PubMed · 14808095
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E BALDUCCI. [Tuberculosis morbidity with reference to working classes].. https://pubmed.ncbi.nlm.nih.gov/14808095/
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Medical students, residents, postdoctoral fellows, and faculty commonly consult with biostatistical experts about study design and data analysis when conducting clinical research. The role of biostatistical training during these consultations is examined, and characterizations of the connections between biostatistical consultation and education are reviewed. The presence and kinds of teaching efforts during biostatistical consults at four academic research institutions over various periods of time between 1999 and 2005 (237 consultations in total) were recorded and are described. By site, 67, 70, 78, and 100 per cent of the consulting sessions included biostatistical training, with an overall 78 per cent (95 per cent CI: 73-83 per cent) of consultations including an educational component when all consultations were combined. Training covered a wide range of biostatistical topics. Seventy-five per cent of the consultations with faculty (120/161), 79 per cent with fellows and residents (31/39), and 100 per cent with medical students (10/10) included some degree of instruction in study design or statistical analysis topics. Results show that both the need and the opportunity exist for specialized biostatistical instruction during one-on-one sessions between a consulting biostatistician and physicians, medical students, and research staff. Academic researchers are ideally positioned to absorb this kind of training when they initiate a request for assistance with their own research project.
This paper considers the application and interpretation of new reliability measures for a classification tree-based medical risk assessment tool. Following the construction of a classification tree reliability measures may then be used to provide an estimate of the precision of the classification and the probability in each terminal node of the classification tree. Identification of unreliable nodes (those that have low precision) in this application may indicate patient groups requiring closer monitoring or scenarios in which further information about the patient is required, thereby providing medical practitioners with an avenue for more informed decision making.
Effect size estimates to be combined in a systematic review are often found to be more variable than one would expect based on sampling differences alone. This is usually interpreted as evidence that the effect sizes are heterogeneous. A random-effects model is then often used to account for the heterogeneity in the effect sizes. A novel method for constructing confidence intervals for the amount of heterogeneity in the effect sizes is proposed that guarantees nominal coverage probabilities even in small samples when model assumptions are satisfied. A variety of existing approaches for constructing such confidence intervals are summarized and the various methods are applied to an example to illustrate their use. A simulation study reveals that the newly proposed method yields the most accurate coverage probabilities under conditions more analogous to practice, where assumptions about normally distributed effect size estimates and known sampling variances only hold asymptotically.