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At least 163 records · Page 9Linked to original sources
PubMed1995

The role of multiple regression and exploratory data analysis in the development of leukemia incidence risk models for comparison of radionuclide air stack emissions from nuclear and coal power industries.

Risks associated with power generation must be identified to make intelligent choices between alternate power technologies. Radionuclide air stack emissions for a single coal plant and a single nuclear plant are used to compute the single plant leukemia incidence risk and total industry leukemia incidence risk. Leukemia incidence is the response variable as a function of radionuclide bone dose for the six proposed dose response curves considered. During normal operation a coal plant has higher radionuclide emissions than a nuclear plant and the coal industry has a higher leukaemia incidence risk than the nuclear industry, unless a nuclear accident occurs. Variation of nuclear accident size allows quantification of the impact of accidents on the total industry leukemia incidence risk comparison. The leukemia incidence risk is quantified as the number of accidents of a given size for the nuclear industry leukemia incidence risk to equal the coal industry leukemia incidence risk. The general linear model is used to develop equations that relate the accident frequency required for equal industry risks to the magnitude of the nuclear emission. Exploratory data analysis revealed that the relationship between the natural log of accident number versus the natural log of accident size is linear.

Journal Article↗
PubMed1998

A risk model approach to the prediction of fetal acidemia.

Known risk factors for adverse perinatal outcome were related to severe fetal acidemia during labor and at birth. Severe fetal acidemia was defined as a fetal scalp or umbilical artery pH > 3 SD below the mean for normal pregnancy. pH values were available for 1,524 singleton births without congenital anomalies. Of these births, 83 (5.4%) had at least one pH value > 3 SD below the reference mean. Known risk factors in the group with severe acidemia were compared with those in a random sample of the remaining 94.6% births. The strongest predictor of severe fetal acidemia was an abnormal cardiotocogram with a Hammacher score > 8. Risk factors shown to lower pH values significantly were combined in a prediction model based on multiple regression analysis. Back-validation of the model showed that half of all cases of severe fetal acidemia could not be adequately predicted. It is concluded that such prediction models do not contribute to a more effective detection of severe fetal acidemia.

Acidosis↗
PubMed1999

Predicting developmental outcomes at school entry using a multiple-risk model: four American communities. The Conduct Problems Prevention Research Group.

The contributions of different risk factors in predicting children's psychological and academic outcomes at the end of 1st grade were examined. Using a regression model, levels of ecobehavioral risk were assessed in the following order: specific demographics, broad demographics, family psychosocial status, mother's depressive symptoms, and neighborhood quality. Participants were 337 families from 4 American communities. Predictor variables were assessed in kindergarten, and teacher, parent, and child outcomes (behavioral and academic) were assessed at the end of 1st grade. Results indicated that (a) each level of analysis contributed to prediction of most outcomes, (b) 18%-29% of the variance was predicted in outcomes, (c) a common set of predictors predicted numerous outcomes, (d) ethnicity showed little unique prediction, and (e) the quality of the neighborhood showed small but unique prediction to externalizing problems.

Achievement↗
PubMed2002

A risk model derived from the National Registry of Myocardial Infarction 2 database for predicting mortality after coronary artery bypass grafting during acute myocardial infarction.

The mortality risk associated with coronary artery bypass grafting (CABG) after acute myocardial infarction (AMI) remains controversial. Although elective CABG is quite safe, the effects of recent myocardial infarction, gender, and other clinical factors on perioperative mortality rates are not completely understood. The objective of this study was to determine in-hospital mortality rates for patients with AMI receiving CABG and to generate a model to predict the risk for any individual patient with specific risk factors. Using the National Registry of Myocardial Infarction 2 database, we identified 71,774 subjects (21,270 women) with AMI who underwent CABG; we excluded those subjects who received immediate surgery as reperfusion therapy. Multivariate logistic regression was used to quantify the independent effects of age, recent myocardial infarction, gender, and other covariates on mortality. A risk score was then generated from the regression model to quantify the mortality risk. The results of logistic regression modeling determined that age was an independent predictor of in-hospital death (adjusted odds ratio [OR] 3.05, 95% confidence interval [CI] 2.76 to 3.37 for age >75), as were previous CABG (OR 2.84, 95% CI 2.55 to 3.16), heart failure on presentation (OR 1.73, 95% CI 1.57 to 1.91 for Killip class II), and female gender (OR 1.58, 95% CI 1.45 to 1.71). The mortality risk score showed that 55% of patients had risk scores of 2 to 5 and mortality rates of 4% to 13%. This moderate risk group experienced 76% of the total predicted mortality. Thus, in-hospital CABG mortality rates after AMI are high compared with elective surgery. Using the described risk score, clinicians can quantify the impact of patient risk factors in making decisions about referral for and timing of CABG.

