Logistic Regression A Self Learning Text 3rd Edition David G. Kleinbaum, Mitchel Klein - Solutions

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Understand how communities have changed over the course of time.
Understand the importance of defining community.
Discuss the effectiveness of police-community relations programs implemented by police beginning in the 1950s and 1960s.
Describe the seven areas revolutionized by Vollmer and Wilson's work in police administration.
Beginning in 1929, August Vollmer, as head of the National Commission on Law Observance and Enforcement, established 10 principles vital in reforming the police. Discuss the importance of the principles in providing the underpinnings for modern policing.
Discuss when and why the Volstead Act passed. What ramifications, if any, did the Volstead Act have on police operations?
What factors were tied to extensive vigilantism in the frontier areas of the country through the 1800s?
When was the Spoils Era in the United States? Briefly discuss how the government worked under this system.What factors led to the demise of the Spoils Era?
Consider what social, economic, and political factors led to the creation of full-time, organized police forces in most major cities in the United States in the mid-1800s.
Discuss and describe the five interrelated pressures that shifted policing from an informal to a formal system.
Discuss the social developments that preceded the passage of the Metropolitan Police Act.
The first sentence of Chapter 2 reads, “The saying that ‘people receive the kind of policing they deserve’ignores the role power plays in the kind, quality, and distribution of police service.” Discuss what this sentence means in the context of contemporary policing in the United States.
Describe how community policing affects officer activity.
Discuss how community policing is sometimes used as a cover for aggressive police tactics.
List and discuss what community policing does NOT constitute.
Discuss how community policing entails the use of discretion and working with other agencies to find other means of dealing with problematic situations.
Understand why community policing is an overarching philosophy, not a technique.
List and describe the four major facets of community policing.
Discuss the sources of confusion surrounding the implementation of community policing.
Describe why community policing encourages decentralized police service and changes in patrol.
Discuss the ways in which the community impacts the police mandate when a department has implemented community policing.
List the factors that set the stage for community policing.
Describe the role of the 1960s in the birth of community policing.
Discuss the problematic relationship police have historically had with minorities.
List August Vollmer's 10 principles of police reform.
Discuss the impact of the Volstead Act on police corruption.
List and describe the three rationalizations of vigilantism.
Describe the spoils system and the problems with policing during this era.
Describe the development of policing in America.
Describe the development of policing in England.
List the five interrelated pressures that influenced the historical shift from informal to formal policing in America.
Discuss the role community policing can play in homeland security. What are some of the pitfalls of a narrow police focus on terrorism?
Discuss “broken windows” theory. How does it relate to “Weed and Seed” programs? Is this an effective approach to helping communities?
The text lists several concepts that are NOT a part of community policing. Discuss four.
Discuss the role of the CPO in community engagement. Include a discussion of the obstacles that may be present and how the officer can work to overcome them.
What is situational crime prevention, and how is it related to community policing?
Briefly discuss how neighborhood variation can impact community policing efforts.
Discuss and describe major facets of community policing.
Have a discussion on what exactly community policing is.
Discuss and describe the sources of confusion surrounding the implementation of community policing.
Discuss the idea that community policing is little more than a continuation of police agencies telling communities what types of police services they need and will therefore receive.
9. How does the interpretation of the odds ratio for RX using Model 2 compare to the interpretation of the odds ratio for RX using a GEE model with the same covariates (see Model 2 in the Test questions of Chap. 15)?
8. Use Model 2 to estimate the odds ratio and 95% confidence intervals for RX.
7. Use Model 1 to estimate the odds ratios for RX (active vs. standard treatment).
6. State the logit form of Model 1 in terms of the mean response of the ith subject.
5. For a GEE model, the user specifies a correlation structure for the response variable, whereas for a GLMM, the user specifies a covariance structure.
4. A mixed logistic model is a generalization of the generalized linear mixed model in which a link function can be specified for the modeling of the mean.
3. The alternating logistic regressions approach models relationships between pairs of responses from the same cluster with odds ratio para- meters rather than with correlation parameters as with GEE.
2. A conditional logistic regression cannot be used to obtain parameter estimates for a predictor variable that does not vary its values within the matched cluster.
1. A model with subject-specific random effects is an example of a marginal model.
15. Calculate the risk ratio for Subject 1 and Subject 2 separately. Calculate the risk ratio after pooling the data for both subjects. How do the risk ratios compare?
14. Compare the baseline risk (where E ¼ 0) of Subject 1 and Subject 2. Is there a difference (i.e., heterogeneity) in the baseline risk between subjects?
13. Suppose an odds ratio is estimated using data in which there are many subjects, each with one observation per subject. Is the odds ratio estimating an individual level odds ratio or a population averaged (marginal) odds ratio?
12. Do Subject 1 and Subject 2 have a different distribution of exposure? This is a criterion for evaluating whether there is confounding by subject.
11. Compare the baseline risk (where E ¼ 0) of Subject 1 and Subject 2. Is there a difference (i.e., heterogeneity) in the baseline risk between subjects? Note that for a model containing subject-specific random effects, the variance of the random intercept is a measure of baseline risk
10. Calculate the odds ratio for Subject 1 and Subject 2 separately. Calculate the odds ratio after pooling the data for both subjects. How do the odds ratios compare? Note: The subject-specific odds ratio as calculated here is a conceptualization of a subject-specific effect, while the pooled
9. Consider a study in which there are five responses per subject. If a model contains two subject-specific random effects (for the intercept and slope), then for subject i, what are the dimensions of the G matrix and of the R matrix?
