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1. The analysis of biological data [2015]
 Whitlock, Michael, author.
 Second edition.  New York, New York : W.H. Freeman and Company, [2015]
 Description
 Book — xxxiii, 818 pages : illustrations (some color) ; 25 cm
 Summary

 PART 1. INTRODUCTION TO STATISTICS
 1. Statistics and samples INTERLEAF
 1 Biology and the history of statistics
 2. Displaying data
 3. Describing data
 4. Estimating with uncertainty INTERLEAF
 2 Pseudoreplication
 5. Probability
 6. Hypothesis testing INTERLEAF
 3 Why statistical significance is not the same as biological importance
 PART 2. PROPORTIONS AND FREQUENCIES
 7. Analyzing proportions INTERLEAF
 4 Correlation does not require causation
 8. Fitting probability models to frequency data INTERLEAF
 5 Making a plan
 9. Contingency analysis: associations between categorical variables
 PART 3. COMPARING NUMERICAL VALUES
 10. The normal distribution INTERLEAF
 6 Controls in medical studies
 11. Inference for a normal population
 12. Comparing two means INTERLEAF
 7 Which test should I use?
 13. Handling violations of assumptions
 14. Designing experiments INTERLEAF
 8 Data dredging
 15. Comparing means of more than two groups INTERLEAF
 9 Experimental and statistical mistakes
 PART 4. REGRESSION AND CORRELATION
 16. Correlation between numerical variables INTERLEAF
 10 Publication bias
 17. Regression INTERLEAF
 11 Using species as data points
 PART 5. MODERN STATISTICAL METHODS
 18. Multiple explanatory variables
 19. Computerintensive methods
 20. Likelihood
 21. Metaanalysis: combining information from multiple studies.
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QH323.5 .W48 2015  Unknown 
 Newcombe, Robert G.
 Boca Raton, FL : CRC Press, c2013.
 Description
 Book — xxvii, 442 p. : ill. ; 24 cm.
 Summary

 Hypothesis Tests and Confidence Intervals Sample and Population Hypothesis Testing and Confidence Intervals: The Fundamentals Why Confidence Intervals Are Generally More Informative Than pValues Measures of Effect Size When Are Point and Interval Estimates Less Helpful? Frequentist, Bayesian and Likelihood Intervals Just What Is Meant by the Population? The Unit of Data Sample Size Planning
 Means and Their Differences Confidence Interval for a Mean Confidence Interval for the Difference between Means of Independent Samples Confidence Interval for the Difference between Two Means Based on Individually Paired Samples Scale Transformation NonParametric Methods The Effect of Dichotomising Continuous Variables
 Confidence Intervals for a Simple Binomial Proportion Introduction The Wald Interval Boundary Anomalies Alternative Intervals Algebraic Definitions for Several Confidence Intervals for the Binomial Proportion Implementation of Wilson Score Interval in MS Excel Sample Size for Estimating a Proportion
 Criteria for Optimality How Can We Say Which Methods Are Good Ones? Coverage Expected Width Interval Location Computational Ease and Transparency
 Evaluation of Performance of Confidence Interval Methods Introduction An Example of Evaluation Approaches Used in Evaluations for the Binomial Proportion The Need for Illustrative Examples
 Intervals for the Poisson Parameter and the Substitution Approach The Poisson Distribution and Its Applications Confidence Intervals for the Poisson Parameter and Related Quantities Widening the Applicability of Confidence Interval Methods: The Substitution Approach
 Difference between Independent Proportions and the SquareandAdd Approach The Ordinary
 2 x
 2 Table for Unpaired Data The Wald Interval The SquareandAdd or MOVER Approach Other WellBehaved Intervals for the Difference between Independent Proportions Evaluation of Performance Number Needed to Treat Bayesian Intervals Interpreting Overlapping Intervals Sample Size Planning
 Difference between Proportions Based on Individually Paired Data The
 2 x
 2 Table for Paired Binary Data Wald and Conditional Intervals Intervals Based on Profile Likelihoods ScoreBased Intervals Evaluation of Performance
 Methods for Triads of Proportions Introduction Trinomial Variables on Equally Spaced Scales Unordered Trinomial Data: Generalising the TailBased pValue to Characterise Conformity to Prescribed Norms A Ternary Plot for Unordered Trinomial Data
