ABSTRACT
The cumulative incidence function is the standard method for estimating the marginal probability of a given event in the presence of competing risks. One basic but important goal in the analysis of competing risk data is the comparison of these curves, for which limited literature exists. We proposed a new procedure that lets us not only test the equality of these curves but also group them if they are not equal. The proposed method allows determining the composition of the groups as well as an automatic selection of their number. Simulation studies show the good numerical behavior of the proposed methods for finite sample size. The applicability of the proposed method is illustrated using real data.
Subject(s)
Models, Statistical , Humans , Incidence , Biometry/methods , Risk Assessment , Computer Simulation , Data Interpretation, StatisticalABSTRACT
Survival analysis includes a wide variety of methods for analyzing time-to-event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for censored data. When the null hypothesis of equality of curves is rejected, leading to the clear conclusion that at least one curve is different, it can be interesting to ascertain whether curves can be grouped or if all these curves are different from each other. A method is proposed that allows determining groups with an automatic selection of their number. The validity and behavior of the proposed method was evaluated through simulation studies. The applicability of the proposed method is illustrated using real data. Software in the form of an R package has been developed implementing the proposed method.
Subject(s)
Data Interpretation, Statistical , Kaplan-Meier Estimate , Models, Statistical , Research Design/statistics & numerical data , Algorithms , Colonic Neoplasms/mortality , Computer Simulation , Humans , Lung Neoplasms/mortalityABSTRACT
A major obstacle hampering the therapeutic application of regulatory T (Treg) cells is the lack of suitable extracellular markers, which complicates their identification/isolation. Treg cells are normally isolated via CD25 (IL-2Rα) targeting, but this protein is also expressed by activated CD4(+) effector T (Teff) lymphocytes. Other extracellular (positive or negative) Treg selection markers (e.g., HLA-DR, CD127) are also nonspecific. CD26 is an extracellular peptidase whose high expression has been traditionally used as an indicator of immune activation and effector functions in T cells. Now, we provide flow cytometry data showing high levels of CD26 within CD4(+)CD25(-) or CD4(+)FoxP3(-/low) effector T (Teff) lymphocytes, but negative or low levels (CD26(-/low)) in Treg cells selected according to the CD4(+)CD25(high) or the CD4(+)FoxP3(high) phenotype. Unlike the negative marker CD127 (IL-7Rα), which is down modulated in CD4(+) Teff lymphocytes after TCR triggering, most of these cells upregulate CD26 and take a CD4(+)CD25(+/high) CD26(+) phenotype upon activation. In contrast, there is only a slight upregulation within Treg cells (CD4(+)CD25(high) CD26(-/low)). Thus, differences in CD26 levels between Treg and Teff subsets are stable, and assessment of this marker, in combination with others like CD25, FoxP3, or CD127, may be useful during the quantitative evaluation or the isolation of Treg cells in samples containing activated Teff lymphocytes (e.g., from patients with autoimmune/inflammatory diseases).