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PubMedDate not supplied

The effect of disease-prevalence adjustments on the accuracy of a logistic prediction model.

The accuracy of a logistic prediction model is degraded when it is transported to populations with outcome prevalences different from that of the population used to derive the model. The resultant errors can have major clinical implications. Accordingly, the authors developed a logistic prediction model with respect to the noninvasive diagnosis of coronary disease based on 1,824 patients who underwent exercise testing and coronary angiography, varied the prevalence of disease in various "test" populations by random sampling of the original "derivation" population, and determined the accuracy of the logistic prediction model before and after the application of a mathematical algorithm designed to adjust only for these differences in prevalence. The accuracy of each prediction model was quantified in terms of receiver operating characteristic (ROC) curve area (discrimination) and chi-square goodness-of-fit (calibration). As the prevalence of the test population diverged from the prevalence of the derivation population, discrimination improved (ROC-curve areas increased from 0.82 +/- 0.02 to 0.87 +/- 0.03; p < 0.05), and calibration deteriorated (chi-square goodness-of-fit statistics increased from 9 to 154; p < 0.05). Following adjustment of the logistic intercept for differences in prevalence, discrimination was unchanged and calibration improved (maximum chi-square goodness-of-fit fell from 154 to 16). When the adjusted algorithm was applied to three geographically remote populations with prevalences that differed from that of the derivation population, calibration improved 87%, while discrimination fell by 1%. Thus, prevalence differences produce statistically significant and potentially clinically important errors in the accuracy of logistic prediction models. These errors can potentially be mitigated by use of a relatively simple mathematical correction algorithm.

Adult↗

PubMedDate not supplied

A predictive model for the polymerization of photo-activated resin composites.

This study investigated the relative significance of irradiation duration (20, 40, 60, or 80 seconds) and intensity; filler type (Silux Plus, a microfill or P-50, a hybrid); and shade (Universal or Gray) on the polymerization of resin composite within the depth of a simulated photopolymerized restoration. From the data, a mathematical model that predicts the extent of resin polymerization based upon the above stated variables was generated. The monomer conversion of specimens was determined by infrared spectroscopy. The results are of great clinical use and indicate that the most significant factor influencing resin composite polymerization is thickness of overlying resin composite. Both duration of exposure and light intensity demonstrate high and equal impact. Color and filler type have only minimal influence. The predictive model for resin composite polymerization provided a very good fit (r2 = .949).

Analysis of Variance↗

PubMedDate not supplied

Prediction model for peak expiratory flow in North Indian population.

OBJECTIVE: To establish a model for predicting peak expiratory flow rate (PEFR) in North Indian healthy population. Study subjects. Eight hundred and ninety-seven healthy, non-smoker individuals (681 males and 216 females) in the age group of 10-60 years. METHODS: The study was carried out at a health exhibition organised by the Government of Uttar Pradesh, at King George's Medical University, Lucknow. Only healthy, non-smoker individuals were enrolled for the study. Age was noted in completed years and weight in kg and height in cm were taken without shoes. PEFR was measured by Mini Wrights peak flow meter in standing position after prior instructions and demonstration of technique to each individual. The test was performed three times on each subject and best of the three attempts was selected for data computation. The statistical solftware SPSS was used to fit the model and perform residual analysis. RESULTS: The highest reading for males was recorded in the age group of 20 to 24 years and for females in the age group of 25 to 29 years. Using age, height and weight, we established a regression model for predicting PEFR values for males and females separately in the age group 19-60 years. In the age group 10-18 years, the model for predicting PEFR was same for both the sexes. PEFR values were found to be more in males as compared to females. The predictive power of the model as described by explained variation was found to be 80 and 82 percent for males and females, respectively. CONCLUSIONS: Prediction model for north Indian subjects was drafted for age range 10-60 years. While separate models were required for males and females because of sex related differences in the age group 19-60 years, a common model sufficed for age group 10-18 years.

Adolescent↗

PubMedDate not supplied

Inverse and predictive modeling of seepage into underground openings.

