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PubMed1975

A review and critique of some models used in competing risk analysis.

We have introduced a notation which allows one to define competing risk models easily and to examine underlying assumptions. We have treated the actuarial model for competing risk in detail, comparing it with other models and giving useful variance formulae both for the case when times of death are available and for the case when they are not. The generality of these methods is illustrated by an example treating two dependent competing risks.

Analysis of Variance↗
PubMed1986

On the application of linear relative risk regression models.

Motivations for considering linear relative risk models are described. These include covariate data reduction and the testing of additivity of covariate effects on the relative risk. A simulation study was conducted in order to study properties of asymptotic distributional approximations and iterative convergence properties. Parameter transformations and likelihood ratio approximations are considered for confidence interval calculation.

Analysis of Variance↗
PubMed1999

Applying risk assessment models in orthopaedic surgery: effective risk stratification.

Deep vein thrombosis (DVT) and pulmonary embolism remain important causes of morbidity and mortality. Without prophylaxis, at least 60% of patients undergoing orthopaedic or trauma surgery develop DVT, and the rate may still be as high as 20-45% even with the best prophylaxis available. The rate of thrombosis may be reduced by wider use of established prophylactic measures and targeting more intense prophylaxis to very-high-risk patients. Novel agents such as pentasaccharides and recombinant hirudins may provide more effective prophylaxis in very-high-risk settings, but their optimal use requires accurate assessment of thromboembolic risk. Risk levels are influenced both by the clinical setting and patient factors, such as obesity and malignancy. There is now growing interest in the influence of molecular risk factors, including acquired thrombophilias and congenital coagulation disorders. Activated protein C resistance and hyperhomocysteinaemia have been recently identified as potential risk factors. Further investigations are needed to clarify the individual contribution of different clinical and molecular factors to overall thromboembolic risk, and the effects of interactions between them. Screening for clotting disorders and other additional risk factors may assist identification of very-high-risk patients and allow appropriate targeting of intensive prophylactic therapy.

Humans↗
PubMed2002

Evaluating breast cancer risk: available models to assess individual breast cancer risk and probability to be a BRCA mutation carrier.

Several models have been generated to estimate breast cancer risk and to predict the probability to identify a BRCA1 or BRCA2 germ line mutation. Each model, based on a slightly different study design and purpose, takes into account a definite number of relevant parameters and ultimately pertains to a specific cohort of women or population. Aim of this work is to underline validity and limits of available tools and their appropriate use in the daily practice.

Adult↗
PubMed1999

Applying risk assessment models in non-surgical patients: effective risk stratification.

Pulmonary embolism and deep vein thrombosis are serious complications of non-surgical patients, but scarcity of data documenting prophylaxis means antithrombotic therapy is rarely used. Prediction of risk is complicated by the variation in the medical conditions associated with venous thromboembolism (VTE), and lack of data defining risk in different groups. Accurate risk assessment is further confounded by inherited or acquired factors for VTE, additional risk due to medical interventions, and interactions between risk factors. Acquired and inherited risk factors may underlie thromboembolic complications in a range of conditions, including pregnancy, ischaemic stroke, myocardial infarction and cancer. Risk stratification may be feasible in non-surgical patients by considering individual risk factors and their cumulative effects. Current risk assessment models require expansion and modification to reflect emerging evidence in the non-surgical field. A large on-going study of prophylaxis with low-molecular-weight heparin in non-surgical patients will clarify our understanding of the components of risk, and assist in developing therapy recommendations.

Drug-Related Side Effects and Adverse Reactions↗
PubMed2000

Models of risk and choice: challenge or danger.

