Stephen Senn

Bio: Stephen Senn is an academic researcher from University of Sheffield. The author has contributed to research in topics: Clinical trial & Statistician. The author has an hindex of 44, co-authored 248 publications receiving 11960 citations. Previous affiliations of Stephen Senn include RWTH Aachen University & University of Dundee.

A 4-Year Trial of Tiotropium in chronic obstructive pulmonary disease

Abstract: In patients with COPD, therapy with tiotropium was associated with improvements in lung function, quality of life, and exacerbations during a 4-year period but did not significantly reduce the rate of decline in FEV1. (ClinicalTrials.gov number, NCT00144339.)

Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations

Abstract: Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so-and yet these misinterpretations dominate much of the scientific literature In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power We conclude with guidelines for improving statistical interpretation and reporting

Statistical tests, P-values, confidence intervals, and power: a guide to misinterpretations

Abstract: Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so-and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.

Cross-over trials in clinical research

Abstract: Preface to the Second Edition Preface to the First Edition 1. Introduction 2. Some Basic Considerations Concerning Estimation in Clinical Trials 3. The AB/BA Design with Normal Data 4. Other Outcomes and the AB/BA Design 5. Normal Data from Designs with Three or More Treatments 6. Other Outcomes from Designs with Three or More Treatments 7. Some Special Designs 8. Graphical and Tabular Presentation of Cross-over Trials 9. Various Design Issues 10. Mathematical Approaches to Carry-over References Author Index Subject Index

Statistical Issues in Drug Development

Abstract: Preface to the Second Edition. Preface to the First Edition. Acknowledgements. 1. Introduction. 1.1 Drug development. 1.2 The role of statistics in drug development. 1.3 The object of this book. 1.4 The author's knowledge of statistics in drug development. 1.5 The reader and his or her knowledge of statistics. 1.6 How to use the book. Part 1: Four Views of Statistics in Drug Development: Historical, Methodological, Technical and Professional. 2. A Brief and Superficial History of Statistics for Drug Developers. 2.1 Introduction. 2.2 Early Probabilists. 2.3 James Bernoulli (1654-1705). 2.4 John Arbuthnott (1667-1753). 2.5 The mathematics of probability in the late 17th, the 18th and early 19th centuries. 2.6 Thomas Bayes (1701-1761). 2.7 Adolphe Quetelet (1796-1874). 2.8 Francis Galton (1822-1911). 2.9 Karl Pearson (1857-1936). 2.10 'Student' (1876-1937). 2.11 R.A. Fisher (1890-1962). 2.12 Modern mathematical statistics. 2.13 Medical statistics. 2.14 Statistics in clinical trials today. 2.15 The current debate. 2.16 A living science. 2.17 Further reading. 3. Design and Interpretation of Clinical Trials as Seen by a Statistician. 3.1 Prefatory warning. 3.2 Introduction. 3.3 Defining effects. 3.4 Practical problems in using the counterfactual argument. 3.5 Regression to the mean. 3.6 Control in clinical trials. 3.7 Randomization. 3.8 Blinding. 3.9 Using concomitant observations. 3.10 Measuring treatment effects. 3.11 Data generation models. 3.12 In conclusion. 3.13 Further reading. 4. Probability, Bayes, P-values, Tests of Hypotheses and Confidence Intervals. 4.1 Introduction. 4.2 An example. 4.3 Odds and sods. 4.4 The Bayesian solution to the example. 4.5 Why don't we regularly use the Bayesian approach in clinical trials? 4.6 A frequentist approach. 4.7 Hypothesis testing in controlled clinical trials. 4.8 Significance tests and P-values. 4.9 Confidence intervals and limits and credible intervals. 4.10 Some Bayesian criticism of the frequentist approach. 4.11 Decision theory. 4.12 Conclusion. 4.13 Further reading . 5. The Work of the Pharmaceutical Statistician. 5.1 Prefatory remarks. 5.2 Introduction. 5.3 In the beginning. 5.4 The trial protocol. 5.5 The statistician's role in planning the protocol. 5.6 Sample size determination. 5.7 Other important design issues. 5.8 Randomization. 5.9 Data collection preview. 5.10 Performing the trial. 5.11 Data analysis preview. 5.12 Analysis and reporting. 5.13 Other activities. 5.14 Statistical research. 5.15 Further reading. Part 2: Statistical Issues: Debatable and Controversial Topics in Drug Development. 6. Allocating Treatments to Patients in Clinical Trials. 6.1 Background. 6.2 Issues. References. 6.A Technical appendix. 7. Baselines and Covariate Information. 7.1 Background. 7.2 Issues. 7.A Technical appendix. 8. The Measurement of Treatment Effects. 8.1 Background. 8.2 Issues. 8.A Technical appendix. 9. Demographic Subgroups: Representation and Analysis. 9.1 Background. 9.2 Issues. 9.A Technical appendix. 10. Multiplicity. 10.1 Background. 10.2 Issues. 10.A Technical appendix. 11. Intention to Treat, Missing Data and Related Matters. 11.1 Background. 11.2 Issues. 11.A Technical appendix. 12. One-sided and Two-sided Tests and Other Issues to Do with Significance and P-values. 12.1 Background. 12.2 Issues. 13. Determining the Sample Size. 13.1 Background. 13.2 Issues. 14. Multicentre Trials. 14.1 Background. 14.2 Issues. 14.A Technical appendix. 15. Active Control Equivalence Studies. 15.1 Background. 15.2 Issues. 15.A Technical appendix. 16. Meta-Analysis. 16.1 Background. 16.2 Issues. 16.A Technical appendix. 17. Cross-over Trials. 17.1 Background. 17.2 Issues. 18. n-of-1 Trials. 18.1 Background. 18.2 Issues. 19. Sequential Tr4ials. 19.1 Background. 19.2 Issues. 20. Dose-finding. 20.1 Background. 20.2 Issues. 21. Concerning Pharmacokinetics and Pharmacodynamics. 21.1 Background. 21.2 Issues. 22. Bioequivalence Studies. 22.1 Background. 22.2 Issues. 23. Safety Data, Harms, Drug Monitoring and Pharmaco-epidemiology. 23.1 Background. 23.2 Issues. 24. Pharmaco-economics and Portfolio Management. 24.1 Background. 24.2 Issues. 25. Concerning Pharmacogenetics, Pharmacogenomics and Related Matters. 25.1 Background. 25.2 Issues. 25.A Technical appendix. Glossary. Index.

