Statistical modeling for biomedical researchers : a simple introduction to the analysis of complex data
This text will enable biomedical researchers to use several advanced statistical methods that have proven valuable in medical research. The emphasis is on understanding the assumptions underlying each method, using exploratory techniques to determine the most appropriate method, and presenting resul...
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| Main Author | |
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| Format | eBook Book |
| Language | English |
| Published |
Cambridge
Cambridge University Press
2002
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| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 0521820618 9780521820615 0521655781 9780521655781 |
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| Abstract | This text will enable biomedical researchers to use several advanced statistical methods that have proven valuable in medical research. The emphasis is on understanding the assumptions underlying each method, using exploratory techniques to determine the most appropriate method, and presenting results in a way that will be readily understood. |
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| AbstractList | This text will enable biomedical researchers to use several advanced statistical methods that have proven valuable in medical research. The emphasis is on understanding the assumptions underlying each method, using exploratory techniques to determine the most appropriate method, and presenting results in a way that will be readily understood. This text will enable biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research. It is intended for people who have had an introductory course in biostatistics. A statistical software package (Stata) is used to avoid mathematics beyond the high school level. The emphasis is on understanding the assumptions underlying each method, using exploratory techniques to determine the most appropriate method, and presenting results in a way that will be readily understood by clinical colleagues. Numerous real examples from the medical literature are used to illustrate these techniques. Graphical methods are used extensively. Topics covered include linear regression, logistic regression, Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measures analysis of variance. Each method is introduced in its simplest form and is then extended to cover situations in which multiple explanatory variables are collected on each study subject. |
| Author | Dupont, William D. (...William Dudley...) |
| Author_xml | – sequence: 1 fullname: Dupont, William D. (...William Dudley...) |
| BackLink | https://cir.nii.ac.jp/crid/1130000797601865600$$DView record in CiNii |
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| Discipline | Medicine |
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| Notes | Includes bibliographical references and index |
| OCLC | 70756558 |
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| Snippet | This text will enable biomedical researchers to use several advanced statistical methods that have proven valuable in medical research. The emphasis is on... This text will enable biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research. It is intended for... |
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| SubjectTerms | Medicine Medicine -- Research -- Mathematical models Medicine -- Research -- Methodology Medicine -- Research -- Statistical methods Methodology |
| TableOfContents | 2.10. 95% Confidence Interval for y[x ] = Alpha + Betax Evaluated at x -- 2.11. 95% Prediction Interval for the Response of a New Patient -- 2.12. Simple Linear Regression with Stata -- Comments -- 2.13. Lowess Regression -- 2.14. Plotting a Lowess Regression Curve in Stata -- Comments -- 2.15. Residual Analyses -- 2.16. Studentized Residual Analysis Using Stata -- Comments -- 2.17. Transforming the x and y Variables -- 2.17.1. Stabilizing the Variance -- 2.17.2. Correcting for Non-linearity -- 2.17.3. Example: Research Funding and Morbidity for 29 Diseases -- 2.18. Analyzing Transformed Data with Stata -- Comments -- 2.19. Testing the Equality of Regression Slopes -- 2.19.1. Example: The Framingham Heart Study -- 2.20. Comparing Slope Estimates with Stata -- Comment -- 2.21. Additional Reading -- 2.22. Exercises -- 3 Multiple Linear Regression -- 3.1. The Model -- 3.2. Confounding Variables -- 3.3. Estimating the Parameters for a Multiple Linear Regression Model -- 3.4. R2 Statistic for Multiple Regression Models -- 3.5. Expected Response in the Multiple Regression Model -- 3.6. The Accuracy of Multiple Regression Parameter Estimates -- 3.7. Leverage -- 3.8. 