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PubMed2003

New directions in the management of chemotherapy-induced neutropenia: Risk models, special populations, and quality of life.

New directions in managing chemotherapy-induced neutropenia include the development and validation of patient-based predictive risk models to guide the use of prophylactic colony-stimulating factors (CSFs) and an emerging recognition of the possible impact of chemotherapy-induced neutropenia on patient quality of life. Predictive risk models are being developed to identify patients who are at greater risk of neutropenic complications so that prophylactic CSF can be targeted to them in a timely and cost-effective manner. Current practice dictates the use of CSF prophylaxis primarily on the basis of the chemotherapy regimen; the risk-model approach is based on individual patient risk factors, which may be unconditional (before treatment) or conditional (after the first cycle). Within this new paradigm are patients with special circumstances, in whom prophylactic CSF treatment is currently recommended. Clinical evidence suggests that elderly patients are at particular risk for myelosuppression and should be considered for prophylactic CSF treatment starting with the first chemotherapy cycle. Analytical tools to measure the facets of quality of life related to neutropenia are being tested for validity and will be incorporated in future clinical trials. Thus, the future of neutropenia management promises to further refine the cost-effective use of CSF, while improving our understanding of the impact that chemotherapy-induced neutropenia has on quality of life.

Antineoplastic Agents↗
PubMed1998

Adjusting and comparing survival curves by means of an additive risk model.

Survival curves may be adjusted for covariates using Aalen's additive risk model. Survival curves may be compared by taking the ratio of two adjusted survival curves; the ratio is denoted the generalized relative survival rate. Adjusting both survival curves for all but one of a common set of covariates gives the partial relative survival rate, which measures the covariate-specific contribution to the generalized relative survival rate. The generalized and partial relative survival rates have interpretations similar to the traditional relative survival rates frequently used in cancer epidemiology. In fact, the traditional relative survival rate can be generalized to a regression context using the additive risk model. This population-adjusted relative survival rate is an alternative and useful method for removing confounding effects of age, cohorts, and sex. The authors use a data set of malignant melanoma patients diagnosed from 1965 to 1974 in Norway. The 25-year survival of 1967 individuals is studied.

Adolescent↗
PubMed2005

Comparison between two partial likelihood approaches for the competing risks model with missing cause of failure.

In many clinical studies where time to failure is of primary interest, patients may fail or die from one of many causes where failure time can be right censored. In some circumstances, it might also be the case that patients are known to die but the cause of death information is not available for some patients. Under the assumption that cause of death is missing at random, we compare the Goetgbebeur and Ryan (1995, Biometrika, 82, 821-833) partial likelihood approach with the Dewanji (1992, Biometrika, 79, 855-857) partial likelihood approach. We show that the estimator for the regression coefficients based on the Dewanji partial likelihood is not only consistent and asymptotically normal, but also semiparametric efficient. While the Goetghebeur and Ryan estimator is more robust than the Dewanji partial likelihood estimator against misspecification of proportional baseline hazards, the Dewanji partial likelihood estimator allows the probability of missing cause of failure to depend on covariate information without the need to model the missingness mechanism. Tests for proportional baseline hazards are also suggested and a robust variance estimator is derived.

Cause of Death↗
PubMed1998

CABG risk model.

Explore the source record for details and available documents.

Chi-Square Distribution↗
PubMed1987

Alternatives to the BEIR relative risk model for explaining atomic-bomb survivor cancer mortality.

The apparent failure of the BEIR absolute risk model to explain the data on the Japanese atomic-bomb survivors does not imply that the BEIR relative risk model (RRM) is correct. RRM is objectionable in that it fits the data only in conjunction with an assumption not in accord with current knowledge and thinking. Contrary to what is widely believed, RRM is not a consequence of, or consistent with, initiator-promoter theories; models derived from initiator-promoter theories fit the data with fewer adjustable parameters and without requiring unpalatable assumptions. The preferable models give substantially lower radiation risks.

Age Factors↗
PubMed1999

A biologic risk model for stage I lung cancer: immunohistochemical analysis of 408 patients with the use of ten molecular markers.