Adult↗
PubMed2001

Risk models used to counsel women for breast and ovarian cancer: a guide for clinicians.

Advances in the identification and treatment of breast and ovarian cancer have lead to a need for reliable estimates of susceptibility risk associated with these two cancers. These estimates may be used in clinical settings to identify individuals at increased risk of developing disease or of being a carrier of a disease susceptibility allele. Accurate assessment of these probabilities is important given the potential implications for medical decision-making including the identification of patients who might benefit from preventive measures, genetic counseling or from entry into clinical trials. A wide range of empirical and statistical models has been proposed, particularly for breast cancer risk prediction, including those that utilize logistic regression or Bayesian modeling. The specific data used to create the various risk models also varies and may include molecular, epidemiologic, or clinical information. This overview presents definitions of risk used in clinical oncology as well as several of the more frequently used methods of risk estimation for breast and ovarian cancer. In addition, the means by which different methods are able to provide a measure of error or uncertainty associated with a given risk estimate will be discussed.

Breast Neoplasms↗
PubMedDate not supplied

Spatial competing risk models in disease mapping.

Often it is required that the 'health status' of an area must be assessed and this involves the analysis of a range of different diseases within one study window. This often arises, for example, when disease cluster alarms are sounded and there is a need to provide a general overview of health in the vicinity of he cluster area. Our approach leads to the consideration of the joint spatial distribution of a 'basket' of diseases. We examine the use of weighting schemes within our general formulation, and extensions to count data and spatio-temporal modelling.

Air Pollution↗
PubMedDate not supplied

Reducing infant mortality rates using the perinatal periods of risk model.

Despite decreases in the last 50 years, infant mortality rates in the United States remain higher than in other industrialized countries. Using overall infant mortality rates to determine the effectiveness of interventions does not help communities focus on particular underlying factors contributing to static, and sometimes increasing, community rates. This study was designed to determine and rank contributing factors to fetal-infant mortality in a specific community using the Perinatal Periods of Risk (PPOR) model. The PPOR model was used to map fetal-infant mortality for 1995 to 1998 in the Tulsa, Oklahoma, Healthy Start Program as compared to traditional calculation methods. The overall fetal-infant mortality rate using the PPOR model was 12.7 compared to 7.11 calculated using the traditional method. The maternal health cell rate was 5.4, maternal care cell rate was 2.9, newborn care cell was 1.9 compared to a 4.1 neonatal death rate calculated using the traditional method, and the infant health cell was 2.4 compared to a 2.9 postneonatal rate calculated using the traditional method. Because the highest infant mortality was in the maternal health cell, intervention strategies were designed to promote the health of women prior to and between pregnancies. The PPOR model was helpful in targeting interventions to reduce fetal-infant mortality based on the prioritization of contributing factors.

Female↗
PubMed2000

A predictive risk model for outcomes of ischemic stroke.

