Proportion Of Explained Variation Research Articles

Staging Systems for Papillary Thyroid Carcinoma

To find out the most applicable and consistent staging system for papillary thyroid carcinoma (PTC) available in the literature. The commonly used staging systems for PTC have predicted cancer-specific survival (CSS) well. However, their applicability and generalizability have not yet been evaluated in different clinical settings. A MEDLINE search from 1965 to 2005 was carried out to identify different staging systems available in the literature and 9 systems were applicable to 1634 PTC patients within 2 tertiary-referral centers. The CSS of each staging system within individual centers were calculated using Kaplan-Meier method and the CSS of each tumor stage in one individual center was compared with that of the other by log-rank test. In addition, within each center, the predictability of each staging system relative to the others was ranked based on the proportion of variation explained (PVE) value. Clinicopathologic features, treatment received, and tumor stages were significantly different between the 2 centers. There were also significant differences in CSS within at least one tumor stage between the 2 centers in 8 of the 9 staging systems. The TNM was a highly predictive and consistent staging system within the 2 centers. Although the absolute PVE values differed between the 2 centers, the relative ranking of the 9 staging systems within each center correlated significantly to each other (P < 0.05). Despite referral, treatment, and data collection biases inherent within each center, the TNM system remained to be the most applicable and consistent staging system for PTC in 2 centers managing the same population group.

Restaging of Differentiated Thyroid Carcinoma by the Sixth Edition AJCC/UICC TNM Staging System: Stage Migration and Predictability

The AJCC/UICC TNM staging system (TNM) is a widely accepted system for differentiated thyroid carcinoma (DTC). The objective of the present study was to evaluate the potential changes in cancer-specific survival (CSS) after reclassification from fifth to sixth edition TNM. A total of 760 DTC patients managed at our institution from 1961 to 2001 were retrospectively restaged from the fifth to sixth edition TNM. CSS were calculated using Kaplan-Meier method and were compared by the log-rank test. The relative ability of each edition in predicting CSS was calculated by the proportion of variance explained (PVE). Upon reclassification, the proportion of T1 and T3 tumors increased from 14.2 to 33.4% and 10.0 to 33.7%; T2 and T4 decreased from 44.2 to 25.0% and 31.6 to 7.9%, respectively; N0 remained unchanged at 66.0%; N1a decreased from 25.7 to 4.7%; N1b increased from 8.4 to 29.3%; stages I and IV tumors increased from 55.7 to 60.3% and 3.4 to 17.6%, respectively; stages II and III tumors decreased from 20.5 to 13.9% and 20.4 to 8.2%, respectively. The sixth edition had a higher PVE value than the fifth edition. Significant differences in CSS were observed between stage III (fifth edition) and stage III (sixth edition) and between stage IV (fifth edition) and stage IVA (sixth edition). The sixth edition TNM caused marked changes in the pT, pN and allocation of patients into different tumor stages. It appeared to have superior predictability over the fifth edition.

Prognostic Impact of Serum Calcitonin and Carcinoembryonic Antigen Doubling-Times in Patients with Medullary Thyroid Carcinoma

After unsuccessful surgery, medullary thyroid carcinoma (MTC) may be fatal or remain stable for decades, and precise survival predictors are needed. This study assesses the prognostic value of calcitonin and carcinoembryonic antigen (CEA) doubling-times (DT). This is a retrospective study on 65 MTC patients from 2.9-29.5 yr after surgery. Data registered in the database of the French Neuroendocrine Tumor Group were analyzed anonymously. All patients had abnormal calcitonin levels after total thyroidectomy and bilateral lymph node dissection. Calcitonin and CEA serum levels were measured during routine disease follow-up. To assess DT as prognostic factors, a patient population was extracted from the database. When calcitonin DT was less than 6 months, 5- and 10-yr survivals were three of 12 (25%) and one of 12 (8%), respectively; when between 6 months and 2 yr, 5- and 10-yr survivals were 11 of 12 (92%) and three of eight (37%), whereas all 41 patients with calcitonin DT greater than 2 yr were alive at the end of the study. Tumor-Node-Metastasis (TNM) stage, European Organization for Research and Treatment of Cancer (EORTC) score, and calcitonin DT were significant predictors of survival by univariate analysis, but only calcitonin DT remained an independent predictor of survival by multivariate analysis (P = 0.002) with a proportion of variance explained (PVE) of 37.4%. Calcitonin DT was a better predictor than CEA (PVE 63.3% and 47.0%, respectively). Calcitonin DT calculated using only the first four measurements was also an independent predictor of survival (P < 0.000001; PVE 40.4%). Calcitonin DT may be superior to initial clinical staging and among the most powerful prognostic indicators in MTC.

Comparing the importance of prognostic factors in Cox and logistic regression using SAS

Two SAS macro programs are presented that evaluate the relative importance of prognostic factors in the proportional hazards regression model and in the logistic regression model. The importance of a prognostic factor is quantified by the proportion of variation in the outcome attributable to this factor. For proportional hazards regression, the program % relimpcr uses the recently proposed measure V to calculate the proportion of explained variation (PEV). For the logistic model, the R 2 measure based on squared raw residuals is used by the program % relimplr. Both programs are able to compute marginal and partial PEV, to compare PEVs of factors, of groups of factors, and even to compare PEVs of different models. The programs use a bootstrap resampling scheme to test differences of the PEVs of different factors. Confidence limits for P-values are provided. The programs further allow to base the computation of PEV on models with shrinked or bias-corrected parameter estimates. The SAS macros are freely available at www.akh-wien.ac.at/imc/biometrie/relimp

A comparison of different staging systems predictability of patient outcome

There is no consensus regarding the comparison of staging classifications. Recently, numerous staging classifications for thyroid carcinoma have been described. This study was performed to evaluate the relative discriminating ability of these different staging systems. A literature review was conducted to identify the available staging classifications used for thyroid carcinoma. To compare the various staging classifications, cause specific survival data from a retrospective review of 382 patients with papillary and follicular thyroid carcinoma treated at Princess Margaret Hospital were applied to the various staging classifications. The ability of these classifications to distinguish the stage groupings were compared in the following ways: 1) tabulating the numbers of patients within each stage group; 2) collapsing stage groupings into high and low risk groups and calculating mortality rates at 10 and 15 years; 3) summing observed deviations at 5, 10, and 15 years; and 4) calculating the proportion of variance explained (PVE) for each staging classification, using each classification separately as a prognostic factor in a Cox regression model. The application of the PVE model, the only method of the four that used statistical inference, showed no statistically significant superiority of any system over the TNM classification of the American Joint Committee on Cancer (AJCC) and the International Union Against Cancer (UICC) in their ability to discriminate stage groupings. Because the TNM classification of the AJCC and UICC is universally available and widely accepted for other disease sites, the authors recommend it for all reports of the treatment and outcome of patients with thyroid carcinoma. Individual research groups also may use an alternative, validated classification to report results, provided that the outcomes are also reported using the TNM classification to facilitate comparison between different centers.