This textbook describes the basics of research in medical, clinical, and biomedical settings as well as the concepts and application of epidemiologic designs in research conduct. Design transcends statistical techniques, and no matter how sophisticated a statistical modeling, errors of design/sampling cannot be corrected. The authors of this textbook have presented a complex field in a very simplified and reader-friendly manner with the intent that such presentation will facilitate the understanding of design process and epidemiologic thinking in clinical and biomedical research. Covers these relevant topics in epidemiology: Case-Cohort Design Prospective Case-Control Quantitative Evidence Synthesis (QES) Instant Cohort Design & Case-Crossover Design Effect Modification & Interaction Epidemiologic Tree - Molecular Epidemiology & Health Disparities Epidemiologic Challenge - "Big Data," mHealth, Social Media 3 "Ts" - Team Science, Transdisciplinary Research, Translational Research Bias, Random error, Confounding Systems Science & Evidence Discovery Research is presented as an exercise around measurement, with measurement error inevitable in its conduct-hence the inherent uncertainties of all findings in clinical and biomedical research. Concise Epidemiologic Principles and Concepts covers research conceptualization, namely research objectives, questions, hypothesis, design, implementation, data collection, analysis, results, and interpretation. While the primary focus of epidemiology is to assess the relationship between exposure (risk or predisposing factor) and outcome (disease or health-related event), causal association is presented in a simplified manner, including the role of quantitative evidence synthesis (meta-analysis) in causal inference. Epidemiology has evolved over the past three decades resulting in several fields being developed. This text presents in brief the perspectives and future of epidemiology in the era of the molecular basis of medicine. With molecular epidemiology, we are better equipped with tools to identify molecular biologic indicators of risk as well as biologic alterations in the early stages of disease.

Biostatistics deals with making sense of data. While statistical inference is essential in our application of the research findings to clinical decision-making regarding the care of our patients, statistical inference without clinical relevance or importance can be very misleading and even meaningless. This textbook has attempted to deemphasize p value in the interpretation of clinical and biomedical data by stressing the importance of confidence intervals, which allow for the quantification of evidence. For example, a large study due to a large sample size that minimizes variability may show a statistically significant difference while in reality the difference is too insignificant to warrant any clinical relevance. Covers these relevant topics in biostatistics: Design Process, Sampling & Reality in Statistical Modeling Basics of Biostatistical Reasoning & Inference Central Tendency Theorem & Measures of Dispersion Most commonly used & abused parametric test - t test Most commonly used & abused non-parametric test - chi squared statistic Sample size and power estimations Logistic/Binomial Regression Models - Binary Outcomes Time-to-Event Data - Survival Analysis & Count Data - Poisson Regression ANOVA, ANCOVA - Mixed Effects Model (Fixed and Random), RANOVA, GEE Simple & Multiple Linear Regression Models Correlation Analysis (Pearson & Spearman Rank) Clinical & Statistical Significance - p value as a function of sample size Clinical and biomedical researchers often ignore an important aspect of evidence discovery from their funded or unfunded projects. Since the attempt is to illustrate some sets of relationships from the data set, researchers often do not exercise substantial amount of time in assessing the reliability and validity of the data to be utilized in the analysis. However, the expected inference or the conclusion to be drawn is based on the analysis of the un-assessed data. Reality in statistical modeling of biomedical and clinical research data remains the focus of scientific evidence discovery, and this book. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample and the randomization process when applicable prior to the selection of the analytic tool. In addition, this book stresses the importance of biologic and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is random sample, which had been perpetually addressed in this book. When studies are conducted without a random sample, except when disease registries/databases or consecutive subjects are utilized, as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value.

This textbook describes the basics of research in medical, clinical and biomedical settings as well as the concepts and application of epidemiologic designs in research conduct. Design transcends statistical techniques, and no matter how sophisticated a statistical modeling, errors of design/sampling cannot be corrected. The authors of this textbook have presented a complex field in a very simplified and friendly manner, with the intent that such presentation will facilitate the understanding of design process and epidemiologic thinking in clinical and biomedical research. Research is presented as exercise around measurement, with measurement error inevitable in its conduct, and hence the inherent uncertainties of all findings in clinical and biomedical research. Concise Epidemiologic Principles and Concepts covers research conceptualization namely research objectives, questions, hypothesis, design, implementation, data collection, analysis, results, and interpretation. While the primary focus of epidemiology is to assess the relationship between exposure (risk or predisposing factor) and outcome (disease or health related events), causal association is presented in a simplified manner including the role of quantitative evidence synthesis (meta-analysis) in causal inference. Epidemiology has evolved during the past three decades resulting in several fields being developed. This text presents in brief the perspectives and future of epidemiology in the era of molecular basis of medicine. With molecular epidemiology, we are better equipped with tools to identify molecular biologic indicators of risk as well as biologic alterations in the early stages of disease.

Biostatistics deals with making sense of data. While statistical inference is essential in our application of the research findings to clinical decision-making regarding the care of our patients, statistical inference without clinical relevance or importance can be very misleading and even meaningless. This textbook has attempted to deemphasize p value in the interpretation of clinical and biomedical data by stressing the importance of confidence intervals, which allow for the quantification of evidence. For example, a large study due to a large sample size that minimizes variability may show a statistically significant difference while in reality the difference is too insignificant to warrant any clinical relevance. Covers these relevant topics in biostatistics: Design Process, Sampling & Reality in Statistical Modeling Basics of Biostatistical Reasoning & Inference Central Tendency Theorem & Measures of Dispersion Most commonly used & abused parametric test - t test Most commonly used & abused non-parametric test - chi squared statistic Sample size and power estimations Logistic/Binomial Regression Models - Binary Outcomes Time-to-Event Data - Survival Analysis & Count Data - Poisson Regression ANOVA, ANCOVA - Mixed Effects Model (Fixed and Random), RANOVA, GEE Simple & Multiple Linear Regression Models Correlation Analysis (Pearson & Spearman Rank) Clinical & Statistical Significance - p value as a function of sample size Clinical and biomedical researchers often ignore an important aspect of evidence discovery from their funded or unfunded projects. Since the attempt is to illustrate some sets of relationships from the data set, researchers often do not exercise substantial amount of time in assessing the reliability and validity of the data to be utilized in the analysis. However, the expected inference or the conclusion to be drawn is based on the analysis of the un-assessed data. Reality in statistical modeling of biomedical and clinical research data remains the focus of scientific evidence discovery, and this book. This text is written to highlight the importance of appropriate design prior to analysis by placing emphasis on subject selection and probability sample and the randomization process when applicable prior to the selection of the analytic tool. In addition, this book stresses the importance of biologic and clinical significance in the interpretation of study findings. The basis for statistical inference, implying the quantification of random error is random sample, which had been perpetually addressed in this book. When studies are conducted without a random sample, except when disease registries/databases or consecutive subjects are utilized, as often encountered in clinical and biomedical research, it is meaningless to report the findings with p value.