 Relative Risk and Rate Ratio A Ratio of Independent Proportions Three Effect Size Measures Comparing Proportions Ratio Measures Behave Best on a Log Scale Intervals Corresponding to the Empirical Estimate Infinite Bias in Ratio Estimates Intervals Based on Mesially Shrunk Estimated Risks A Ratio of Proportions Based on Paired Data A Ratio of Sizes of Overlapping Groups A Ratio of Two Rates Implementation in MS Excel
 The Odds Ratio and Logistic Regression The Rationale for the Odds Ratio Disadvantages of the Odds Ratio Intervals Corresponding to the Empirical Estimate Deterministic Bootstrap Intervals Based on Median Unbiased Estimates Logistic Regression An Odds Ratio Based on Paired Data Implementation
 Screening and Diagnostic Tests Background Sensitivity and Specificity Positive and Negative Predictive Values TradeOff between Sensitivity and Specificity: The ROC Curve Simultaneous Comparison of Sensitivity and Specificity between Two Tests
 Widening the Applicability of Confidence Interval Methods: The Propagating Imprecision Approach Background The Origin of the PropImp Approach The PropImp Method Defined PropImp and MOVER Wilson Intervals for Measures Comparing Two Proportions Implementation of the PropImp Method Evaluation The Thorny Issue of Monotonicity Some Issues Relating to MOVER and PropImp Approaches
 Several Applications of the MOVER and PropImp Approaches Introduction AdditiveScale Interaction for Proportions Radiation Dose Ratio Levin's Attributable Risk Population Risk Difference and Population Impact Number Quantification of Copy Number Variations Standardised Mortality Ratio Adjusted for Incomplete Data on Cause of Death RD and NNT from Baseline Risk and Relative Risk Reduction Projected Positive and Negative Predictive Values Estimating Centiles of a Gaussian Distribution Ratio Measures Comparing Means Winding the Clock Back: The Healthy Hearts Study Grass Fires Incremental RiskBenefit Ratio Adjustment of Prevalence Estimate Using Partial Validation Data Comparison of Two Proportions Based on Overlapping Samples Standardised Difference of Proportions
 Generalised MannWhitney Measure Absolute and Relative Effect Size Measures for Continuous and Ordinal Scales The Generalised MannWhitney Measure Definitions of Eight Methods Illustrative Examples Evaluation Results of the Evaluation Implementation in MS Excel Interpretation
 Generalised Wilcoxon Measure The Rationale for the Generalised Wilcoxon Measure psi Paired and Unpaired Effect Size Measures Compared Estimating the Index psi Development of a Confidence Interval for psi Evaluation of Coverage Properties: Continuous Case Results of Evaluation for the Continuous Case Coverage Properties for Discrete Distributions Discussion
 References Appendices.
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QH323.5 .N49 2013  Unknown 
3. Regression methods in biostatistics : linear, logistic, survival, and repeated measures models [2012]
 2nd ed.  New York : Springer, c2012.
 Description
 Book — xx, 509 p. : ill. ; 25 cm.
 Summary

 Introduction. Exploratory and Descriptive Methods. Basic Statistical Methods. Linear Regression. Logistic Regression. Survival Analysis. Repeated Measures Analysis. Generalized Linear Models. Strengthening Casual Inference. Predictor Selection. Complex Surveys. Summary.
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QH323.5 .R43 2012  Unknown 
4. Bayesian biostatistics [2012]
 Lesaffre, Emmanuel.
 Chichester, West Sussex : John Wiley & Sons, 2012.
 Description
 Book — xviii, 516 p. : ill ; 25 cm.
 Summary

 Preface xiii Notation, terminology and some guidance for reading the book xvii Part I BASIC CONCEPTS IN BAYESIAN METHODS
 1 Modes of statistical inference
 3 1.1 The frequentist approach: A critical reflection
 4 1.2 Statistical inference based on the likelihood function
 10 1.3 The Bayesian approach: Some basic ideas
 14 1.4 Outlook
 18
 2 Bayes theorem: Computing the posterior distribution
 20 2.1 Introduction
 20 2.2 Bayes theorem
 the binary version
 20 2.3 Probability in a Bayesian context
 21 2.4 Bayes theorem
 the categorical version
 22 2.5 Bayes theorem
 the continuous version
 23 2.6 The binomial case
 24 2.7 The Gaussian case
 30 2.8 The Poisson case
 36 2.9 The prior and posterior distribution of h(theta)
 40 2.10 Bayesian versus likelihood approach
 40 2.11 Bayesian versus frequentist approach
 41 2.12 The different modes of the Bayesian approach
 41 2.13 An historical note on the Bayesian approach