95% Confidence Interval for… -- 3.9. 95% Prediction Intervals -- 3.10. Example: The Framingham Heart Study -- 3.10.1. Preliminary Univariate Analyses -- 3.11. Scatterplot Matrix Graphs -- 3.11.1. Producing Scatterplot Matrix Graphs with Stata -- Comments -- 3.12. Modeling Interaction in Multiple Linear Regression -- 3.12.1 The Framingham Example -- 3.13. Multiple Regression Modeling of the Framingham Data -- 3.14. Intuitive Understanding of a Multiple Regression Model -- 3.14.1. The Framingham Example -- 3.15. Calculating 95% Confidence and Prediction Intervals -- 3.16. Multiple Linear Regression with Stata -- Comments -- 3.17. Automatic Methods of Model Selection 3.17.1. Forward Selection using Stata -- Comments -- 3.17.2. Backward Selection -- 3.17.3. Forward Stepwise Selection -- 3.17.4. Backward Stepwise Selection -- 3.17.5. Pros and Cons of Automated Model Selection -- 3.18. Collinearity -- 3.19. Residual Analyses -- 3.20. Influence -- 3.20.1. DeltaBeta Influence Statistic -- 3.20.2. Cook's Distance -- 3.20.3. The Framingham Example -- 3.21. Residual and Influence Analyses Using Stata -- Comments -- 3.22. Additional Reading -- 3.23. Exercises -- 4 Simple Logistic Regression -- 4.1. Example: APACHE Score and Mortality in Patients with Sepsis -- 4.2. Sigmoidal Family of Logistic Regression Curves -- 4.3. The Log Odds of Death Given a Logistic Probability Function -- 4.4. The Binomial Distribution -- 4.5. Simple Logistic Regression Model -- 4.6. Generalized Linear Model -- 4.7. Contrast Between Logistic and Linear Regression -- 4.8. Maximum Likelihood Estimation -- 4.8.1. Variance of Maximum Likelihood Parameter Estimates -- 4.9. Statistical Tests and Confidence Intervals -- 4.9.1. Likelihood Ratio Tests -- 4.9.2. Quadratic Approximations to the Log Likelihood Ratio Function -- 4.9.3. Score Tests -- 4.9.4. Wald Tests and Confidence Intervals -- 4.9.5. Which Test Should You Use? -- 4.10. Sepsis Example -- 4.11. Logistic Regression with Stata -- Comments -- 4.12. Odds Ratios and the Logistic Regression Model -- 4.13. 95% Confidence Interval for the Odds Ratio Associated with a Unit Increase in x -- 4.13.1. Calculating this Odds Ratio with Stata -- Comments -- 4.14. Logistic Regression with Grouped Response Data -- 4.15. 95% Confidence Interval for pi[x] -- 4.16. 95% Confidence Intervals for Proportions -- 4.17. Example: The Ibuprofen in Sepsis Trial -- 4.18. Logistic Regression with Grouped Data using Stata -- Comments -- 4.19. Simple 2 x 2 Case-Control Studies 4.19.1. Example: The Ille-et-Vilaine Study of Esophageal Cancer and Alcohol -- 4.19.2. Review of Classical Case-Control Theory -- 4.19.3. 95% Confidence Interval for the Odds Ratio: Woolf's Method -- 4.19.4. Test of the Null Hypothesis that the Odds Ratio Equals One -- 4.19.5. Test of the Null Hypothesis that Two Proportions are Equal -- 4.20. Logistic Regression Models for 2 × 2 Contingency Tables -- 4.20.1. Nuisance Parameters -- 4.20.2. 95% Confidence Interval for the Odds Ratio: Logistic Regression -- 4.21. Creating a Stata Data File -- Comments -- 4.22. Analyzing Case-Control Data with Stata -- Comments -- 4.23. Regressing Disease Against Exposure -- 4.24. Additional Reading -- 4.25. Exercises -- 5 Multiple Logistic Regression -- 5.1. Mantel-Haenszel Estimate of an Age-Adjusted Odds Ratio -- 5.2. Mantel-Haenszel Chi2 Statistic for Multiple 2 x 2 Tables -- 5.3. 95% Confidence Interval for the Age-Adjusted Odds Ratio -- 5.4. Breslow and Day's Test for Homogeneity -- 5.5. Calculating the Mantel-Haenszel Odds Ratio using Stata -- Comments -- 5.6. Multiple Logistic Regression Model -- 5.7. 