Community Policing A Contemporary Perspective 6th Edition Victor E Kappeler, Larry K Gaines - Solutions

Unlock the full potential of "Community Policing A Contemporary Perspective 6th Edition" by accessing our comprehensive online resources. Our answers key provides precise answers and detailed solutions in PDF format, ensuring every question is addressed. Explore solved problems with step-by-step answers, designed to enhance your understanding. Benefit from the instructor manual and test bank for deeper insights and chapter solutions. Whether you're a student or an instructor, our textbook solutions are available for free download, offering a complete guide to mastering community policing concepts.

The example in Sections 7.5 through 7.7 used only a few of the variables available in the Benign Breast Disease Study. Repeat the modeling using all the covariates.
Data from the 1-3 matched Benign Breast Disease Study are used in this chapter to illustrate methods for a 1-M matched study. The data are described in Table 7.1 0.Find the best logistic regression model for a 1-1 matched design using the first of the three controls.(Note: It would have been
We noted in Section 5.5 that an argument could be made for combining the previous and recent levels of IV drug use into one group yielding a dichotomous "never-ever" covariate. Evaluate the model using this covariate in place of history of IV drug use coded at three levels.Is the dichotomous
Obtain 95 percent confidence intervals for the estimates in Exercises 5 and6.
As a more complicated exercise in using the method of logit differences used in Section 5.5, estimate the odds ratio for a 25 year old white subject with 1 previous drug treatment, no history of IV drug use and on the longer treatment at site A compared to a 30 year old nonwhite subject with 5
Use the method of logit differences to estimate the odds ratio for site A versus B within racial groups.
Fit the final model for the UIS shown in Table 5.10 and obtain the estimated covariance matrix of the estimated coefficients.
Repeat Exercise 2 for the models developed for the low birth weight data and the prostate cancer data in Chapter 4, Exercise 4.
In the exercises in Chapter 4, Problem 3, multivariable models for the ICU study were formed. Assess the fit of the model(s) that you feel was (were) best among those considered. This assessment should include an overall assessment of fit and use of the diagnostic statistics.Does the model fit? Are
As is the case in linear regression, effective use of diagnostic statistics depends on our ability to interpret and understand the values of the statistics. The purpose of this problem is to provide a few structured examples to examine the effect on the fitted logistic regression model and
Repeat Exercises 1 and 3 for the Low Birthweight data and the Prostatic Cancer data.
Consider the variable vital status (STA) as the outcome variable and the remainder of the variables in the ICU data set as potential covariates. Use each of the variable selection methods discussed in this chapter to find a "best" model. Document thoroughly the rationale for each step in each
Consider the variable level of consciousness at ICU admission(LOC) as a covariate and vital status (ST A) as the outcome variable.Compare the estimates of the odds ratios obtained from the cross-classification of ST A by LOC and the logistic regression of STA on LOC. Use LOC = 0 as the reference
Selection of the scale for continuous covariates is an important step in any modeling process. The variable systolic blood pressure at admission, SYS, in the ICU study described in Section 1. 6 presents a particularly challenging example. Consider the variable vital status (STA) as the outcome
Use the data from the Prostatic Cancer Study described in Section 1.6.3 to answer the following questions:(a) By fitting a series of logistic regression models show that RACE is not a confounder of the PSA CAPSULE odds ratio but is an effect modifier (at the 10 percent level).(b) Graph the
Consider the ICU data described in Section 1.6.1 and use as the outcome variable vital status (ST A) and CPR prior to ICU admission (CPR) as a covariate.(a) Demonstrate that the value of the log-odds ratio obtained from the cross-classification of ST A by CPR is identical to the estimated slope
Use the Prostate Cancer data described in Section 1.6.3 and consider the multiple logistic regression model of capsule penetration (CAPSULE),on AGE, RACE, results of the digital rectal exam (DPROS and DCAPS), prostate specific antigen (PSA), Gleason score (GLEASON)and tumor volume (VOL).(a) The
Use the ICU data described in Section 1.6.1 and consider the multiple logistic regression model of vital status, STA, on age (AGE), cancer part of the present problem (CAN), CPR prior to ICU admission (CPR), infection probable at ICU admission (INF), and race (RACE).(a) The variable RACE is coded
In the Prostate Cancer Study described in Section 1.6.3, one variable thought to be particularly predictive of capsule penetration is the prostate specific antigen level, PSA. Repeat steps (a) - (g) and (j) of Exercise 1 using CAPSULE as the outcome variable and PSA as the covariate. For Exercises
In the Low Birth Weight Study described in Section 1.6.2, one variable that physicians felt was important to control for was the weight of the mother at the last menstrual period, LWT. Repeat steps (a) -(g) of Exercise 1, but for Exercise 3(c) use intervals [80, 99], [100, 109], [110, 114], [115,
Use the ICU Study and repeat Exercises 1(a), l(b), l(d), 1(e) and 1 (g) using the variable "type of admission," TYP, as the covariate.
