Practical Statistics for Medical Research. Edited by Douglas G. Altman. ISBN 0- , Chapman and Hall, London, , pp., $ Until recently. Statistics in Medicine. Book Review. Practical statistics for medical research. Douglas G. Altman, Chapman and Hall, London, No. of pages: Price: £. Statistics in Medicine · Volume 10, Issue 10 Practical statistics for medical research. Douglas G. Altman, Chapman and Hall, London, No. of pages:
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Practical statistics for medical research / Douglas G. Altman. p. cm. Originally published: London: Chapman & Hall, Includes bibliographical references . Douglas-G-Altman-Practical-Statistics-for-Medical-Research- Chapman-Hall-CRCpdf - Ebook download as PDF File .pdf) or read book . PRACTICAL. STATISTICS. FOR. MEDICAL. RESEARCH. Douglas G. Altman. Head of Medical Statistical Laboratory. Imperial Cancer Research Fund. London.
Conventional hypothesis testing serves to either reject or retain a null hypothesis. A CI, while also functioning as a hypothesis test, provides additional information on the variability of an observed sample statistic ie, its precision and on its probable relationship to the value of this statistic in the population from which the sample was drawn ie, its accuracy. Thus, the CI focuses attention on the magnitude and the probability of a treatment or other effect. It thereby assists in determining the clinical usefulness and importance of, as well as the statistical significance of, findings. The CI is appropriate for both parametric and nonparametric analyses and for both individual studies and aggregated data in meta-analyses.
Close Preview. Toggle navigation Additional Book Information. Description Table of Contents Reviews. Summary Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates.
Explores topics of particular importance in clinical practice, including diagnostic tests, method comparison, and observer agreement Utilizes real data throughout and includes dozens of interesting data sets Discusses the use and misuse of statistics, enabling readers to judge the appropriateness of methods and interpretations published in medical journals Describes the main statistical methods for comparing data collected from different groups of individuals or for relating different observations from the same people Provides many problems, all with detailed solutions The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research.
The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research. Mathematical Notation Appendix B: Reviews " Request an e-inspection copy. Share this Title. Related Titles. Statistics for Environmental Biology and Toxicology. Shopping Cart Summary. Items Subtotal. View Cart. Offline Computer — Download Bookshelf software to your desktop so you can view your eBooks with or without Internet access.
The country you have selected will result in the following: Please review our Terms and Conditions of Use and check box below to share full-text version of article. No abstract is available for this article. Citing Literature Number of times cited according to CrossRef: Volume 10 , Issue 10 October Pages Related Information. Email or Customer ID. Forgot password? Old Password. New Password. Key strengths of the CI as a means of statistical is an estimate because the true value of the population is inference are then considered, in particular, the preci- almost always unknown.
Address all correspondence to Dr Sim. This article was submitted April 9, , and was accepted August 23, Physical Therapy.
February Sim and Reid. That is, the observed difference between or median values of a variable between 2 or more groups is considered to be no greater than the differ- subgroups within the sample.
The way in which the P ,. That is, it is considered more What is the likelihood of finding this relationship in the plausible that the difference in outcome is due to an sample, if, in fact, no such relationship exists in the underlying difference between the groups than that it is population from which it was drawn?
This assumption of due to chance factors such as variation in sampling or no relationship is referred to as the null hypothesis, and group assignment.
The null hypothesis is rejected in the rival assumption that the relationship does exist favor of the alternative hypothesis. A statistical test for differences was used in this A hypothetical example may serve to illustrate the way in study. Because the data concerned are continuous, are which a statistical hypothesis test is utilized. A sample of approximately normally distributed, and can be argued 50 patients with fibromyalgia syndrome FMS is drawn to lie on an interval scale of measurement, a t test for randomly from a population of such patients and then independent measures was performed.
The probability assigned again randomly to receive 1 of 2 treatments associated with the test statistic P5. The null hypothesis is that a the conventional critical value of. This finding pro- difference in pain relief will not exist between the 2 vides sufficient grounds for doubting the null hypothe- groups following treatment. The alternative hypothesis is sis, which is therefore rejected in favor of the alternative that such a difference will exist. The chosen outcome hypothesis. The difference in pain relief between the 2 variable, pain intensity as measured in millimeters on a groups is said to be real.
The mean not yet been answered with respect to this study. The first pain relief score can then be calculated for each group. Although a mean difference in pain This pain relief score will almost certainly differ in the 2 relief between the 2 treatments has been shown to be groups, but the question is whether such a difference in real, based on probabilistic statistics, it may be of little outcome is attributable to an underlying difference in practical importance.
