Methods and Applications of Statistics in the Life and Health Sciences
- ID: 1208919
- January 2010
- 1016 Pages
- John Wiley and Sons Ltd
Inspired by the Encyclopedia of Statistical Sciences, Second Edition, this volume outlines the statistical tools for successfully working with modern life and health sciences research
Data collection plays an essential part in dictating the future of health sciences and public health, as the compilation of statistics allows researchers and medical practitioners to monitor trends, identify problems, and evaluate the impact of health policies and programs. Methods and Applications of Statistics in the Life and Health Sciences serves as a single, one–of–a–kind resource on the wide range of statistical methods, techniques, and applications that are used across modern life and health sciences research. Specially designed to present encyclopedic content in an accessible and self–contained format, this book outlines thorough coverage of underlying quantitative techniques and illustrates their relevance to research in related disciplines such as biology, epidemiology, clinical trials, and public health.
Uniquely combining established literature with cutting–edge research, this volume features classic articles from the acclaimed Encyclopedia of Statistical Sciences, Second Edition and more than twenty–five new articles and completely revised contributions. The result is a compilation of more than eighty articles that explores classic methodology and new topics, including:
Sequential methods in biomedical research
Statistical measures of human quality of life
Change–point methods in genetics
Sample size determination for clinical trials
Mixed–effects regression models for predicting preclinical disease
Probabilistic and statistical models for conception
Statistical methods are explored and applied to population growth, disease detection and treatment, genetic and genomic research, drug development, screening and prevention, and the assessment of rehabilitation, recovery, and quality of life. These topics are explored in contributions written by more than 100 leading academics, researchers, and practitioners who utilize various statistical practices, such as election bias, survival analysis, missing data techniques, and cluster analysis for handling the wide array of modern issues in the life and health sciences.
With its combination of traditional methodology and newly developed research, Methods and Applications of Statistics in the Life and Health Sciences has everything students, academics, and researchers in the life and health sciences need to build and apply their knowledge of statistical methods and applications. SHOW LESS READ MORE >
1 Aalen s Additive Risk Model.
3 AIDS Stochastic Models.
4 All–or–None Compliance.
5 Ascertainment Sampling.
6 Assessment Bias.
7 Bioavailability and Bioequivalence.
8 Cancer Stochastic Models.
9 Centralized Genomic Control: A Simple Approach Correcting for Population Structures in Case–Control Association Studies.
10 Change Point Methods in Genetics.
11 Classical Biostatistics.
12 Clinical Trials–II.
13 Cluster Randomization.
14 Cohort Analysis .
15 Comparisons with a Control.
16 Competing Risks.
17 Countermatched Sampling.
18 Counting Processes.
19 Cox s Proportional Hazards Model.
20 Crossover Trials.
21 Design and Analysis for Repeated Measurements.
22 DNA Fingerprinting.
24 Epidemiological Statistics I.
25 Epidemiological Statistics II.
26 Event History Analysis.
27 FDA Statistical Programs: An Overview.
28 FDA Statistical Programs: Human Drugs.
30 Frailty Models.
31 Framingham: An Evolving Longitudinal Study.
32 Genetic Linkage.
33 Group–Sequential Methods in Biomedical Research.
34 Group–Sequential Tests.
35 Grouped Data in Survival Analysis.
36 Image Processing.
37 Image Restoration and Reconstruction.
38 Imputation and Multiple Imputation.
39 Incomplete Data.
40 Interval Censoring.
41 Interrater Agreement.
42 Kaplan–Meier Estimator I.
43 Kaplan–Meier Estimator II.
44 Landmark Data.
45 Longitudinal Data Analysis.
47 Missing Data: Sensitivity Analysis.
48 Multiple Testing in Clinical Trials.
49 Mutation Processes.
50 Nested Case–Control Sampling.
51 Observational Studies.
52 One– and Two–Armed Bandit Problems.
54 Panel Count Data.
55 Planning and Analysis of Group–Randomized Trials.
56 Predicting Preclinical Disease Using the Mixed–Effects Regression Model.
57 Predicting Random Effects in Group–Randomized Trials.
58 Probabilistic and Statistical Models for Conception.
59 Probit Analysis.
60 Prospective Studies.
61 Quality Assessment for Clinical Trials.
62 Repeated Measurements.
63 Reproduction Rates.
64 Retrospective Studies.
65 Sample Size Determination for Clinical Trials.
66 Scan Statistics.
67 Semiparametric Analysis of Competing–Risk Data.
68 Size and Shape Analysis.
69 Stability Study Designs.
70 Statistical Analysis of DNA Microarray Data.
71 Statistical Genetics.
72 Statistical Methods in Bioassay.
73 Statistical Modeling of Human Fecundity.
74 Statistical Quality of Life.
75 Statistics at CDC.
76 Statistics in Dentistry.
77 Statistics in Evolutionary Genetics.
78 Statistics in Forensic Science.
79 Statistics in Human Genetics I.
80 Statistics in Human Genetics II.
81 Statistics in Medical Diagnosis.
82 Statistics in Medicine.
83 Statistics in the Pharmaceutical Industry.
84 Statistics in Spatial Epidemiology.
85 Stochastic Compartment Models.
86 Surrogate Markers.
87 Survival Analysis.