Book Description
A comprehensive user-friendly introduction to biostatistics and epidemiology applied to medicine, clinical practice, and research. Features “Presenting Problems” (case studies) drawn from studies published in the medical literature, end-of-chapter, and a CD-ROM with data sets and statistical software programs.
Customer Reviews:
Try another book.......2007-09-01
I had to purchase this book for a class I am taking. This is a fourth edition book and I have to correct errors before reading each chapter. I give publishers some leeway on a first edition, but by the fourth the kinks should be worked out. Also, there is some crucial information missing when making the calculations and sometimes it does not give enough detail on how to work through the problems.
Basic and Clincial Biostats, Scientific Perspective.......2007-06-28
The book is very detailed and an excellent read and resource for anyone who is in the field of science and someone who reads scientific peer-reviewed journal articles. Though there were some errors in some calculations in the book, overall, it is very helpful and a great way to learn biostatistics and applications in the clinical setting and research settings.
definitely not helpful for 'basic' or introductory level.......2007-04-08
I have very little background in biostats, and need to learn it for medical research and research design.
This book is difficult to read, has far too much emphasis on mathematics and far too little empahisis on concept. After a whole quarter in Biostats using this book, I can tell you very little about how and when to employ certain basis statistics tests or interpret them with confidence. The answers in the back are ofter erroneous, as is some of the text (according to my professor).
The only thing I can seay that is good about this test is that the NCSS software that comes with it I think may be helpful at some point.
I am back in the market for a better book!
Good Primer.......2005-08-18
I bought this book in 1990 (an older version) and have never had a statistics course. I have found it to be an extremely helpful starting point for the application of statistical tests to biomedical problems. From this I have gone on and used more sophisticated tools for computation and have only rarely needed to seek out the advice of experts.
The book is packed full of information and covers the broad range of problems most often encountered in biomedical science. It emphasizes an understanding of the choice an appropriate test for a given problem. Flow charts also help guide the user to the right test and the correct chapter. For this reason I have come back to it repeatedly over the years and it has become well worn. It is sparse on explanation of the statistical or mathematical proofs of methods so it is more of a cookbook than as a theoretical treatise.
Pretty Good.......2004-08-19
I actually thought this book was pretty good. I haven't used the cd. The definitions are pretty clear. I use this in conjunction with Gordis' Epidemiology for quick review. I find Rothman and Greenland is not good for that. When I am thinking about research design and elements of statistical analysis this book is easy to turn to to just go over why one test may be better than another--I am a medical student with an MPH in epi who has worked on several research projects, and I think this book is pretty helpful as a simple aid in the transition from research assistant to co-investigator.
Book Description
Sample size calculation plays an important role in clinical research. It is not uncommon, however, to observe discrepancies among study objectives (or hypotheses), study design, statistical analysis (or test statistic), and sample size calculation. Focusing on sample size calculation for studies conducted during the various phases of clinical research and development, Sample Size Calculation in Clinical Research explores the causes of discrepancies and how to avoid them. This volume provides formulas and procedures for determination of sample size required not only for testing equality, but also for testing non-inferiority/superiority, and equivalence (similarity) based on both untransformed (raw) data and log-transformed data under a parallel-group design or a crossover design with equal or unequal ratio of treatment allocations. It contains a comprehensive and unified presentation of statistical procedures for sample size calculation that are commonly employed at various phases of clinical development. Each chapter includes, whenever possible, real examples of clinical studies from therapeutic areas such as cardiovascular, central nervous system, anti-infective, oncology, and women's health to demonstrate the clinical and statistical concepts, interpretations, and their relationships and interactions. The book highlights statistical procedures for sample size calculation and justification that are commonly employed in clinical research and development. It provides clear, illustrated explanations of how the derived formulas and/or statistical procedures can be used.
