PubMed · 7837137
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N M Hadler. 1994. The imperious p value.. https://pubmed.ncbi.nlm.nih.gov/7837137/
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The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low-pass filtering in relation to functional-anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non-inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.
BACKGROUND: As part of the international cooperative effort to develop a complete set of International Neuroblastoma Risk Groups, the International Neuroblastoma Pathology Committee (INPC) initiated activities in 1994 to devise a morphologic classification of neuroblastic tumors (NTs; neuroblastoma, ganglioneuroblastoma, and ganglioneuroma). METHODS: Six member pathologists (H.S., I.M.A., L.P.D., J.H., V.V.J., and B.R.) discussed and defined morphologically based classifications (Shimada classification; risk group and modified risk group proposed by Joshi et al.) on the basis of a review of 227 cases, using various pathologic characteristics of the NTs. The classification-grading system was evaluated for prognostic significance and biologic relevance. RESULTS: The INPC has adopted a prognostic system modeled on one proposed by Shimada et al. It is an age-linked classification dependent on the differentiation grade of the neuroblasts, their cellular turnover index, and the presence or absence of Schwannian stromal development. Based on morphologic criteria defined in this article, NTs were classified into four categories and their subtypes: 1) neuroblastoma (Schwannian stroma-poor), undifferentiated, poorly differentiated, and differentiating; 2) ganglioneuroblastoma, intermixed (Schwannian stroma-rich); 3) ganglioneuroma (Schwannian stroma-dominant), maturing and mature; and 4) ganglioneuroblastoma, nodular (composite Schwannian stroma-richlstroma-dominant and stroma-poor). Specific features, such as the mitosis-karyorrhexis index, the mitotic rate, and calcification, were also included to allow the prognostic significance of the classification to be tested. Recommendations are made regarding the surgical materials to use for an optimal pathobiologic assessment and the practical handling of samples. CONCLUSIONS: The current article covers the essentials and important points regarding the histopathologic evaluation of NTs. Using the morphologic criteria described herein, the INPC is proposing the International Neuroblastoma Pathology Classification. It is reported in a companion article in this issue (Cancer 1999;86:363-71).