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Validation Sample (validation + sample)
Selected AbstractsHealth state values for the HUI 2 descriptive system: results from a UK surveyHEALTH ECONOMICS, Issue 3 2005Christopher McCabe Abstract This paper reports the results of a study to estimate a statistical health state valuation model for a revised version of the Health Utilities Index Mark 2, using Standard Gamble health state preference data. A sample of 51 health states were valued by a sample of the 198 members of the UK general population. Models are estimated for predicting health state valuations for all 8000 states defined by the revised HUI2. The recommended model produces logical and significant coefficients for all levels of all dimensions in the HUI2. These coefficients appear to be robust across model specifications. This model performs well in predicting the observed health state values within the valuation sample and for a separate validation sample of health states. However, there are concerns over large prediction errors for two health states in the valuation sample. These problems must be balanced against concerns over the validity of using the VAS based health state valuation data of the original HUI2 valuation model. Copyright © 2004 John Wiley & Sons, Ltd. [source] Confirmatory factor analysis and recommendations for improvement of the Autonomy-Preference-Index (API)HEALTH EXPECTATIONS, Issue 3 2010Daniela Simon PhD Dipl Psych Abstract Objective, Validation of the German version of the Autonomy-Preference-Index (API), a measure of patients' preferences for decision making and information seeking. Methods, Stepwise confirmatory factor analysis was conducted on a sample of patients (n = 1592) treated in primary care for depression (n = 186), surgical and internal medicine inpatients (n = 811) and patients with minor trauma treated in an emergency department (n = 595). An initial test of the model was done on calculation and validation halves of the sample. Both local and global indexes-of-fit suggested modifications to the scale. The scale was modified and re-tested in the calculation sample and confirmed in the validation sample. Subgroup analyses for age, gender and type of treatment setting were also performed. Results, The confirmatory analysis led to a modified version of the API with better local and global indexes-of-fit for samples of German-speaking patients. Two items of the sub-scale, ,preference for decision-making', and one item of the sub-scale, ,preference for information seeking', showed very low reliability scores and were deleted. Thus, several global indexes-of-fit clearly improved significantly. The modified scale was confirmed on the validation sample with acceptable to good indices of fit. Results of subgroup analyses indicated that no adaptations were necessary. Discussion and conclusions, This first confirmatory analysis for a German-speaking population showed that the API was improved by the removal of several items. There were theoretically plausible explanations for this improvement suggesting that the modifications might also be appropriate in English and other language versions. [source] Assessment of Individual Risk of Death Using Self-Report Data: An Artificial Neural Network Compared with a Frailty IndexJOURNAL OF AMERICAN GERIATRICS SOCIETY, Issue 7 2004Xiaowei Song PhD Objectives: To evaluate the potential of an artificial neural network (ANN) in predicting survival in elderly Canadians, using self-report data. Design: Cohort study with up to 72 months follow-up. Setting: Forty self-reported characteristics were obtained from the community sample of the Canadian Study of Health and Aging. An individual frailty index score was calculated as the proportion of deficits experienced. For the ANN, randomly selected participants formed the training sample to derive relationships between the variables and survival and the validation sample to control overfitting. An ANN output was generated for each subject. A separate testing sample was used to evaluate the accuracy of prediction. Participants: A total of 8,547 Canadians aged 65 to 99, of whom 1,865 died during 72 months of follow-up. Measurements: The output of an ANN model was compared with an unweighted frailty index in predicting survival patterns using receiver operating characteristic (ROC) curves. Results: The area under the ROC curve was 86% for the ANN and 62% for the frailty index. At the optimal ROC value, the accuracy of the frailty index was 70.0%. The ANN accuracy rate over 10 simulations in predicting the probability of individual survival mean±standard deviation was 79.2±0.8%. Conclusion: An ANN provided more accurate survival classification than an unweighted frailty index. The data suggest that the concept of biological redundancy might be operationalized from health survey data. [source] Failure time regression with continuous covariates measured with errorJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 4 2000Halbo Zhou We consider failure time regression analysis with an auxiliary variable in the presence of a validation sample. We extend the nonparametric inference procedure of Zhou and Pepe to handle a continuous auxiliary or proxy covariate. We estimate the induced relative risk function with a kernel smoother and allow the selection probability of the validation