## Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex DataFor biomedical researchers, the new edition of this standard text guides readers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is used to perform the analyses, in this edition employing the intuitive version 10. Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University. |

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### Contents

Probability density function | 25 |

Simple linear regression | 45 |

l | 82 |

Multiple linear regression | 97 |

Simple logistic regression | 159 |

Multiple logistic regression | 201 |

covariates on the response variable | 211 |

Introduction to survival analysis | 287 |

inferences | 373 |

Multiple Poisson regression | 401 |

IO Fixed effects analysis of variance | 429 |

GEE | 476 |

A Summary of statistical models discussed | 485 |

B Summary of Stata commands used in this text | 491 |

References | 507 |

513 | |

### Other editions - View all

Statistical Modeling for Biomedical Researchers: A Simple Introduction to ... William D. Dupont No preview available - 2009 |

### Common terms and phrases

_cons _Ismoke_2 alcohol arterial pH body mass index calculate chdfate coefﬁcient command is Statistics Conf conﬁdence interval covariate pattern covariates data set deaths default deﬁned degrees of freedom denote deviance dose equals Equation esophageal cancer example expected value Figure ﬁrst ﬁt follow-up Framingham Heart Study genotype groups hazards regression Hence histogram Ibuprofen Independent variables inﬂuence isoproterenol ith patient knots label lincom linear model log ﬁle logbmi logistic regression logistic regression model lowess regression male missing values mortality multiple newvar null hypothesis Number of obs number of patients observations odds ratio option Output omitted P-value panel parameter estimates person—years point-and-click point-and-click command Poisson regression predict prediction interval Prob record regression line relative risk response variable risk of CHD sample scatter plot Section slope speciﬁed standard deviation standard error Stata studentized residuals sunﬂower survival curves Table test the null twoway varlist varname women xvar y-axis