In the ICU data described in Section 1.6.1 the primary outcome variable is vital status at hospital discharge, STA. Clinicians associated with the study felt that a key determinant of survival was the patient's age at admission, AGE.(a) Write down the equation for the logistic regression model of
Much of the discussion in this section requires familiarity with the material in Section 3.5. As such, we recommend that readers review that section before proceeding.|| Melanie Wall from the Departments of Psychiatry and Biostatistics and Sharon Schwartz from the Department of Epidemiology at
The log link model is appropriate only for prospectively collected data.It is not useful for binary outcome regression in case–control studies.§ Sharon Schwartz from the Department of Epidemiology and Melanie Wall from the Departments of Psychiatry and Biostatistics at Columbia University are
We encourage readers to review the material Section 1.1 again to refresh their memory on the rationale for using the logistic regression model.
Explore using the log link model and the identity link model with the covariates in Table 4.27. Is it possible to fit the model on the full data set?If not what restrictions are required to be able to fit the models?* We choose the second largest value as the largest, 0.79, was the larger by 0.13
Explore using an alternative link function to predict the probability of death using the same covariates as in the logit model in Table 4.27. As part of this process see what model is suggested when you add Stukel's two additional covariates. Did prediction improve?
For the model of the Problem 12, use Bayesian residuals and/or posterior simulated values to examine the model fit.
Perform model building using MCMC and at least two additional predictors from the GLOW_RAND data set along with WEIGHT and HEIGHT. For the final model, perform diagnostic checks to ensure you have a viable MCMC sample and then interpret your results in a Bayesian framework including a credible
For the MCMC simulation of Problem 10, change the thinning and look at the impact on the ACF. Explain the results.
Use MCMC simulation for the GLOW_RAND data set with WEIGHT5 as the only predictor. Use three chains, a burn-in of 400 and thin every three steps and check the convergence diagnostics for both intercept and slope. In particular, consider trace plots, the ACF, the BGR statistic, MCSE and the Geweke
Repeat Problem 7 assuming that you plan to use a model that contains age, smoking status during pregnancy and race.
Repeat Problem 6 assuming that you plan to use a model that contains age, weight of the mother at the last menstrual period and race.
Consider the low birth weight study. What sample size would be needed in a new study to be able to detect that the odds of a low birth weight baby decreases at a rate of 10% per 10 pound increase in weight at the last menstrual period, using a 5% type I error probability and 80% power?
Consider the low birth weight study. What sample size would be needed in a new study to be able to detect that the odds of a low birth weight baby among women who smoke during pregnancy is 2.5 times that of women who do not smoke, using a 5% type I error probability and 80% power?
Produce diagnostic plots as in Chapter 5 for each of the five logistic regression models fit to the imputed data sets from Problem 4. Is there evidence of issues with the imputation models? Is there evidence of issues with the logistic regression model used to analyze the data?