A dimensional model of perceived risk and a model of risk acceptance based on risk rates are proposed in this paper. In line with the proposed model of perceived risk, risk is a linear combination of the three basic dimensions of a risky situation: the amount and probability of loss and the amount of gain. It is assumed that psychological transformations are made on these dimensions. According to the model of acceptance, acceptance is judged by making a trade-off between perceived risk and the amount of gain. These models have been investigated in two experiments, in which risk judgments and acceptance rates for a set of descriptions of risky investments were collected from managers in Poland. The proposed dimensional model of perceived risk was compared to the distributional models of risk and to the risk models based on the expectation principle. The best fit was obtained for the proposed model. It was also found that perceived risk was useful in predicting acceptance rates. Better fits were obtained for the models of acceptance, based on perceived risk, than for the expected/weighted utility models, including bilinear models.

Administrative Personnel↗
PubMed2006

MADCAP: a graphical method for assessing risk scoring systems.

OBJECTIVE: We set out to develop a method for assessing the performance of clinical risk models over the spectrum of risks and to assess the performance of the EuroSCORE risk model used in cardiac surgery. METHODS: We developed a graphical method for assessing the performance of clinical risk models over the spectrum of risks. To illustrate the technique, we analysed retrospective data concerning 9268 patients that underwent cardiac surgery and for whom both the additive EuroSCORE prediction of risk of morality and vital status at 30 days were available. RESULTS: The graphical tool developed, called MADCAP (Mean Adjusted Deaths Compared Against Predictions), can be used to highlight systematic features of the performance of a clinical risk model. Its use in the current study indicates that the additive version of the EuroSCORE model seems to underestimate risk amongst low-risk cases (0% and 1%). Otherwise the score systematically favours risk avoiding behaviour as the risk model underestimates mortality for 2--6% prediction but not at 7% and above. CONCLUSION: The robustness of case-mix adjusted audit is dependent on the performance of the risk scoring system over the entire spectrum of risk. If we are to use risk adjustment of mortality rates when comparing outcomes obtained by different units or individual surgeons, it is essential that we continually review the performance of the risk adjustment method. The MADCAP method presented here provides a useful tool to this end.

Cardiac Surgical Procedures↗
PubMed1999

How generalizable are coronary risk prediction models? Comparison of Framingham and two national cohorts.

BACKGROUND: Previous models used to predict individual risk of death from coronary heart disease (CHD) were developed from data of 3 decades ago from the Framingham Heart Study. CHD mortality rates have declined markedly since that period as a result of improvement in both risk factor status and medical interventions. Generalization of the results from this one study to the population at large remains a matter of concern. We compared predictive functions derived from the major risk factors for CHD from Framingham and 2 more recent national cohorts, the First and Second National Health and Nutrition Examination Survey (NHANES I and NHANES II). METHODS AND RESULTS: The participants included 1846 men and 2323 women 35 to 69 years of age and free of CHD at the fourth examination (1954 to 1958) from the Framingham Study; 2753 men and 3858 women from the NHANES I (1971 to 1975); and 2655 men and 3050 women from NHANES II (1976 to 1980). The 3 cohorts were monitored for 24, 20, and 15 years, respectively. Significant heterogeneity existed among studies in the magnitude of the Cox coefficients for the individual factors (ie, age, systolic blood pressure, serum total cholesterol, and smoking status), especially among men. When risk factors were considered collectively, however, functions derived from and applied to different cohorts had a similar ability to rank individual risk. The areas under the receiver operating characteristic curves were 0. 71 to 0.76 in men and 0.76 to 0.81 in women when different risk functions were applied to their own population or to a second population. The cumulative CHD survival observed in women in the 2 national cohorts was close to what was predicted from the Framingham equation. However, Framingham overestimated the cumulative CHD mortality rates in men in NHANES I and NHANES II. CONCLUSIONS: The Framingham risk model for the prediction of CHD mortality rates provides a reasonable rank ordering of risk for individuals in the US white population for the period 1975 to 1990. However, prediction of absolute risk is less accurate.

Adolescent↗
PubMed2006

Brownfields and health risks--air dispersion modeling and health risk assessment at landfill redevelopment sites.