We discuss the development and calibration of a model for predicting seepage into underground openings. Seepage is a key factor affecting the performance of the potential nuclear-waste repository at Yucca Mountain, Nevada. Three-dimensional numerical models were developed to simulate field tests in which water was released from boreholes above excavated niches. Data from air-injection tests were geostatistically analyzed to infer the heterogeneous structure of the fracture permeability field. The heterogeneous continuum model was then calibrated against the measured amount of water that seeped into the opening. This approach resulted in the estimation of model-related, seepage-specific parameters on the scale of interest. The ability of the calibrated model to predict seepage was examined by comparing calculated with measured seepage rates from additional experiments conducted in different portions of the fracture network. We conclude that an effective capillary strength parameter is suitable to characterize seepage-related features and processes for use in a prediction model of average seepage into potential waste-emplacement drifts.

Calibration↗

PubMed2004

Formulation and evaluation of a predictive model to identify the sites of future diabetic retinopathy.

PURPOSE: To formulate and test a model to predict the development of local patches of nonproliferative diabetic retinopathy (NPDR), based on multifocal electroretinogram (mfERG) implicit times and candidate diabetic risk factors. METHODS: mfERGs and fundus photographs were obtained from 28 eyes of 28 diabetic patients during an initial and 12-month follow-up examination. mfERG implicit times were derived at 103 locations using a template-stretching method, and a z-score was calculated in comparison with 20 age-matched normal subjects. Thirty-five nonoverlapping retinal zones were constructed by grouping two to three adjacent stimulated locations, and each zone was assigned the maximum z-score within it. Zones containing initial retinopathy were excluded from further analysis. The probability that new retinopathy would develop in the remaining zones by the follow-up examination was modeled based on the mfERG implicit time z-score for the zone and other candidate diabetic risk factors determined during the initial visit. Data collected from four previously untested diabetic subjects and the other eye of eight previous subjects during their second year follow-up were used to test the predictive model. RESULTS: After 1 year, new retinopathy developed in 11 of the 12 NPDR eyes and 1 of the 16 eyes without initial retinopathy. After accounting for the correlation among zones within each eye, a predictive model was formulated with the variables mfERG implicit time, duration of diabetes, presence of retinopathy (NPDR or no retinopathy), and blood glucose level at initial visit. The area under the receiver operating characteristic (ROC) curve of this multivariate model is 0.90 (P <0.001). The predictive model has an expected sensitivity of 86% and a specificity of 84%, which was verified by the test data. CONCLUSIONS: The development of diabetic retinopathy over a 1-year period can be well predicted by a multivariate model. The inclusion of local mfERG implicit times allowed the model to identify the specific sites of future retinopathy.

Adult↗

PubMed2003

Multivariate multiple regression prediction models: a Euclidean distance approach.

An extension of a multiple regression prediction model to multiple response variables is presented. An algorithm using least sum of Euclidean distances between the multivariate observed and model-predicted response values provides regression coefficients, a measure of effect size, and inferential procedures for evaluating the extended multivariate multiple regression prediction model.

Adolescent↗

PubMed1996

Prediction models for insulin resistance.

A prediction model for estimating insulin resistance in hypertensive patients is presented. Body-mass index, serum triglyceride concentrations and liver enzyme activity in plasma correlate to insulin resistance determined with the euglycaemic, hyperinsulinaemic clamp technique. Prediction models using body-mass index and either triglycerides or serum alanine-amino transferase were equally good in predicting insulin resistance and gave results that were as reliable as those obtained in a model using fasting-insulin concentrations. The hyperinsulinaemic clamp had a reproducibility error of 14%, and body-mass index and serum triglycerides had a multiple correlation of 0.57 to the insulin-sensitivity results. The model predicts insulin resistance with acceptable statistical power, whereas the power to predict high values of insulin sensitivity is less good.

Aged↗

PubMed2001

A framework for predictive modeling of anatomical deformations.

A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.

Biomechanical Phenomena↗

PubMed1989

Future imperfect: the limitations of clinical prediction models and the limits of clinical prediction.

