In this paper, we consider the use of the EM algorithm for the fitting of distributions by maximum likelihood to overdispersed count data. In the course of this, we also provide a review of various approaches that have been proposed for the analysis of such data. As the Poisson and binomial regression models, which are often adopted in the first instance for these analyses, are particular examples of a generalized linear model (GLM), the focus of the account is on the modifications and extensions to GLMs for the handling of overdispersed count data.

Background This study aimed to determine the impact of preoperative exposure to intravenous contrast for CT and the risk of developing postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. Methods This prospective, multicentre cohort study included adults undergoing gastrointestinal resection, stoma reversal or liver resection. Both elective and emergency procedures were included. Preoperative exposure to intravenous contrast was defined as exposure to contrast administered for the purposes of CT up to 7 days before surgery. The primary endpoint was the rate of AKI within 7 days. Propensity score‐matched models were adjusted for patient, disease and operative variables. In a sensitivity analysis, a propensity score‐matched model explored the association between preoperative exposure to contrast and AKI in the first 48 h after surgery. Results A total of 5378 patients were included across 173 centres. Overall, 1249 patients (23·2 per cent) received intravenous contrast. The overall rate of AKI within 7 days of surgery was 13·4 per cent (718 of 5378). In the propensity score‐matched model, preoperative exposure to contrast was not associated with AKI within 7 days (odds ratio (OR) 0·95, 95 per cent c.i. 0·73 to 1·21; P = 0·669). The sensitivity analysis showed no association between preoperative contrast administration and AKI within 48 h after operation (OR 1·09, 0·84 to 1·41; P = 0·498). Conclusion There was no association between preoperative intravenous contrast administered for CT up to 7 days before surgery and postoperative AKI. Risk of contrast‐induced nephropathy should not be used as a reason to avoid contrast‐enhanced CT.

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The peri-operative use of angiotensin-converting enzyme inhibitors or angiotensin-2 receptor blockers is thought to be associated with an increased risk of postoperative acute kidney injury. To reduce this risk, these agents are commonly withheld during the peri-operative period. This study aimed to investigate if withholding angiotensin-converting enzyme inhibitors or angiotensin-2 receptor blockers peri-operatively reduces the risk of acute kidney injury following major non-cardiac surgery. Patients undergoing elective major surgery on the gastrointestinal tract and/or the liver were eligible for inclusion in this prospective study. The primary outcome was the development of acute kidney injury within seven days of operation. Adjusted multi-level models were used to account for centre-level effects and propensity score matching was used to reduce the effects of selection bias between treatment groups. A total of 949 patients were included from 160 centres across the UK and Republic of Ireland. From this population, 573 (60.4%) patients had their angiotensin-converting enzyme inhibitors or angiotensin-2 receptor blockers withheld during the peri-operative period. One hundred and seventy-five (18.4%) patients developed acute kidney injury; there was no difference in the incidence of acute kidney injury between patients who had their angiotensin-converting enzyme inhibitors or angiotensin-2 receptor blockers continued or withheld (107 (18.7%) vs. 68 (18.1%), respectively; p = 0.914). Following propensity matching, withholding angiotensin-converting enzyme inhibitors or angiotensin-2 receptor blockers did not demonstrate a protective effect against the development of postoperative acute kidney injury (OR (95%CI) 0.89 (0.58-1.34); p = 0.567).

Researchers are frequently faced with the analysis of microarray data of a relatively large number of genes using a small number of tissue samples. We examine the application of two statistical methods for clustering such microarray expression data: EMMIX-GENE and GeneClust. EMMIX-GENE is a mixture-model based clustering approach, designed primarily to cluster tissue samples on the basis of the genes. GeneClust is an implementation of the gene shaving methodology, motivated by research to identify distinct sets of genes for which variation in expression could be related to a biological property of the tissue samples. We illustrate the use of these two methods in the analysis of Affymetrix oligonucleotide arrays of well-known data sets from colon tissue samples with and without tumors, and of tumor tissue samples from patients with leukemia. Although the two approaches have been developed from different perspectives, the results demonstrate a clear correspondence between gene clusters produced by GeneClust and EMMIX-GENE for the colon tissue data. It is demonstrated, for the case of ribosomal proteins and smooth muscle genes in the colon data set, that both methods can classify genes into co-regulated families. It is further demonstrated that tissue types (tumor and normal) can be separated on the basis of subtle distributed patterns of genes. Application to the leukemia tissue data produces a division of tissues corresponding closely to the external classification, acute myeloid meukemia (AML) and acute lymphoblastic leukemia (ALL), for both methods. In addition, we also identify genes specific for the subgroup of ALL-Tcell samples. Overall, we find that the gene shaving method produces gene clusters at great speed; allows variable cluster sizes and can incorporate partial or full supervision; and finds clusters of genes in which the gene expression varies greatly over the tissue samples while maintaining a high level of coherence between the gene expression profiles. The intent of the EMMIX-GENE method is to cluster the tissue samples. It performs a filtering step that results in a subset of relevant genes, followed by gene clustering, and then tissue clustering, and is favorable in its accuracy of ranking the clusters produced.