8. Which of the presented covariance structures allow for both variance heterogeneity and correlation within a cluster.
7. Which of the above covariance structures allow for variance heterogeneity within a cluster?
6. Use the model to obtain the odds ratio (RX ¼ 1 vs. RX ¼ 0) averaged over all subjects.
5. Use the model to obtain the baseline risk for subject i (i.e., risk when RX ¼ 0).
4. Use the model to obtain the odds ratio for RX ¼ 1 vs. RX ¼ 0 for subject i.
3. Show that if AD ¼ BC, then the correlation between Yj and Yk is 0 and the odds ratio is 1 (assuming nonzero cell counts).
2. Show that if both the B and C cells are equal to 0, then the correlation between Yj and Yk is equal to 1 (assuming A and D are nonzero). What is the corresponding odds ratio if the B and C cells are equal to 0? Did both the B and C cells have to equal 0 to obtain this corresponding odds ratio?
1. Calculate and compare the respective odds ratios and correlations between Yj and Yk for the data summarized in Tables 1 and 2 to show that the same odds ratio can correspond to different correlations. Table 1 Yk ¼ 1 Yk ¼ 0 Yj ¼ 13 1 Yj ¼ 01 3 Table 2 Yk ¼ 1 Yk ¼ 0 Yj ¼ 19 1 Yj ¼ 01 1
6. Estimate the odds ratio for RX using Model 2 and Model 3. Is there a suggestion that SEQUENCE is confounding the association between RX and heartburn relief. Answer this question from a data-based perspective (i.e., comparing the odds ratios) and a theoretical perspective (i.e., what it means
5. Use Model 1 to perform the Score test on the interaction term RX* SEQ at a 0.05 level of significance.
4. Use Model 1 to perform the Wald test on the interaction term RX* SEQ at a 0.05 level of significance.
3. In Model 1, what is the difference between the working covariance matrix and the covariance matrix for parameter estimates used to obtain the 95% confidence interval in the previous question?
2. Use Model 1 to estimate the odds ratios and 95% confidence intervals for RX (active vs. standard treatment). Hint. Make use of the variance–covariance matrix for the parameter estimates.
1. State the logit form of the model for Model 1, Model 2, and Model 3.
10. Suppose that a parameter estimate obtained from running a GEE model on a correlated data set was not affected by the choice of correlation structure. Would the corresponding Wald test statistic also be unaffected by the choice of correlation structure?
9. If the GEE approach yields consistent estimates of the “true odds ratio” even if the correlation structure is misspecified, why are the odds ratio estimates different using a stationary 4-dependent correlation structure (Exercise 2) and a stationary 8-dependent correlation structure
8. Estimate the odds ratio and 95% confidence interval for DIARRHEA (1 vs. 0) using the model with the stationary 8-dependent working correlation structure.
7. How can the unstructured correlation structure be used to assess assumptions underlying other more constrained correlation structures?
6. For the Infant Care data, how many more correlation parameters would be included in a model that uses an unstructured correlation structure rather than a stationary 8-dependent correlation structure?
5. Compare the underlying assumptions of the stationary 8-dependent correlation structure with the unstructured correlation structure as it pertains to this model.
4. Referring again to Exercise 2: Which estimate of the 95% confidence interval do you prefer?
3. Referring to Exercise 2: Explain the circumstances in which the model-based variance estimators yield consistent estimates.
2. Estimate the odds ratio and 95% confidence interval for the variable DIARRHEA (1 vs. 0) on a low weightfor-height z-score (i.e., outcome ¼ 1). Compute the 95% confidence interval in two ways: first using the empirical standard errors and then using the modelbased standard errors.
1. Explain the underlying assumptions of a stationary 4-dependent correlation structure as it pertains to the Infant Care Study.
10. Quasi-likelihood estimates may be obtained even if the distribution of the response variable is unknown. What should be specified is a func- tion relating the variance to the mean response.
9. The empirical variance estimator allows for consistent estimation of the variance of the response variable even if the correlation struc- ture is misspecified.
8. If a consistent estimator is used for a model, then the estimate should be correct even if the number of clusters is small.
7. The decision as to whether to use model-based variance estimators or empirical variance esti- mators can affect both the estimation of the regression parameters and their standard errors.
6. To run a GEE model, the user specifies a work- ing correlation structure which provides a framework for the estimation of the correlation parameters.
5. In a GLM, a function of the mean response is modeled as linear with respect to the regression parameters. That function is called the link function.
4. In a GLM, the mean response is modeled as linear with respect to the regression para- meters.
3. If a dichotomous response, coded Y = 0 and Y=1, follows a binomial distribution, then the mean response is the probability that Y = 1.
2. If an exchangeable correlation structure is spe- cified in a GEE model, then the correlation between a subject's first and second responses is assumed equal to the correlation between the subject's first and third responses. However, that correlation can be different for each sub- ject.
1. It is typically the regression coefficients, not the correlation parameters, that are the parameters of primary interest in a correlated analysis.
10. If the correlation between X and Y is zero, then X and Y are independent.
9. Maximum likelihood estimates are obtained in a GLM by solving a system of score equations.The estimating equations used for GEE models have a similar structure to those score equations but are generalized to accommodate multiple responses from the same subject.
8. Since GEE models produce consistent estimates for the regression parameters even if the correla- tion structure is misspecified (assuming the mean response is modeled correctly), there is no particular advantage in specifying the cor- relation structure correctly.
7. A likelihood ratio test can test the statistical sig- nificance of several parameters simultaneously in a GEE model.
6. If there are two responses for each cluster, then the exchangeable, AR1, and unstructured work- ing correlation structure reduce to the same correlation structure.
5. Matrix E is an example of which correlation structure?
4. Matrix D is an example of which correlation structure?
3. Matrix C is an example of which correlation structure?
2. Matrix B is an example of which correlation structure?

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Logistic Regression A Self Learning Text 3rd Edition David G. Kleinbaum, Mitchel Klein - Solutions

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