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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.
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 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.
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