BACKGROUND AND PURPOSE: The great variability of outcome seen in stroke patients has led to an interest in identifying predictors of outcome. The combination of clinical and imaging variables as predictors of stroke outcome in a multivariable risk adjustment model may be more powerful than either alone. The purpose of this study was to determine the multivariable relationship between infarct volume, 6 clinical variables, and 3-month outcomes in ischemic stroke patients. METHODS: Included in the study were 256 eligible patients from the Randomized Trial of Tirilazad Mesylate in Acute Stroke (RANTTAS). Six clinical variables and 1-week infarct volume were the prespecified predictor variables. The National Institutes of Health Stroke Scale, Barthel Index, and Glasgow Outcome Scale were the outcomes. Multivariable logistic regression techniques were used to develop the model equations, and bootstrap techniques were used for internal validation. Predictive performance of the models was assessed for discrimination with receiver operator characteristic (ROC) curves and for calibration with calibration curves. RESULTS: The predictive models had areas under the ROC curve of 0.79 to 0.88 and demonstrated nearly ideal calibration curves. The areas under the ROC curves were statistically greater (P<0.001) with both clinical and imaging information combined than with either alone for predicting excellent recovery and death or severe disability. CONCLUSIONS: Combined clinical and imaging variables are predictive of 3-month outcome in ischemic stroke patients. Demonstration of this relationship with acute clinical variables and 1-week infarct information supports future attempts to predict 3-month outcome with all acute variables.

Aged↗
PubMed1997

Improved probabilistic decompression model risk predictions using linear-exponential kinetics.

Using a data base of 2,383 air and nitrogen-oxygen dives resulting in 131 cases of decompression sickness (DCS), risk functions were developed for a set of probabilistic decompression models according to survival analysis techniques. Parameters were optimized using the method of maximum likelihood Gas kinetics were either traditional exponential uptake and elimination, or an exponential uptake followed by linear elimination (LE kinetics) when calculated supersaturation was excessive. Risk functions either used the calculated relative gas supersaturation directly, or a delayed risk using a time integral of prior supersaturation. The most successful model (considering both incidence and time of onset of DCS) used supersaturation risk, and LE kinetics (in only 1 of 3 parallel compartments). Several methods of explicitly incorporating metabolic gases in physiologically plausible functions were usually found in lumped threshold terms and did not explicitly affect the overall data fit. The role of physiologic fidelity vs. empirical data fitting ability in accounting for model success is discussed.

Decompression Sickness↗
PubMed2002

A risk model for predicting the effects of lake-side development on wildfowl populations.

The lack of quantitative analysis and general scientific rigour in environmental impact assessment (EIA) is well documented. While reasons for this may include political and economic factors, the lack of high-level statistical knowledge and skills in environmental consultancies probably contributes to the problem, particularly with regard to ecological studies. This paper develops a simple model for wintering wildfowl populations that predicts different levels of risk from lake-side development. The aim was to create a model that could be used easily and quickly by consultants, is readily understandable for developers and various groups associated with the planning process, with explicit assumptions that can be criticised, and predictions that can be tested with post-development audits. The model is used in a case study. The basic parameters of the model are (i) average wildfowl abundance on lakes before development, (ii) maximum potential density of wildfowl across lakes (Kmax) before development, and (iii) reduction in lake area suitable for wildfowl after development. The model includes abundance-area relationships that are useful for highlighting the importance of particular lakes at a site. In the case study, abundance-area relationships focused attention on two lakes with thick charophyte beds which supported higher than expected numbers of pochard and coot given their size. As well as being robust (confidence limits are calculated), the model's predictions are quantitative and testable, making it possible to compare the predictions with on-going post-development monitoring of wildfowl population levels. The predictions rely on the effectiveness of path screening, and post-development monitoring can suggest where screening should be strengthened if the model's predictions are not met. Similarly, if other assumptions in the model are not met by the development, appropriate action can be implemented.

Animals↗
PubMed1999

Occupational tuberculosis among deputy sheriffs in Connecticut: a risk model of transmission.