Redevelopment of landfill sites in the New Jersey-New York metropolitan area for recreational (golf courses), commercial, and even residential purposes seems to be gaining acceptance among municipal planners and developers. Landfill gas generation, which includes methane and potentially toxic nonmethane compounds usually continues long after closure of the landfill exercise phase. It is therefore prudent to evaluate potential health risks associated with exposure to gas emissions before redevelopment of the landfill sites as recreational, commercial, and, especially, residential properties. Unacceptably high health risks would call for risk management measures such as limiting the development to commercial/recreational rather than residential uses, stringent gas control mechanisms, interior air filtration, etc. A methodology is presented for applying existing models to estimate residual landfill hazardous compounds emissions and to quantify associated health risks. Besides the toxic gas constituents of landfill emissions, other risk-related issues concerning buried waste, landfill leachate, and explosive gases were qualitatively evaluated. Five contiguously located landfill sites in New Jersey intended for residential and recreational redevelopment were used to exemplify the approach.

Adult↗
PubMed2004

Comparison of sensitivity analysis methods based on applications to a food safety risk assessment model.

Sensitivity analysis (SA) methods are a valuable tool for identifying critical control points (CCPs), which is one of the important steps in the hazard analysis and CCP approach that is used to ensure safe food. There are many SA methods used across various disciplines. Furthermore, food safety process risk models pose challenges because they often are highly nonlinear, contain thresholds, and have discrete inputs. Therefore, it is useful to compare and evaluate SA methods based upon applications to an example food safety risk model. Ten SA methods were applied to a draft Vibrio parahaemolyticus (Vp) risk assessment model developed by the Food and Drug Administration. The model was modified so that all inputs were independent. Rankings of key inputs from different methods were compared. Inputs such as water temperature, number of oysters per meal, and the distributional assumption for the unrefrigerated time were the most important inputs, whereas time on water, fraction of pathogenic Vp, and the distributional assumption for the weight of oysters were the least important inputs. Most of the methods gave a similar ranking of key inputs even though the methods differed in terms of being graphical, mathematical, or statistical, accounting for individual effects or joint effect of inputs, and being model dependent or model independent. A key recommendation is that methods be further compared by application on different and more complex food safety models. Model independent methods, such as ANOVA, mutual information index, and scatter plots, are expected to be more robust than others evaluated.

Analysis of Variance↗
PubMed1994

Validation of a breast cancer risk assessment model in women with a positive family history.

BACKGROUND: Gail et al. developed a statistical model for estimating the risk of developing breast cancer in white women screened annually with mammography. This model is used for counseling and for admission to clinical trials. PURPOSE: We evaluated the model prospectively in a cohort of women with a family history of breast cancer. METHODS: We followed women who participated in the American Cancer Society 1987 Texas Breast Screening Project. The model was evaluated by comparing the observed (O) and expected (E) numbers of breast cancers using composite background rates from both the Breast Cancer Detection and Demonstration Project and the Surveillance, Epidemiology, and End Results program of the National Cancer Institute. Data were partitioned by adherence to American Cancer Society screening guidelines. RESULTS: The Gail et al. model predicted the risk well among women who adhered to the American Cancer Society guidelines (O/E = 1.12; 95% confidence interval = 0.75-1.61) but overpredicted risk for women who did not adhere to the guidelines. There was an indication that the model overpredicted risk for women younger than 60 years old and underpredicted risk in women aged 60 years and older. CONCLUSIONS: Overall, the Gail et al. model accurately predicts risk in women with a family history of breast cancer and who adhere to American Cancer Society screening guidelines. Thus, the model should be used as it was intended, for women who receive annual mammograms.

Adult↗
PubMed2000

Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression.