Customer Reviews:
A reasonable reference book, but my expectations were higher.......2007-08-08
I am a PhD statistician working in a pharma company. My overall impression about this book is that it is not a handbook for use on a daily basis for clinical trialists. I expected the book to be more insightful about sample size calculations. Examples are very artificial, not taken from real-life.
The book has an academic flavor, however the intended audience is clinical trials practicioners. It would be much better to start each chapter with a couple of strong examples, then description of the methodology, and finally sample size calculations.
I think this book needs a lot of improvement before it can be used as a good reference
This *could* have been great..........2006-04-25
At first browse, this book looks a bit like one of Julius Bendat's excellent texts on time series analysis: dense in formulas, but rewarding the wade.
And then I tried to actually _work through_ their examples. A formula-rich book is NO place for typos.
I don't mind when the text uses "lossed" for "lost;" I can quickly figure out what was meant. I resent having to do forensics to rebuild what formulas and/or results I should have seen in the examples.
That three-star rating reflects two things: the potential this book could have had, and my expectation that sooner or later there will be an ERRATA listing that helps sort this beast out.
Average customer rating:
- Medical Uses of Statistics
|
Medical Uses Of Statistics
JOHN, ED. BAILAR
Manufacturer: CRC
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Similar Items:
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Statistics in Medicine
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How to Report Statistics in Medicine: Annotated Guidelines for Authors, Editors, and Reviewers (Medical Writing and Communication) (Medical Writing and ... (Medical Writing and Communication)
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Intuitive Biostatistics
ASIN: 0910133360 |
Book Description
McGill University, Montreal, Canada. New edition of a practical text on the clinical use of statistics in medicine, for physicians and medical students without formal statistics education. Emphasizes explaining the purpose of statistical methods rather than the performance of calculations.
Customer Reviews:
Medical Uses of Statistics.......2000-08-29
I've been writing an overview of bio-statistics and have been immersed in a stack of statistics books and this one is my favorite, from the perspective of a non-statistician. Chapters are well organized and the material is covered thoroughly without bogging down in formulas. Medical professionals will find this to be an excellent resource for understanding the types of statistical methods used and reported in the well known medical journals.
Book Description
In recent years many introductory textbooks on clinical trial methodology have been published, some of which are excellent, in addition to a very extensive specialist literature. Nevertheless, here is a new book on methods and issues in clinical research. The objectives can be summarized in three points. 1. Integrate medical and statistical components of clinical research. 2. Do justice to the operational and practical requirements of clinical research. 3. Give space to the ethical implications of methodological issues in clinical research.
The scope of clinical research is to evaluate the effect of a treatment on the evolution of a disease in the human species. The treatment can be pharmacological, surgical, psychological/behavioral or organizational/logistic. The disease, intended as an impairment of a state of well-being or a condition capable of provoking such impairment over time, can be universally accepted as such (e.g. a cancer or a bone fracture) or perceived as such only by limited groups of individuals in a given cultural context (e.g. hair loss or weight gain). The course of the disease that ones wishes to change can be the one with no intervention or, more frequently, the one observed with the available treatment. The evaluation of the effect of a treatment on the course of a disease is a lengthy process, which progresses in increasingly complex stages.
A detailed coverage of the logistic, administrative and legal aspects of clinical research is outside the scope of this book. However, throughout the book we keep reminding the reader of these aspects because, as already mentioned, we firmly believe they have a crucial role in determining the success of a study. The history of clinical research is paved with relics of studies started with great pomp, riding great ideas and great hopes, which drowned miserably because of inadequate logistical preparation. In our experience, the excessive complexity of a clinical trial is the single most frequent cause of failure: the study is perfect on paper, but impossible to implement by patients and staff alike. The distance between the principal investigators and the reality of clinical research in its day-to-day practice is often the main cause of such disasters. We warmly encourage everyone involved in clinical research to get involved in the logistics of a study, learning from colleagues responsible for its practical conduct (clinical research associates, data managers, etc.) and to take part, in person, in the practical implementation of a trial before attempting to design a study protocol.