OBJECTIVES: The purpose of this study was to identify risk factors for tuberculin skin test conversions among non-infected deputy sheriffs in Connecticut and during an out-break in an urban lock-up. METHODS: 377 tuberculin skin-test negative sheriffs statewide in 1994-1995 and 37 sheriffs in the lock-up in 1995 were screened and questionnaires administered. Cellblock volumetric air flow was measured. The Wells-Riley model of airborne transmission [C = S(1-e-Iqpt/Q)] was applied to the epidemiologic and ventilation data. RESULTS: Statewide, of 377 sheriffs screened, 22 (5.8%) had positive skin tests. Risk factors included older age (p < .0075) and longer tenure (p < .036). In the lock-up, of 37 sheriffs screened, 4 (10.8%) had positive skin tests. Measured mean outside air flow per occupant (8.0 cfm; S.D. = 2.2) was below design (15 cfm) and recommended (20 cfm) standards. Mathematical modeling estimated that TB transmission would be reduced by 62.5 percent with ventilation at design specifications. CONCLUSIONS: Inadequate ventilation may increase the risk for occupational transmission of tuberculosis. Since lock-ups do not have the time to conduct inmate TB screenings, supplemental engineering controls may be required.

Adult↗
PubMed1998

Modelling risk from a disease in time and space.

This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is implemented via Markov chain Monte Carlo methods. The methodology is illustrated by a tentative re-analysis of Ohio lung cancer data 1968-1988. Two approaches that adjust for unmeasured spatial covariates, particularly tobacco consumption, are described. The first includes random effects in the model to account for unobserved heterogeneity; the second adds a simple urbanization measure as a surrogate for smoking behaviour. The Ohio data set has been of particular interest because of the suggestion that a nuclear facility in the southwest of the state may have caused increased levels of lung cancer there. However, we contend here that the data are inadequate for a proper investigation of this issue.

Adolescent↗
PubMed2003

Ping-pong cross-validation in real space: a method for increasing the phasing power of a partial model without risk of model bias.

Experimental phases could only be obtained to 4.4 A resolution for crystals of the SecA translocation ATPase. Density modification of these phases exploiting the 65% solvent content of the crystal produced a map from which an approximate backbone model could be built for 80% of the structure. Combining the phases inferred from this partial model with the MIR phases and repeating the density modification produced an improved map from which a more complete backbone model could be built. However, this procedure converged before yielding a map, that allowed unambiguous sequence assignment for the majority of the protein molecule. In order to avoid the likely model bias associated with a speculative attempt at sequence assignment, a real-space cross-validation procedure was employed to facilitate completion of the crystal structure based on partial model phasing. The protein was partitioned into two disjoint sets of residues. Models in which the side chains were built for residues in one of the two sets were used for phase combination and density modification in order to produce improved electron density for interpretation of residues in the other set that had not been included in the model. Residues in the two sets were therefore omitted from the model in alternation except at sites where the side chain could be identified definitively based on phasing with the other set. This ping-pong cross-validation procedure allowed partial model phasing to be used to complete the crystal structure of SecA without being impeded by model bias. These results show that the structure of a large protein molecule can be solved with exclusively low-resolution experimental phase information based on intensive use of partial model phasing and density modification. Real-space cross-validation can be applied to reduce the risk of model bias associated with partial model phasing, streamlining this approach and expanding its range of applicability.

Adenosine Triphosphatases↗
PubMed1992

Accuracy of investment risk models varies.

Financial analysis of proposed capital investments must include an assessment of risk as well as return. Four assessment techniques can be used to provide insight into the risk inherent in any capital investment project: breakeven analysis, sensitivity analysis, scenario analysis, and Monte Carlo simulation. The most complete picture of a project's stand-alone risk is provided by a Monte Carlo simulation, which generates a profitability probability distribution. The primary concern of most hospital decision makers, however, is the amount of risk the capital investment adds to the riskiness of the hospital in the aggregate.

Capital Expenditures↗
PubMed2006

Mis-specified and non-robust mortality risk models for nasopharyngeal cancer in the National Cancer Institute formaldehyde worker cohort study.

