LEADER 05301nam 2200661Ia 450 001 9910145557403321 005 20230828231224.0 010 $a1-118-70980-2 010 $a1-281-32049-8 010 $a9786611320492 010 $a0-470-75583-0 010 $a0-470-75502-4 035 $a(CKB)1000000000410981 035 $a(EBL)351461 035 $a(OCoLC)437218709 035 $a(SSID)ssj0000251173 035 $a(PQKBManifestationID)11216565 035 $a(PQKBTitleCode)TC0000251173 035 $a(PQKBWorkID)10249319 035 $a(PQKB)10892512 035 $a(MiAaPQ)EBC351461 035 $a(Au-PeEL)EBL351461 035 $a(CaPaEBR)ebr10233109 035 $a(CaONFJC)MIL132049 035 $a(EXLCZ)991000000000410981 100 $a20060109d2006 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistics at square two$b[electronic resource] $eunderstanding modern statistical applications in medicine /$fMichael J. Campbell 205 $a2nd ed. 210 $aMalden, Mass. ;$aOxford $cBMJ Books/Blackwell$d2006 215 $a1 online resource (146 p.) 300 $aDescription based upon print version of record. 311 $a1-4051-3490-9 320 $aIncludes bibliographical references and index. 327 $aStatistics at Square Two: Understanding modern statistical applications in medicine; Contents; Preface; Chapter 1: Models, tests and data; 1.1 Basics; 1.2 Models; 1.3 Types of data; 1.4 Significance tests; 1.5 Confidence intervals; 1.6 Statistical tests using models; 1.7 Model fitting and analysis: confirmatory and exploratory analyses; 1.8 Computer-intensive methods; 1.9 Bayesian methods; 1.10 Missing values; 1.11 Reporting statistical results in the literature; 1.12 Reading statistics in the literature; Chapter 2: Multiple linear regression; 2.1 The model; 2.2 Uses of multiple regression 327 $a2.3 Two independent variables 2.4 Interpreting a computer output; 2.5 Multiple regression in action; 2.6 Assumptions underlying the models; 2.7 Model sensitivity; 2.8 Stepwise regression; 2.9 Reporting the results of a multiple regression; 2.10 Reading the results of a multiple regression; Chapter 3: Logistic regression; 3.1 The model; 3.2 Uses of logistic regression; 3.3 Interpreting a computer output: grouped analysis; 3.4 Logistic regression in action; 3.5 Model checking; 3.6 Interpreting computer output: ungrouped analysis; 3.7 Case-control studies 327 $a3.8 Interpreting computer output: unmatched case-control study 3.9 Matched case-control studies; 3.10 Interpreting computer output: matched case-control study; 3.11 Conditional logistic regression in action; 3.12 Reporting the results of logistic regression; 3.13 Reading about logistic regression; Chapter 4: Survival analysis; 4.1 Introduction; 4.2 The model; 4.3 Uses of Cox regression; 4.4 Interpreting a computer output; 4.5 Survival analysis in action; 4.6 Interpretation of the model; 4.7 Generalisations of the model; 4.8 Model checking; 4.9 Reporting the results of a survival analysis 327 $a4.10 Reading about the results of a survival analysis Chapter 5: Random effects models; 5.1 Introduction; 5.2 Models for random effects; 5.3 Random vs fixed effects; 5.4 Use of random effects models; 5.5 Random effects models in action; 5.6 Ordinary least squares at the group level; 5.7 Computer analysis; 5.8 Model checking; 5.9 Reporting the results of random effects analysis; 5.10 Reading about the results of random effects analysis; Chapter 6: Other models; 6.1 Poisson regression; 6.2 Ordinal regression; 6.3 Time series regression 327 $a6.4 Reporting Poisson, ordinal or time series regression in the literature 6.5 Reading about the results of Poisson, ordinal or time series regression in the literature; Appendix 1: Exponentials and logarithms; A1.1 Logarithms; Appendix 2: Maximum likelihood and significance tests; A2.1 Binomial models and likelihood; A2.2 Poisson model; A2.3 Normal model; A2.4 Hypothesis testing: LR test; A2.5 Wald test; A2.6 Score test; A2.7 Which method to choose?; A2.8 Confidence intervals; Appendix 3: Bootstrapping and variance robust standard errors; A3.1 Computer analysis; A3.2 The bootstrap in action 327 $aA3.3 Robust or sandwich estimate SE 330 $aUpdated companion volume to the ever popular Statistics at Square One (SS1) Statistics at Square Two, Second Edition, helps you evaluate the many statistical methods in current use. Going beyond the basics of SS1, it covers sophisticated methods and highlights misunderstandings. Easy to read, it includes annotated computer outputs and keeps formulas to a minimum. Worked examples of methods such as multiple and logical regression reinforce the text. Each chapter concludes with exercises to stimulate learning. All those who need to understand stati 606 $aMedical statistics 606 $aStatistics 615 0$aMedical statistics. 615 0$aStatistics. 676 $a610.2/1 700 $aCampbell$b Michael J.$cPhD.$0125733 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910145557403321 996 $aStatistics at square two$92238656 997 $aUNINA