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1. Statistical analysis of clinical data on a pocket calculator. Part 2 [electronic resource] [2012]
 Cleophas, Ton J. M.
 Dordrecht ; London : Springer, 2012.
 Description
 Book — 1 online resource.
 Summary

 Preface.
 Chapter 1 Introduction.
 Chapter 2 Basic logarithm for a better understanding of statistical methods.
 Chapter 3 Missing data imputation.
 Chapter 4 Assessing manipulated data.
 Chapter 5 Propensity scores and propensity score matching for assessing multiple confounders.
 Chapter 6 Markov modeling for predicting outside the range of observations.
 Chapter 7 Uncertainty in the evaluation of diagnostic tests.
 Chapter 8 Robust tests for imperfect data.
 Chapter 9 Nonlinear modeling on a pocket calculator.
 Chapter 10 Fuzzy modeling for imprecise and incomplete data.
 Chapter 11 Goodness of fit tests for normal and cumulatively normal data.
 Chapter 12 Bhattacharya modeling for unmasking hidden Gaussian curves.
 Chapter 13 Item response modeling instead of classical linear analysis of questionnaires.
 Chapter 14 Superiority testing instead of null hypothesis testing.
 Chapter 15 Variability analysis with the Bartlett's test.
 Chapter 16: Binary partitioning for CART (classification and regression tree) methods.
 Chapter 17 Metaanalysis of continuous data.
 Chapter 18 Metaanalysis of binary data.
 Chapter 19 Physicians' daily life and the scientific method.
 Chapter 20 Incident analysis and the scientific method. Final remarks. Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
 Cleophas, Ton J. M.
 5th ed.  Dordrecht ; New York : Springer, c2012.
 Description
 Book — 1 online resource (xxxv, 743 p.)
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
 Cleophas, Ton J. M.
 3rd ed.  Dordrecht, the Netherlands : Springer, c2006.
 Description
 Book — xiv, 366 p. : ill.
 4th ed.  [Dordrecht] : Springer, c2009.
 Description
 Book — xxi, 559 p. : ill. ; 25 cm.
 Summary

 Foreword
 Chapter 1: Hypotheses, Data, Stratification
 Chapter 2: The Analysis of Efficacy Data
 Chapter 3: The Analysis of Safety Data
 Chapter 4: Log Likelihood Ratio Tests for Safety Data Analysis
 Chapter 5: Equivalence Testing
 Chapter 6: Statistical Power and Sample Size
 Chapter 7: Interim Analyses
 Chapter 8: Clinical Trials Are Often False Positive
 Chapter 9: Multiple Statistical Inferences
 Chapter 10: The Interpretation of the PValues
 Chapter 11: Research Data Closer to Expectation than Compatible with Random Sampling
 Chapter 12: Statistical Tables for Testing Data Closer to Expectation than Compatible with Random Sampling
 Chapter 13: Principles of Linear Regression
 Chapter 14: Subgroup Analysis Using Multiple Linear Regression: Confounding, Interaction, Synergism
 Chapter 15: Curvilinear Regression
 Chapter 16: Logistic and Cox Regression, Markow Models, Regression with Laplace Transformations
 Chapter 17: Regression Modeling For Improved Precision
 Chapter 18: PostHoc Analysis in Clinical Trials, A Case For Logistic Regression Analysis
 Chapter 19: Confounding
 Chapter 20: Interaction
 Chapter 21: MetaAnalysis, Basic Approach
 Chapter 22: MetaAnalysis, Review and Update of Methodologies
 Chapter 23: Crossover Studies with Continuous Variables
 Chapter 24: Crossover Studies with Binary Responses
 Chapter 25: CrossOver Trials Should Not Be Used To Test Treatments with Different Chemical Class
 Chapter 26: QualityOfLife Assessments in Clinical Trials
 Chapter 27: Statistics for the Analysis of Genetic Data
 Chapter 28: Relationship among Statistical Distributions
 Chapter 29: Testing Clinical Trials for Randomness
 Chapter 30: Clinical Trials Do Not Use Random Samples Anymore
 Chapter 31: Clinical Data Where Variability Is More Important than Averages
 Chapter 32: Testing Reproducibility
 Chapter 33: Validating Qualitative Diagnostic Tests
 Chapter 34: Uncertainty of Qualitative Diagnostic Tests
 Chapter 35: MetaAnalyses of Qualitative Diagnostic Tests
 Chapter 36: Validating Quantitative Diagnostic Tests
 Chapter 37: Summary of Validation Procedures for Diagnostic Tests
 Chapter 38: Validating Surrogate Endpoints of Clinical Trials
 Chapter 39: Methods for Repeated Measures Analysis
 Chapter 40: Advanced Analysis Of Variance, Random Effects and Mixed Effects Models
 Chapter 41: Monte Carlo Methods for Data Analysis
 Chapter 42: Physicians' Daily Life and the Scientific Method
 Chapter 43: SuperiorityTesting
 Chapter 44: TrendTesting
 Chapter 45: Odds Ratios and Multiple Regression, Why and How to Use Them
 Chapter 46: Statistics Is No "Bloodless" Algebra
 Chapter 47: Bias Due to Conflicts of Interests, Some Guidelines
 Appendix Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
5. Statistics applied to clinical trials [2006]
 Cleophas, Ton J. M.
 3rd ed.  Dordrecht, the Netherlands : Springer, c2006.
 Description
 Book — computer files ( xiv, 366 pages : illustrations ; 25 cm)
 Online
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(no call number)  Unknown 
 Cleophas, Ton J. M. author.
 Switzerland : Springer, [2016]
 Description
 Book — online resource (x, 234 pages) : illustrations (some color)
 Summary