OBJECTIVE: The standard treatment of patients with stage I non-small cell lung cancer is resection of the primary tumor; however, the recurrence rate is 28% to 45%. This study evaluates a panel of molecular markers in a large population of patients with stage I non-small cell lung cancer to determine the prognostic value of each marker and to create a biologic risk model. METHODS: Pathologic specimens were collected from 408 consecutive patients after complete resection for stage I non-small cell lung cancer at a single institution, with follow-up of at least 5 years. A panel of 10 molecular markers was chosen for immunohistochemical analysis of the primary tumor on the basis of differing oncogenic mechanisms. Local tumor expansion requires growth regulating proteins (epidermal growth factor receptor, the protooncogene erb-b2); apoptosis proteins (p53, bcl-2); and cell cycle regulating proteins (retinoblastoma recessive oncogene, KI-67). Local tumor invasion requires angiogenesis (factor viii). The development of distant metastases involves the expression of adhesion proteins (CD-44, sialyl-Tn, blood group A). Cox proportional hazards regression analysis was used to construct an independent risk model for cancer recurrence and death. RESULTS: Multivariable analysis demonstrated significantly elevated risk for the following molecular markers: p53 (hazard ratio, 1.68; P =.004); factor viii (hazard ratio, 1.47 P =. 033); erb-b2 (hazard ratio, 1.43; P =.044); CD-44 (hazard ratio, 1. 40; P =.050); and retinoblastoma recessive oncogene (hazard ratio, 0. 747; P =.084). CONCLUSIONS: Five molecular markers were associated with the risk of recurrence and death, representing independent metastatic pathways: apoptosis (p53), angiogenesis (factor viii), growth regulation (erb-b2), adhesion (CD-44), and cell cycle regulation (retinoblastoma recessive oncogene). This study demonstrates the validity of this molecular biologic risk model in patients with stage I non- small cell lung cancer.

Biomarkers, Tumor↗
PubMed2005

Validation and refinement of mortality risk models for heart valve surgery.

BACKGROUND: The Northern New England Cardiovascular Disease Study Group (NNE) recently published risk models for hospital mortality after heart valve surgery. The Providence Health System Cardiovascular Study Group (PHS) has been collecting similar heart valve data for 8 years, providing an ideal opportunity to both validate the NNE risk models and attempt to produce an improved model, by using some different modeling techniques. METHODS: From 1997 to 2004, 3,324 patients aged 30 to 95 years underwent aortic valve replacement (AVR), and 1,596 underwent mitral valve replacement or repair (MVRR) at one of nine PHS medical centers. We used area under the receiver operating characteristic curve (c-index) to measure model discrimination, and Hosmer-Lemeshow statistic (H-L) to measure calibration. We modified the NNE models by ungrouping continuous variables, seeking optimal transformations of continuous variables, and imputing missing values by multiple regression. RESULTS: The prevalence and the lethality of risk factors were similar in PHS and NNE patients. The NNE models fit PHS patients well: c-index (95% confidence interval) = 0.75 (0.70 to 0.80) for AVR and 0.81 (0.76 to 0.86) for MVRR; H-L = 3.95 (p = 0.861) for AVR and 7.10 (p = 0.526) for MVRR. A single PHS model performed slightly better for both positions: c-index = 0.79 (0.75 to 0.83) for AVR and 0.84 (0.80 to 0.88) for MVRR; H-L = 2.75 (p = 0.949) for AVR and 12.21 (p = 0.142) for MVRR. CONCLUSIONS: The NNE models for aortic and mitral valve surgery were successfully validated using PHS patients. Using some different statistical approaches to modeling, we produced a new, unified model for both positions.

Adult↗
PubMed1999

Confidence bands for cumulative incidence curves under the additive risk model.

In the context of competing risks, the cumulative incidence function is often used to summarize the cause-specific failure-time data. As an alternative to the proportional hazards model, the additive risk model is used to investigate covariate effects by specifying that the subject-specific hazard function is the sum of a baseline hazard function and a regression function of covariates. Based on such a formulation, we present an approach to constructing simultaneous confidence intervals for the cause-specific cumulative incidence function of patients with given risk factors. A melanoma data set is used for the purpose of illustration.

Adult↗
PubMed2006

Validating the Clinical Outcomes Assessment Program risk model for percutaneous coronary intervention.

