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Objectives and study design: In this population-based study of 602 patients, we amended C-reactive protein (CRP) and plasma albumin (PA) levels around the diagnosis of diffuse large B-cell lymphoma (DLBCL) to the International Prognostic Index (IPI) and assessed 0–90, 91–365, and +365-day survival. Results: The CRP did not contribute to the IPI's prognostic or discriminatory ability, regardless of time period, particularly not in models with PA. In contrast, the PA was an important contributor, especially in the 0–90 day period, but also up to one year after the diagnosis. For day 0–90, the model with the IPI only had an Area Under the Receiver Operating Characteristics (AUROC) of 0.742, whereas the IPI with PA as a continuous variable rendered an AUROC of 0.841. Especially the lower PA quartile (18–32 g/L) contributed to the worse prognosis. Conclusions: The amendment of PA to the IPI may significantly improve the short-term prognostic and discriminative ability. The amendment of the plasma albumin (PA) level to the International Prognostic Index significantly improved the prediction of mortality up to one year after the diagnosis of diffuse large B-cell lymphoma. It was especially the lower quartile of the PA level (18–32 g/L) that contributed to the worse prognosis. [ABSTRACT FROM AUTHOR]

In order to show the effect of treatment, the change between two repeated psychometric measurements at the individual level should be estimated. The simplest method is to calculate the absolute difference between two measurements. However, measurements obtained in a clinical setting are often influenced by other changes not related to the treatment. One of the typical sources of error is regression to the mean (RTM). Iachina and Bilenberg (2012) propose a new method to adjust to the RTM effect. In this work, we will evaluate the performance of absolute difference method and adjusted method in a simulation study. [ABSTRACT FROM AUTHOR]

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Although longitudinal studies are designed to collect data on all individuals at each assessment time, often longitudinal studies suffer from missing values, especially when studying processes in late life. These missing values not only mean less efficient estimates because of the reduced size of the data base but also that the standard methods cannot be immediately used to analyze the data. Moreover, possible biases exist because the respondents are usually systematically different from the nonrespondents. Several methods was developing to accommodate missing data in models for longitudinal data analysis. One of the approaches developed by Robins et al. (1995) is the co-called Inverse Probability Weighted Generalized Estimating Equations (IPWGEE). This approach involves modelling the missing data process and weighting the estimating equations by the invers probability of participation. In this work, we will evaluate the performance of IPWGEE in a simulation study and prove the consistency of the mean parameters. [ABSTRACT FROM AUTHOR]