PubMed Health⌕ Search

PubMed · 3772220

Learning statistical methods.

Abstract

Since at least 1951 anesthesia journals have called for a more rigorous application of statistical methods in research reports. This appeal for statistical excellence actually applies to the researcher, to the clinician journal reader, and to the editor. Thirty five years ago the obligations of these three groups was made clear in an unsigned editorial in Anesthesiology; there is now a widespread consensus on these responsibilities. The researcher must create valid science. The clinician reader must bring sufficient intellectual skills to understand a journal article; using these skills the reader must critique the research report to judge its applicability to his patients. The editor must decline manuscripts showing poor or absent application of the scientific method, experimental design, and statistical analysis. Though the editors continue to exhort further improvements, even a casual perusal of their journals demonstrates a tremendous improvement in the handling of numeric data over the last four decades. With the increasing sophistication of statistical methods in journals, the reader must continue to expand his statistical understanding. In this short review, a few highlights of statistical methods useful either in planning and accomplishing a research project or in reading a research report will be discussed; these include the planning of a study design, data collection, data analysis, and interpretation of the research. Comments about using these concepts to better understand a research article will be included. Also included will be an annotated reading list for further study and reference. Mathematical formulas have been avoided as much as possible. Actual use of statistics requires use of equations which can be found in the books of the reading list.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

N L Pace. 1986. Learning statistical methods.. https://doi.org/10.1007/bf02916392

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Tutorial in biostatistics: competing risks and multi-state models.

Standard survival data measure the time span from some time origin until the occurrence of one type of event. If several types of events occur, a model describing progression to each of these competing risks is needed. Multi-state models generalize competing risks models by also describing transitions to intermediate events. Methods to analyze such models have been developed over the last two decades. Fortunately, most of the analyzes can be performed within the standard statistical packages, but may require some extra effort with respect to data preparation and programming. This tutorial aims to review statistical methods for the analysis of competing risks and multi-state models. Although some conceptual issues are covered, the emphasis is on practical issues like data preparation, estimation of the effect of covariates, and estimation of cumulative incidence functions and state and transition probabilities. Examples of analysis with standard software are shown.

Biometry↗

The role of education in biostatistical consulting.

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.

Biometry↗

Improving the quality of patient care using reliability measures: a classification tree approach.

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.

Biometry↗