PubMed · 11147503
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2000. Security HotList.. https://pubmed.ncbi.nlm.nih.gov/11147503/
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This study provides new standards of fetal organ weights (brain, heart, liver, pancreas, spleen, lungs, kidneys, adrenals, thymus, and thyroid) and body dimensions (crown-heel and crown-rump lengths, head circumference, and foot length). Subjects came from a large dataset including more than 4,000 fetuses autopsied in fetopathology units of pediatric hospitals in Paris between 1986 and 2001. From this dataset, 673 subjects were carefully selected by exclusion of multiple pregnancies, macerated and malformed fetuses, subjects with abnormal karyotypes, and those with severe infections. Fetal age ranged from 9 to 42 gestational weeks, with a very large sample of fetuses in the first half of gestation. Each organ was weighed after fixation in formalin. The standards were computed in relation to age and body weight. The mathematical models used to fit the percentile growth curves were carefully selected for each organ or dimension. This study, based on reliable methodology, affords a whole set of accurate growth standards useful for pathologists.
At both of our universities we teach (with colleagues) introductory courses in statistics for fellows and physicians. We do not expect that those taking these courses will be able to do their own statistical work, but rather the intention is for them to 'learn the language' and to facilitate future collaboration. Basic principles of study design are introduced in the courses, as well as some of the most common statistical procedures. We will discuss the factors (what works and what does not) that may contribute to a successful course, a comparison to other courses, and our self-evaluation strategy. Finally, we will cover the financial arrangements that we have made when teaching these courses.
Focused on interpreting data as statistical evidence, the evidential paradigm uses likelihood ratios to measure the strength of statistical evidence. Under this paradigm, re-examination of accumulating evidence is encouraged because (i) the likelihood ratio, unlike a p-value, is unaffected by the number of examinations and (ii) the probability of observing strong misleading evidence is naturally low, even for study designs that re-examine the data with each new observation. Further, the controllable probabilities of observing misleading and weak evidence provide assurance that the study design is reliable without affecting the strength of statistical evidence in the data. This paper illustrates the ideas and methods associated with using likelihood ratios to measure statistical evidence. It contains a comprehensive introduction to the evidential paradigm, including an overview of how to quantify the probability of observing misleading evidence for various study designs. The University Group Diabetes Program (UGDP), a classic and still controversial multi-centred clinical trial, is used as an illustrative example. Some of the original UGDP results, and subsequent re-analyses, are presented for comparison purposes.