases. Data included the procedure code, estimated operating time, estimated case difficulty, and potential factors that might affect the duration. In phase 3, identical data were collected from five selected surgeons by personal interview. RESULTS: In phase 1, 26 of 43 surgeons provided significantly better estimates than did the scheduling system (P < 0.01), and no surgeon was significantly worse, although the absolute errors were large (34% of 157 min average case length). In phase 2, modeling improved the accuracy of the surgeons' estimates by 11.5%, compared with the scheduling system. In phase 3, applying the model from phase 2 improved the accuracy of the surgeons' estimates by 18.2%. CONCLUSIONS: Surgeons provide more accurate time estimates than does the scheduling software as it is used in our institution. Regression modeling effects modest improvements in accuracy. Further improvements would be likely if the hospital information system could provide timely historical data and feedback to the surgeons.
Appointments and Schedules↗