## Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data, Part 938This text enables biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research, and uses a statistical software package (Stata® ) to avoid mathematics beyond the high school level. Intended for people who have had an introductory course in biostatistics, the volume emphasizes the assumptions underlying each method, using exploratory techniques to determine the most appropriate method. It presents results in a way that will be readily understood by clinical colleagues. Numerous real examples from medical literature and graphical methods are used to illustrate these techniques. |

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### Contents

Simple Linear Regression | 34 |

Multiple Linear Regression | 72 |

Simple Logistic Regression | 108 |

Multiple Logistic Regression 1435 | 143 |

Hazard Regression Analysis | 228 |

Ragged Entry | 256 |

Inferences | 269 |

Multiple Poisson Regression | 295 |

Fixed Effects Analysis of Variance | 320 |

RepeatedMeasures Analysis of Variance | 338 |

Using GEE | 362 |

Summary of Stata Commands Used in this Text | 369 |

378 | |

### Other editions - View all

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

### Common terms and phrases

adjusted alcohol analysis APACHE score associated baseline blood Calculate called cancer command compared Conf confidence interval correlation covariates curve data set deaths defined degrees of freedom denote disease distribution dose effect equals equation error estimate example expected Figure follow-up Framingham function genotype given gives graph groups hazard Heart Hence Ibuprofen increases indicates label likelihood lincom linear regression logistic regression male mean methods missing values multiple Note null hypothesis observed odds ratio option Output omitted parameter patients pattern perform plot Poisson population predict pressure probability proportional provides record regression model relative risk residuals respectively response sample shows similar simple slope smoke specified standard standard deviation Stata statistic stratum subjects Suppose Table treat true values variable variance women