Stepwise regression procedures are often used to identify a small set of variables that serve as important predictors of clinical outcome and to construct prediction models based on those variables. Several theoretical and practical limitations of this process are discussed and highlighted with a variety of examples from published reports. Wider appreciation of these limitations should encourage the development of more relevant models, and thereby improve the quality of clinical prediction.

Models, Statistical↗

PubMed2005

Cancer risk prediction models: a workshop on development, evaluation, and application.

Cancer researchers, clinicians, and the public are increasingly interested in statistical models designed to predict the occurrence of cancer. As the number and sophistication of cancer risk prediction models have grown, so too has interest in ensuring that they are appropriately applied, correctly developed, and rigorously evaluated. On May 20-21, 2004, the National Cancer Institute sponsored a workshop in which experts identified strengths and limitations of cancer and genetic susceptibility prediction models that were currently in use and under development and explored methodologic issues related to their development, evaluation, and validation. Participants also identified research priorities and resources in the areas of 1) revising existing breast cancer risk assessment models and developing new models, 2) encouraging the development of new risk models, 3) obtaining data to develop more accurate risk models, 4) supporting validation mechanisms and resources, 5) strengthening model development efforts and encouraging coordination, and 6) promoting effective cancer risk communication and decision-making.

Breast Neoplasms↗

PubMed2005

Development of radiology prediction models using feature analysis.

RATIONALE AND OBJECTIVES: This article provides an introduction to prediction models and their application in diagnostic imaging research. Prediction models capitalize on the different degrees of association among variables to make a prediction of a health state, formulate a rule, or quantify individual contributions of various predictor variables. The purpose of this article is to elucidate the rationale, implication, and interpretation of prediction models using imaging features. MATERIALS AND METHODS: The techniques and challenges of developing, testing, and implementing prediction models are described. Prediction model development methods are similar to data-mining techniques. RESULTS: Learning objectives are to review prediction rule (model) methods, learn how prediction models may be applied to feature analysis, and understand the challenges of developing, testing, and implementing prediction models.

Decision Support Techniques↗

PubMed2005

A resampling approach for adjustment in prediction models for covariate measurement error.

Recent works on covariate measurement errors focus on the possible biases in model coefficient estimates. Usually, measurement error in a covariate tends to attenuate the coefficient estimate for the covariate, i.e., a bias toward the null occurs. Measurement error in another confounding or interacting variable typically results in incomplete adjustment for that variable. Hence, the coefficient for the covariate of interest may be biased either toward or away from the null. This paper presents a new method based on a resampling technique to deal with covariate measurement errors in the context of prediction modeling. Prediction accuracy is our primary parameter of interest. Prediction accuracy of a model is defined as the success rate of prediction when the model predicts new response. We call our method bootstrap regression calibration (BRC). We study logistic regression with interacting covariates as our prediction model. We measure the prediction accuracy of a model by receiver operating characteristic (ROC) method. Results from simulations show that bootstrap regression calibration offers consistent enhancement over the commonly used regression calibration (RC) method in terms of improving prediction accuracy of the model and reducing bias in the estimated coefficients.

Calibration↗

PubMed2006

Do clinical prediction models improve concordance of treatment decisions in reproductive medicine?