The book ends with a brief description of the drug development process and to the phases of clinical development.
Book Description
Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances--particularly in software--made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis. The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail. All of the data sets used in the book are available for download from www.crcpress.com/e_products/downloads. Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical practices.
Customer Reviews:
Good introduction.......2000-03-30
A well-written introductory book. Broad range of material make it a good reference for new comers in survival analysis.
Book Description
Contents
Preface To The Third Edition
Acknowledgments
Chapter 1. The Scientific Method
1.1 The Logic of Scientific Reasoning
1.2 Variability of Phenomena Requires Statistical Analysis
1.3 Inductive Inference: Statistics as the Technology of the Scientific Method
1.4 Design of Studies
1.5 How to Quantify Variables
1.6 The Null Hypothesis
1.7 Why Do We Test the Null Hypothesis?
1.8 Types of Errors
1.9 Significance Level and Types of Error
1.10 Consequences of Type I and Type II Errors
Chapter 2. A Little Bit Of Probability
2.1 What Is Probability?
2.2 Combining Probabilities
2.3 Conditional Probability
2.4 Bayesian Probability
2.5 Odds and Probability
2.6 Likelihood Ratio
2.7 Summary of Probability
Chapter 3. Mostly About Statistics
3.1 Chi-Square for 2 x 2 Tables
3.2 McNemar Test
3.3 Kappa
3.4 Description of a Population: Use of the Standard Deviation
3.5 Meaning of the Standard Deviation: The Normal Distribution
3.6 The Difference Between Standard Deviation and Standard Error
3.7 Standard Error of the Difference Between Two Means
3.8 Z Scores and the Standardized Normal Distribution
3.9 The t Statistic
3.10 Sample Values and Population Values Revisited
3.11 A Question of Confidence
3.12 Confidence Limits and Confidence Intervals
3.13 Degrees of Freedom
3.14 Confidence Intervals for Proportions
3.15 Confidence Intervals Around the Difference Between Two Means
3.16 Comparisons Between Two Groups
3.17 Z-Test for Comparing Two Proportions
3.18 t-Test for the Difference Between Means of Two Independent Groups: Principles
3.19 How to Do a t-Test: An Example
3.20 Matched Pair t-Test
3.21 When Not to Do a Lot of t-Tests: The Problem of Multiple Tests of Significance
3.22 Analysis of Variance: Comparison Among Several Groups
3.23 Principles
3.24 Bonferroni Procedure: An Approach to Making Multiple Comparisons
3.25 Analysis of Variance When There Are Two Independent Variables: The Two-Factor ANOVA
3.26 Interaction Between Two Independent Variables
3.27 Example of a Two-Way ANOVA
3.28 Kruskal-Wallis Test to Compare Several Groups
3.29 Association and Causation: The Correlation Coefficient
3.30 How High Is High?
3.31 Causal Pathways
3.32 Regression
3.33 The Connection Between Linear Regression and the Correlation Coefficient
3.34 Multiple Linear Regression
3.35 Summary So Far
Chapter 4. Mostly About Epidemiology
4.1 The Uses of Epidemiology
4.2 Some Epidemiologic Concepts: Mortality Rates
4.3 Age-Adjusted Rates
4.4 Incidence and Prevalence Rates
4.5 Standardized Mortality Ratio
4.6 Person-Years of Observation
4.7 Dependent and Independent Variables
4.8 Types of Studies
4.9 Cross-Sectional Versus Longitudinal Looks at Data
4.10 Measures of Relative Risk: Inferences From Prospective Studies: the Framingham Study
4.11 Calculation of Relative Risk from Prospective Studies
4.12 Odds Ratio: Estimate of Relative Risk from Case-Control Studies
4.13 Attributable Risk
4.14 Response Bias
4.15 Confounding Variables
4.16 Matching
4.17 Multiple Logistic Regression
4.18 Confounding By Indication
4.19 Survival Analysis: Life Table Methods
4.20 Cox Proportional Hazards Model
4.21 Selecting Variables For Multivariate Models
4.22 Interactions: Additive and Multiplicative Models
Summary:
