Structural equation modeling in medical research: a primer

<p>Abstract</p> <p>Background</p> <p>Structural equation modeling (SEM) is a set of statistical techniques used to measure and analyze the relationships of observed and latent variables. Similar but more powerful than regression analyses, it examines linear causal relationships among variables, while simultaneously accounting for measurement error. The purpose of the present paper is to explicate SEM to medical and health sciences researchers and exemplify their application.</p> <p>Findings</p> <p>To facilitate its use we provide a series of steps for applying SEM to research problems. We then present three examples of how SEM has been utilized in medical and health sciences research.</p> <p>Conclusion</p> <p>When many considerations are given to research planning, SEM can provide a new perspective on analyzing data and potential for advancing research in medical and health sciences.</p

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Reclassification of SIDS cases - a need for adjustment of the San Diego classification?

A study was undertaken reclassifying cases of sudden infant death syndrome (SIDS) taken from two geographically separate locations utilizing the San Diego definition with subclassifications. One hundred twenty-eight infant cases were examined from files at Forensic Science South Australia in Adelaide, SA, Australia over a 7.5-year period from July 1999 to January 2007. Thirty-one cases (24%) had initially been diagnosed as SIDS and 30 (23%) as undetermined while 67 (52%) had an explainable cause of death. After reclassification, the number of SIDS cases had increased to 49 of the 128 cases, now representing 38% of the cases; category IB SIDS constituted 10 (20%) and II SIDS 39 (80%) of the SIDS cases. No cases were classified as IA SIDS. Two hundred eighteen infant cases were identified from the files of the Department of Forensic Medicine, Aarhus University, Denmark over a 16-year period from 1992 to 2007. Eighty-two (38%) were originally diagnosed as SIDS, 128 (59%) with identifiable causes of death, and 8 (4%) as unexplained. After review, 77 (35%) cases were reclassified as SIDS, a decrease of 6%. Twenty (26%) infants were classified as category IB SIDS and 57 (74%) as II SIDS. None of the cases met the criteria for IA SIDS. Problems arose in assessing cases with failure to thrive, fever, and possible asphyxia. Modifications to the San Diego subclassifications might improve the consistency of categorizing these cases.Lisbeth Lund Jensen, Marianne Cathrine Rohde, Jytte Banner, Roger William Byar