OBJECTIVE: To assess whether the use of clinical prediction models improves concordance between gynaecologists with respect to treatment decisions in reproductive medicine. DESIGN: We constructed 16 vignettes of subfertile couples by varying fertility history, postcoital test, sperm motility, follicle-stimulating hormone level and Chlamydia antibody titre. SETTING: Thirty-five gynaecologists estimated three probabilities, i.e. the 1-year probability of spontaneous pregnancy, the pregnancy chance after intrauterine insemination (IUI) and the pregnancy chance after in vitro fertilisation (IVF). Subsequently they proposed therapeutic regimens for these 16 fictional couples, i.e. expectant management, IUI or IVF. Three months later, the participant gynaecologists again had to propose therapeutic regimes for the same 16 fictional cases but this time accompanied by pregnancy chances obtained from prediction models: predictions on spontaneous pregnancy, IUI and IVF. POPULATION: Thirty-five gynaecologists working in academic and nonacademic hospitals in the Netherlands. METHODS: Setting section. Main outcome measures The concordance between gynaecologists of probability estimates, expressed as interclass correlation coefficient (ICC) and the concordance between gynaecologists of treatment decisions, analysed by calculating Cohen's kappa (kappa). RESULTS: The gynaecologists differed widely in estimating pregnancy chances (ICC: 0.34). Furthermore, there was a huge variation in the proposed therapeutic regimens (kappa: 0.21). The treatment decisions made by gynaecologists were consistent with the ranking of their probability estimates. When prediction models were used, the concordance (kappa) for treatment decisions increased from 0.21 to 0.38. The number of gynaecologists counselling for expectant management increased from 39 to 51%, whereas counselling for IVF dropped from 23 to 14%. CONCLUSION: Gynaecologists differed widely in their estimation of prognosis in 16 fictional cases of subfertile couples. Their therapeutic regimens showed likewise huge variation. After confrontation with prediction models in the same 16 fictional cases, the proposed therapeutic regimens showed only slightly better concordance. Therefore a simple introduction of validated prediction models is insufficient to introduce concordant management between doctors.

Adult↗

PubMed2003

[Characteristics of ovarian tumors with color Doppler sonography: a comparison of predictive models derived from two academic centers data].

OBJECTIVE: To construct and cross-validate logistic regression models used for the prediction of ovarian malignancies in two groups of women with adnexal tumors. MATERIALS AND METHODS: Preoperative clinical, gray-scale and color Doppler ultrasound data of 307 women treated in the Ist Dept of Gynecology of the Medical University in Lublin (group I) and 464 of women treated in the Dept of Surgical Gynecology, Medical University in Poznan (group II) were analyzed retrospectively. These data were used to construct predictive models which were developed for both groups separately and then cross-validated (12 cases in each group, six malignant and 6 benign) between the groups. Multiple logistic regression analysis was chosen to calculate probability of malignancy in each examined mass. RESULTS: There were 228 (74.2%) benign tumors and 79 (25.7%) malignant tumors in group I. Group II consisted of 299 (64.4%) benign tumors and 165 (35.6%) malignant masses. Only six variables were included in the logistic regression model in group I. These were age, bilaterality, septae, papillary projections, volume and color score. In group II there were also 6 variables included in the regression model (menopausal status, septae, bilaterality, ascites, blood vessel localization and PI). At 50% probability of malignancy the model constructed for group I had a sensitivity and specificity of 74.6% i 94.7%, respectively. At the same probability level in group II sensitivity and specificity were 86.1% i 93.6%, respectively. Cross-validation of the predictive model constructed for group I in randomly selected cases from group II had sensitivity of 66.4% and specificity of 79.2%. Sensitivity and specificity of the group II model tested in cases from group I were 64.3% and 75.2%, respectively. CONCLUSIONS: We conclude that predictive models created with the use of multiple logistic regression analysis may be useful in preoperative discrimination of adnexal tumors. However, much better definition of diagnostic criteria, especially color Doppler score must be achieved before these models could be widely used in clinical practice.

Academic Medical Centers↗

PubMed1985

Discriminant analysis for predicting dystocia in beef cattle. II. Derivation and validation of a prebreeding prediction model.

Discriminant analysis was utilized to derive and validate a model for predicting dystocia using only data available at the beginning of the breeding season. Data were collected from 211 Chianina crossbred cows (2 to 6 yr old) bred to Chianina bulls. A proportionally stratified sampling procedure divided females into an analysis sample (n = 134) on which the model was derived and a hold-out sample (n = 77) on which the prediction model was validated (tested). Variables available during the derivation stage were cow age, cow weight, pelvic height, pelvic width, pelvic area and calf sire. Dystocia was categorized as either unassisted or assisted. Occurrence of dystocia was 17.2 and 18.2% in the analysis and hold-out samples, respectively. All data were standardized to a mean of zero and a variance of one before statistical analysis. The centroid of cows experiencing dystocia differed (P less than .01) from that of cows calving unassisted in the analysis sample. Significant variables were pelvic area and cow age (standardized coefficients = .56 and .51, respectively). This model correctly classified 85.1% of the cows in the analysis sample. This was 13.5% greater than the proportional chance criterion. For model validation, prediction accuracy was 84.4% in the hold-out group, which was 14.2% greater than the proportional chance criterion. However, only 57.1% of the cows that experienced dystocia were correctly classified. Examination of the data revealed that those cows misclassified were 3 yr of age or older.(ABSTRACT TRUNCATED AT 250 WORDS)

Age Factors↗

PubMed2004

Comparison of hospital charge prediction models for colorectal cancer patients: neural network vs. decision tree models.