Chapter 5. Mostly About Screening
5.1 Sensitivity, Specificity, and Related Concepts
5.2 Cutoff Point and Its Effects on Sensitivity and Specificity
Chapter 6. Mostly About Clinical Trials
6.1 Features of Randomized Clinical Trials
6.2 Purposes of Randomization
6.3 How to Perform Randomized Assignment
6.4 Two-Tailed Tests Versus One-Tailed Test
6.5 Clinical Trial as "Gold Standard"
6.6 Regression Toward the Mean
6.7 Intention-to-Treat Analysis
6.8 How Large Should the Clinical Trial Be?
6.9 What Is Involved in Sample Size Calculation?
6.10 How to Calculate Sample Size for the Difference Between Two Proportions
6.11 How to Calculate Sample Size for Testing the Difference Between Two Means
Chapter 7. Mostly About Quality Of Life
7.1 Scale Construction
7.2 Reliability
7.3 Validity
7.4 Responsiveness
7.5 Some Potential Pitfalls
Chapter 8. Mostly About Genetic Epidemiology
8.1 A New Scientific Era
8.2 Overview of Genetic Epidemiology
8.3 Twin Studies
8.4 Linkage and Association Studies
8.5 LOD Score: Linkage Statistic
8.6 Association Studies
8.7 Transmission Disequilibrium Tests (TDT)
8.8 Some Additional Concepts and Complexities of Genetic Studies
Chapter 9. Research Ethics And Statistics
9.1 What does statistics have to do with it?
9.2 Protection of Human Research Subjects
9.3 Informed Consent
9.4 Equipoise
9.5 Research Integrity
9.6 Authorship policies
9.7 Data and Safety Monitoring Boards
9.8 Summary
Postscript A Few Parting Comments On The Impact Of Epidemiology On Human Lives
Appendix A. Critical Values Of Chi-square, Z, And T
Appendix B. Fisher'S Exact Test
Appendix C. Kruskal-wallis Nonparametric Test To Compare Several Groups
Appendix D. How To Calculate A Correlation Coefficient
Appendix E. Age-adjustment
Appendix F. Confidence Limits On Odds Ratios
Appendix G. "J" Or "U" Shaped Relationship Between Two Variables
Appendix H. Determining Appropriateness Of Change Scores
Appendix I. Genetic Principles
References
Suggested Readings
Index
Book Description
Statistical Evidence in Medical Trials is a lucid, well-written and entertaining text that addresses common pitfalls in evaluating medical research. Including extensive use of publications from the medical literature and a non-technical account of how to appraise the quality of evidence presented in these publications, this book is ideal for health care professionals, students in medical or nursing schools, researchers and students in statistics, and anyone needing to assess the evidence published in medical journals. Stephen D. Simon earned a Ph.D. in statistics from the University of Iowa in 1982. He currently works as a research biostatistician at Children's Mercy Hospitals and Clinics in Kansas City, MO. He has authored or co-authored over 60 publications in a variety of medical and statistical journals, four of which have won awards. He has given a wide range of lectures and classes on statistics, evidence based medicine, research ethics, and quality control.
Average customer rating:
- not worth the paper it is printed on
- This book offers nothing!
- I've never seen a garbage like this before
- this is not a book on statistical methods
|
Statistical Methods for Clinical Trials (Biostatistics)
Mark X. Norleans
Manufacturer: CRC
ProductGroup: Book
Binding: Hardcover
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ASIN: 0824704673 |
Book Description
"Summarizes graphical analysis, analysis of variance, meta-analysis, and design of comparable treatment groups. Streamlines the analytical techniques for continuous, categorical, longitudinal, and survival data-focusing on generalized linear models, GEEs, and mixed linear models, -ahd highlihgts p-value, and more."
