Introduction Overview Human Growth Human Development Statistical Considerations Causal Reasoning and Study Designs Causality Study Designs Basic Statistical Concepts and Tools Normal Distribution Statistical Inference and Significance Standardized Scores Statistical Programming Quantifying Growth and Development: Use of Existing Tools Growth Development Change Scores Regression Analysis of Quantitative Outcomes Least-Squares Regression Quantile Regression Covariate-Adjusted Variables Regression Analysis of Binary Outcomes Basic Concepts Introduction to Generalized Linear Models Logistic Regression Log-Binomial and Binomial Regression Models Regression Analysis of Censored Outcomes Fundamentals Regression Analysis of Right-Censored Data Analysis of Interval-Censored Data Analysis of Repeated Measurements and Clustered Data Introduction Robust Variance Estimator Analysis of Subject-Level Summary Statistics Mixed Models Quantifying Growth: Development of New Tools Capturing Nonlinear Relationships Modeling Quantifying Development: Development of New Tools Summary Index Based on Binary Items Summary Index Based on Quantitative Variables Defining Growth and Development: Longitudinal Measurements Expected Change and Unexplained Residuals Reference Intervals for Longitudinal Monitoring Conditional Scores by Quantile Regression Trajectory Characteristics Validity and Reliability Concepts Statistical Methods Further Topics Missing Values and Imputation Introduction When Is Missing Data (Not) a Problem? Interpolation and Extrapolation Mixed Models Multiple Imputation Imputing Censored Data Multiple Comparisons The Problem When Not to Do Multiplicity Adjustment Strategies of Analysis to Prevent Multiplicity P-Value Adjustments Close Testing Procedure Regression Analysis Strategy Introduction Rationale of Using Multivariable Regression Point Measures, Change Scores, and Unexplained Residuals Issues in Variable Selection Interaction Role of Prior Knowledge References Appendix A: Stata Codes to Generate Simulated Clinical Trial (SCT) Dataset Appendix B: Stata Codes to Generate Simulated Longitudinal Study (SLS) Dataset Appendix C: Stata Program for Detrended Q-Q Plot Appendix D: Weighted Maximum Likelihood Estimation for Binary Items Index mes Basic Concepts Introduction to Generalized Linear Models Logistic Regression Log-Binomial and Binomial Regression Models Regression Analysis of Censored Outcomes Fundamentals Regression Analysis of Right-Censored Data Analysis of Interval-Censored Data Analysis of Repeated Measurements and Clustered Data Introduction Robust Variance Estimator Analysis of Subject-Level Summary Statistics Mixed Models Quantifying Growth: Development of New Tools Capturing Nonlinear Relationships Modeling Quantifying Development: Development of New Tools Summary Index Based on Binary Items Summary Index Based on Quantitative Variables Defining Growth and Development: Longitudinal Measurements Expected Change and Unexplained Residuals Reference Intervals for Longitudinal Monitoring Conditional Scores by Quantile Regression Trajectory Characteristics Validity and Reliability Concepts Statistical Methods Further Topics Missing Values and Imputation Introduction When Is Missing Data (Not) a Problem? Interpolation and Extrapolation Mixed Models Multiple Imputation Imputing Censored Data Multiple Comparisons The Problem When Not to Do Multiplicity Adjustment Strategies of Analysis to Prevent Multiplicity P-Value Adjustments Close Testing Procedure Regression Analysis Strategy Introduction Rationale of Using Multivariable Regression Point Measures, Change Scores, and Unexplained Residuals Issues in Variable Selection Interaction Role of Prior Knowledge References Appendix A: Stata Codes to Generate Simulated Clinical Trial (SCT) Dataset Appendix B: Stata Codes to Generate Simulated Longitudinal Study (SLS) Dataset Appendix C: Stata Program for Detrended Q-Q Plot Appendix D: Weighted Maximum Likelihood Estimation for Binary Items Index e Variables Defining Growth and Development: Longitudinal Measurements Expected Change and Unexplained Residuals Reference Intervals for Longitudinal Monitoring Conditional Scores by Quantile Regression Trajectory Characteristics Validity and Reliability Concepts Statistical Methods Further Topics Missing Values and Imputation Introduction When Is Missing Data (Not) a Problem? Interpolation and Extrapolation Mixed Models Multiple Imputation Imputing Censored Data Multiple Comparisons The Problem When Not to Do Multiplicity Adjustment Strategies of Analysis to Prevent Multiplicity P-Value Adjustments Close Testing Procedure Regression Analysis Strategy Introduction Rationale of Using Multivariable Regression Point Measures, Change Scores, and Unexplained Residuals Issues in Variable Selection Interaction Role of Prior Knowledge References Appendix A: Stata Codes to Generate Simulated Clinical Trial (SCT) Dataset Appendix B: Stata Codes to Generate Simulated Longitudinal Study (SLS) Dataset Appendix C: Stata Program for Detrended Q-Q Plot Appendix D: Weighted Maximum Likelihood Estimation for Binary Items Index s Close Testing Procedure Regression Analysis Strategy Introduction Rationale of Using Multivariable Regression Point Measures, Change Scores, and Unexplained Residuals Issues in Variable Selection Interaction Role of Prior Knowledge References Appendix A: Stata Codes to Generate Simulated Clinical Trial (SCT) Dataset Appendix B: Stata Codes to Generate Simulated Longitudinal Study (SLS) Dataset Appendix C: Stata Program for Detrended Q-Q Plot Appendix D: Weighted Maximum Likelihood Estimation for Binary Items Index.