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.
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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.
The appropriate CI for hypothesis testing is deter- confidence. The null value is that of Physical Therapy. Table 4. The t test performed rejected the difference that would be found in the population of null hypothesis that the population difference between patients with FMS would be greater than zero, which is treatments is zero Tab.
This advantage of the CI can be hypothesis, in respect to this particular sample, based on illustrated by considering the results of a second exper- any population difference outside the limits of the CI.
The t test, in contrast, only serves to depending on the size of the sample. Thus, the proba- exclude a difference of zero and does not allow infer- bility value is not a direct function of the effect size ences to be made about other possible values of the alone, but is instead a function of both the effect size and population parameter.
It follows If a series of CIs are calculated on a given data set, the that knowledge of the probability value returned by a risk of a Type I error ie, the risk of rejecting the null hypothesis test does not, on its own, provide an indica- hypothesis when it is true will rise accordingly, just as in tion of the likely effect size in the population, just as the the case of multiple tests of significance. The CI, in contrast, provides this sons are made. In such circumstances, it is often appro- information.
In the third FMS study Tab. Such information is ence in pain relief of just over 7 mm on the VAS was potentially important to the clinician but is not revealed sufficient to reject the null hypothesis of no difference by inspection of the probability value alone.
The Physi- with an independent t test. The CI for the hood of death from stroke in the group that received pain-relief scores is wider in this FMS study than in the aspirin to the likelihood of death in the group that previous FMS study Tab.
Although a mean difference of 7.
Inspection of the upper limit of mm, which is arguably not likely to be clinically impor- the CI for the odds ratio reveals that an almost fold tant, is also compatible with rejection of the null hypoth- difference in fatal stroke between the 2 groups cannot be esis at the P ,.
Thus, there is considerable lack of precision in the odds ratio for this study, which is due to the low 1. Consequently, despite the nonsignificant find- patients with FMS between the 2 interventions tested. The odds ratio is an appropriate measure of Figure 3.
The solid reference line an outcome variable measured on a continuous scale. The horizontal scale is logarithmic. The narrow width of the CI for the total odds ratio found an overall odds ratio for death or deterioration of reflects the precision that results from aggregating data, 0.
This odds ratio means that the likelihood of death and the fact that it excludes 1. Thus, CIs assist conventional therapy. Studies will vary in the contributions they make to the total odds ratio, based on sample sizes and other factors Conclusion that may or may not control random error.
Displaying Interval estimation is a valuable form of statistical infer- the CI for each study on what is known as a forest plot ence that we believe has certain advantages over conven- illustrates clearly the relative merits of the separate tional hypothesis testing based on tests of significance.
The sented as an estimate of the corresponding popu- figure also shows the total odds ratio, which is calculated lation parameter the standard deviation of the by statistical analysis of the aggregated data from the mean should be presented if no inference to the individual studies odds ratios can be calculated retro- population is intended. A ratio of less than 1. Interpreting the results of observational provided by CIs to assess the clinical importance of research: chance is not such a fine thing.
Distribution-Free Tests. Calculating confidence intervals for risk of a Type 1 error. London, England: British Medical Association; — Ordinal Methods for Behavioral Data Analysis.
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