set to depend on the observed covariates. We present some asymptotic properties for the kernel estimator and provide some simulation results. The method proposed is illustrated with a data set from an on-going epidemiologic study. [source] A unified approach to regression analysis under double-sampling designsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2000Yi-Hau Chen We propose a unified approach to the estimation of regression parameters under double-sampling designs, in which a primary sample consisting of data on the rough or proxy measures for the response and/or explanatory variables as well as a validation subsample consisting of data on the exact measurements are available. We assume that the validation sample is a simple random subsample from the primary sample. Our proposal utilizes a specific parametric model to extract the partial information contained in the primary sample. The resulting estimator is consistent even if such a model is misspecified, and it achieves higher asymptotic efficiency than the estimator based only on the validation data. Specific cases are discussed to illustrate the application of the estimator proposed. [source] Relationship between eye symptoms and blepharospasm: A multicenter case,control studyMOVEMENT DISORDERS, Issue 12 2005Davide Martino MD Abstract Although patients with primary blepharospasm (BSP) commonly report experiencing ocular symptoms before the onset of orbicular spasms, the precise frequency and pathogenic role of this subjective ocular discomfort are poorly understood. We conducted a multicenter case,control study to investigate symptoms related to disorders of the anterior segment of the eye, administering a questionnaire to 165 patients with BSP and 180 age- and gender-matched control patients with hemifacial spasm. On a validation sample, our questionnaire yielded high accuracy in detecting eye diseases (predominantly, dry eye syndrome) using detailed ophthalmological examination as the criterion. Logistic regression analysis indicated a significant association between ocular symptoms at disease onset and BSP. Ocular symptoms starting in the year preceding disease onset (short-latency symptoms) showed a stronger association with BSP than ocular symptoms occurring earlier in time (long-latency symptoms). The association was stronger when short-latency symptoms developed from 40 to 59 years of age, whereas this was not observed for long-latency symptoms. Our findings support the view that eye symptoms associated with BSP result from eye diseases and may be involved in the pathogenesis of BSP. The differential risk of developing BSP, based on age at onset of ocular symptoms, suggests that age and eye diseases may interact in giving rise to BSP. © 2005 Movement Disorder Society [source] A computer case definition for sudden cardiac death,PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 6 2010Cecilia P. Chung M.D. Abstract Purpose To facilitate studies of medications and sudden cardiac death, we developed and validated a computer case definition for these deaths. The study of community dwelling Tennessee Medicaid enrollees 30,74 years of age utilized a linked database with Medicaid inpatient/outpatient files, state death certificate files, and a state ,all-payers' hospital discharge file. Methods The computerized case definition was developed from a retrospective cohort study of sudden cardiac deaths occurring between 1990 and 1993. Medical records for 926 potential cases had been adjudicated for this study to determine if they met the clinical definition for sudden cardiac death occurring in the community and were likely to be due to ventricular tachyarrhythmias. The computerized case definition included deaths with (1) no evidence of a terminal hospital admission/nursing home stay in any of the data sources; (2) an underlying cause of death code consistent with sudden cardiac death; and (3) no terminal procedures inconsistent with unresuscitated cardiac arrest. This definition was validated in an independent sample of 174 adjudicated deaths occurring between 1994 and 2005. Results The positive predictive value of the computer case definition was 86.0% in the development sample and 86.8% in the validation sample. The positive predictive value did not vary materially for deaths coded according to the ICO-9 (1994,1998, positive predictive value,=,85.1%) or ICD-10 (1999,2005, 87.4%) systems. Conclusion A computerized Medicaid database, linked with death certificate files and a state hospital discharge database, can be used for a computer case definition of sudden cardiac death. Copyright © 2009 John Wiley & Sons, Ltd. [source] ORIGINAL RESEARCH,ENDOCRINOLOGY: ANDROTEST©: A Structured Interview for the Screening of Hypogonadism in Patients with Sexual DysfunctionTHE JOURNAL OF SEXUAL MEDICINE, Issue 4 2006Giovanni Corona MD ABSTRACT Introduction., Detecting hypogonadism, which is important in the general population, becomes crucial in patients with sexual dysfunctions, because hypogonadism can have a causal role for them and testosterone (T) substitution represents a milestone for the therapy. Aim., No inventories are available for the screening of hypogonadism in patients with sexual dysfunction. We wished to set up a brief structured interview providing scores useful for detecting hypogonadism defined as low total T (<10.4 nmol/L, 