BACKGROUND: The Clinical Outcomes Assessment Program (COAP) from the state of Washington recently published a risk model for hospital mortality after percutaneous coronary intervention (PCI), which was validated by a consortium of hospitals in 4 northeastern states. The Providence Health System (PHS) Cardiovascular Study Group data was used to further validate this COAP model using data from PHS hospitals in 3 western states. METHODS: All 13124 consecutive PCI procedures performed in 6 PHS hospitals from July 2001 through June 2004 were included. The c index was used to test model discrimination. The Hosmer-Lemeshow test, the le Cessie-van Houwelingen-Copas test, and the cumulative sum method were used to test model calibration. RESULTS: The patient profiles of the COAP data and the PHS data were similar. The overall mortality was 1.6% for COAP and 1.4% for PHS. The subgroup mortalities were also similar. When applying the COAP model to the PHS data, the c index (95% CI) was 0.893 (0.859-0.928), indicating excellent discrimination, and the le Cessie-van Houwelingen-Copas test and the cumulative sum method showed good global goodness of fit. CONCLUSION: The COAP model for hospital mortality was successfully validated using PHS data. With the advance of technology and changing patient profile, PCI models must be periodically checked for possible updating to reflect contemporary practice. Predictors in a PCI risk model should be objective, have standard definitions, and be easy to obtain to facilitate the transportability of the model.

Adult↗
PubMed1989

Heterogeneous risk models.

"This paper briefly reviews the heterogeneous risk models in demography. With a specific model, it is shown that in a heterogeneous population, the marginal hazard need not be monotonic. Procedures described in the literature to recover individual hazards from marginal hazards without precise knowledge of structural parameters can lead to biased results. Strategies of parameter estimation and testing are discussed. The results are extended to [the] multistate life table."

Biology↗
PubMed1997

Assessing open heart surgery mortality in Catalonia (Spain) through a predictive risk model.

OBJECTIVE: To develop a risk stratification model to assess open heart surgery mortality in Catalonia (Spain) in order to use risk-adjusted hospital mortality rates as an approach to analyze quality of care. METHODS: Data were prospectively collected through a specific data-sheet during 6 1/2 months in consecutive adult patients subjected to open heart surgery. The dependent variable was surgical mortality, and independent variables included were presurgical (sociodemographic data, clinical antecedents, morphological and functional studies) and surgical. The model was built on a subsample (70% of study population) through univariate and logistic regression analysis and validated in the rest of the sample. RESULTS: The total sample was of 1309 procedures in seven hospitals; 47% of them were valve procedures. The overall crude mortality rate was 10.9% and varied among centers (range, 2.8-14.8%). Risk factors included in the model received a weight based on the logistic regression coefficient and a score was generated for each patient. The factors with the highest weight were patient older than 80 and second reoperation. Score was stratified in five categories of increasing risk. There was a good agreement between observed and predicted mortality rates in the validation group. Overall patient distribution was as follows: 52% low risk level, 16% fair, 13% high, 12% very high, and 6% extremely high risk level. Mortality rate increased from 4.2% in the low risk to 54.4% in the highest risk group. Case mix adjustment was performed through the risk score level. There were statistically significant differences in the risk profiles of patients admitted among centers. After adjustment by risk profiles, there were no differences in mortality by hospital. CONCLUSION: A risk stratification model through a multicentric, prospective and exhaustive collection of data in all types of open heart procedures was developed. In spite of wide differences on crude rates and in the risk profiles of patients admitted, we did not find statistically significant differences in adjusted mortality rates among centers. Timely and accurate information about surgical outcomes can lead to improvements in clinical practice and quality of care.

Adolescent↗
PubMed1989

Cell proliferation kinetics and multistage cancer risk models.

Cell-kinetic multistage (CKM) cancer-risk models account for clonal proliferation of postulated intermediate (initiated, premalignant) cell populations during tumorigenesis. To date, almost all CKM models considered have assumed that intermediate, premalignant cells may proliferate exponentially over time in vivo. This "exponential growth" assumption, however, may not always be as biologically plausible as the alternative assumption that cells tend to grow geometrically in time. The general CKM model and applications of it that presume exponential cell growth are reviewed here. Geometric CKM models are then considered, previous erroneous analyses of these models are reviewed, and a corrected mathematical treatment is provided. It is pointed out that the presumption of exponential instead of geometric proliferation kinetics may lead to underestimates of small increments in CKM-predicted cancer risk above background if the geometric assumption is true. An evaluation of pertinent biological evidence is provided, which indicates that precancerous cells may typically proliferate geometrically. Consequently, if CKM models are used for environmental risk assessment, it may be prudent for one to presume geometric cell growth unless specific data support an alternative assumption.

Animals↗
PubMed2004

Gail model risk assessment and risk perceptions.

Patients can benefit from accessible breast cancer risk information. The Gail model is a well-known means of providing risk information to patients and for guiding clinical decisions. Risk presentation often includes 5-year and life-time percent chances for a woman to develop breast cancer. How do women perceive their risks after Gail model risk assessment? This exploratory study used a randomized clinical trial design to address this question among women not previously selected for breast cancer risk. Results suggest a brief risk assessment intervention changes quantitative and comparative risk perceptions and improves accuracy. This study improves our understanding of risk perceptions by evaluating an intervention in a population not previously selected for high-risk status and measuring perceptions in a variety of formats.

