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008			100429s2011 njua b 001 0 eng
010			_a 2010018482
016	7		_a101553323 _2DNLM
020			_a9780470508220 (hardback)
020			_a0470508221 (hardback)
035			_a(OCoLC)ocn613993075
040			_aDLC _cDLC _dYDX _dYDXCP _dCDX _dCUX _dNLM _dDLC
042			_apcc
050	0	0	_aRA409 _b.L33 2011
060	0	0	_a2011 B-401
060	1	0	_aWA 950
082	0	0	_a610.151 95 LAC _222
100	1		_aLachin, John M., _d1942-
245	1	0	_aBiostatistical methods : _bthe assessment of relative risks / _cJohn M. Lachin.
250			_a2nd ed.
260			_aHoboken, N.J. : _bWiley, _cc2011.
300			_axxiii, 622 p. : _bill. ; _c25 cm.
490	1		_aWiley series in probability and statistics
504			_aIncludes bibliographical references and index.
505			_aPreface. Preface to First Edition. 1 Biostatistics and Biomedical Science. 1.1 Statistics and the Scientific Method. 1.2 Biostatistics. 1.3 Natural History of Disease Progression. 1.4 Types of Biomedical Studies. 1.5 Studies of Diabetic Nephropathy. 2 Relative Risk Estimates and Tests for Independent Groups. 2.1 Probability As a Measure of Risk. 2.2 Measures of Relative Risk. 2.3 Large Sample Distribution. 2.4 Sampling Models Likelihoods. 2.5 Exact Inference. 2.6 Large Sample Inferences. 2.7 SAS PROC FREQ. 2.8 Other Measures of Differential Risk. 2.9 Polychotomous and Ordinal Data. 2.10 Two Independent Groups With Polychotomous Response. 2.11 Multiple Independent Groups. 2.12 Problems. 3 Sample Size, Power, and Efficiency. 3.1 Estimation Precision. 3.2 Power of Z-Tests. 3.3 Test for Two Proportions. 3.4 Power of Chi-Square Tests. 3.5 SAS PROC POWER. 3.6 Efficiency. 3.7 Problems. 4 Stratified-Adjusted Analysis for Independent Groups. 4.1 Introduction. 4.2 Mantel-Haenszel Test and Cochran's Test. 4.3 Stratified-Adjusted Estimators. 4.4 Nature of Covariate Adjustment. 4.5 Multivariate Tests of Hypotheses. 4.6 Tests of Homogeneity. 4.7 Efficient Tests of No Partial Association. 4.8 Asymptotic Relative Efficiency of Competing Tests. 4.9 Maximin-Efficient Robust Tests. 4.10 Random Effects Model. 4.11 Power and Sample Size for Tests of Association. 4.12 Polychotomous and Ordinal Data. 4.13 Problems. 5 Case-Control and Matched Studies. 5.1 Unmatched Case-Control (Retrospective) Sampling. 5.2 Matching. 5.3 Tests of Association for Matched Pairs. 5.4 Measures of Association for Matched Pairs. 5.5 Pair-Matched Retrospective Study. 5.6 Power Function of McNemar's Test. 5.7 Stratified Analysis of Pair-Matched Tables. 5.8 Multiple Matching-Mantel-Haenszel Analysis. 5.9 Matched Polychotomous Data. 5.10 Kappa Index of Agreement. 5.11 Problems. 6 Applications of Maximum Likelihood and Efficient Scores. 6.1 Binomial. 6.2 2x2 Table: Product Binomial (Unconditionally). 6.3 2x2 Table, Conditionally. 6.4 Score-Based Estimate. 6.5 Stratified Score Analysis of Independent 2x2 Tables. 6.6 Matched Pairs. 6.7 Iterative Maximum Likelihood. 6.8 Problems. 7 Logistic Regression Models. 7.1 Unconditional Logistic Regression Model. 7.2 Interpretation of the Logistic Regression Model. 7.3 Tests of Significance. 7.4 Interactions. 7.5 Measures of the Strength of Association. 7.6 Conditional Logistic Regression Model for Matched Sets. 7.7 Models for Polychotomous or Ordinal Data. 7.8 Random Effects and Mixed Models. 7.9 Models for Multivariate or Repeated Measures. 7.10 Problems. 8 Analysis of Count Data. 8.1 Event Rates and the Homogeneous Poisson Model. 8.2 Over Dispersed Poisson Model. 8.3 Poisson Regression Model. 8.4 Over Dispersed and Robust Poisson Regression. 8.5 Conditional Poisson Regression for Matched Sets. 8.6 Negative Binomial Models. 8.7 Power and Sample Size. 8.8 Multiple Outcomes. 8.9 Problems. 9 Analysis of Event-Time Data. 9.1 Introduction to Survival Analysis. 9.2 Lifetable Construction. 9.3 Family of Weighted Mantel-Haenszel Tests. 9.4 Proportional Hazards Models. 9.5 Evaluation of Sample Size and Power. 9.6 Additional Models. 9.7 Analysis of Recurrent Events. 9.8 Problems. Appendix Statistical Theory. A.1 Introduction. A.2 Central Limit Theorem and the Law of Large Numbers. A.3 Delta Method. A.4 Slutsky's Convergence Theorem. A.5 Least Squares Estimation. A.6 Maximum Likelihood Estimation and Efficient Scores. A.7 Tests of Significance. A.8 Explained Variation. A.9 Robust Inference. A.10 Generalized Linear Models and Quasi-Likelihood. A.11 Generalized Estimating Equations (GEE). References. Author Index. Subject Index.
520			_a"This book focuses on the comparison, contrast, and assessment of risks on the basis of clinical investigations. It develops basic concepts as well as deriving biostatistical methods through both the application of classical mathematical statistical tools and more modern likelihood-based theories. The first half of the book presents methods for the analysis of single and multiple 2x2 tables for cross-sectional, prospective, and retrospective (case-control) sampling, with and without matching using fixed and two-stage random effects models. The text then moves on to present a more modern likelihood- or model-based approach, which includes unconditional and conditional logistic regression; the analysis of count data and the Poisson regression model; the analysis of event time data, including the proportional hazards and multiplicative intensity models; and elements of categorical data analysis (expanded in this edition). SAS subroutines are both showcased in the text and embellished online by way of a dedicated author website. The book contains a technical, but accessible appendix that presents the core mathematical statistical theory used for the development of classical and modern statistical methods"--Provided by publisher.
650		0	_aMedical statistics.
650		0	_aHealth risk assessment _xStatistical methods.
650		0	_aMedicine _xResearch _xStatistical methods.
650	1	2	_aBiostatistics _xmethods.
650	2	2	_aData Interpretation, Statistical.
650	2	2	_aModels, Statistical.
650	2	2	_aRisk Assessment _xmethods.
830		0	_aWiley series in probability and statistics.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cRFT