95% Confidence Interval for an Adjusted Odds Ratio -- 5.8. Logistic Regression for Multiple 2 x 2 Contingency Tables -- 5.9. Analyzing Multiple 2 x 2 Tables with Stata -- Comments -- 5.10. Handling Categorical Variables in Stata -- Comments -- 5.11. Effect of Dose of Alcohol on Esophageal Cancer Risk -- 5.11.1. Analyzing Model (5.24) with Stata -- Comments -- 5.12. Effect of Dose of Tobacco on Esophageal Cancer Risk -- 5.13. Deriving Odds Ratios from Multiple Parameters -- 5.14. The Standard Error of a Weighted Sum of Regression Coefficients -- 5.15. Confidence Intervals for Weighted Sums of Coefficients -- 5.16. Hypothesis Tests for Weighted Sums of Coefficients -- 5.17. The Estimated Variance-Covariance Matrix -- 5.18. Multiplicative Models of Two Risk Factors 5.19. Multiplicative Model of Smoking, Alcohol, and Esophageal Cancer -- 5.20. Fitting a Multiplicative Model with Stata -- Comments -- 5.21. Model of Two Risk Factors with Interaction -- 5.22. Model of Alcohol, Tobacco, and Esophageal Cancer with Interaction Terms -- 5.23. Fitting a Model with Interaction using Stata -- Comments -- 5.24. Model Fitting: Nested Models and Model Deviance -- 5.25. Effect Modifiers and Confounding Variables -- 5.26. Goodness-of-Fit Tests -- 5.26.1. The Pearson chi2 Goodness-of-Fit Statistic -- 5.27. Hosmer-Lemeshow Goodness-of-Fit Test -- 5.27.1. An Example: The Ille-et-Vilaine Cancer Data Set -- 5.28. Residual and Influence Analysis -- 5.28.1. Standardized Pearson Residual -- 5.28.2. Delta Betaj Influence Statistic -- 5.28.3. Residual Plots of the Ille-et-Vilaine Data on Esophageal Cancer -- 5.29. Using Stata for Goodness-of-Fit Tests and Residual Analyses -- Comments -- 5.30. Frequency Matched Case-Control Studies -- 5.31. Conditional Logistic Regression -- 5.32. Analyzing Data with Missing Values -- 5.32.1. Cardiac Output in the Ibuprofen in Sepsis Study -- 5.32.2. Modeling Missing Values with Stata -- Comments -- 5.33. Additional Reading -- 5.34. Exercises -- 6 Introduction to Survival Analysis -- 6.1. Survival and Cumulative Mortality Functions -- 6.2. Right Censored Data -- 6.3. Kaplan-Meier Survival Curves -- 6.4. An Example: Genetic Risk of Recurrent Intracerebral Hemorrhage -- 6.5. 95% Confidence Intervals for Survival Functions -- 6.6. Cumulative Mortality Function -- 6.7. Censoring and Bias -- 6.8. Logrank Test -- 6.9. Using Stata to Derive Survival Functions and the Logrank Test -- Comments -- 6.10. Logrank Test for Multiple Patient Groups -- 6.11. Hazard Functions -- 6.12. Proportional Hazards -- 6.13. Relative Risks and Hazard Ratios -- 6.14. Proportional Hazards Regression Analysis 6.15. Hazard Regression Analysis of the Intracerebral Hemorrhage Data Cover -- Half-title -- Title -- Copyright -- Contents -- Preface -- 1 Introduction -- 1.1. Algebraic Notation -- 1.2. Descriptive Statistics -- 1.2.1. Dot Plot -- 1.2.2. Sample Mean -- 1.2.3. Residual -- 1.2.4. Sample Variance -- 1.2.5. Sample Standard Deviation -- 1.2.6. Percentile and Median -- 1.2.7. Box Plot -- 1.2.8. Histogram -- 1.2.9. Scatter Plot -- 1.3. The Stata Statistical Software Package -- 1.3.1. Downloading Data from My Web Site -- 1.3.2. Creating Dot Plots with Stata -- Comments -- 1.3.3. Stata Command Syntax -- 1.3.4. Obtaining Interactive Help from Stata -- 1.3.5. Stata Log Files -- 1.3.6. Displaying Other Descriptive Statistics with Stata -- Comments -- 1.4. Inferential Statistics -- 1.4.1. Probability Density Function -- 1.4.2. Mean, Variance and Standard Deviation -- 1.4.3. Normal Distribution -- 1.4.4. Expected Value -- 1.4.5. Standard Error -- 1.4.6. Null Hypothesis, Alternative Hypothesis and P Value -- 1.4.7. 95% Confidence Interval -- 1.4.8. Statistical Power -- 1.4.9. The z and Student's t Distributions -- 1.4.10. Paired t Test -- 1.4.11. Performing Paired t Tests with Stata -- Comments -- 1.4.12. Independent t Test Using a Pooled Standard Error Estimate -- 1.4.13. Independent t Test using Separate Standard Error Estimates -- 1.4.14. Independent t Tests using Stata -- Comments -- 1.4.15. The Chi-Squared Distribution -- 1.5. Additional Reading -- 1.6. Exercises -- 2 Simple Linear Regression -- 2.1. Sample Covariance -- 2.2. Sample Correlation Coefficient -- 2.3. Population Covariance and Correlation Coefficient -- 2.4. Conditional Expectation -- 2.5. Simple Linear Regression Model -- 2.6. Fitting the Linear Regression Model -- 2.7. Historical Trivia: Origin of the Term Regression -- 2.8. Determining the Accuracy of Linear Regression Estimates -- 2.9. Ethylene Glycol Poisoning Example |
| Title | Statistical modeling for biomedical researchers : a simple introduction to the analysis of complex data |
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