Use the GLOW500_MISSING data and repeat the analysis of Section 10.4 by producing five imputed data sets using multiple imputations. How do your results compare to those presented in Section 10.4? If there is a large difference, why is this case and what does it suggest?
Perform a full model assessment of the propensity score model in Table 10.2.
Perform a full model assessment of the propensity score model in Table 10.1.
In the development of a propensity score for treatment in the GLOW study we defined treatment as being under treatment on enrollment and during follow up. Repeat the analysis using treatment on follow up.
Use the GLOW_RAND data from Section 9.4 and fit a cluster-specific model using adaptive quadrature for estimation. Use FRACTURE as the response and covariates: calculated fracture risk score (FRAC_SCORE), age (AGE), and self-perception of risk (RATERISK). Include a random intercept for physician id
Fit the final polypharmacy cluster-specific model of Table 9.20 then produce the empirical Bayes predicted random intercept values. What do these values mean in the context of this model? Compare values for different subjects and comment on whether they make sense by examining the actual data for
If you have a software package that can produce the QIC, examine the effect on the QIC statistics of adding two-way interactions for race by age and inpatient by outpatient MHV to the final polypharmacy population average model of Table 9.12. Are the changes in QIC consistent with other indicators
Develop a preliminary main effects model for the GLOW_RAND data using a cluster-specific model with random intercept based on the SITE.
Develop a preliminary main effects model for the GLOW_RAND data using a population average model with exchangeable correlation structure.
Fit a univariable model for the polypharmacy data with the single cluster-level covariate, race dichotomized to white and other. Using the resulting model compute and interpret the IOR.
Compute and interpret the MOR for the covariates in the final clusterspecific model for the polypharmacy data in Table 9.20.
Using techniques described in this chapter, as well as design variables, examine the scale of the age covariate in the population average and cluster-specific models of Section 9.5. Do you agree with the transformations proposed?
Fit the population average model for the polypharmacy data of Table 9.12 using the independent correlation structure, in other words using a standard logistic regression model. Compare the two models. Does including the correlation due to subjects in the model seem necessary?Explain.
Use equation 9.19 to approximate the population average coefficients using the parameter estimates for the final cluster-specific polypharmacy model of Table 9.20. Compare these approximations to the parameter estimates produced by fitting the corresponding population average model in Table 9.12.
Interpret the odds ratios for a 2-year increase in age and the odds ratio for gender for the population average model of Table 9.12 and the clusterspecific model of Table 9.20 in Section 9.5. Are the differences in odds ratios from the two models what you expect? Explain.
Interpret the odds ratios for self-reported risk (RATERISK) produced in the cluster-specific model of Table 9.3 of Section 9.4.
Show that if the correlation is 0 (i.e., an independent correlation structure is chosen) that the GEE of equation 9.9 reduces to the likelihood equations for a standard logistic regression model.
Suppose that a dichotomous covariate, D, for race comparing WHITE to OTHER is included in the GLOW models and the estimated coefficient is 2.(a) Interpret the odds ratio for race assuming that a population average model was fit.(b) Interpret the odds ratio assuming that a cluster-specific model was
We performed preliminary analyses, not shown here, that justify pooling these two outcome categories. We use the four category outcome in the exercises.
Fit the multinomial logistic regression model using the DANGER as the outcome and covariates in Table 8.23. Compare the results of this fit to the one shown in Table 8.23.
Estimate and interpret the odds ratios for the dichotomous covariates in Table 8.21. Be sure to account for the interaction term.
Obtain and evaluate the diagnostic statistics for the proportional odds model fit to the outcome NEURO shown in Table 8.21. This should use the separate binary regressions with outcome defined as in equation 8.32.If at all possible with the software package you are using calculate the diagnostics
Obtain and evaluate the diagnostic statistics for the multinomial logistic regression model fit to the outcome PLACE3 in Table 8.10.