 42 2.14 Closing remarks
 44
 3 Introduction to Bayesian inference
 46 3.1 Introduction
 46 3.2 Summarizing the posterior by probabilities
 46 3.3 Posterior summary measures
 47 3.4 Predictive distributions
 51 3.5 Exchangeability
 58 3.6 A normal approximation to the posterior
 60 3.7 Numerical techniques to determine the posterior
 63 3.8 Bayesian hypothesis testing
 72 3.9 Closing remarks
 78
 4 More than one parameter
 82 4.1 Introduction
 82 4.2 Joint versus marginal posterior inference
 83 4.3 The normal distribution with mu and sigma2 unknown
 83 4.4 Multivariate distributions
 89 4.5 Frequentist properties of Bayesian inference
 92 4.6 Sampling from the posterior distribution: The Method of Composition
 93 4.7 Bayesian linear regression models
 96 4.8 Bayesian generalized linear models
 101 4.9 More complex regression models
 102 4.10 Closing remarks
 102
 5 Choosing the prior distribution
 104 5.1 Introduction
 104 5.2 The sequential use of Bayes theorem
 104 5.3 Conjugate prior distributions
 106 5.4 Noninformative prior distributions
 113 5.5 Informative prior distributions
 121 5.6 Prior distributions for regression models
 129 5.7 Modeling priors
 134 5.8 Other regression models
 136 5.9 Closing remarks
 136
 6 Markov chain Monte Carlo sampling
 139 6.1 Introduction
 139 6.2 The Gibbs sampler
 140 6.3 The Metropolis(Hastings) algorithm
 154 6.4 Justification of the MCMC approaches*
 162 6.5 Choice of the sampler
 165 6.6 The Reversible Jump MCMC algorithm*
 168 6.7 Closing remarks
 172
 7 Assessing and improving convergence of the Markov chain
 175 7.1 Introduction
 175 7.2 Assessing convergence of a Markov chain
 176 7.3 Accelerating convergence
 189 7.4 Practical guidelines for assessing and accelerating convergence
 194 7.5 Data augmentation
 195 7.6 Closing remarks
 200
 8 Software
 202 8.1 WinBUGS and related software
 202 8.2 Bayesian analysis using SAS
 215 8.3 Additional Bayesian software and comparisons
 221 8.4 Closing remarks
 222 Part II BAYESIAN TOOLS FOR STATISTICAL MODELING
 9 Hierarchical models
 227 9.1 Introduction
 227 9.2 The Poissongamma hierarchical model
 228 9.3 Full versus empirical Bayesian approach
 238 9.4 Gaussian hierarchical models
 240 9.5 Mixed models
 244 9.6 Propriety of the posterior
 260 9.7 Assessing and accelerating convergence
 261 9.8 Comparison of Bayesian and frequentist hierarchical models
 263 9.9 Closing remarks
 265
 10 Model building and assessment
 267 10.1 Introduction
 267 10.2 Measures for model selection
 268 10.3 Model checking
 288 10.4 Closing remarks
 316
 11 Variable selection
 319 11.1 Introduction
 319 11.2 Classical variable selection
 320 11.3 Bayesian variable selection: Concepts and questions
 325 11.4 Introduction to Bayesian variable selection
 326 11.5 Variable selection based on Zellner's gprior
 333 11.6 Variable selection based on Reversible Jump Markov chain Monte Carlo
 336 11.7 Spike and slab priors
 339 11.8 Bayesian regularization
 345 11.9 The many regressors case
 351 11.10 Bayesian model selection
 355 11.11 Bayesian model averaging
 357 11.12 Closing remarks
 359 Part III BAYESIAN METHODS IN PRACTICAL APPLICATIONS
 12 Bioassay
 365 12.1 Bioassay essentials
 365 12.2 A generic in vitro example
 369 12.3 Ames/Salmonella mutagenic assay
 371 12.4 Mouse lymphoma assay (L5178Y TK+/)
 373 12.5 Closing remarks
 374
 13 Measurement error
 375 13.1 Continuous measurement error
 375 13.2 Discrete measurement error
 382 13.3 Closing remarks
 389
 14 Survival analysis
 390 14.1 Basic terminology
 390 14.2 The Bayesian model formulation
 394 14.3 Examples
 397 14.4 Closing remarks
 406
 15 Longitudinal analysis
 407 15.1 Fixed time periods
 407 15.2 Random event times
 417 15.3 Dealing with missing data
 420 15.4 Joint modeling of longitudinal and survival responses
 424 15.5 Closing remarks
 429
 16 Spatial applications: Disease mapping and image analysis
 430 16.1 Introduction
 430 16.2 Disease mapping
 430 16.3 Image analysis
 444
 17 Final
 chapter 456 17.1 What this book covered
 456 17.2 Additional Bayesian developments
 456 17.3 Alternative reading
 459 Appendix: Distributions
 460 A.1 Introduction
 460 A.2 Continuous univariate distributions
 461 A.3 Discrete univariate distributions
 477 A.4 Multivariate distributions
 481 References
 484 Index 509.
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