300 ng/dL) in a symptomatic population (sexual dysfunction). Methods., A minimum set of items was identified within a larger structured interview through iterative receiver-operating characteristic curve analysis, with assessment of sensitivity and specificity for hypogonadism in a sample of 215 patients. Main Outcome Measures., Sensitivity and specificity were verified in a further sample of 664 patients. Correlation of test scores with prostate-specific antigen (PSA), testis volume, and others clinical and psychological parameters, was assessed for concurrent validity. Results., In the validation sample, the final 12-item version of the interview (ANDROTEST,©) had a sensitivity and specificity of 68% and 65%, in detecting low total T (<10.4 nmol/L) and of 71% and 65%, in the screening for low free T (<37 pmol/L). Furthermore, patients with a pathological test (i.e., score >8) showed higher prevalence of hypogonadism-related signs, such as lower testis volume and higher depressive symptoms. Finally, when only younger patients (<54 years, which represents the median age of the sample) were considered, Log10 [PSA] levels were significantly lower in those with ANDROTEST,© score >8. Conclusion., ANDROTEST,© is a quick and easy-to-administer interview that provides scores for the screening of male hypogonadism in patients with sexual dysfunction. Corona G, Mannucci E, Petrone L, Balercia G, Fisher AD, Chiarini V, Forti G, and Maggi M. ANDROTEST,©: A structured interview for the screening of hypogonadism in patients with sexual dysfunction. J Sex Med 2006;3:706,715. [source] Identification of a novel susceptibility locus for juvenile idiopathic arthritis by genome-wide association analysisARTHRITIS & RHEUMATISM, Issue 1 2009Anne Hinks Objective Juvenile idiopathic arthritis (JIA) is a chronic rheumatic disease of childhood. Two well-established genetic factors known to contribute to JIA susceptibility, HLA and PTPN22, account for less than half of the genetic susceptibility to disease; therefore, additional genetic factors have yet to be identified. The purpose of this study was to perform a systematic search of the genome to identify novel susceptibility loci for JIA. Methods A genome-wide association study using Affymetrix GeneChip 100K arrays was performed in a discovery cohort (279 cases and 184 controls). Single-nucleotide polymorphisms (SNPs) showing the most significant differences between cases and controls were then genotyped in a validation sample of cases (n = 321) and controls, combined with control data from the 1958 UK birth cohort (n = 2,024). In one region in which association was confirmed, fine-mapping was performed (654 cases and 1,847 controls). Results Of the 112 SNPs that were significantly associated with JIA in the discovery cohort, 6 SNPs were associated with JIA in the independent validation cohort. The most strongly associated SNP mapped to the HLA region, while the second strongest association was with a SNP within the VTCN1 gene. Fine-mapping of that gene was performed, and 10 SNPs were found to be associated with JIA. Conclusion This study is the first to successfully apply a SNP-based genome-wide association approach to the investigation of JIA. The replicated association with markers in the VTCN1 gene defined an additional susceptibility locus for JIA and implicates a novel pathway in the pathogenesis of this chronic disease of childhood. [source] Combining Information from Cancer Registry and Medical Records Data to Improve Analyses of Adjuvant Cancer TherapiesBIOMETRICS, Issue 3 2009Yulei He Summary Cancer registry records contain valuable data on provision of adjuvant therapies for cancer patients. Previous studies, however, have shown that these therapies are underreported in registry systems. Hence direct use of the registry data may lead to invalid analysis results. We propose first to impute correct treatment status, borrowing information from an additional source such as medical records data collected in a validation sample, and then to analyze the multiply imputed data, as in Yucel and Zaslavsky (2005,,Journal of the American Statistical Association,100, 1123,1132). We extend their models to multiple therapies using multivariate probit models with random effects. Our model takes into account the associations among different therapies in both administration and probability of reporting, as well as the multilevel structure (patients clustered within hospitals) of registry data. We use Gibbs sampling to estimate model parameters and impute treatment status. The proposed methodology is applied to the data from the Quality of Cancer Care project, in which stage II or III colorectal cancer patients were eligible to receive adjuvant chemotherapy and radiation therapy. [source] A Note on Estimating Crude Odds Ratios in Case,Control Studies with Differentially Misclassified ExposureBIOMETRICS, Issue 4 2002Robert H. Lyles Summary. Morrissey and Spiegelman (1999, Biometrics55, 338,344) provided a comparative study of adjustment methods for exposure misclassification in case-control studies equipped with an internal validation sample. In addition to the maximum likelihood (ML) approach, they considered two intuitive procedures based on proposals in the literature. Despite appealing ease of computation associated with the latter two methods, efficiency calculations suggested