Adult↗
PubMed1998

Age-dependent U-shaped risk functions and Aalen's additive risk model.

Epidemiologic studies of time to some failure often show a quadratic relation between the risk of failure and covariate(s). We study the nadir for a given covariate, i.e., the value of the covariate associated with the lowest risk (supposing a U-shape), within Aalen's additive risk model. This model was applied since the effect of the covariate(s) is allowed to vary over time and, as a consequence, a given nadir can vary over time. We propose a test for the null hypothesis that the nadir is time independent and, if this is the case, an estimate of the nadir. Large sample properties of the test statistic and estimator are derived. The methods are illustrated with data where time to death is related to body mass index.

Adult↗
PubMed2003

Patient selection for carotid endarterectomy: how far is risk modeling applicable to the individual?

BACKGROUND AND PURPOSE: Risk-factor modeling has been proposed to identify patients with carotid stenosis who will most benefit from surgery. Validation by independent institutions performing carotid endarterectomy is necessary to determine the applicability of such models to the individual patient. METHODS: A series of patients with a recently symptomatic high-grade carotid stenosis were selected for surgery according to current guidelines and were consecutively operated on in a single institution. In addition, a prognostic model was applied to the patients to analyze the concordance of both selection methods. RESULTS: The study included 134 patients operated on between 1999 and 2001. The risk model predicted that 49% of the patients should have been excluded from surgery because the operation was found to be possibly harmful in 1 patient (1%) and not significantly beneficial in 65 patients (48%). This resulted from the predominant negative weight of the surgical risk factors in the model. However, this predominance was negated in our series by the fact that only 1 major complication (0.75%) occurred during follow-up. CONCLUSIONS: Exclusion of single patients on the basis of risk modeling may be problematic when the rate of perioperative complications is very low.

Adult↗
PubMed2000

An integrated risk model of a drinking-water-borne cryptosporidiosis outbreak.

A dynamic risk model is developed to track the occurrence and evolution of a drinking-water-borne cryptosporidiosis outbreak. The model characterizes and integrates the various environmental, medical, institutional, and behavioral factors that determine outbreak development and outcome. These include contaminant delivery and detection, water treatment efficiency, the timing of interventions, and the choices that people make when confronted with a known or suspected risk. The model is used to evaluate the efficacy of alternative strategies for improving risk management during an outbreak, and to identify priorities for improvements in the public health system. Modeling results indicate that the greatest opportunity for curtailing a large outbreak is realized by minimizing delays in identifying and correcting a drinking-water problem. If these delays cannot be reduced, then the effectiveness of risk communication in preemptively reaching and persuading target populations to avoid exposure becomes important.

Choice Behavior↗
PubMed1984

Multistage risk models and the age pattern in familial polyposis coli.

Multistage risk models provide a close fit to most age patterns of adult-onset cancers. Such models posit that a number of events must occur before some cell in a tissue is transformed from normal to neoplastic. When an approximate version of the models has been fitted to data, this number has been estimated to be about 4-6. In fitting the same approximate model to the age pattern of onset of colon cancer in bearers of the Familial Polyposis coli (FPC) gene, several authors have found that the number of stages estimated was about two to three fewer than those for colon cancer in the general population. However, when an exact multistage model is used rather than an approximation to it, this is no longer the case: the number of stages estimated from the general population becomes too large to be compatible with what is known about carcinogenesis from laboratory experiments, and the number estimated from FPC victims is larger than that for the general population. "Inherited-hit" models of the nature of the FPC gene may be correct at the cellular level, but multistage models as commonly formulated cannot be used on data on the age-onset patterns in populations of individuals to infer such mechanisms or estimate their parameters.

Age Factors↗
PubMed2006

Inference for the dependent competing risks model with masked causes of failure.

The competing risks model is useful in settings in which individuals/units may die/fail for different reasons. The cause specific hazard rates are taken to be piecewise constant functions. A complication arises when some of the failures are masked within a group of possible causes. Traditionally, statistical inference is performed under the assumption that the failure causes act independently on each item. In this paper we propose an EM-based approach which allows for dependent competing risks and produces estimators for the sub-distribution functions. We also discuss identifiability of parameters if none of the masked items have their cause of failure clarified in a second stage analysis (e.g. autopsy). The procedures proposed are illustrated with two datasets.

Algorithms↗