 Randomness
 Randomized and Observational Research
 Randomized Clinical Trials, history, Designs
 Randomized Clinical Trials, Analysis Sets, Statistical Analysis, Reporting Issues
 Discrete Data Analysis, Failure Time Data Analysis
 Quantitative Data Analysis
 Subgroup Analysis
 Interim Analysis
 Multiplicity Analysis
 Medical Statistics : a Discipline at the Interface of Biology and Mathematics.
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SPRINGER  Unknown 
 SPSS for starters
 Cleophas, Ton J. M., author.
 Second edition.  Cham : Springer, [2016]
 Description
 Book — 1 online resource
 Summary

 Preface
 Chapter 1: Introduction
 Part 1 Continuous outcome data
 Chapter 2: One sample continuous data
 Chapter 3: Paired continuous outcome data normality assumed
 Chapter 4: Paired continuous outcome data nonnormality accounted
 Chapter 5: Paired continuous outcome data with predictors
 Chapter 6: Unpaired continuous outcome data normality assumed
 Chapter 7: Unpaired continuous outcome data nonnormality accounted
 Chapter 8: Linear regression for continuous outcome data
 Chapter 9: Recoding for categorical predictor data
 Chapter 10: Repeatedmeasuresanalysis of variance normality assumed
 Chapter 11: Repeatedmeasuresanalysis of variance nonnormality accounted
 Chapter 12: Doublyrepeatedmeasuresanalysis of variance
 Chapter 13: Multilevel modeling with mixed linear models
 Chapter 14: Random multilevel modeling with generalized mixed linear models
 Chapter 15: Onewayanalysis of variance normality assumed
 Chapter 16: Onewayanalysis of variance nonnormality accounted
 Chapter 17: Trend tests of cont inuous outcome data
 Chapter 18: Multistage regression
 Chapter 19: Multivariate analysis with path statistics
 Chapter 20: Multivariate analysis of variance
 Chapter 21: Averageranktesting for multiple outcome variables and categorical predictors
 Chapter 22: Missing data imputation
 Chapter 23: Metaregression
 Chapter 24: Poisson regression including a weight variable (time of observation) for rates
 Chapter 25: Confounding
 Chapter 26: Interaction
 Chapter 27: Curvilinear analysis
 Chapter 28: Loess and spline modeling for nonlinear data, where curvilinear models lack fit
 Chapter 29: Monte Carlo analysis, the easy alternative for continuous outcome data
 Chapter 30: Artificial intelligence as a distribution free alternative for nonlinear data
 Chapter 31: Robust tests for data with large outliers
 Chapter 32: Nonnegative outcome data using the gamm a distribution
 Chapter 33: Nonnegative outcome data with a big spike at zero using the Tweedie distribution
 Chapter 34: Polynomial analysis for continuous outcome data with a sinusoidal pattern
 Chapter 35: Validating quantitative diagnostic tests
 Chapter 36: Reliability assessment of quantitative diagnostic tests
 Part 2 Binary outcome data
 Chapter 37: One sample binary data
 Chapter 38: Unpaired binary data
 Chapter 39: Binary logistic regression with a binary predictor
 Chapter 40: Binary logistic regression with categorical predictors
 Chapter 41: Binary logistic regression with a continuous predictor
 Chapter 42: Trend tests of binary data
 Chapter 43: Paired binary outcome data without predictors
 Chapter 44: Paired binary outcome data with predictors
 Chapter 45: Repeated measures binary data
 Chapter 46: Multinomial logistic regression for outcome categories
 Chapter 47: Multinomial logistic regression with random intercepts for both categorical
 outcome and predictor data.
 Chapter 48: Comparing the performance of diagnostic tests
 Chapter 49: Poisson regression for binary outcome data
 Chapter 50: Loglinear models for the exploration of multidimensional contingency tables
 Chapter 51: Probit regression for binary outcome data reported as response rates
 Chapter 52: Monte Carlo analysis, the easy alternative for binary outcomes
 Chapter 53: Validating qualitative diagnostic tests
 Chapter 54: Reliability assessment of qualitative diagnostic tests
 Part 3 Survival and longitudinal data
 Chapter 55: Log rank tests
 Chapter 56: Cox regression
 Chapter 57: Cox regression with timedependent variables
 Chapter 58: Segmented Cox regression
 Chapter 59: Assessing seasonality
 Chapter 60: Probability assessment of survival with interval censored data analysis Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
8. SPSS for starters [2010]
 Cleophas, Ton J. M.
 Dordrecht ; New York : Springer, c2010.
 Description
 Book — online resource (ix, 76 pages : illustrations (some color) ; 24 cm)
 Summary

