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Biomedical subjects

Alicia Y Toledano

Publications and source records attributed to Alicia Y Toledano.

8 recordsLinked to original sources
PubMed2005

Interobserver reliability of computed tomography-derived primary tumor volume measurement in patients with supraglottic carcinoma.

BACKGROUND: Prior studies have determined that macroscopic ("gross") tumor volume (GTV), as calculated from pretreatment computer tomography (CT), was capable of predicting local control in squamous cell carcinoma arising in different subsites in the head and neck in patients who were treated with nonsurgical organ-preservation therapy. The majority of these studies were single-institution, retrospective investigations. Consequently, there has been concern that GTV measurements may not be reproducible by different readers at different institutions. The objective of the current study was to measure the interobserver reliability for GTV measurements for squamous cell carcinoma of the supraglottic larynx (SGSCCA) performed by different readers at different institutions. METHODS: Eight experienced readers (4 neuroradiologists and 4 radiation oncologists) from different institutions independently measured the pretreatment GTV of 20 patients with SGSCCA. The CT scans were obtained from patients entered into the definitive radiation therapy arm of Radiation Therapy Oncology Group protocol 91-11, who had supraglottic carcinoma and underwent pretreatment CT scans of the neck. Statistical analysis focused on interobserver reliability as measured by the intraclass correlation coefficient. RESULTS: The intraclass correlation coefficient was 0.81 (95% lower confidence bound, 0.71). This value was interpreted as "excellent." CONCLUSIONS: GTV measurements were reliable and reproducible when performed by neuroradiologists and radiation oncologists who were experienced in the interpretation of CT scans of the extracranial head and neck in patients with SGSCCA. The result implied that the correlation between GTV and local control should be reproducible across institutions.

Adult↗
PubMed2005

Weaknesses of goodness-of-fit tests for evaluating propensity score models: the case of the omitted confounder.

PURPOSE: Propensity scores are used in observational studies to adjust for confounding, although they do not provide control for confounders omitted from the propensity score model. We sought to determine if tests used to evaluate logistic model fit and discrimination would be helpful in detecting the omission of an important confounder in the propensity score. METHODS: Using simulated data, we estimated propensity scores under two scenarios: (1) including all confounders and (2) omitting the binary confounder. We compared the propensity score model fit and discrimination under each scenario, using the Hosmer-Lemeshow goodness-of-fit (GOF) test and the c-statistic. We measured residual confounding in treatment effect estimates adjusted by the propensity score omitting the confounder. RESULTS: The GOF statistic and discrimination of propensity score models were the same for models excluding an important predictor of treatment compared to the full propensity score model. The GOF test failed to detect poor model fit for the propensity score model omitting the confounder. C-statistics under both scenarios were similar. Residual confounding was observed from using the propensity score excluding the confounder (range: 1-30%). CONCLUSIONS: Omission of important confounders from the propensity score leads to residual confounding in estimates of treatment effect. However, tests of GOF and discrimination do not provide information to detect missing confounders in propensity score models. Our findings suggest that it may not be necessary to compute GOF statistics or model discrimination when developing propensity score models.

Confounding Factors, Epidemiologic↗
PubMed2004

Principles for modeling propensity scores in medical research: a systematic literature review.

PURPOSE: To document which established criteria for logistic regression modeling researchers consider when using propensity scores in observational studies. METHODS: We performed a systematic review searching Medline and Science Citation to identify observational studies published in 2001 that addressed clinical questions using propensity score methods to adjust for treatment assignment. We abstracted aspects of propensity score model development (e.g. variable selection criteria, continuous variables included in correct functional form, interaction inclusion criteria), model discrimination and goodness of fit for 47 studies meeting inclusion criteria. RESULTS: We found few studies reporting on the propensity score model development or evaluation of model fit. CONCLUSIONS: Reporting of aspects related to propensity score model development is limited and raises questions about the value of these principles in developing propensity scores from which unbiased treatment effects are estimated.

Bias↗
PubMed2003

Three methods for analysing correlated ROC curves: a comparison in real data sets from multi-reader, multi-case studies with a factorial design.

This paper compares three published methods for analysing multiple correlated ROC curves: a method using generalized estimating equations with marginal non-proportional ordinal regression models; a method using jackknifed pseudovalues of summary statistics; a method using a corrected F-test from analysis of variance of summary statistics. Use of these methods is illustrated through six real data examples from studies with the common factorial design, that is, multiple readers interpreting images obtained with each test modality on each study subject. The issue of the difference between typical summary statistics and summary statistics from typical ROC curves is explored. The examples also address similarities and differences among the analytical methods. In particular, while point estimates of differences between test modalities are similar, the standard errors of these differences do not agree for all three methods. A simulation study supports the standard errors provided by the generalized estimating equations with marginal non-proportional ordinal regression models.

Analysis of Variance↗
PubMed2003

Computerized tomographic colonography: performance evaluation in a retrospective multicenter setting.

BACKGROUND & AIMS: No multicenter study has been reported evaluating the performance and interobserver variability of computerized tomographic colonography. The aim of this study was to assess the accuracy of computerized tomographic colonography for detecting clinically important colorectal neoplasia (polyps >or=10 mm in diameter) in a multi-institutional study. METHODS: A retrospective study was developed from 341 patients who had computerized tomographic colonography and colonoscopy among 8 medical centers. Colonoscopy and pathology reports provided the standard. A random sample of 117 patients, stratified by criterion standard, was requested. Ninety-three patients were included (47% with polyps >or=10 mm; mean age, 62 years; 56% men; 84% white; 40% reported colorectal symptoms; 74% at increased risk for colorectal cancer). Eighteen radiologists blinded to the criterion standard interpreted computerized tomography colonography examinations, each using 2 of 3 different software display platforms. RESULTS: The average area under the receiver operating characteristic curve for identifying patients with at least 1 lesion >or=10 mm was 0.80 (95% lower confidence bound, 0.74). The average sensitivity and specificity were 75% (95% lower confidence bound, 68%) and 73% (95% lower confidence bound, 66%), respectively. Per-polyp sensitivity was 75%. A trend was observed for better performance with more observer experience. There was no difference in performance across software display platforms. CONCLUSIONS: Computerized tomographic colonography performance compared favorably with reported performance of fecal occult blood testing, flexible sigmoidoscopy, and barium enema. A prospective study evaluating the performance of computerized tomography colonography in a screening population is indicated.

Colonic Neoplasms↗