that ML was often to be recommended for the analyst with access to a numerical routine to facilitate it. Here, a reparameterization of the likelihood reveals that one of the intuitive approaches, the inverse matrix method, is in fact ML under differential misclassification. This correction is intended to alert readers to the existence of a simple closed-form ML estimator for the odds ratio in this setting so that they may avoid assuming that a commercially inaccessible optimization routine must be sought to implement ML. [source] Identifying Combinations of Cancer Markers for Further Study as Triggers of Early InterventionBIOMETRICS, Issue 4 2000Stuart G. Baker Summary. In many long-term clinical trials or cohort studies, investigators repeatedly collect and store tissue or serum specimens and later test specimens from cancer cases and a random sample of controls for potential markers for cancer. An important question is what combination, if any, of the molecular markers should be studied in a future trial as a trigger for early intervention. To answer this question, we summarized the performance of various combinations using Receiver Operating Characteristic (ROC) curves, which plot true versus false positive rates. To construct the ROC curves, we proposed a new class of nonparametric algorithms which extends the ROC paradigm to multiple tests. We fit various combinations of markers to a training sample and evaluated the performance in a test sample using a target region based on a utility function. We applied the methodology to the following markers for prostate cancer, the last value of total prostate-specific antigen (PSA), the last ratio of total to free PSA, the last slope of total PSA, and the last slope of the ratio. In the test sample, the ROC curve for last total PSA was slightly closer to the target region than the ROC curve for a combination of four markers. In a separate validation sample, the ROC curve for last total PSA intersected the target region in 77% of bootstrap replications, indicating some promise for further study. We also discussed sample size calculations. [source] Improved prediction of recurrence after curative resection of colon carcinoma using tree-based risk stratificationCANCER, Issue 5 2004Martin Radespiel-Tröger M.D. Abstract BACKGROUND Patients who are at high risk of recurrence after undergoing curative (R0) resection for colon carcinoma may benefit most from adjuvant treatment and from intensive follow-up for early detection and treatment of recurrence. However, in light of new clinical evidence, there is a need for continuous improvement in the calculation of the risk of recurrence. METHODS Six hundred forty-one patients with R0-resected colon carcinoma who underwent surgery between January 1, 1984 and December 31, 1996 were recruited from the Erlangen Registry of Colorectal Carcinoma. The study end point was time until first locoregional or distant recurrence. The factors analyzed were: age, gender, site in colon, International Union Against Cancer (UICC) pathologic tumor classification (pT), UICC pathologic lymph node classification, histologic tumor type, malignancy grade, lymphatic invasion, venous invasion, number of examined lymph nodes, number of lymph node metastases, emergency presentation, intraoperative tumor cell spillage, surgeon, and time period. The resulting prognostic tree was evaluated by means of an independent sample using a measure of predictive accuracy based on the Brier score for censored data. Predictive accuracy was compared with several proposed stage groupings. RESULTS The prognostic tree contained the following variables: pT, the number of lymph node metastases, venous invasion, and emergency presentation. Predictive accuracy based on the validation sample was 0.230 (95% confidence interval [95% CI], 0.227,0.233) for the prognostic tree and 0.212 (95% CI, 0.209,0.215) for the UICC TNM sixth edition stage grouping. CONCLUSIONS The prognostic tree showed superior predictive accuracy when it was validated using an independent sample. It is interpreted easily and may be applied under clinical circumstances. Provided that their classification system can be validated successfully in other centers, the authors propose using the prognostic tree as a starting point for studies of adjuvant treatment and follow-up strategies. Cancer 2004;100:958,67. © 2004 American Cancer Society. [source] PREDICTION OF TEXTURE IN GREEN ASPARAGUS BY NEAR INFRARED SPECTROSCOPY (NIRS)JOURNAL OF FOOD QUALITY, Issue 4 2002D. PEREZ NIR spectroscopy was used to estimate three textural parameters of green asparagus: maximum cutting force, energy and toughness. An Instron 1140 Texturometer provided reference data. A total of 199 samples from two asparagus varieties (Taxara and UC-157) were used to obtain the calibration models between the reference data and the NIR spectral data. Standard errors of cross validation (SECV) and r2 were (5.73, 0.84) for maximum cutting force, (0.58, 0.66) for toughness, and (0.04, 0.85) for cutting energy. The mathematical models developed as calibration models were tested using independent validation samples (n =20); the resulting standard errors of prediction (SEP) and r2 for the same parameters were (6.73, 0.82), (0.61, 0.57) and (0.04, 0.89), respectively. For toughness, substantially improved r2 (0.85) and SEP (0.36) when four