On a cm VAS, a difference the treatments received by the groups, rather than between treatments of 3. If there is a sufficiently successful treatment eg, it might be more expensive or high probability that the observed difference in outcome require more frequent attendance by the patient.
Sim and Reid Physical Therapy. It is worth noting that the converse of between It is also the case that the situation just outlined may also arise. A difference in values toward the extremes of this interval are rather less pain relief may be found not to be real, perhaps due to likely to represent the population mean than those insufficient sample size or a high degree of variance in nearer the center.
If we were to draw repeated samples from a in the population. This The population mean is either included or not difference is an estimate of the difference that would included. All we know is that the difference found in this The function of a CI, therefore, is essentially an inferen- particular sample is sufficiently great for it to be attrib- tial one.
A CI is used when examining a characteristic of uted to a genuine difference between the treatments a sample in this case, the mean pretest VAS score in rather than to chance variation in sampling or allocation terms of its degree of variability in the corresponding to groups. We do not know how good an estimate it is of population.
As or the semi-interquartile range for a median , should Abrams and Scragg11 point out, a probability value be used. This is information, however, that we need in order to Width of Confidence Intervals inform clinical practice. For a given level of confidence, the narrower the CI, the greater the precision of the sample mean as an estimate The CI assists in addressing these questions as to the of the population mean.
There are 3 factors that will and remedies some of the shortcomings of more con- influence the width of a CI at a given level of confidence. These points will be considered in detail following an account of First, the width of the CI is related to the variance of the interval estimation.
If this sample variance can be reduced eg, by increasing the reliability The Nature of Confidence Intervals of measurements , the CI will be narrower, reflecting the A sample statistic, such as a sample mean, provides an greater precision of the individual measurements. It provides a single Selecting a sample that is more homogeneous will estimate of the specific value of the parameter on the reduce the variance of scores and thereby increase their basis of the observed value of the statistic.
As an adjunct precision. This factor, however, is often outside the to a single estimate, an interval estimate can be calculated. This increase in precision the mean pretest VAS score for the sample of patients is occurs because the variance of a statistic, as expressed by Physical Therapy.
The usefulness of a CI depends on the point statistic eg, the sample mean on which it is based being an unbiased point estimate. If systematic error is present in a study, the point estimate will lie at some distance from the true value of the parameter.
In such a case, a CI based on a large sample will, paradoxically, be more misleading than one based on a small sample. Means and confidence intervals of years of qualified experience for sider again Figure 1. Imagine that the point estimate of progressively larger samples of physical therapists drawn from a single 9. It is evident that, unlike the 2 wider CIs, the narrow CIs, based on the larger samples, actually exclude this value. In the presence of systematic error, its standard error, decreases as sample size increases.
This example illustrates the fundamental and the mean period of postqualification experience for point that increases in sample size will only assist in each sample. The mean is precisely the same in each dealing with random, not systematic, error. Systematic case, but the CI becomes narrower as the sample size error is usually an issue in study design rather than a increases. As sampling precision is related to the square function of the statistics used.
With a tcv , and the standard error of the sample mean SE , higher level of confidence, the interval needs to be wider according to the following formula: in order to support the claim of having included the population parameter at the chosen level of confidence.
It is not the case, however, that, at a given confidence level, a narrow CI is any more or less likely than a wider 5 The probability of including the parameter is deter- mined by the chosen confidence level, not by the width 5 The probability of inclusion, however, is the same in both cases.
A statistic such as a sample mean is just one estimate of the population mean, and, because the population mean is nearly always unknown, it is not possible to know how good the estimate is. In this case, the population mean is 9. Any one of these point estimates of the mean, taken on its own, gives no indication of how precise an estimate it is or of how near it lies to the population mean.
Moreover, the width Figure 2.
The solid horizontal reference line represents the population mean of 9. The point estimate. Because each CI differs from sample 10 samples. In many cases, The way in which the null hypothesis is tested by means however, researchers may want to test a specific hypoth- of a CI is by determining whether the null value ie, the esis, as in the example of the FMS study considered value specified in the null hypothesis lies within the CI.
Although the orthodox approach is to conduct a If the null value lies within the CI, we cannot exclude it hypothesis test, in this case using the independent- as being the population parameter at the chosen level of measures t test Tab. In contrast, if the null value lies outside the reject or retain the null hypothesis, and thereby perform CI, we can exclude the null value from the possible precisely the same function as the orthodox significance values of the population parameter at this level of test.