Analysis and prediction of the care charges related to colorectal cancer in Korea are important for the allocation of medical resources and the establishment of medical policies because the incidence and the hospital charges for colorectal cancer are rapidly increasing. But the previous studies based on statistical analysis to predict the hospital charges for patients did not show satisfactory results. Recently, data mining emerges as a new technique to extract knowledge from the huge and diverse medical data. Thus, we built models using data mining techniques to predict hospital charge for the patients. A total of 1,022 admission records with 154 variables of 492 patients were used to build prediction models who had been treated from 1999 to 2002 in the Kyung Hee University Hospital. We built an artificial neural network (ANN) model and a classification and regression tree (CART) model, and compared their prediction accuracy. Linear correlation coefficients were high in both models and the mean absolute errors were similar. But ANN models showed a better linear correlation than CART model (0.813 vs. 0.713 for the hospital charge paid by insurance and 0.746 vs. 0.720 for the hospital charge paid by patients). We suggest that ANN model has a better performance to predict charges of colorectal cancer patients.

Algorithms↗

PubMed2002

Biopsychosocial multivariate predictive model of occupational low back disability.

STUDY DESIGN: To establish outcome, 253 workers with subacute and chronic low back conditions were assessed with a comprehensive multimethod biopsychosocial protocol at baseline, 3 days after the initial examination, and 3 months later. OBJECTIVE: To validate empirically a biopsychosocial model for prediction of occupational low back disability. SUMMARY OF BACKGROUND DATA: Costs of low back occupational disability continue to spiral despite stabilization of low back injury rates. An empirically based model to predict occupational disability in workers with low back injuries is required. METHODS: Workers with subacute low back injuries (4-6 weeks after injury, n = 192) and those with chronic back pain (6-12 months after injury, n = 61) were the study participants. The biopsychosocial protocol included five groups of variables: 1) sociodemographic, 2) medical, 3) psychosocial, 4) pain behavior, and 5) workplace-related factors. Predictive validity was investigated through a 3-month follow-up assessment, at which time the return to work outcome was determined. Stepwise logistic regression models were developed to predict work status. RESULTS: The final integrated model consisted of variables from a wide biopsychosocial spectrum: vitality, health transition, feeling that job is threatened due to injury, expectations of recovery, guarding behavior, perception of severity of disability, time to complete walk, and right leg typical sciatica. CONCLUSIONS: The "winning" variables identified in the integrated model are dominated by cognitions, which are accompanied by disability behaviors. A cognitive-behavioral model with an adaptation-oriented rather than a pathology-oriented focus is favored for early intervention with high-risk workers since cognitions are amenable to change.

Adult↗

PubMed2005

Sources of uncertainty in model predictions: lessons learned from the IAEA Forest and Fruit Working Group model intercomparisons.

The International Atomic Energy Agency (IAEA), through the BIOMASS program, has provided a unique international forum for assessing the relative contribution of different sources of uncertainty associated with environmental modeling. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte-Carlo modeling are often recommended. The issue of model uncertainty is still rarely addressed in practical applications and the use of several alternative models to derive a range of model outputs (similar to what was done in IAEA model intercomparisons) is one of a few available techniques. This paper addresses the often overlooked issue of what we call 'modeler uncertainty,' i.e., differences in problem formulation, model implementation and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit and Forest Working Groups created under the BIOMASS program (BIOsphere Modeling and ASSessment). The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that the parameter uncertainty (as evaluated by a probabilistic Monte-Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in fate and transport modeling and risk characterization.

Fruit↗