LEADER 05972nam 2200769 450 001 9910139141103321 005 20200520144314.0 010 $a1-118-76360-2 010 $a1-118-76345-9 010 $a1-118-76359-9 035 $a(CKB)2550000001272937 035 $a(EBL)1662762 035 $a(SSID)ssj0001180049 035 $a(PQKBManifestationID)11798011 035 $a(PQKBTitleCode)TC0001180049 035 $a(PQKBWorkID)11186096 035 $a(PQKB)11185519 035 $a(OCoLC)865452430 035 $a(MiAaPQ)EBC1662762 035 $a(DLC) 2013049660 035 $a(Au-PeEL)EBL1662762 035 $a(CaPaEBR)ebr10856835 035 $a(CaONFJC)MIL595064 035 $a(OCoLC)875820381 035 $a(PPN)191923109 035 $a(EXLCZ)992550000001272937 100 $a20140415h20142014 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHow to design, analyse and report cluster randomised trials in medicine and health related research /$fMichael J. Campbell and Stephen J. Walters 210 1$aChichester, England :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (268 p.) 225 1 $aStatistics in Practice 300 $aDescription based upon print version of record. 311 $a1-119-99202-8 311 $a1-306-63813-5 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Preface; Acronyms and abbreviations; Chapter 1 Introduction; 1.1 Randomised controlled trials; 1.1.1 A-Allocation at random; 1.1.2 B-Blindness; 1.1.3 C-Control; 1.2 Complex interventions; 1.3 History of cluster randomised trials; 1.4 Cohort and field trials; 1.5 The field/community trial; 1.5.1 The REACT trial; 1.5.2 The Informed Choice leaflets trial; 1.5.3 The Mwanza trial; 1.5.4 The paramedics practitioner trial; 1.6 The cohort trial; 1.6.1 The PoNDER trial; 1.6.2 The DESMOND trial; 1.6.3 The Diabetes Care from Diagnosis trial; 1.6.4 The REPOSE trial 327 $a1.6.5 Other examples of cohort cluster trials 1.7 Field versus cohort designs; 1.8 Reasons for cluster trials; 1.9 Between- and within-cluster variation; 1.10 Random-effects models for continuous outcomes; 1.10.1 The model; 1.10.2 The intracluster correlation coefficient; 1.10.3 Estimating the intracluster correlation (ICC) coefficient; 1.10.4 Link between the Pearson correlation coefficient and the intraclass correlation coefficient; 1.11 Random-effects models for binary outcomes; 1.11.1 The model; 1.11.2 The ICC for binary data; 1.11.3 The coefficient of variation 327 $a1.11.4 Relationship between cvc and ? for binary data 1.12 The design effect; 1.13 Commonly asked questions; 1.14 Websources; Exercise; Appendix 1.A; Chapter 2 Design issues; 2.1 Introduction; 2.2 Issues for a simple intervention; 2.2.1 Phases of a trial; 2.2.1.1 Preclinical; 2.2.1.2 Sequence of phases; 2.2.2 'Pragmatic' and 'explanatory' trials; 2.2.3 Intention-to-treat and per-protocol analyses; 2.2.4 Non-inferiority and equivalence trials; 2.3 Complex interventions; 2.3.1 Design of complex interventions; 2.3.1.1 Theory (preclinical); 2.3.2 Phase I modelling/qualitative designs 327 $a2.3.3 Pilot or feasibility studies 2.3.4 Example of pilot/feasibility studies in cluster trials; 2.4 Recruitment bias; 2.5 Matched-pair trials; 2.5.1 Design of matched-pair studies; 2.5.2 Limitations of matched-pairs designs; 2.5.3 Example of matched-pair design: The Family Heart Study; 2.6 Other types of designs; 2.6.1 Cluster factorial designs; 2.6.2 Example cluster factorial trial; 2.6.3 Cluster crossover trials; 2.6.4 Example of a cluster crossover trial; 2.6.5 Stepped wedge; 2.6.6 Pseudorandomised trials; 2.7 Other design issues; 2.8 Strategies for improving precision; 2.9 Randomisation 327 $a2.9.1 Reasons for randomisation 2.9.2 Simple randomisation; 2.9.3 Stratified randomisation; 2.9.4 Restricted randomisation; 2.9.5 Minimisation; Exercise; Appendix 2.A; Chapter 3 Sample size: How many subjects/clusters do I need for my cluster randomised controlled trial?; 3.1 Introduction; 3.1.1 Justification of the requirement for a sample size; 3.1.2 Significance tests, P-values and power; 3.1.3 Sample size and cluster trials; 3.2 Sample size for continuous data-comparing two means; 3.2.1 Basic formulae; 3.2.2 The design effect (DE) in cluster RCTs; 3.2.3 Example from general practice 327 $a3.3 Sample size for binary data-comparing two proportions 330 $a"A much-needed guide to the design and analysis of cluster randomized trials, How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research delivers practical guidance on the design and analysis of cluster randomised trials (cRCTs) in health care research. Detailing how to use Stata and SPSS and R for statistical analysis, each analysis technique is carefully explained with mathematics kept to a minimum. Written in a clear, accessible style by experienced statisticians, the text provides a practical approach for applied statisticians and biomedical researchers"--Provided by publisher. 410 0$aStatistics in practice. 606 $aRandomized Controlled Trials as Topic 606 $aData Interpretation, Statistical 606 $aHealth Services Research$xmethod 606 $aResearch Design 615 0$aRandomized Controlled Trials as Topic. 615 0$aData Interpretation, Statistical. 615 0$aHealth Services Research$xmethod. 615 0$aResearch Design. 676 $a610.72/4 700 $aCampbell$b Michael J.$f1950-$0125733 702 $aWalters$b Stephen John 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139141103321 996 $aHow to design, analyse and report cluster randomised trials in medicine and health related research$92043960 997 $aUNINA