{"product_id":"methods-and-applications-of-sample-size-calculation-and-recalculation-in-clinical-trials-9783030495305","title":"Methods and Applications of Sample Size","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides an extensive overview of the principles and methods of sample size calculation and recalculation in clinical trials. Appropriate calculation of the required sample size is crucial for the success of clinical trials. At the same time, a sample size that is too small or too large is problematic due to ethical, scientific, and economic reasons. Therefore, state-of-the art methods are required when planning clinical trials. \u003c\/p\u003e  \u003cp\u003ePart I describes a general framework for deriving sample size calculation procedures. This enables an understanding of the common principles underlying the numerous methods presented in the following chapters. Part II addresses the fixed sample size design, where the required sample size is determined in the planning stage and is not changed afterwards. It covers sample size calculation methods for superiority, non-inferiority, and equivalence trials, as well as comparisons between two and more than two groups. A wide range of further topics is discussed, including sample size calculation for multiple comparisons, safety assessment, and multi-regional trials. There is often some uncertainty about the assumptions to be made when calculating the sample size upfront. Part III presents methods that allow to modify the initially specified sample size based on new information that becomes available during the ongoing trial. Blinded sample size recalculation procedures for internal pilot study designs are considered, as well as methods for sample size reassessment in adaptive designs that use unblinded data from interim analyses. The application is illustrated using numerous clinical trial examples, and software code implementing the methods is provided.\u003c\/p\u003e  \u003cp\u003eThe book offers theoretical background and practical advice for biostatisticians and clinicians from the pharmaceutical industry and academia who are involved in clinical trials. Covering basic as well as more advanced and recently developed methods, it is suitable for beginners, experienced applied statisticians, and practitioners. To gain maximum benefit, readers should be familiar with introductory statistics. The content of this book has been successfully used for courses on the topic.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The R source code is shown by chapter, well-documented, and easy to find and follow as brief descriptions and necessary specifications for the function calls are given by means of comments. … a wide area of application fields is covered and exhaustive literature references for further reading are given. … The presentation of the material is very reader-friendly, easily accessible and pedagogical … . It is likewise highly recommended … . This is an effective and nicely written reference textbook.” (Oke Gerke, ISCB News, iscb.info, Vol. 72, December, 2021)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003ePart I Basics\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e1        \u003c\/b\u003e\u003cb\u003eIntroduction\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e1.1       Background and outline\u003c\/p\u003e  \u003cp\u003e1.2       Examples\u003c\/p\u003e  \u003cp\u003e1.2.1        The ChroPac trial\u003c\/p\u003e  \u003cp\u003e1.2.2        The Parkinson trial \u003c\/p\u003e  \u003cp\u003e1.3       General considerations when calculating sample sizes\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e2        \u003c\/b\u003e\u003cb\u003eStatistical test and sample size calculation\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e2.1       The main principle of statistical testing\u003c\/p\u003e  2.2       The main principle of sample size calculation\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003ePart II Sample size calculation\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e3        \u003c\/b\u003e\u003cb\u003eComparison of two groups for normally distributed outcomes and test for difference or superiority\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e3.1       Background and notation\u003c\/p\u003e  \u003cp\u003e3.2       \u003ci\u003ez\u003c\/i\u003e-test\u003c\/p\u003e  \u003cp\u003e3.3       t-test\u003c\/p\u003e  \u003cp\u003e3.4       Analysis of covariance\u003c\/p\u003e  \u003cp\u003e3.5       Bayesian approach\u003c\/p\u003e  \u003cp\u003e3.5.1        Background\u003c\/p\u003e  \u003cp\u003e3.5.2        Methods\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e4        \u003c\/b\u003e\u003cb\u003eComparison of two groups for continuous and ordered categorical outcomes and test for difference or superiority\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e4.1       Background and notation\u003c\/p\u003e  \u003cp\u003e4.2       Continuous outcomes\u003c\/p\u003e  \u003cp\u003e4.3       Ordered categorical outcomes\u003c\/p\u003e  \u003cp\u003e4.3.1        Assumption-free approach\u003c\/p\u003e  \u003cp\u003e4.3.2        Assuming proportional odds\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e5        \u003c\/b\u003e\u003cb\u003eComparison of two groups for binary outcomes and test for difference and superiority\u003c\/b\u003e\u003c\/p\u003e  5.1       Background and notation\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e5.2       Asymptotic tests\u003c\/p\u003e  \u003cp\u003e5.2.1        Difference of rates as effect measure\u003c\/p\u003e  5.2.2        Risk ratio as effect measure\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e5.2.3        