Surveying and optimizing the predictors for ependymoma specific survival using SEER data.

Purpose: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) ependymoma data to identify predictive models and potential disparity in outcome.

Materials And Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ependymoma. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome ('brain and other nervous systems' specific death in yes/no).

Association between urinary cadmium and all cause, all cancer and prostate cancer specific mortalities for men: an analysis of national health and nutrition examination survey (NHANES III) data.

Aim: This study employed public use National Health and Nutrition Examination Survey (NHANES III) data to investigate the association between urinary cadmium (UDPSI) and all cause, all cancer and prostate cancer mortalities in men.

Patients And Methods: NHANES III household adult, laboratory and mortality data were merged. The sampling weight used was WTPFEX6, with SDPPSU6 applied for the probability sampling unit and SDPSTRA6 to designate the strata for the survey analysis.

Socio-economic factors affect the outcome of soft tissue sarcoma: an analysis of SEER data.

Background: This study analyzed whether socio-economic factors affect the cause specific survival of soft tissue sarcoma (STS).

Methods: Surveillance, Epidemiology and End Results (SEER) soft tissue sarcoma (STS) data were used to identify potential socio-economic disparities in outcome. Time to cause specific death was computed with Kaplan-Meier analysis.

Assessing the impact of socio-economic variables on breast cancer treatment outcome disparity.

Background: We studied Surveillance, Epidemiology and End Results (SEER) breast cancer data of Georgia USA to analyze the impact of socio-economic factors on the disparity of breast cancer treatment outcome.

Materials And Methods: This study explored socio-economic, staging and treatment factors that were available in the SEER database for breast cancer from Georgia registry diagnosed in 2004-2009. An area under the receiver operating characteristic curve (ROC) was computed for each predictor to measure its discriminatory power.