RESUMEN
OBJECTIVE: To ascertain the survival by stage of cervical cancer in Mallorca (Spain), to explore factors collected by the Mallorcan Cancer Registry associated with survival, and to determine the distribution of cervical cases by stage. METHOD: Retrospective follow-up study of cases diagnosed with cervical cancer between 2006 and 2012 through the Mallorcan Cancer Registry. Cases identified only by death certificate were excluded. VARIABLES: age; date and method of diagnosis; histology (ICD-O 3rd ed.); TNM and stage (UICC 7th ed.); date of follow-up or death and cause of death. Follow-up ended on 31 of December 2015. Multiple imputation was used for missing stage cases. Actuarial and Kaplan-Meier methods were used for survival analysis and Cox regression models to identify factors that explain and predict survival. RESULTS: 321 cases were identified. The stage was missing in 8.4% of cases. After multiple imputation, 42.63% were stage I, 24.01% stage II, 19.94% stage III and 13.42% stage IV. Survival was 63% at 5 years: 92% for women diagnosed in stage I, 59% in stage II, 37% in stage III and 18% in stage IV. Stage and age were associated to survival. CONCLUSIONS: Diagnosis of cervical cancer in stage I is essential. Less than half of the women were diagnosed in stage I. Cervical cancer screening programmes must be improved.
Asunto(s)
Neoplasias del Cuello Uterino , Detección Precoz del Cáncer , Femenino , Estudios de Seguimiento , Humanos , Estadificación de Neoplasias , Pronóstico , Sistema de Registros , Estudios Retrospectivos , España/epidemiología , Tasa de SupervivenciaRESUMEN
La mayoría de los datos en ciencias sociales y educación presentan valores perdidos debido al abandono del estudio o la ausencia de respuesta. Los métodos para el manejo de datos perdidos han mejorado dramáticamente en los últimos años, y los programas computacionales ofrecen en la actualidad una variedad de opciones sofisticadas. A pesar de la amplia disponibilidad de métodos considerablemente justificados, muchos investigadores e investigadoras siguen confiando en técnicas viejas de imputación que pueden crear análisis sesgados. Este artículo presenta una introducción conceptual a los patrones de datos perdidos. Seguidamente, se introduce el manejo de datos perdidos y el análisis de los mismos con base en los mecanismos modernos del método de máxima verosimilitud con información completa (FIML, siglas en inglés) y la imputación múltiple (IM). Asimismo, se incluye una introducción a los diseños de datos perdidos así como nuevas herramientas computacionales tales como la función Quark y el paquete semTools. Se espera que este artículo incentive el uso de métodos modernos para el análisis de los datos perdidos.
Most of the social and educational data have missing observations due to either attrition or nonresponse. Missing data methodology has improved dramatically in recent years, and popular computer programs as well as software now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, many researchers still rely on old imputation techniques that can create biased analysis. This article provides conceptual introductions to the patterns of missing data. In line with that, this article introduces how to handle and analyze the missing information based on modern mechanisms of full-information maximum likelihood (FIML) and multiple imputation (MI). An introduction about planned missing designs is also included and new computational tools like Quark function, and semTools package are also mentioned. The authors hope that this paper encourages researchers to implement modern methods for analyzing missing data.