Odds ratio as effect measure\u003c\/p\u003e  \u003cp\u003e5.2.4        Logistic regression\u003c\/p\u003e  5.3       Exact unconditional tests\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e5.3.1        Background\u003c\/p\u003e  \u003cp\u003e5.3.2        Fisher-Boschloo test\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e6        \u003c\/b\u003e\u003cb\u003eComparison of two groups for time-to-event outcomes and test for differences or superiority\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e6.1       Background and notation\u003c\/p\u003e  \u003cp\u003e6.1.1        Time-to-event data\u003c\/p\u003e  \u003cp\u003e6.1.2        Sample size calculation for time-to-event data\u003c\/p\u003e  \u003cp\u003e6.2       Exponentially distributed time-to-event data\u003c\/p\u003e  \u003cp\u003e6.3       Time-to-event data with proportional hazards\u003c\/p\u003e  \u003cp\u003e6.3.1        Approach of Schoenfeld\u003c\/p\u003e  \u003cp\u003e6.3.2        Approach of Freedman\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e7        \u003c\/b\u003e\u003cb\u003eComparison of more than two groups and test for difference\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e7.1       Background and notation\u003c\/p\u003e  \u003cp\u003e7.2       Normally distributed outcomes\u003c\/p\u003e  \u003cp\u003e7.3       Continuous outcomes\u003c\/p\u003e  \u003cp\u003e7.4       Binary outcomes \u003c\/p\u003e  \u003cp\u003e7.4.1        Analysis with chi-square test\u003c\/p\u003e  \u003cp\u003e7.4.2        Analysis with Cochran-Armitage test\u003c\/p\u003e  \u003cp\u003e7.5       Time-to-event outcomes\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e8        \u003c\/b\u003e\u003cb\u003eComparison of two groups and test for non-inferiority\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e8.1       Background and notation\u003c\/p\u003e  \u003cp\u003e8.2       Normally distributed outcomes\u003c\/p\u003e  \u003cp\u003e8.2.1        Difference of means\u003c\/p\u003e  8.2.2        Ratio of means\u003cp\u003e\u003c\/p\u003e  8.3       Continuous and ordered categorical outcomes\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e8.4       Binary outcomes\u003c\/p\u003e  \u003cp\u003e8.4.1        Analysis with asymptotic tests\u003c\/p\u003e  \u003cp\u003e8.4.1.1  Difference of rates as effect measure\u003c\/p\u003e  \u003cp\u003e8.4.1.2  Risk ratio as effect measure\u003c\/p\u003e  8.4.1.3  Odds ratio as effect measure\u003cp\u003e\u003c\/p\u003e  8.4.2        Exact unconditional tests\u003cp\u003e\u003c\/p\u003e  8.4.2.1  Background\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e8.4.2.2  Difference of rates as effect measure\u003c\/p\u003e  \u003cp\u003e8.4.2.3  Risk ratio as effect measure\u003c\/p\u003e  \u003cp\u003e8.4.2.4  Odds ratio as effect measure\u003c\/p\u003e  \u003cp\u003e8.5       Time-to-event outcomes\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e9        \u003c\/b\u003eComparison of three groups in the gold standard non-inferiority design\u003c\/p\u003e  \u003cp\u003e9.1       Background and notation\u003c\/p\u003e  9.2       Net effect approach\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.3       Fraction effect approach\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e10    \u003c\/b\u003eComparison of two groups for normally distributed outcomes and test for equivalence\u003c\/p\u003e  \u003cp\u003e10.1   Background and notation\u003c\/p\u003e  \u003cp\u003e10.2   Difference of means\u003c\/p\u003e  10.3   Ratio of means\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e11    \u003c\/b\u003e\u003cb\u003eMultiple comparisons\u003c\/b\u003e\u003c\/p\u003e  11.1   Background and notation\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e11.2   Generally applicable sample size calculation methods and applications\u003c\/p\u003e  \u003cp\u003e11.2.1    Methods\u003c\/p\u003e  \u003cp\u003e11.2.2    Applications\u003c\/p\u003e  \u003cp\u003e11.3   Multiple endpoints\u003c\/p\u003e  \u003cp\u003e11.3.1    Background and notation\u003c\/p\u003e  \u003cp\u003e11.3.2    Methods\u003c\/p\u003e  \u003cp\u003e11.4   More than two groups\u003c\/p\u003e  11.4.1    Background and notation\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e11.4.2    Dunnett test\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e12    \u003c\/b\u003eAssessment of safety\u003c\/p\u003e  \u003cp\u003e12.1   Background and notation\u003c\/p\u003e  12.2   Testing hypotheses on the event probability\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e12.3   Estimating the occurrence probability of an event with specified precision\u003c\/p\u003e  \u003cp\u003e12.4   Observing at least one event\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e13    \u003c\/b\u003e\u003cb\u003eCluster-randomized trials\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e13.1   Background and notation\u003c\/p\u003e  \u003cp\u003e13.2   Normally distributed outcomes\u003c\/p\u003e  \u003cp\u003e13.2.1    Cluster-level analysis\u003c\/p\u003e  \u003cp\u003e13.2.2    Individual-level analysis\u003c\/p\u003e  \u003cp\u003e13.2.3    Dealing with unequal cluster size\u003c\/p\u003e  \u003cp\u003e13.3   Other scale levels of the outcome\u003c\/p\u003e  \u003cb\u003e \u003c\/b\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e14    \u003c\/b\u003e\u003cb\u003eMulti-regional trials\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e14.1   Background and notation\u003c\/p\u003e  \u003cp\u003e14.2   Sample size calculation for demonstrating consistency of global results and results for a specified region\u003c\/p\u003e  \u003cp\u003e14.3   Sample size calculation for demonstrating a consistent trend across all regions\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e15    \u003c\/b\u003e\u003cb\u003eIntegrated planning of phase II\/III drug development programs\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e15.1   Background and notation\u003c\/p\u003e  \u003cp\u003e15.2   Optimizing phase II\/III programs\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e16    \u003c\/b\u003e\u003cb\u003eSimulation-based sample size calculation\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart III Sample size recalculation\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e17    \u003cb\u003eBackground\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart IIIA Blinded sample size recalculation in internal pilot study designs\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e18    \u003cb\u003eBackground and notation\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e19    \u003cb\u003eA general approach for controlling the type I error rate for blinded sample size recalculation\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cb\u003e20    \u003c\/b\u003e\u003cb\u003eComparison of two groups for normally distributed outcomes and test for difference or superiority\u003c\/b\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e20.1   \u003ci\u003et\u003c\/i\u003e-Test\u003c\/p\u003e  \u003cp\u003e20.1.1    Background and notation\u003c\/p\u003e  \u003cp\u003e20.1.2    Blinded variance estimation\u003c\/p\u003e  20.1.3    Type I error rate\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e20.1.4    Power and sample size\u003c\/p\u003e  \u003cp\u003e20.2   Analysis of covariance\u003c\/p\u003e  20.2.1    Background and notation\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e20.2.2    Blinded variance estimation\u003c\/p\u003e  \u003cp\u003e20.2.3    Type I error rate\u003c\/p\u003e  20.2.4    Power and sample size\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e21    \u003cb\u003eComparison of two groups for binary outcomes and test for difference or superiority\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e21.1   Background and notation\u003c\/p\u003e  \u003cp\u003e21.2   Asymptotic tests\u003c\/p\u003e  21.2.1    Difference of rates as effect measure\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e21.2.2    Risk ratio and odds ratio as effect measure\u003c\/p\u003e  \u003cp\u003e21.3   Fisher-Boschloo test\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e22    \u003c\/b\u003e\u003cb\u003eComparison of two groups for normally distributed outcomes and test for non-inferiority\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e22.1   \u003ci\u003et\u003c\/i\u003e-Test\u003c\/p\u003e  22.1.1    Background and notation\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e22.1.2    Blinded variance estimation\u003c\/p\u003e  \u003cp\u003e22.1.3    Type I error rate\u003c\/p\u003e  22.1.4    Power and sample size\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e22.2   Analysis of covariance\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e23    \u003c\/b\u003eComparison of two groups for binary outcomes and test for non-inferiority\u003c\/p\u003e  \u003cp\u003e23.1   Background and notation\u003c\/p\u003e  \u003cp\u003e23.2   Difference of rates as effect measure\u003c\/p\u003e  \u003cp\u003e23.3   Risk ratio and odds ratio as effect measure\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e24    \u003c\/b\u003e\u003cb\u003eComparison of two groups for normally distributed outcomes and test for equivalence\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e25    \u003cb\u003eRegulatory and operational aspects\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e26    \u003c\/b\u003e\u003cb\u003eConcluding remarks\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart IIIB Unblinded sample size recalculation in adaptive designs\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e27    \u003c\/b\u003e\u003cb\u003eBackground and notation\u003c\/b\u003e\u003c\/p\u003e  27.1   Group-sequential designs\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e27.2   Adaptive designs\u003c\/p\u003e  \u003cp\u003e27.2.1    Combination function approach\u003c\/p\u003e  27.2.2    Conditional error function approach\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e28    \u003cb\u003eSample size recalculation based on conditional power\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e28.1   Background and notation\u003c\/p\u003e  \u003cp\u003e28.2   Using the interim estimate of the effect\u003c\/p\u003e  28.3   Using the initially specified effect\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e28.4   Using prior information as well as the interim effect estimate\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e29    \u003c\/b\u003eSample size recalculation by optimization\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e30    \u003c\/b\u003e\u003cb\u003eRegulatory and operational aspects\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e31    \u003c\/b\u003eConcluding remarks\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAppendix: Selected R software code\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eReferences\u003c\/b\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":51021119488343,"sku":"9783030495305","price":49.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030495305.jpg?v=1750785222","url":"https:\/\/bookcurl.com\/products\/methods-and-applications-of-sample-size-calculation-and-recalculation-in-clinical-trials-9783030495305","provider":"Book Curl","version":"1.0","type":"link"}