 Introduction
 OneSample Continuous and Binary Data (tTest, zTest) (10 and
 55 Patients)
 Paired Continuous Data (Pairedt, Wilcoxon) (10 Patients)
 Unpaired Continuous Data (Unpaired tTests, MannWhitney) (20 Patients)
 Linear Regression (20 Patients)
 Repeated Measures ANOVA, Friedman (10 Patients)
 Mixed Models (20 Patients)
 OneWayANOVA, KruskallWallis (30 Patients)
 Trend Test for Continuous Data (30 Patients)
 Unpaired Binary Data (ChiSquare, Crosstabs) (55 Patients)
 Logistic Regression (55 Patients)
 Trend Tests for Binary Data (106 Patients)
 Paired Binary (McNemar Test) (139 General Practitioners)
 Multiple Paired Binary Data (Cochran's Q Test) (139 Patients)
 Cox Regression (60 Patients)
 Cox Regression with Timedependent Variables (60 Patients)
 Validating Qualitative Diagnostic Tests (575 Patients)
 Validating Quantitative Diagnostic Tests (17 Patients)
 Reliability Assessment of Qualitative Diagnostic Tests (17 Patients)
 Reliability Assessment of Quantitative Diagnostic Tests (17 Patients)
 Final Remarks.
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Medical Library (Lane)
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SPRINGER  Unknown 
 Cleophas, Ton J. M.
 Dordrecht ; Boston : Kluwer Academic Publishers, ©2000.
 Description
 Book — 1 online resource (xi, 97 pages) : illustrations
 Summary

 Ch.
 1. Hypotheses, data, stratification
 Ch.
 2. The analysis of efficacy data of drug trials
 Ch.
 3. The analysis of safety data of drug trials
 Ch.
 4. Equivalence testing
 Ch.
 5. Statistical power and sample size
 Ch.
 6. Interim analyses
 Ch.
 7. Multiple statistical inferences
 Ch.
 8. Subgroup analysis using multiple linear regression: confounding, interaction, synergism
 Ch.
 9. Metaanalysis
 ch.
 10. Capitum selectum, crossover studies with continuous variables: power analysis
 Ch.
 11. Capitum selectum: posthoc analysis in clinical trails, a case for logistic regression analysis.
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