Multivariable analysis : a practical guide for clinicians /
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| Author / Creator: | Katz, Mitchell H. |
|---|---|
| Edition: | 2nd ed. |
| Imprint: | Cambridge ; New York : Cambridge University Press, 2006. |
| Description: | xv, 203 p. : ill. ; 26 cm. |
| Language: | English |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/6861782 |
Table of Contents:
- Preface
- 1. Introduction
- 1.1. Why should I do multivariable analysis?
- 1.2. What are confounders and how does multivariable analysis help me to deal with them?
- 1.3. What are suppressers and how does multivariable analysis help me to deal with them?
- 1.4. What are interactions and how does multivariable analysis help me to deal with them?
- 2. Common uses of multivariable models
- 2.1. What are the most common uses of multivariable models in clinical research?
- 2.2. How do I choose what type of multivariable analysis to use?
- 3. Outcome variables in multivariable analysis
- 3.1. How does the nature of my outcome variable influence my choice of which type of multivariable analysis to do?
- 3.2. What type of multivariable analysis should I use with an interval outcome?
- 3.3. What are the different types of analysis of variance and when are they used?
- 3.4. What should I do if my outcome variable is ordinal or nominal?
- 3.5. What type of multivariable analysis should I use with a dichotomous outcome?
- 3.6. What type of multivariable analysis should I use with a time-to-outcome variable?
- 3.7. What type of multivariable analysis should I use with a rare outcome or a count?
- 4. Type of independent variables in multivariable analysis
- 4.1. What type of independent variables can I use with multivariable analysis?
- 4.2. What should I do with my ordinal and nominal independent variables?
- 5. Assumptions of multiple linear regression, multiple logistic regression, and proportional hazards analysis
- 5.1. What are the assumptions of multiple linear regression, multiple logistic regression, and proportional hazards analysis?
- 5.2. What is being modeled in multiple linear regression, multiple logistic regression, and proportional hazards analysis?
- 5.3. What is the relationship of multiple independent variables to outcome in multiple linear regression, multiple logistic regression, and proportional hazards analysis?
- 5.4. What is the relationship of an interval-independent variable to the outcome in multiple linear regression, multiple logistic regression, and proportional hazards analysis?
- 5.5. What if my interval-independent variable does not have a linear relationship with my outcome?
- 5.6. Assuming that my interval-independent variable fits a linear assumption, is there any reason to group it into interval categories or create multiple dichotomous variables?
- 5.7. What are the assumptions about the distribution of the outcome and the variance?
- 5.8. What should I do if I find significant violations of the assumptions of normal distribution and equal variance in my multiple linear regression analysis?
- 5.9. What are the assumptions of censoring?
- 5.10. How likely is it that the censoring assumption is valid in my study?
- 5.11. How can I test the validity of the censoring assumption for my data?
- 6. Relationship of independent variables to one another
- 6.1. Does it matter if my independent variables are related to each other?
- 6.2. How do I assess whether my variables are multi collinear?
- 6.3. What should I do with multicollinear variables?
- 7. Setting up a multivariable analysis
- 7.1. What independent variables should I include in my multivariable model?
- 7.2. How do I decide what confounders to include in my model?
- 7.3. What independent variables should I exclude from my multivariable model?
- 7.4. How many subjects do I need to do multivariable analysis?
- 7.5. What if I have too many independent variables given my sample size?
- 7.6. What should I do about missing data on my independent variables?
- 7.7. What should I do about missing data on my outcome variable?
- 8. Performing the analysis
- 8.1. What numbers should I assign for dichotomous or ordinal variables in my analysis?
- 8.2. Does it matter what I choose as my reference category for multiple dichotomous ("dummied") variables?
- 8.3. How do I enter interaction terms into my analysis?
- 8.4. How do I enter time into my proportional hazards or other survival analysis?
- 8.5. What about subjects who experience their outcome on their start date?
- 8.6. What about subjects who have a survival time shorter than physiologically possible?
- 8.7. What are variable selection techniques?
- 8.8. What value should I specify for tolerance in my logistic regression or proportional hazards model?
- 8.9. How many iterations (attempts to solve) should I specify for my logistic regression or proportional hazards model?
- 8.10. What value should I specify for the convergence criteria for my logistic regression or proportional hazards model?
- 8.11. My model won't converge. What should I do?
- 9. Interpreting the analysis
- 9.1. What information will the printout from my analysis provide?
- 9.2. How do I assess how well my model accounts for the outcome?
- 9.3. What do the coefficients tell me about the relationship between each variable and the outcome?
- 9.4. How do I get odds ratios and relative hazards from the multivariable analysis? What do they mean?
- 9.5. How do I interpret the odds ratio and relative hazard when the independent variable is interval?
- 9.6. How do I compute the confidence intervals for the odds ratios and relative hazards?
- 9.7. What are standardized coefficients and should I use them?
- 9.8. How do I test the statistical significance of my coefficients?
- 9.9. How do I interpret the results of interaction terms?
- 9.10. Do I have to adjust my multivariable regression coefficients for multiple comparisons?
- 10. Checking the assumptions of the analysis
- 10.1. How do I know if my data fit the assumptions of my multivariable model?
- 10.2. How do I assess the linearity, normal distribution, and equal variance assumptions of multiple linear regression?
- 10.3. How do I assess the linearity assumption of multiple logistic regression and proportional hazards analysis?
- 10.4. What are outliers and how do I detect them in my multiple linear regression model?
- 10.5. How do I detect outliers in my multiple logistic regression model?
- 10.6. What about analysis of residuals with proportional hazards analysis?
- 10.7. What should I do when I detect outliers?
- 10.8. What is the additive assumption and how do I assess whether my multiple independent variables fit this assumption?
- 10.9. What does the additive assumption mean for interval-independent variables?
- 10.10. What is the proportionality assumption?
- 10.11. How do I test the proportionality assumption?
- 10.12. What if the proportionality assumption does not hold for my data?
- 11. Propensity scores
- 11.1. What are propensity scores? Why are they used?
- 12. Correlated observations
- 12.1. How do I analyze correlated observations?
- 12.2. How do I calculate the needed sample size for studies with correlated observations?
- 13. Validation of models
- 13.1. How can I validate my models?
- 14. Special topics
- 14.1. What if the independent variable changes value during the course of the study?
- 14.2. What are the advantages and disadvantages of time-dependent covariates?
- 14.3. What are classification and regression trees (CART) and should I use them?
- 14.4. How can I get best use of my biostatistician?
- 14.5. How do I choose which software package to use?
- 15. Publishing your study
- 15.1. How much information about how I constructed my multivariable models should I put in the Methods section?
- 15.2. Do I need to cite a statistical reference for my choice of multivariable model?
- 15.3. Which parts of my multivariable analysis should I report in the Results section?
- 16. Summary: Steps for constructing a multivariable model
- Index
