Modern Applied Biostatistical Methods: Using S-Plus

✍ Scribed by Steve Selvin

• Year: 1998
• Tongue: English
• Leaves: 476
• Edition: 1
• Category: Library

✦ Synopsis

Statistical analysis typically involves applying theoretically generated techniques to the description and interpretation of collected data. In this text, theory, application and interpretation are combined to present the entire biostatistical process for a series of elementary and intermediate analytic methods. The theoretical basis for each method is discussed with a minimum of mathematics and is applied to a research data example using a computer system called S-PLUS. This system produces concrete numerical results and increases one's understanding of the fundamental concepts and methodology of statistical analysis.Combining statistical logic, data and computer tools, the author explores such topics as random number generation, general linear models, estimation, analysis of tabular data, analysis of variance and survival analysis. The end result is a clear and complete explanation of the way statistical methods can help one gain an understanding of collected data. Modern Applied Biostatistical Methods is unlike other statistical texts, which usually deal either with theory or with applications. It integrates the two elements into a single presentation of theoretical background, data, interpretation, graphics, and implementation. This all-around approach will be particularly helpful to students in various biostatistics and advanced epidemiology courses, and will interest all researchers involved in biomedical data analysis. This text is not a computer manual, even though it makes extensive use of computer language to describe and illustrate applied statistical techniques. This makes the details of the statistical process readily accessible, providing insight into how and why a statistical method identifies the properties of sampled data. The first chapter gives a simple overview of the S-PLUS language. The subsequent chapters use this valuable statistical tool to present a variety of analytic approaches.

✦ Table of Contents

Cover
......Page 1
Modem Applied Biostatistical Methods
Using S-Plus......Page 4
Copyright
......Page 5
Preface
......Page 8
Contents......Page 12
In the beginning......Page 18
Three data types—and some input conventions......Page 20
Reading values into SPLUS......Page 25
S-tools—a beginning set......Page 26
S-arithmetic......Page 34
More S-tools—intermediate set......Page 35
S-tools for statistics......Page 39
Statistical distributions in SPLUS......Page 44
Arrays and tables......Page 48
Matrix algebra tools......Page 54
Some additional S-tools......Page 58
Four S-code examples......Page 64
The .Data file......Page 72
Addendum: Built-in editors......Page 75
Problem set I......Page 76
Description of descriptive statistics......Page 80
Basic statistical measures......Page 82
Histogram smoothing—density estimation......Page 87
Stem-and-leaf display......Page 91
Comparison of groups—t-test......Page 94
Comparison of groups—boxplots......Page 95
Comparison of data to a theoretical distribution—quantile plots......Page 99
Comparison of groups—qqplots......Page 104
xy-plot......Page 110
Three-dimensional plots—perspective plots......Page 113
Three-dimensional plots—contour plots......Page 116
Three-dimensional plots—rotation......Page 119
Smoothing......Page 124
Two-dimensional smoothing of spatial data......Page 128
Clusters as a description of data......Page 131
Additivity—"sweeping" an array......Page 141
Example—geographic calculations using S-functions......Page 148
Estimation of the center of a two-dimensional distribution......Page 150
Addendum: S-geometry......Page 151
Problem set II......Page 154
3. Simulation: Random Values......Page 156
Random uniform values......Page 157
An example......Page 167
Sampling without and with replacement......Page 170
Random sample from a discrete probability distribution—acceptance/rejection sampling......Page 171
Random sample from a discrete probability distribution—inverse transform method......Page 175
Binomial probability distribution......Page 177
Hypergeometric probability distribution......Page 180
Poisson probability distribution......Page 183
Geometric probability distribution......Page 187
Random samples from a continuous distribution......Page 189
Inverse transform method......Page 192
Simulating values from the normal distribution......Page 195
Four other statistical distributions......Page 198
Simulating minimum and maximum values......Page 200
Butler's method......Page 201
Random values over a complex region......Page 203
Multivariate normal variables......Page 205
Problem set III......Page 207
Simplest case—univariate linear regression......Page 210
Multivariable linear model......Page 214
A closer look at residual values......Page 232
Predict—pointwise confidence intervals......Page 236
Formulas for glm( )......Page 237
Polynomial regression......Page 238
Discriminant analysis......Page 242
Linear logistic model......Page 251
Categorical data—bivariate linear logistic model......Page 254
Multivariable data—linear logistic model......Page 258
Goodness-of-fit......Page 263
Poisson model......Page 265
Multivariable Poisson model......Page 272
Problem set IV......Page 278
Estimator properties......Page 281
Maximum likelihood estimator......Page 282
Scoring to find maximum likelihood estimates......Page 287
Multiparameter estimation......Page 292
Generalized scoring......Page 294
Background......Page 298
General outline......Page 299
Sample mean from a normal population......Page 300
Confidence limits......Page 303
An example—relative risk......Page 304
Median......Page 305
Simple linear regression......Page 306
Jackknife estimation......Page 312
Bias estimation......Page 315
Two-sample test—bootstrap approach......Page 316
Two-sample test—randomization approach......Page 317
Least squares properties......Page 320
Non-linear least squares estimation......Page 324
Problem set V......Page 334
Two by two tables......Page 338
Matched pairs—binary response......Page 343
Two by k table......Page 345
Measures of association—2 x 2 table......Page 349
Measures of association—r x c table......Page 351
Measures of association—table with ordinal variables......Page 353
Loglinear model......Page 356
Multidimensional—k-level variables......Page 363
High dimensional tables......Page 369
Problem set VI......Page 373
One-way design......Page 376
Nested design......Page 382
Two-way classification with one observation per cell......Page 384
Matched pairs—measured response......Page 393
Two-way classification with more than one observation per cell......Page 396
Leaps—a model selection technique......Page 399
Principal components......Page 406
Canonical correlations......Page 416
Problem set VII......Page 422
Rates......Page 425
Life tables......Page 432
Survival analysis—an introduction......Page 438
Nonparametric estimation of a survival curve......Page 444
Hazard rate—estimation......Page 445
Mean/median survival time......Page 447
Proportional hazards model......Page 452
Problem set VIII......Page 465
B......Page 468
D......Page 469
H......Page 470
L......Page 471
N......Page 472
P......Page 473
S......Page 474
V......Page 475
Z......Page 476

✦ Subjects

Библиотека;Компьютерная литература;R;