Customer Reviews:
not worth the paper it is printed on.......2007-08-18
In 2001 under the pen name statman13 I wrote the first amazon customer review of this book. It was a very detailed review and one of the very few reviews that I have written that really panned a book. I am happy to see that in the intevening time two other reviewers expressed similar negative opinions. Unfortunately if they bought the book they must not have read mine! In 2001 I bought the book at a "discount price" because the author was presenting the work as a tutorial at the Deming conference, the title interested me and the abstract was intriguing. The book was just coming out. So I had no reviews to base a purchasing decision on and I knew that the series editor had a great reputation as an author and editor on these topics. I was on the conference organizing committee and although I had not been the member who invited Mark, I was duped into buying the book and I agreed to moderate his session. The session was an embarrassment. It was not a tutorial but a diatribe against classical statistics. The author carrying degrees in both medicine and statistics simply demonstrated his ignorance of statistics and presented his own nonsense methods. I can't really blame my colleague who pick him for the tutorial as he was duped just the way I was. It is unusual for the Deming conference to have such a terrible presentation. All the rest of the tutorials and short courses were excellent as they have been before and since and Walter Young and his committee volunteers deserve congratulations over the consistent high quality and state-of-the-art presentations. I served on the committee in the past and have given tutorials there also.
Because it is so easy for prospective buyers to be misled into purchasing this book for the reasons I have given and because editorial reviews are just sugar-coated publisher sales pitches that are also very misleading I continue to warn amazon readers not to waste their money on this book!
This book offers nothing!.......2003-08-29
From whichever perspective you look at it - this books offers absolutely nothing. I wish I could get my $$ back!
I've never seen a garbage like this before.......2002-02-07
I don't know why Dr. Chow put this garbage in his series.
this is not a book on statistical methods.......2001-12-14
The author has an MD from China and a PhD from Louisiana State University Medical Center. However, his understanding of probability and statistics is less than what would be expected from a person with a Ph.D. in biostatistics. The book has one theme. It rejects the use of classical statistical methods of hypothesis testing that began with Neyman and Pearson's work in the 1930s. Although the author claims that he is rejecting it in the context of clincial trials, it appears that his strong statements indicate that he believes it is not very useful and provides no information to draw conclusions. His arguments, provided most strikingly in Chapter 10 "The Fiction behind the Current Statistics and Its Consequences", reveal this and best display his ignorance.
It is true that most clinical trials are conducted on restricted sets of subjects who must meet carefully planned inclusion/exclusion criteria. Also, clinical centers are chosen by the sponsor (often a pharmaceutical company) and are picked because they have performed well in the past or have a well known investigator. Therefore, the common paradigm of statistical inference that the subjects are a random sample from a larger population is not even closely true. This seems to be a good argument for not taking formal statistical hypothesis testing too seriously in this context and too much attention has been placed on p-values and rigid decision making in the regulatory arena so I was very much interested in seeing how Norleans would get around these problems.
But instead of recognizing the inadequacies of the models and finding better models, he resorts to a subjective form of decision making based solely on graphics and a misunderstanding of methods such as maximum likelihood and analysis of variance. These problems require increased sophistication for solution not the abandonment of statistical modeling.
Graphical techniques are clearly useful and the pioneering work of Tukey led to a deeper appreciation for good exploratory data analysis techniques beginning in the 1960s. However, Norleans ignores or is ignorant of this literature as he fails to use any of the techniques of Tukey. Rather he concentrates on unconventional and not always very informative graphical displays. These techniques seem to be designed to look at individuals more than groups and can often be very cluttered. There is no reference to Tukey, Cleveland or Tufte, the pioneers in statistical graphics.