samples exhibiting large residual values were removed. The results indicated that NIRS could accurately predict texture parameters of green asparagus. [source] Discovering robust protein biomarkers for disease from relative expression reversals in 2-D DIGE data.PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 8 2007Troy J. Anderson Abstract This study assesses the ability of a novel family of machine learning algorithms to identify changes in relative protein expression levels, measured using 2-D DIGE data, which support accurate class prediction. The analysis was done using a training set of 36 total cellular lysates comprised of six normal and three cancer biological replicates (the remaining are technical replicates) and a validation set of four normal and two cancer samples. Protein samples were separated by 2-D DIGE and expression was quantified using DeCyder-2D Differential Analysis Software. The relative expression reversal (RER) classifier correctly classified 9/9 training biological samples (p<0.022) as estimated using a modified version of leave one out cross validation and 6/6 validation samples. The classification rule involved comparison of expression levels for a single pair of protein spots, tropomyosin isoforms and ,-enolase, both of which have prior association as potential biomarkers in cancer. The data was also analyzed using algorithms similar to those found in the extended data analysis package of DeCyder software. We propose that by accounting for sources of within- and between-gel variation, RER classifiers applied to 2-D DIGE data provide a useful approach for identifying biomarkers that discriminate among protein samples of interest. [source] Regression Analysis with a Misclassified Covariate from a Current Status Observation SchemeBIOMETRICS, Issue 2 2010Leilei Zeng Summary Naive use of misclassified covariates leads to inconsistent estimators of covariate effects in regression models. A variety of methods have been proposed to address this problem including likelihood, pseudo-likelihood, estimating equation methods, and Bayesian methods, with all of these methods typically requiring either internal or external validation samples or replication studies. We consider a problem arising from a series of orthopedic studies in which interest lies in examining the effect of a short-term serological response and other covariates on the risk of developing a longer term thrombotic condition called deep vein thrombosis. The serological response is an indicator of whether the patient developed antibodies following exposure to an antithrombotic drug, but the seroconversion status of patients is only available at the time of a blood sample taken upon the discharge from hospital. The seroconversion time is therefore subject to a current status observation scheme, or Case I interval censoring, and subjects tested before seroconversion are misclassified as nonseroconverters. We develop a likelihood-based approach for fitting regression models that accounts for misclassification of the seroconversion status due to early testing using parametric and nonparametric estimates of the seroconversion time distribution. The method is shown to reduce the bias resulting from naive analyses in simulation studies and an application to the data from the orthopedic studies provides further illustration. [source] Revised Pediatric Emergency Assessment Tool (RePEAT): A Severity Index for Pediatric Emergency CareACADEMIC EMERGENCY MEDICINE, Issue 4 2007MSCE, Marc H. Gorelick MD Abstract Objectives: To develop and validate a multivariable model, using information available at the time of patient triage, to predict the level of care provided to pediatric emergency patients for use as a severity of illness measure. Methods: This was a retrospective cohort study of 5,521 children 18 years of age or younger treated at four emergency departments (EDs) over a 12-month period. Data were obtained from abstraction of patient records. Logistic regression was used to develop (75% of sample) and validate (25% of sample) models to predict any nonroutine diagnostic or therapeutic intervention in the ED and admission to the hospital. Data on ED length of stay and hospital costs were also obtained. Results: Eight predictor variables were included in the final models: presenting complaint, age, triage acuity category, arrival by emergency medical services, current use of prescription medications, and three triage vital signs (heart rate, respiratory rate, and temperature). The resulting models had adequate goodness of fit in both derivation and validation samples. The area under the receiver operating characteristic curve was 0.73 for the ED intervention model and 0.85 for the admission model. The Revised Pediatric Emergency Assessment Tool (RePEAT) score was then calculated as the sum of the predicted probability of receiving intervention and twice the predicted probability of admission. The RePEAT score had a significant univariate association with ED costs (r= 0.44) and with ED length of stay (r= 0.27) and contributed significantly to the fit of multivariable models comparing these outcomes across sites. Conclusions: The RePEAT score accurately predicts level of care provided for pediatric emergency patients and may provide a useful means of risk adjustment when benchmarking outcomes. [source] | ||||||||||||||||