Inroductory Biostatistics

Автор(ы):Le T. Chap
06.10.2007
Год изд.:2003
Описание: A course in introductory biostatistics is often required for professional students in public health, dentistry, nursing, and medicine, and for graduate students in nursing and other biomedical sciences, a requirement that is often considered a roadblock, causing anxiety in many quarters. These feelings are expressed in many ways and in many different settings, but all lead to the same conclusion: that students need help, in the form of a user-friendly and real data-based text, in order to provide enough motivation to learn a subject that is perceived to be difficult and dry. This introductory text is written for professionals and beginning graduate students in human health disciplines who need help to pass and benefit from the basic biostatistics requirement of a one-term course or a full-year sequence of two courses. Our main objective is to avoid the perception that statistics is just a series of formulas that students need to "get over with," but to present it as a way of thinking—thinking about ways to gather and analyze data so as to benefit from taking the required course. There is no better way to do that than to base a book on real data, so many real data sets in various fields are provided in the form of examples and exercises as aids to learning how to use statistical procedures, still the nuts and bolts of elementary applied statistics.
Оглавление:
Inroductory Biostatistics — обложка книги. Обложка книги.
1 Descriptive Methods for Categorical Data [1]
  1.1 Proportions [1]
    1.1.1 Comparative Studies [2]
    1.1.2 Screening Tests [5]
    1.1.3 Displaying Proportions [8]
  1.2 Rates [11]
    1.2.1 Changes [11]
    1.2.2 Measures of Morbidity and Mortality [13]
    1.2.3 Standardization of Rates [16]
  1.3 Ratios [18]
    1.3.1 Relative Risk [19]
    1.3.2 Odds and Odds Ratio [19]
    1.3.3 Generalized Odds for Ordered 2 x k Tables [22]
    1.3.4 Mantel-Haenszel Method [26]
    1.3.5 Standardized Mortality Ratio [30]
  1.4 Notes on Computations [31]
      Exercises [34]
2 Descriptive Methods for Continuous Data [57]
  2.1 Tabular and Graphical Methods [57]
    2.1.1 One-Way Scatter Plots [57]
    2.1.2 Frequency Distribution [58]
    2.1.3 Histogram and the Frequency Polygon [62]
    2.1.4 Cumulative Frequency Graph and Percentiles [67]
    2.1.5 Stem-and-Leaf Diagrams [70]
  2.2 Numerical Methods [72]
    2.2.1 Mean [73]
    2.2.2 Other Measures of Location [76]
    2.2.3 Measures of Dispersion [77]
    2.2.4 Box Plots [80]
  2.3 Special Case of Binary Data [81]
  2.4 Coefficients of Correlation [83]
    2.4.1 Pearson's Correlation Coefficient [85]
    2.4.2 Nonparametric Correlation Coefficients [88]
  2.5 Notes on Computations [90]
      Exercises [92]
3 Probability and Probability Models [108]
  3.1 Probability [108]
    3.1.1 Certainty of Uncertainty [109]
    3.1.2 Probability [109]
    3.1.3 Statistical Relationship [111]
    3.1.4 Using Screening Tests [115]
    3.1.5 Measuring Agreement [118]
  3.2 Normal Distribution [120]
    3.2.1 Shape of the Normal Curve [120]
    3.2.2 Areas under the Standard Normal Curve [123]
    3.2.3 Normal Distribution as a Probability Model [128]
  3.3 Probability Models for Continuous Data [131]
  3.4 Probability Models for Discrete Data [132]
    3.4.1 Binomial Distribution [133]
    3.4.2 Poisson Distribution [136]
  3.5 Brief Notes on the Fundamentals [137]
    3.5.1 Mean and Variance [137]
    3.5.2 Pair-Matched Case-Control Study [138]
  3.6 Notes on Computations [140]
      Exercises [141]
4 Estimation of Parameters [147]
  4.1 Basic Concepts [148]
    4.1.1 Statistics as Variables [149]
    4.1.2 Sampling Distributions [149]
    4.1.3 Introduction to Confidence Estimation [152]
  4.2 Estimation of Means [152]
    4.2.1 Confidence Intervals for a Mean [154]
    4.2.2 Uses of Small Samples [156]
    4.2.3 Evaluation of Interventions [158]
  4.3 Estimation of Proportions [160]
  4.4 Estimation of Odds Ratios [165]
  4.5 Estimation of Correlation Coefficients [168]
  4.6 Brief Notes on the Fundamentals [171]
  4.7 Notes on Computations [173]
      Exercises [173]
5 Introduction to Statistical Tests of Significance [188]
  5.1 Basic Concepts [190]
    5.1.1 Hypothesis Tests [190]
    5.1.2 Statistical Evidence [191]
    5.1.3 Errors [192]
  5.2 Analogies [194]
    5.2.1 Trials by Jury [194]
    5.2.2 Medical Screening Tests [195]
    5.2.3 Common Expectations [195]
  5.3 Summaries and Conclusions [196]
    5.3.1 Rejection Region [197]
    5.3.2 p Values [198]
    5.3.3 Relationship to Confidence Intervals [201]
  5.4 Brief Notes on the Fundamentals [203]
    5.4.1 Type I and Type II Errors [203]
    5.4.2 More about Errors and p Values [203]
      Exercises [204]
6 Comparison of Population Proportions [208]
