Author
Martin Bland
Other affiliations: University of Notre Dame Australia, St George's, University of London
Bio: Martin Bland is an academic researcher from University of York. The author has contributed to research in topics: Randomized controlled trial & Population. The author has an hindex of 50, co-authored 171 publications receiving 12809 citations. Previous affiliations of Martin Bland include University of Notre Dame Australia & St George's, University of London.
Papers published on a yearly basis
Papers
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Book•
23 Nov 1987
TL;DR: The design of experiments, analysis of the means of small samples using the t-c Distribution, and selection of the statistical method for clinical measurement and the structure of human populations are reviewed.
Abstract: Introduction The design of experiments Sampling and observational studies Summarizing data Presenting data Probability The Normal Distribution Estimation, standard error, and confidence intervals Significance tests Analysis of the means of small samples using the t-c Distribution Choosing the statistical method Clinical measurement Mortality statistics and the structure of human populations Solutions to exercises.
2,245 citations
TL;DR: Completion of long-term follow-up is needed to establish the efficacy of carotid artery stenting compared with endarterectomy, but in the meantime, carotin artery stent should remain the treatment of choice for patients suitable for surgery.
1,115 citations
TL;DR: The authors pointed out that it could be highly misleading to analyse such data by combining repeated observations from several subjects and then calculating the correlation coefficient as if the data were a simple sample, as shown in table I.
Abstract: In an earlier Statistics Note1 we commented on the analysis of paired data where there is more than one observation per subject, as shown in table I. We pointed out that it could be highly misleading to analyse such data by combining repeated observations from several subjects and then calculating the correlation coefficient as if the data were a simple sample. This note is a response to several letters about the appropriate analysis for such data.
View this table:
TABLE I
Repeated measurements of intramural pH and PaCO2 for eight subjects2
The choice of analysis for the data in table I depends on the question we want to …
918 citations
TL;DR: Several measurements of the same quantity on the same subject will not in general be the same, because of natural variation in the subject, variations in the measurement process, or both.
Abstract: Several measurements of the same quantity on the same subject will not in general be the same. This may be because of natural variation in the subject, variation in the measurement process, or both. For example, table 1 shows four measurements of lung function in each of 20 schoolchildren (taken from a larger study1). The first child shows typical variation, having peak expiratory flow rates of 190, 220, 200, and 200 l/min.
View this table:
Table 1
Repeated peak expiratory flow rate (PEFR) measurements for 20 schoolchildren
Let us suppose that the child has a “true” average value over all possible measurements, which is what we really want to know when we make a measurement. Repeated measurements on the same subject will vary around the true value because of measurement error. The standard deviation of repeated measurements on the same subject …
870 citations
TL;DR: This protocol incorporates many features of the American National Standard for Non-Automated Sphygmomanometers but includes many additional features, such as strict criteria for observer training, interdevice variability testing before and after a month of ambulatory use, and a new system of analysis which permits the test system to be graded.
Abstract: With the increasing manufacture of expensive systems for the measurement of ambulatory blood pressure there is a need for potential purchasers to be able to satisfy themselves that the systems have been evaluated according to agreed criteria. The British Hypertension Society has, therefore, drawn up
749 citations
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23 Sep 2019TL;DR: The Cochrane Handbook for Systematic Reviews of Interventions is the official document that describes in detail the process of preparing and maintaining Cochrane systematic reviews on the effects of healthcare interventions.
Abstract: The Cochrane Handbook for Systematic Reviews of Interventions is the official document that describes in detail the process of preparing and maintaining Cochrane systematic reviews on the effects of healthcare interventions.
21,235 citations
TL;DR: Authors/Task Force Members: Piotr Ponikowski* (Chairperson) (Poland), Adriaan A. Voors* (Co-Chair person) (The Netherlands), Stefan D. Anker (Germany), Héctor Bueno (Spain), John G. F. Cleland (UK), Andrew J. S. Coats (UK)
13,400 citations
University of Bristol1, Harvard University2, University Hospitals Bristol NHS Foundation Trust3, Research Triangle Park4, University of Toronto5, University of Oxford6, University of Ottawa7, Paris Descartes University8, University of London9, University of York10, University of Birmingham11, University of Southern Denmark12, University of Liverpool13, University of East Anglia14, Loyola University Chicago15, University of Aberdeen16, Kaiser Permanente17, Baruch College18, McMaster University19, Cochrane Collaboration20, McGill University21, Ottawa Hospital Research Institute22, University of Louisville23, University of Melbourne24
TL;DR: Risk of Bias In Non-randomised Studies - of Interventions is developed, a new tool for evaluating risk of bias in estimates of the comparative effectiveness of interventions from studies that did not use randomisation to allocate units or clusters of individuals to comparison groups.
Abstract: Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
8,028 citations
TL;DR: The 95% limits of agreement, estimated by mean difference 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie.
Abstract: Agreement between two methods of clinical measurement can be quantified using the differences between observations made using the two methods on the same subjects. The 95% limits of agreement, estimated by mean difference +/- 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie. We describe how graphical methods can be used to investigate the assumptions of the method and we also give confidence intervals. We extend the basic approach to data where there is a relationship between difference and magnitude, both with a simple logarithmic transformation approach and a new, more general, regression approach. We discuss the importance of the repeatability of each method separately and compare an estimate of this to the limits of agreement. We extend the limits of agreement approach to data with repeated measurements, proposing new estimates for equal numbers of replicates by each method on each subject, for unequal numbers of replicates, and for replicated data collected in pairs, where the underlying value of the quantity being measured is changing. Finally, we describe a nonparametric approach to comparing methods.
7,976 citations
TL;DR: ACCF/AHAIAI: angiotensin-converting enzyme inhibitor as discussed by the authors, angio-catabolizing enzyme inhibitor inhibitor inhibitor (ACS inhibitor) is a drug that is used to prevent atrial fibrillation.
Abstract: ACC/AHA
: American College of Cardiology/American Heart Association
ACCF/AHA
: American College of Cardiology Foundation/American Heart Association
ACE
: angiotensin-converting enzyme
ACEI
: angiotensin-converting enzyme inhibitor
ACS
: acute coronary syndrome
AF
: atrial fibrillation
7,489 citations