I find his condemnation of Jerzy Neyman particularly insulting and it shows both naivety and lack of understanding. He doesn't even know how to spell Neyman's first name (sic Jersey)! He claims that statistical hypothesis testing is based on assuming the truth can be known when in fact it is just the opposite. The framework assumes that there is a true state of nature but the truth can never be known and that we can only express our degree of belief in probabilities that particular decision rules lead to incorrect conclusions. Sample size requirements and decisions are made when these error probabilities are sufficiently small.
Neyman-Pearson theory has also been criticized for using a sharp null hypothesis but extensions of the theory got around that problem as can be seen from the famous text by Lehmann on hypothesis testing in the late 1950s. It is commonplace now to use both composite null and alternative hypotheses and appropriate methods for equivalence teting have been devised by switching the usual null and alternative hypothesis. But Norleans appears to be unaware of these advances. Also the generalization by Wald and others to statistical decision theory based on loss functions or utility functions is likewise overlooked.
On the one hand he reject parametric statistical inference because he does not believe in the use of parametric distributions to represent test statistics but yet he accepts the method of maximum likelihood which he does not recognize as parametric. But maximum likelihood methods are not always robust and in some cases not even sensible.
The analysis of clinical trials is challenging. Mixed effects models, censored survival models, handling of missing data, multiplicity adjustment are among the many tools and issues associated with these problems. Probability and statistics have subtleties that cannot always be simplified. It takes sophistication and the clever use of probability to conquer these problems but Norleans offers us none of this.
He appears to be ignorant of the asymptotic theory of statistics which is based on convergence concepts from probability. The only asymptotic result he mentions is the central limit theorem and that he seems to think is based on a Taylor series approximation. With the Poisson model he mentions the problem of overdispersion but instead of recognizing that with medical data more complex models such as compound Poisson can adequate address the issue and make sense clinically he rejects the methodology itself.
Medical researchers who want to understand statistics and its useful role in medicine and other research would be better served by reading David Salsburg's "A Lady Tasting Tea" than the garbage in this book.
I really have serious issue about Dr. Chow's selection of this book for this series and I cannot understand how he can characterize it so favorably in his introduction.
Book Description
From â€~Abcissa’ to â€~Zygosity determination’ - this accessible introduction to the terminology of medical statistics describes more than 1500 terms all clearly explained, illustrated and defined in non-technical language, without any mathematical formulae! With the majority of terms revised and updated and the addition of more than 100 brand new definitions, this new edition will enable medical students to quickly grasp the meaning of any of the statistical terms they encounter when reading the medical literature. Furthermore, annotated comments are used judiciously to warn the unwary of some of the common pitfalls that accompany some cherished biomedical statistical techniques. Wherever possible, the definitions are supplemented with a reference to further reading where the reader may gain a deeper insight, so whilst the definitions are easily disgestible, they also provide a stepping stone to a more sophisticated comprehension. Statistical terminology can be quite bewildering for clinicians: this guide will be a lifesaver.
Book Description
Statistical Questions in Evidence-based Medicine is a book of questions and answers about the statistical principles and methods used in medical research. Based entirely on material published in the medical literature and popular media, it will prove invaluable to medical students, doctors, nurses, medical researchers and others concerned with medical data. This book is a companion volume to the new 3rd edition of An Introduction to Medical Statistics but can also be used in conjunction with the 2nd edition or with other good texts. Short excerpts of material from published papers or summaries of their results are presented with questions. These test and develop the reader's understanding and interpretation of statistics and extend the reader's research and critical appraisal skills, thus encouraging an evidence-based approach. Questions are presented on the left-hand page with detailed answers on the right-hand page. Answers include references to core material in An Introduction to Medical Statistics. The book is intended as a series of examples for self-teaching but could also be read as a series of case studies with detailed commentaries. The questions are clearly graded, using icons, in terms of difficulty and undergraduate or postgraduate level. The book is easy to use and a model of clarity for the reader.
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