The estimate of a multivariate risk is now required in guidelines for cardiovascular prevention. Limitations of existing statistical risk models lead to explore machine-learning methods. This study evaluates the implementation and performance of a decision tree (CART) and a multilayer perceptron (MLP) to predict cardiovascular risk from real data. The study population was randomly splitted in a learning set (n = 10,296) and a test set (n = 5,148). CART and the MLP were implemented at their best performance on the learning set and applied on the test set and compared to a logistic model. Implementation, explicative and discriminative performance criteria are considered, based on ROC analysis. Areas under ROC curves and their 95% confidence interval are 0.78 (0.75-0.81), 0.78 (0.75-0.80) and 0.76 (0.73-0.79) respectively for logistic regression, MLP and CART. Given their implementation and explicative characteristics, these methods can complement existing statistical models and contribute to the interpretation of risk.

Artificial Intelligence↗
PubMed2000

The cardiovascular event reduction tool (CERT)--a simplified cardiac risk prediction model developed from the West of Scotland Coronary Prevention Study (WOSCOPS).

The clinical decision to treat hypercholesterolemia is premised on an awareness of patient risk, and cardiac risk prediction models offer a practical means of determining such risk. However, these models are based on observational cohorts where estimates of the treatment benefit are largely inferred. The West of Scotland Coronary Prevention Study (WOSCOPS) provides an opportunity to develop a risk-benefit prediction model from the actual observed primary event reduction seen in the trial. Five-year Cox model risk estimates were derived from all WOSCOPS subjects (n = 6,595 men, aged 45 to 64 years old at baseline) using factors previously shown to be predictive of definite fatal coronary heart disease or nonfatal myocardial infarction. Model risk factors included age, diastolic blood pressure, total cholesterol/ high-density lipoprotein ratio (TC/HDL), current smoking, diabetes, family history of fatal coronary heart disease, nitrate use or angina, and treatment (placebo/ 40-mg pravastatin). All risk factors were expressed as categorical variables to facilitate risk assessment. Risk estimates were incorporated into a simple, hand-held slide rule or risk tool. Risk estimates were identified for 5-year age bands (45 to 65 years), 4 categories of TC/HDL ratio (<5.5, 5.5 to <6.5, 6.5 to <7.5, > or = 7.5), 2 levels of diastolic blood pressure (<90, > or = 90 mm Hg), from 0 to 3 additional risk factors (current smoking, diabetes, family history of premature fatal coronary heart disease, nitrate use or angina), and pravastatin treatment. Five-year risk estimates ranged from 2% in very low-risk subjects to 61% in the very high-risk subjects. Risk reduction due to pravastatin treatment averaged 31%. Thus, the Cardiovascular Event Reduction Tool (CERT) is a risk prediction model derived from the WOSCOPS trial. Its use will help physicians identify patients who will benefit from cholesterol reduction.

Age Factors↗
PubMed2002

How profitable is risk selection? A comparison of four risk adjustment models.

To mitigate selection triggered by capitation payments, risk-adjustment models bring capitation payments closer on average to individuals' expected expenditure. We examine the maximum potential profit that plans could hypothetically gain by using their own private information to select low-cost enrollees when payments are made using four commonly used risk adjustment models. Simulations using a privately insured sample suggest that risk selection profits remain substantial. The magnitude of potential profit varies according to the risk adjustment model and the private information plans can employ to identify profitable enrollees.

Actuarial Analysis↗
PubMed2000

Biologically based risk assessment models for developmental toxicity.

It is obvious that the task of incorporating mechanistic information into dose-response assessment for developmental toxicity is, by and large, still at its conceptual stage. Our immature understanding of embryogenesis and teratogenesis forces us to make simplifying biological assumptions that may turn out to be erroneous; therefore, these mechanistically based models should be constructed so as to be easily modified as new information becomes available. The data-intensive (and costly) nature of these modeling efforts may also limit their practice to extraordinary situations where, for instance, large segments of the human population are exposed to low levels of a compound and the determination of a safe level of exposure is of utmost importance, or where compounds are of such immense economic or therapeutic value that their use would warrant a concerted effort to minimize the uncertainties inherent in the current methods of extrapolation. Nevertheless, this chapter has presented several attempts to translate the concept of BBDR into practice. When these applications become successful, these models should provide risk assessors with more reliable response indicators at low doses (where human exposures are realistic) and more accurate cross-strain/cross-species comparisons as well as extrapolations across exposure conditions. Although the BBDR models do not necessarily redefine the current practice of risk assessment using either the NOAEL or the benchmark approach, information derived from these efforts should provide a credible scientific basis for the estimates of RfD.