The following exercise is designed to enhance the idea expressed in Figure 8.3 and Figure 8.4 that one way to obtain the proportional odds model is via categorization of a continuous variable.(a) Form the scatterplot of BWT versus LWT.(b) Fit the linear regression of BWT on LWT and add the
The steps should include: (i) a complete univariate analysis, (ii) an appropriate selection of variables for a multivariate model (this should include scale identification for continuous covariates and assessment of the need for interactions), (iii) an assessment of fit of the multivariate model,
However in this problem use the outcome variable BWT4 and fit the multinomial or baseline logistic regression model with as the referent outcome and consider as possible model covariates all other variables in Table 1.6, except birth weight.
The data for the low birth weight study are described in Section 1.6.2.These data are used in Section 8.2 to illustrate ordinal logistic regression models via the four-category outcome BWT4,
Continue model building, evaluation, and presenting estimated odds ratios with the interaction between AGE and TBSA added to the model in Table 7.9.
Repeat the analysis in Section 7.4 as an unmatched case-control study including age as a covariate. Compare the results of this analysis to those in Section 7.4. Which analysis yields the more precise estimates of the odds ratios for TBSA, RACE, and INH_INJ?
Using the first and third controls and the case in each of the 97 strata of the Burn matched study, perform the analysis in this matched data.
Repeat the analysis in Section 7.4 using the covariate in place of TBSA.
Using the first control and the case in each of the 97 strata of the BURN matched data set, perform a complete matched analysis.
Estimate the odds ratios and confidence intervals for obesity using your final model from Problem 2 and interpret them in context.
Assess the fit and evaluate the diagnostics for the model developed in Problem 2.
Using all of the covariates in Table 6.1 build, using purposeful selection, a model assessing risk factors for obesity, BMI > 35.
Fit the model in Table 6.4 using a model quadratic in AGE10. Graph the logit functions for the (3, 3) model and (1, 2) model using the method shown in Chapter 5. Which model do you prefer and why? Estimate the odds ratio, with 95% confidence intervals, using the estimated coefficients from the
Present and interpret the results of the final model for the Burn Study that includes AGE and AGEFP1.
Based on the analysis in exercise 5 is there evidence that the interaction is due to the same three covariate patterns? Would you recommend a model that excludes the interaction? If so, evaluate its fit.
When we built the model for the Burn Study data in Chapter 4 we used the one-term fractional transformation for age, . One could argue, and we often do, that the lower order term, AGE, should also be included in the model. Perform a full model assessment of the model that adds AGE to the model in
As is the case in linear regression, effective use of diagnostic statistics depends on our ability to interpret and understand the values of the statistics. The purpose of this problem is to provide a few structured examples to examine the effect on the fitted logistic regression model and
Perform a full evaluation of model assessment for the smaller Burn Study Model in Table 5.12. This should include all goodness of fit tests, diagnostic statistics and an ROC analysis.
Using the diagnostic statistics verify that the significance of the interaction term in the fitted model for the Burn Study in Table 4.27 is due to only three covariate patterns.
Evaluate the goodness of fit for the Burn Study model in Table 4.27 using the standardized sum-of-squares statistic.
Consider the data from the Myopia Study described in Section 1.6.6 whose variables are described in Table 1.10. The binary outcome variable is MYOPIC Consider as independent variables all others in Table 1.10 except spherical equivalent refraction (SPHEQ) as it is used to define the outcome
The restricted cubic spline analysis for age in the Burn Injury Study shown in Table 4.21 used four knots at the 5th, 35th, 65th, and 95th percentiles (see Table 4.1). Verify that spline functions formed from these four knots provide a better model than using three or five knots placed at the
Demonstrate best subset selection of interactions by beginning with the main effects model from the GLOW500 data.
In the modeling of the Burn Injury data questions came up as to how to model age. There were essentially three choices: linear (power 1), quadratic (powers 1 and 2) and the best fractional polynomial model(power 2). In the text we proceeded with power 2. Perform selection of interactions for the
In the modeling of the GLOW500 data using purposeful selection age was modeled as linear in the logit. We noted that the estimated coefficients for the quartile design variables for age in Table 4.10 suggested an alternative parameterization: using the design variable for the fourth quartile AGE_4.
