Sufficient Covariate, Propensity Variable and Doubly Robust Estimation

Statistical causal inference from observational studies often requires adjustment for a possibly multi-dimensional variable, where dimension reduction is crucial. The propensity score, first introduced by Rosenbaum and Rubin, is a popular approach to such reduction. We address causal inference within Dawid's decision-theoretic framework, where it is essential to pay attention to sufficient covariates and their properties. We examine the role of a propensity variable in a normal linear model. We investigate both population-based and sample-based linear regressions, with adjustments for a multivariate covariate and for a propensity variable. In addition, we study the augmented inverse probability weighted estimator, involving a combination of a response model and a propensity model. In a linear regression with homoscedasticity, a propensity variable is proved to provide the same estimated causal effect as multivariate adjustment. An estimated propensity variable may, but need not, yield better precision than the true propensity variable. The augmented inverse probability weighted estimator is doubly robust and can improve precision if the propensity model is correctly specified

Matching Methods for Causal Inference: A Review and a Look Forward

When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970s, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods---or developing methods related to matching---do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.Comment: Published in at http://dx.doi.org/10.1214/09-STS313 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

FNA based diagnosis of head and neck nodal lymphoma [Citomorfološka dijagnoza limfoma u području glave i vrata]

Fine-needle aspiration (FNA) biopsy has become a well established technique in the diagnosis, staging, and follow-up of patients with head and neck lesions. As in lymphoma diagnostics, FNA serves as a screening method in evaluating potentially affected lymph node for open or core biopsy. According to the World Health Organization classification of lymphoid neoplasms, today it is important to recognize cell morphology and reveal its phenotype, then combine it with different genotypic information and clinical data to provide appropriate therapy. The aim of this study was to assess the efficacy of FNA and immunocytochemistry based lymphoma diagnostic in head and neck region. We conducted a retrospective study during a period of three years where cases with either FNA diagnosis or clinical suspicion of newly recognized or relapsing lymphoma were reviewed. In the study were included patients that were referred to our laboratory from hematology department, in whom head and neck lymphadenopathia was found and lymph node FNA preceded other procedures. Two hundred eighty-five aspirations from 248 patients fulfilled study criteria. Adequate specimens were diagnosed as lymphoma in 100 cases (36%), in 65 male and 35 female patients, 76 in patients with newly discovered disease and 24 in patients with prior lymphoma diagnosis. Overall sensitivity of FNA specimens in the diagnosis of head and neck lymphomas was 90%, specificity 88%, predictive value of a positive result 97%, and predictive value of negative result 61%. Based on our results FNA corroborated with immunophenotyping by immunocytochemistry can be method of choice in primary lymphoma diagnosis as a method complementary to histopathology in lymphoma diagnostics

Comparative Direct Analysis of Type Ia Supernova Spectra. V. Insights from A Larger Sample and Quantitative Subclassification

A comparative study of optical spectra of Type Ia supernovae (SNe Ia) is extended, in the light of new data. The discussion is framed in terms of the four groups defined in previous papers of this series: core normal (CN); broad line (BL); cool (CL); and shallow silicon (SS). Emerging features of the SN Ia spectroscopic diversity include evidence (1) that extreme CL SN 1991bg-likes are not a physically distinct subgroup and (2) for the existence of a substantial number of SN 1999aa-like SSs that are very similar to each other and distinguishable from CN even as late as three weeks after maximum light. SN 1999aa-likes may be relatively numerous, yet not a physically distinct subgroup. The efficacy of quantitative spectroscopic subclassification of SNe Ia based on the equivalent widths of absorption features near 5750 A and 6100 A near maximum light is discussed. The absolute magnitude dispersion of a small sample of CNs is no larger than the characteristic absolute magnitude uncertainty.Comment: 32 pages including 14 figures and 1 table, accepted by PAS

Regulatory T cells in melanoma revisited by a computational clustering of FOXP3+ T cell subpopulations

CD4+ T cells that express the transcription factor FOXP3 (FOXP3+ T cells) are commonly regarded as immunosuppressive regulatory T cells (Treg). FOXP3+ T cells are reported to be increased in tumour-bearing patients or animals, and considered to suppress anti-tumour immunity, but the evidence is often contradictory. In addition, accumulating evidence indicates that FOXP3 is induced by antigenic stimulation, and that some non-Treg FOXP3+ T cells, especially memory-phenotype FOXP3low cells, produce proinflammatory cytokines. Accordingly, the subclassification of FOXP3+ T cells is fundamental for revealing the significance of FOXP3+ T cells in tumour immunity, but the arbitrariness and complexity of manual gating have complicated the issue. Here we report a computational method to automatically identify and classify FOXP3+ T cells into subsets using clustering algorithms. By analysing flow cytometric data of melanoma patients, the proposed method showed that the FOXP3+ subpopulation that had relatively high FOXP3, CD45RO, and CD25 expressions was increased in melanoma patients, whereas manual gating did not produce significant results on the FOXP3+ subpopulations. Interestingly, the computationally-identified FOXP3+ subpopulation included not only classical FOXP3high Treg but also memory-phenotype FOXP3low cells by manual gating. Furthermore, the proposed method successfully analysed an independent dataset, showing that the same FOXP3+ subpopulation was increased in melanoma patients, validating the method. Collectively, the proposed method successfully captured an important feature of melanoma without relying on the existing criteria of FOXP3+ T cells, revealing a hidden association between the T cell profile and melanoma, and providing new insights into FOXP3+ T cells and Treg