An IARC (International Agency for Research on Cancer) working group categorized formaldehyde as a human carcinogen (Group 1) in 2004. A major component of the epidemiologic evidence evaluated by IARC to reach this decision was the analysis published by Hauptmann et al. [Hauptmann, M., Lubin, J. H., Stewart, P. A., Hayes, R. B., Blair, A. 2004. Mortality from solid cancers among workers in formaldehyde industries. Am. J. Epidemiol. 159, 1117-1130.] of the National Cancer Institute (NCI) historical cohort comprising industrial workers exposed to formaldehyde in 10 U.S. plants. The NCI authors emphasized the relationship found between highest formaldehyde peak exposure and death from nasopharyngeal cancer (NPC). We performed two additional types of re-analyses of the NCI cohort data with focus on peak exposure and NPC mortality. The analyses were aimed at (1) investigating whether the model specification chosen by Hauptmann et al. (2004) was appropriate (interaction assessment) and (2) exploring the degree of instability of the risk estimates for NPC in relation to highest peak exposure (sensitivity analysis). Hauptmann et al. (2004) failed to account for an important interaction structure between plant group and the exposure variable that prohibits a generalization of formaldehyde effects within the NCI cohort and, in particular, beyond the NCI cohort. In addition, our sensitivity analysis demonstrates considerable uncertainties in the risk estimates and points convincingly to instability problems particularly related to Plant 1. Even a simple sensitivity model taking only one additional death into account produced a variation of the risk estimates beyond the instability conveyed by standard confidence intervals. The results of our current reanalysis of the NCI study do not support NCI's suggestion of a causal association with formaldehyde exposure and nasopharyngeal cancer. The decision by the IARC working group to reclassify formaldehyde as a Group 1 substance was clearly premature considering: (1) the missing evidence of an NPC excess from the large British and NIOSH cohort studies; (2) the absence of an association with formaldehyde and NPC in the independent and expanded study of Plant 1; and (3) the mis-specified and non-robust internal analysis of the NCI cohort study brought to light in our current re-analysis. Thus, the 2004 IARC decision to reclassify formaldehyde as a Group 1 substance should be reconsidered.

Adenocarcinoma↗
PubMed2003

Risk assessment in oncology clinical practice. From risk factors to risk models.

Myelosuppression and neutropenia represent the major dose-limiting toxicity of cancer chemotherapy. Chemotherapy-induced neutropenia may be accompanied by fever, presumably due to life-threatening infection, which generally requires hospitalization for evaluation and treatment with empiric broad-spectrum antibiotics. The resulting febrile neutropenia is a major cause of the morbidity, mortality, and costs associated with the treatment of patients with cancer. Furthermore, the threat of febrile neutropenia often results in chemotherapy dose reductions and delays, which can compromise long-term clinical outcomes. Prophylactic colony-stimulating factor (CSF) has been shown to reduce the incidence, severity, and duration of neutropenia and its complications. Guidelines from the American Society of Clinical Oncology recommend the use of CSF on the basis of the myelosuppressive potential of the chemotherapy regimen. The challenge in ensuring the appropriate and cost-effective use of prophylactic CSF is to determine which patients would be most likely to benefit from it. A number of patient-, disease-, and treatment-related factors are associated with an increased risk of neutropenia and its complications. A number of clinical predictive models have been developed from retrospective datasets to identify patients at greater risk for neutropenia and its complications. Early studies have demonstrated the potential of such models to guide the targeted use of CSF to those patients who are most likely to benefit from the early use of these supportive agents. Additional prospective research is needed to develop more accurate and valid risk models and to evaluate the efficacy and cost-effectiveness of model-targeted use of CSF in high-risk patients.

Antineoplastic Agents↗
PubMedDate not supplied

Aspects of the use of relative risk models in the design and analysis of cohort studies and prevention trials.

Relative risk regression methods provide a unifying and powerful approach to a range of problems in the design and analysis of cohort studies and prevention trials. Standard partial likelihood-based estimation procedures do not, however, encompass several features that are important in such contexts. Specifically, one may wish to relate disease rates marginally to 'recent' risk factor measurements, whereas a partial likelihood approach requires one to condition on an accumulating risk factor history. Secondly, risk factor values may be ascertained with considerable measurement error, thereby requiring specialized procedures to estimate relative risk parameters. Thirdly, analysis of raw materials to obtain desired covariate (risk factor) histories may involve considerable expense if carried out for the entire cohort. Case-control and case-cohort sampling procedures can avoid much of this expense, but once again partial likelihood estimation procedures require generalization. Such generalizations are described herein.

Blood Pressure↗