Medical Statistics Made Easy for the Medical Practitioner (original) (raw)
Related papers
Essential statistics for the clinician
Surgery (United Kingdom), 2012
This article provides a working introduction to statistics for the core trainee, specialty trainee and keen medical student to aid their daily practice, interaction at journal clubs and scientific meetings and for examination preparation with a focus on surgical research. Data can be described as metric (quantitative) or categorical (qualitative). The mode, median or mean can be used as measures of location and the variation ratio, range, interquartile range and standard deviation as measures of spread. Cohort studies can be used to define risk reduction of a given intervention; casecontrol studies rely on odds ratios to approximate risk. Hypothesis tests can be used to establish statistical significance, which is distinct from clinical significance. Confounding can be controlled with multi-variable analyses. Correlation coefficients can be used to establish the relationship between two sets of continuous data, but is distinct from causation. Power calculations can be used to estimate the sample size required to generate a clinically and statistically significant result from a trial. It is important for clinicians to be able to understand these concepts and translate them into simple and understandable format for patients, colleagues and commissioners to maintain high quality care within the NHS.
MEDICAL STATISTICS from A to Z
Preface to the second edition In the second edition of Medical Statistics from A to Z I have addedmany new definitions and taken the opportunity to correct and clarify a number of entries. More references are also provided that point readers to more detailed accounts of topics. cambridge university press
Statistics and Medicine: the Indispensable Know - How of the Researcher
2013
Statistics has often been misunderstood in Medicine, but it is indispensable knowledge both for the experimenter and the reader. Statistical methods allow to study diseases, patients, and epidemiological events. The modern researcher cannot refuse to know and to use statistics. A deeper understanding is required to prepare a research project and to avoid colossal mistakes of misleading.
Meta-analysis involves combining summary information from related but independent studies. The objectives of a meta-analysis include increasing power to detect an overall treatment e ect, estimation of the degree of beneÿt associated with a particular study treatment, assessment of the amount of variability between studies, or identiÿcation of study characteristics associated with particularly e ective treatments. This article presents a tutorial on meta-analysis intended for anyone with a mathematical statistics background. Search strategies and review methods of the literature are discussed. Emphasis is focused on analytic methods for estimation of the parameters of interest. Three modes of inference are discussed: maximum likelihood; restricted maximum likelihood, and Bayesian. Finally, software for performing inference using restricted maximum likelihood and fully Bayesian methods are demonstrated. Methods are illustrated using two examples: an evaluation of mortality from prophylactic use of lidocaine after a heart attack, and a comparison of length of hospital stay for stroke patients under two di erent management protocols.
The Ochsner journal, 2010
The Accreditation Council for Graduate Medical Education sets forth a number of required educational topics that must be addressed in residency and fellowship programs. We sought to provide a primer on some of the important basic statistical concepts to consider when examining the medical literature. It is not essential to understand the exact workings and methodology of every statistical test encountered, but it is necessary to understand selected concepts such as parametric and nonparametric tests, correlation, and numerical versus categorical data. This working knowledge will allow you to spot obvious irregularities in statistical analyses that you encounter.
A quick guide to medical statistics
The Foundation Years, 2005
Study design is a fundamental aspect of statistics, and possibly more important than analysis. If the analysis is incorrect it can be repeated, but if the design is wrong the results may be meaningless and a lot of time, effort and resources will have been wasted. There are two types of study in medical research: observational and planned experiments.
The Use of Statistics in Medical Research
The American …, 2007
There is widespread evidence of the extensive use of statistical methods in medical research. Just the same, standards are generally low and a growing body of literature points to statistical errors in most medical journals. However, there is no comprehensive study contrasting the top medical journals of basic and clinical science for recent practice in their use of statistics. were screened for their statistical content. Types, frequencies, and complexity of applied statistical methods were systematically recorded. A 46-item checklist was used to evaluate statistical quality for a subgroup of papers.
The Use of Statistics in Health Sciences: Situation Analysis and Perspective
Statistics in Biosciences, 2016
Statistics plays a crucial role in research, planning and decision-making in the health sciences. Progress in technologies and continued research in computational statistics has enabled us to implement sophisticated mathematical models within software that are handled by non-statistician researchers. As a result, over the last decades, medical journals have published a host of papers that use some novel statistical method. The aim of this paper is to present a review on how the statistical methods are being applied in the construction of scientific knowledge in health sciences, as well as, to propose some improvement actions. From the early 20th century, there has been a remarkable surge in scientific evidence alerting on the errors that many non-statistician researchers were making in applying statistical methods. Today, several studies continue showing that a large percentage of articles published in high impact factor journals contain errors in data analysis or interpretation of results, with the ensuing repercussions on the validity and efficiency of the research conducted. Scientific community should reflect on the causes that have led to this situation, the consequences to the advancement of scientific knowledge and the solutions to this problem.
A Brief Outline of Bio-Statistics in Medical Research
International journal of scientific and research publications, 2018
Bio-statistics is the important branch of statistics and is related to medical field. Also it plays an important role in the field of research. The main role of statistics in research is to designing a research, analyzing data and draw meaningful conclusions. The meaningful conclusion can be drawn by using proper statistical tests. Statistics also helps to reduce large volume of raw data which must be suitably reduced so that the same can be read easily and can be used for further analysis. This article covers the brief outline of data, qualitative data, and quantitative data. Also give a brief outline of measures of central tendency (location), measures of variability (dispersion) , statistical inference(parametric and non-parametric tests) and give brief outline of sample size calculations.[4]