Cochrane Handbook for Systematic Reviews of 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.

Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary.

Abstract: Chronic obstructive pulmonary disease (COPD) remains a major public health problem. It is the fourth leading cause of chronic morbidity and mortality in the United States, and is projected to rank fifth in 2020 in burden of disease worldwide, according to a study published by the World Bank/World Health Organization. Yet, COPD remains relatively unknown or ignored by the public as well as public health and government officials. In 1998, in an effort to bring more attention to COPD, its management, and its prevention, a committed group of scientists encouraged the U.S. National Heart, Lung, and Blood Institute and the World Health Organization to form the Global Initiative for Chronic Obstructive Lung Disease (GOLD). Among the important objectives of GOLD are to increase awareness of COPD and to help the millions of people who suffer from this disease and die prematurely of it or its complications. The first step in the GOLD program was to prepare a consensus report, Global Strategy for the Diagnosis, Management, and Prevention of COPD, published in 2001. The present, newly revised document follows the same format as the original consensus report, but has been updated to reflect the many publications on COPD that have appeared. GOLD national leaders, a network of international experts, have initiated investigations of the causes and prevalence of COPD in their countries, and developed innovative approaches for the dissemination and implementation of COPD management guidelines. We appreciate the enormous amount of work the GOLD national leaders have done on behalf of their patients with COPD. Despite the achievements in the 5 years since the GOLD report was originally published, considerable additional work is ahead of us if we are to control this major public health problem. The GOLD initiative will continue to bring COPD to the attention of governments, public health officials, health care workers, and the general public, but a concerted effort by all involved in health care will be necessary.

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials

Abstract: Overwhelming evidence shows the quality of reporting of randomised controlled trials (RCTs) is not optimal. Without transparent reporting, readers cannot judge the reliability and validity of trial findings nor extract information for systematic reviews. Recent methodological analyses indicate that inadequate reporting and design are associated with biased estimates of treatment effects. Such systematic error is seriously damaging to RCTs, which are considered the gold standard for evaluating interventions because of their ability to minimise or avoid bias. A group of scientists and editors developed the CONSORT (Consolidated Standards of Reporting Trials) statement to improve the quality of reporting of RCTs. It was first published in 1996 and updated in 2001. The statement consists of a checklist and flow diagram that authors can use for reporting an RCT. Many leading medical journals and major international editorial groups have endorsed the CONSORT statement. The statement facilitates critical appraisal and interpretation of RCTs. During the 2001 CONSORT revision, it became clear that explanation and elaboration of the principles underlying the CONSORT statement would help investigators and others to write or appraise trial reports. A CONSORT explanation and elaboration article was published in 2001 alongside the 2001 version of the CONSORT statement. After an expert meeting in January 2007, the CONSORT statement has been further revised and is published as the CONSORT 2010 Statement. This update improves the wording and clarity of the previous checklist and incorporates recommendations related to topics that have only recently received recognition, such as selective outcome reporting bias. This explanatory and elaboration document-intended to enhance the use, understanding, and dissemination of the CONSORT statement-has also been extensively revised. It presents the meaning and rationale for each new and updated checklist item providing examples of good reporting and, where possible, references to relevant empirical studies. Several examples of flow diagrams are included. The CONSORT 2010 Statement, this revised explanatory and elaboration document, and the associated website (www.consort-statement.org) should be helpful resources to improve reporting of randomised trials.

2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines

Abstract: Since 1980, the American College of Cardiology (ACC) and American Heart Association (AHA) have translated scientific evidence into clinical practice guidelines (guidelines) with recommendations to improve cardiovascular health. In 2013, the National Heart, Lung, and Blood Institute (NHLBI) Advisory