follow site Wiley Hochberg, Y. Sterne JAC Teaching hypothesis tests - time for significant change? Statistics in Medicine 21 : Sifting the evidence - what's wrong with significance tests? BMJ : Wang, R. Siegel, S. Design and Regression. Doull, I. Duffy, S. MSc Dissertation. University of London.
Hoaglin, D. Hsu, J. Kadir, R. Miners, A. Synek, M. Multivariate analysis Afifi, A. Barnet, V. John Wiley. Brayn, F. Chatfield, C. Diez Roux, A. Health , 56 , Dillon, W. Hair, J. Prentice Hall. Johnson, R. Kendall , M. Krzanowski, W. Part 1. Distributions, Ordination and Inference. Hodder Arnold. Part 2. Classification, Covariance Structures and Repeated Measurements. Manly, B. Mardia, K. Raab, G. Sharma, Subhash. Tabachnick, B. Allyn and Bacon. Wulder, M. Canadian Forest Service. Testing proportions Agresti, A.
Kurtiz, S. Health 9 , Radcliffe, M.
Correlation and regression Chatterjee, S. Draper, N. Harrell, Jr, F. Miller, A. Sage University Press. Health , 46 , Regression to the mean Galton, F. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. Journal of Clin. Bland, J. Hosmer, D. Kleinbaum, D. Menard, S. Sage Publications. Pampel, F. Peduzzi, P. A simulation study of the number of events per variable in logistic regression analysis. Steyerberg, E. Jr, Borsboom, G. Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis.
What do we mean by validating a prognostic model? Bleeker, S. Collet, D.
Dobson, A. Dupont, W. Gilthorpe, M. Community Dental Health , 17 , Goldstein, H. Harrel Jun, F. Hastie, T. Hoffman, J. Katz, M. Kreft, I. Leylan, A. May, M. Mocroft, A. Snijders, T. Sullivan, L. Wong, M. Sample size calculation Altman, D. In: Statistics in Practice S. Altman eds. Amir, J. Chow, S-C. Laurence Erlbaum Associates. Donner, A. Julious , S.
Lehr, R. Machin, D. Wittes, J. Additional Topics Diagnostic tests and prognostic scores Begg, C. Burroughs, A. Greenhouse, S. Biometrics , 6 , Harper, R. Ingelsson, E. Leeflang, M. McGeechan, K. Reynolds, T. Wald, N. Zethelius, B. Measuring agreement Bland, J. Lancet , i , Chinn, S. Chmura Kraemer, H. Cohen, J.
Dunn, G. Kraemer, H. Landis, J. Biometrics , 33 , Lin, I-K A concordance correlation coefficient to evaluate reproducibility. Biometrics 45 McDowell, I. Shoukri, M. Streiner, D.
Evidence-based Medicine Bero, L. Preparing, maintaining, and disseminating systematic reviews of the effects of health care. An Introduction to Evidence Based Medicine. Edward Arnold. Elward, J. A new approach to teaching the practice of medicine. Greenhalgh, T. Ioannidis, J. Peat, J. Sackett, D. Churchill Livingstone. Straus SE, Richardson, W. S, Glasziou, P. G eds Systematic Reviews. Egger, M. Meta-analysis in Context 2nd edn of Systematic Reviews.
BMJ Publications. Elphick, H. Freemantle, N. The role and limitation of meta-analysis. Glasziou, P. Higgins, P. Houwelingen, H. Leandro, G.
BMJ Books. Moher, D. Quality of Reporting of Meta-analyses. Normand, S. Oakes, M. Stangl, D. Stroup, D. JAMA, , Sutton, A. Thompson, S. Whitehead, A. Albert, P. Bryk, A.
For any MA, a forest plot is typically produced for summary and publication purposes. This will particularly help clinical practitioners to apply these methodologies in their own scientific problems. CRC Kenward, M. The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods. Professor Chen was a professor in biostatistics at the University of Rochester from to and the Karl E. The Q statistic only assesses the presence or absence of heterogeneity.
Carr, G. Data: a generalized estimating equations approach. Collins, L. American Psychological Association. Crowder, M. Davis , C. Springer-Verlag Ditlevsen, S. A structural equation approach for estimating the proportion of exposure effect on outcome explained by an intermediate variable. Epidemiology , 16 , Statistician , 44 , Kendall Library of Statistics. Hanley, J.
Hardin, J. Hayes, R.
CRC Kenward, M. Lindsey, J. Longford, N. Matthews, J. Naumova, E. Tutorial in biostatistics: evaluating the impact of 'critical periods' in longitudinal studies of growth using piecewise mixed effects models. Omar, R. Rouz, A. A glossary for multilevel analysis. Senn, S. Taylor , R. Survival analysis Bradburn, M. Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies.
It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making. This book presents in depth the Clinical Scenario Evaluation CSE framework, and discusses optimization strategies, including the quantitative assessment of tradeoffs.
A variety of common development challenges are evaluated as case studies, and used to show how this framework both simplifies and optimizes strategy selection. After this book, the reader will be equipped to extend the CSE framework to their particular development challenges as well. Alex Dmitrienko is President at Mediana Inc. He has been actively involved in biostatistical research with emphasis on multiplicity issues in clinical trials, subgroup analysis, innovative trial designs and clinical trial optimization.
Dmitrienko is a Fellow of the American Statistical Association. Erik Pulkstenis is Vice President, Clinical Biostatistics and Data Management at MedImmune, and has worked in the medical device and biopharmaceutical industry for over 20 years. In addition, he served as a faculty member for the Institute for Professional Education teaching on categorical data analysis.
His research interests include evidence-based decision making, precision medicine, and applications of statistical methods in oncology. Introduction Clinical Scenario Evaluation framework Case study 2. Introduction Clinical Scenario Evaluation in confirmatory subgroup analysis Case study 3. See All Customer Reviews. Shop Books. Read an excerpt of this book!
Add to Wishlist. USD Sign in to Purchase Instantly. Overview Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. Show More. Average Review. Write a Review. Related Searches.
A Practical Guide to Managing Clinical Trials is a basic, comprehensive guide to conducting clinical A Practical Guide to Managing Clinical Trials is a basic, comprehensive guide to conducting clinical trials. Designed for individuals working in research site operations, this user-friendly reference guides the reader through each step of the clinical trial process from site View Product.
Setting up a GXP environment where none existed previously is a very daunting task.