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.
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