  6.1 One-Sample Problem with Binary Data [208]
  6.2 Analysis of Pair-Matched Data [210]
  6.3 Comparison of Two Proportions [213]
  6.4 Mantel-Haenszel Method [218]
  6.5 Inferences for General Two-Way Tables [223]
  6.6 Fisher's Exact Test [229]
  6.7 Ordered 2 x k Contingency Tables [230]
  6.8 Notes on Computations [234]
      Exercises [234]
7 Comparison of Population Means [246]
  7.1 One-Sample Problem with Continuous Data [246]
  7.2 Analysis of Pair-Matched Data [248]
  7.3 Comparison of Two Means [253]
  7.4 Nonparametric Methods [257]
    7.4.1 Wilcoxon Rank-Sum Test [257]
    7.4.2 Wilcoxon Signed-Rank Test [261]
  7.5 One-Way Analysis of Variance [263]
  7.6 Brief Notes on the Fundamentals [269]
  7.7 Notes on Computations [270]
      Exercises [270]
8 Correlation and Regression [282]
  8.1 Simple Regression Analysis [283]
    8.1.1 Simple Linear Regression Model [283]
    8.1.2 Scatter Diagram [283]
    8.1.3 Meaning of Regression Parameters [284]
    8.1.4 Estimation of Parameters [285]
    8.1.5 Testing for Independence [289]
    8.1.6 Analysis-of-Variance Approach [292]
  8.2 Multiple Regression Analysis [294]
    8.2.1 Regression Model with Several Independent Variables [294]
    8.2.2 Meaning of Regression Parameters [295]
    8.2.3 Effect Modifications [295]
    8.2.4 Polynomial Regression [296]
    8.2.5 Estimation of Parameters [296]
    8.2.6 Analysis-of-Variance Approach [297]
    8.2.7 Testing Hypotheses in Multiple Linear Regression [298]
  8.3 Notes on Computations [305]
      Exercises [306]
9 Logistic Regression [314]
  9.1 Simple Regression Analysis [316]
    9.1.1 Simple Logistic Regression Model [317]
    9.1.2 Measure of Association [318]
    9.1.3 Effect of Measurement Scale [320]
    9.1.4 Tests of Association [321]
    9.1.5 Use of the Logistic Model for Different Designs [322]
    9.1.6 Overdispersion [323]
  9.2 Multiple Regression Analysis [325]
    9.2.1 Logistic Regression Model with Several Covariates [326]
    9.2.2 Effect Modifications [327]
    9.2.3 Polynomial Regression [328]
    9.2.4 Testing Hypotheses in Multiple Logistic Regression [329]
    9.2.5 Receiver Operating Characteristic Curve [336]
    9.2.6 ROC Curve and Logistic Regression [337]
  9.3 Brief Notes on the Fundamentals [339]
      Exercise [341]
10 Methods for Count Data [350]
  10.1 Poisson Distribution [350]
  10.2 Testing Goodness of Fit [354]
  10.3 Poisson Regression Model [356]
    10.3.1 Simple Regression Analysis [357]
    10.3.2 Multiple Regression Analysis [360]
    10.3.3 Overdispersion [368]
    10.3.4 Stepwise Regression [370]
      Exercise [372]
11 Analysis of Survival Data and Data from Matched Studies [379]
  11.1 Survival Data [381]
  11.2 Introductory Survival Analyses [384]
    11.2.1 Kaplan-Meier Curve [384]
    11.2.2 Comparison of Survival Distributions [386]
  11.3 Simple Regression and Correlation [390]
    11.3.1 Model and Approach [391]
    11.3.2 Measures of Association [392]
    11.3.3 Tests of Association [395]
  11.4 Multiple Regression and Correlation [395]
    11.4.1 Proportional Hazards Model with Several Covariates [396]
    11.4.2 Testing Hypotheses in Multiple Regression [397]
    11.4.3 Time-Dependent Covariates and Applications [401]
  11.5 Pair-Matched Case-Control Studies [405]
    11.5.1 Model [406]
    11.5.2 Analysis [407]
  11.6 Multiple Matching [409]
    11.6.1 Conditional Approach [409]
    11.6.2 Estimation of the Odds Ratio [410]
    11.6.3 Testing for Exposure Effect [411]
  11.7 Conditional Logistic Regression [413]
    11.7.1 Simple Regression Analysis [414]
    11.7.2 Multiple Regression Analysis [418]
      Exercises [426]
12 Study Designs [445]
  12.1 Types of Study Designs [446]
  12.2 Classification of Clinical Trials [447]
  12.3 Designing Phase I Cancer Trials [448]
  12.4 Sample Size Determination for Phase II Trials and Surveys [451]
  12.5 Sample Sizes for Other Phase II Trials [453]
      12.5.1 Continuous Endpoints [454]
      12.5.2 Correlation Endpoints [454]
  12.6 About Simon's Two-Stage Phase II Design [456]
  12.7 Phase II Designs for Selection [457]
    12.7.1 Continuous Endpoints [457]
    12.7.2 Binary Endpoints [458]
  12.8 Toxicity Monitoring in Phase II Trials [459]
  12.9 Sample Size Determination for Phase III Trials [461]
    12.9.1 Comparison of Two Means [462]
    12.9.2 Comparison of Two Proportions [464]
    12.9.3 Survival Time as the Endpoint [466]
  12.10 Sample Size Determination for Case-Control Studies [469]
    12.10.1 Unmatched Designs for a Binary Exposure [469]
    12.10.2 Matched Designs for a Binary Exposure [471]
    12.10.3 Unmatched Designs for a Continuous Exposure [473]
      Exercises [476]
Bibliography [483]
Appendices [489]
Answers to Selected Exercises [499]
Index [531]
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