LEADER 05372nam 2200673 450 001 9910811547103321 005 20230803220319.0 010 $a1-118-72198-5 010 $a1-118-72195-0 010 $a1-118-72196-9 035 $a(CKB)2550000001130061 035 $a(EBL)1471797 035 $a(OCoLC)860923490 035 $a(SSID)ssj0001002363 035 $a(PQKBManifestationID)11551095 035 $a(PQKBTitleCode)TC0001002363 035 $a(PQKBWorkID)10995861 035 $a(PQKB)10455825 035 $a(OCoLC)868971811 035 $a(MiAaPQ)EBC1471797 035 $a(Au-PeEL)EBL1471797 035 $a(CaPaEBR)ebr10784792 035 $a(CaONFJC)MIL530108 035 $a(EXLCZ)992550000001130061 100 $a20131109d2014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRegression methods for medical research /$fBee Choo Tai, David Machin 210 1$aChichester, West Sussex, UK :$cJohn Wiley and Sons,$d2014. 210 4$dİ2014 215 $a1 online resource (313 p.) 300 $aDescription based upon print version of record. 311 $a1-4443-3144-2 311 $a1-299-98857-1 320 $aIncludes bibliographical references and index. 327 $aRegression Methods for Medical Research; Copyright; Contents; Preface; 1 Introduction; Introduction; Statistical models; Comparing two means; Linear regression; Types of dependent variables (y-variables); Some completed studies; Further reading; Technical details; Student's t-test; Linear regression; Predicting a mean value of y for a particular x; Predicting an individual's value of y for a particular x; Analysis of Variance (ANOVA); Coefficient of determination; Extending the simple linear model; Correlation; Logarithms and the exponential constant, e 327 $a2 Linear Regression: Practical IssuesTypes of covariates (independent variables); Ordered categorical covariates; Numerically discrete covariates; Unordered categorical covariates; Verifying the assumptions; Ordered categorical covariate; Continuous covariate; Do the assumptions matter?; Precautions; Computation; Simple models; Study design; Clinical and statistical significance; Reporting; Technical details; Global tests; Ordered Normal scores; 3 Multiple Linear Regression; Linear regression: two covariates; How good is the fitted model?; Quadratic models; Multiple linear regression 327 $aExtending the 2-covariate modelNotation; Interactions; Non-nested models; Precautions; Nested models; Collinearity; Parsimonious models; Verifying assumptions; Technical details; Nested models; Akaike's criterion; 4 Logistic Regression; The logit transformation; Odds ratio; The logit transformation; Logistic regression; Categorical and continuous covariates; Unordered categorical covariate; Ordered categorical covariate; Continuous covariate; Multiple logistic regression; Interactions; Model checking; Tabulations; Lack of an important covariate; Outlying or influential observations 327 $aGoodness-of-fitConditional logistic regression; Ordered logistic regression; Technical details; Odds ratio (OR) and relative risk (RR); Binomial distribution; Maximum likelihood estimation (MLE); Likelihood ratio (LR) test; The empirical logit transformation; 5 Poisson Regression; Introduction; Poisson or Binomial models; Unknown population size at risk; Over-dispersion and robust estimates; Over-dispersion; Robust procedures; Known population size at risk; Known cumulative exposure; Zero-inflated models; Residuals; Technical details; Poisson distribution; Maximum Likelihood Estimation (MLE) 327 $aRelationship between Poisson and logit models6 Time-to-Event Regression; Time-to-event data; Kaplan-Meier survival curve; The hazard rate and hazard ratio; Hazard ratio; The Cox regression model; Single covariate; Two covariates; More than two covariates; Verifying proportional hazards; Complementary log-log plot; Observed and predicted K-M plots; Schoenfeld residuals; What if the proportional hazards assumption is wrong?; Stratified Cox; Technical details; Calculating a Kaplan-Meier survival curve; The hazard function; The complementary log-log transformation; Residuals; 7 Model Building 327 $aIntroduction 330 $a Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to app 606 $aRegression analysis 606 $aMedicine$xResearch 615 0$aRegression analysis. 615 0$aMedicine$xResearch. 676 $a610.72/4 700 $aTai$b Bee-Choo$01658558 701 $aMachin$b David$f1939-$0520765 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910811547103321 996 $aRegression methods for medical research$94012646 997 $aUNINA