Animals↗
PubMed2002

The proportion of thyroid cancers in the Japanese atomic bomb survivors associated with natural background radiation.

Generalised absolute and relative risk models (with adjustment to the excess absolute risk for time since exposure and age at exposure, and with adjustment to the excess relative risk for age at exposure) are fitted to the Japanese atomic bomb survivor thyroid cancer incidence data followed up over the period 1958-87, taking account of natural background radiation. Thyroid cancers associated with natural background radiation and atomic bomb radiation are overwhelmingly accounted for by exposure at young ages. Over 50% of the excess cases associated with either the atomic bomb radiation or natural background radiation are linked to exposures under the age of 20, irrespective of the assumed risk model or natural background dose rate. The excess risk is overwhelmingly concentrated among females, again irrespective of the assumed model or natural background dose rate. Depending on the assumed natural background dose rate (in the range 0.5-2.0 mSv/year) between 17.3 and 32.0% of the thyroid cancer in this cohort may be associated with natural background radiation if an absolute risk model applies; between 4.2 and 17.1% of the thyroid cancers may be associated with natural background radiation if the relative risk model applies. The proportion of the thyroid tumours attributed to the atomic bomb radiation is between 21.1 and 22.0% for the absolute risk model, and is between 18.7 and 19.1% for the relative risk model, in both cases irrespective of the assumed background radiation dose. In particular, these proportions are not very different from the proportions calculated when fitting models that do not take account of natural background radiation, namely 22.0% for the absolute risk model and 18.6% for the relative risk model. The proportion of thyroid cancers accounted for by natural background radiation progressively increases with attained age, from 0.3% of cancers among those under the age of 15 to 30.5% for those over the age of 60, assuming that the absolute risk model applies. There is a similar increase in this percentage, if to a rather lower level (from 0.2 to 10.2%), assuming that the relative risk model applies.

Adolescent↗
PubMed2006

Deficiencies of cardiovascular risk prediction models for type 1 diabetes.

OBJECTIVE: Cardiovascular risk prediction models are available for the general population (Framingham) and for type 2 diabetes (U.K. Prospective Diabetes Study [UKPDS] Risk Engine) but may not be appropriate in type 1 diabetes, as risk factors including younger age at diabetes onset and presence of diabetes complications are not considered. Therefore, our objective was to examine the accuracy of Framingham and UKPDS models for predicting coronary heart disease (CHD) in a type 1 diabetic cohort. RESEARCH DESIGN AND METHODS: Ten-year follow-up data from the Pittsburgh Epidemiology of Diabetes Complications (EDC) study, a prospective cohort study of 658 subjects with childhood-onset type 1 diabetes diagnosed between 1950 and 1980 first seen in 1986-1988, were analyzed. EDC study data were used to calculate the 10-year probability of CHD (fatal CHD, nonfatal myocardial infarction, or Q-waves) applying to the Framingham and UKPDS equations. RESULTS: Mean age at CHD onset was 39 years. When fatal/nonfatal myocardial infarction and CHD death were modeled, both the UKPDS and Framingham models showed significant lack of calibration (P < 0.0001) but moderate discrimination (0.76 UKPDS, 0.77 Framingham men, and 0.88 Framingham women). Both the UKPDS and Framingham models underestimated probability of events in highest risk deciles. CONCLUSIONS: Currently available CHD models poorly predict events in type 1 diabetes. Future research should focus on determining the risk factors accounting for the lack of fit and developing prediction models specific to this high-risk group.

Adult↗