Erratum to: What impact do assumptions about missing data have on conclusions? a practical sensitivity analysis for a cancer survival registry
نویسندگان
چکیده
Erratum After the publication of the original article [1], it came to the author’s attention that an error affecting Figures 2 and 3, and the Additional files, has occurred. During the author proofing stage, an instruction was made to move Figs. 2 and 3 to the Additional files. The instruction was misinterpreted by the Production team, resulting in the files being retained as Figs. 2 and 3, and the Additional files being subsequently incorrect. The original article has now been updated to rectify this error.
منابع مشابه
What impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry
BACKGROUND Within epidemiological and clinical research, missing data are a common issue and often over looked in publications. When the issue of missing observations is addressed it is usually assumed that the missing data are 'missing at random' (MAR). This assumption should be checked for plausibility, however it is untestable, thus inferences should be assessed for robustness to departures ...
متن کاملپیشبینی بقای بیماران مبتلا به سرطان پستان با استفاده از دو مدل رگرسیون لجستیک و شبکه عصبی مصنوعی
Background and Objectives : recent years, considerable attention has been paid to statistical models for classification of medical data according to various diseases and their outcomes. Artificial neural networks have been successfully used for pattern recognition and prediction since they are not based on prior assumptions in clinical studies. This study compared two statistical models, arti...
متن کاملVariance estimation in reference based sensitivity analysis for longitudinal trials with protocol deviation
The statistical analysis of longitudinal randomised clinical trials is frequently complicated by the occurrence of protocol deviations, which result in incomplete data sets for analysis. Analysis and inference then rest on inherently untestable assumptions about the distribution of the unobserved data. It is therefore important to perform sensitivity analysis to explore the robustness of conclu...
متن کاملمقایسه مدل شبکه عصبی مصنوعی و رگرسیون پارامتری در پیش‌بینی بقای بیماران مبتلا به سرطان معده
Background & Objective: Using parametric models is common approach in survival analysis. In the recent years, artificial neural network (ANN) models have increasingly used in survival prediction. The aim of this study was to predict of survival rate of patients with gastric cancer by using a parametric regression and ANN models and compare these methods. Methods: We used the data of 436 gast...
متن کاملSpatial Varying Coefficient Regression Model For Relative Risk Factors of Esophageal Cancer Patients
In conventional methods for spatial survival data modeling, it is often assumed that the coefficients of explanatory variables in different regions have a constant effect on survival time. Usually, the spatial correlation of data through a random effect is also included in the model. But in many practical issues, the factors affecting survival time do not have the same effects in different regi...
متن کامل