Show algebraically and with a numerical example of your choice that the restricted cubic spline functions in equation 4.3 meet at the three knots.
In the Low Birth Weight Study described in Section 1.6.2, determine the crude odds ratio of smoking (SMOKE) on the outcome low birthweight (LOW). Stratify on RACE and note the odds ratios within the three strata. Do the odds ratios appear to be homogeneous across strata?Compute the
The outcome variable in the Myopia Study described in Section 1.6.6 is becoming myopic during the first five years of follow up (MYOPIC).Consider a logistic regression model containing spherical equivalent refraction (SHPQ), gender (GENDER), sports hours (SPORTHR), reading hours (RESDHR), computer
In the Burn Injury Data described in Section 1.6.5 vital status at hospital discharge (DEATH) is the outcome variable.(a) Show that age (AGE) is not a confounder of the effect of inhalation injury (INH_INJ) but is an effect modifier.(b) Using the interactions model from part 5(a) and the four-step
Repeat problem 3 using cancer part of the present problem (CAN) as the factor of interest and type of admission (TYP) as a potential adjustment and interaction variable.
In the ICU data vital status at discharge (STA) is the outcome variable and consider history of chronic renal failure (CRN) as the factor of interest. Using logistic regression, demonstrate and then explain why age(AGE) is needed to adjust the effect of CRN. Using logistic regression modeling,
Consider data from the Low Birth Weight Study described in Section 1.6.2 and use as the outcome variable low birth weight (LOW) and race of the mother (RACE) as the covariate.(a) Prepare a table showing the coding of the two design variables for RACE using the value RACE = 1, white, as the
Consider the ICU data described in Section 1.6.1 and use as the outcome variable vital status (STA) and infection probable at ICU admission (INF) as a covariate.(a) Demonstrate that the value of the log-odds ratio obtained from the cross-classification of STA by INF is identical to the estimated
Use the Myopia Study data described in Section 1.6.6 and use MYOPIC as the outcome and as possible variables for a model: AGE, GENDER, family history of myopia (MOMMY and DADMY), number of hours playing sports (SPORTHR) and number of hours watching television(TVHR).(a) Repeat parts 2(b)–2(h) of
Use the ICU data described in Section 1.6.1 and consider the multiple logistic regression model of vital status, STA, on age (AGE), cancer part of the present problem (CAN), CPR prior to ICU admission (CPR), infection probable at ICU admission (INF), and race (RACE).(a) The variable race is coded
In Section 2.4 we stated, but did not provide details for, the likelihood ratio test for the addition of weight and early menopause to the model containing AGE, prior fracture (PRIORFRAC) and self-reported risk(RATERISK).(a) Using the GLOW500 data and a logistic regression package verify the values
Using the data from the ICU study create a dichotomous variable NONWHITE ( if or 3 and if). Fit the logistic regression of STA on NONWHITE and show that the 95 percent profile likelihood confidence interval for the coefficient for nonwhite has asymmetry of and that this interval is 26% wider than
In the Myopia Study described in Section 1.6.2, one variable that is clearly important is the initial value of spherical equivalent refraction.(SPHREQ). Repeat steps (a)–(g) of Exercise 1, but for 2(c) use eight intervals containing approximately equal numbers of subjects (i.e., cut points at
In the ICU data described in Section 1.6.1 the primary outcome variable is vital status at hospital discharge, STA. Clinicians associated with the study felt that a key determinant of survival was the patient's age at admission, AGE.(a) Write down the equation for the logistic regression model of
Discuss some current events involving the police and consider their consequences for community policing.
Discuss each of the seven trends that undermine the spirit of community policing.

Showing 200 - 300 of 976

Community Policing A Contemporary Perspective 6th Edition Victor E Kappeler, Larry K Gaines - Solutions

Step by Step Answers