A pediatric clinic is interested in reducing the rate of missed opportunities to vaccinate its patients...

   

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A pediatric clinic is interested in reducing the rate of missed opportunities to vaccinate its patients for info. An opportunity is defined as a clinic visit by a patient who has not been vaccinated for influenza. An opportunity is missed if the unvaccinated patient leaves the clinic without receiv ing the vaccination and not missed if the patient receives the vaccination. In June of 2018, the clinic implemented a new program designed to decrease the rate of mid opportunities. To evaluate whether the program has had an effect, the clinic administration has assembled a dates that contains the following information for each patient visit that was an op- portunity to vaccinate, covering the 5 months before and the 5 months after the start of the progra Mouth of visit: Jan 2018 through Oct 2018, coded as through t Sex: 1 male, female MO: 1-missed opportunity and not. Assume that no patient appears in the data set more than once and that each observation independent. The overall rates of missed opportunities by month are as follow Month Number of visits that Number of missed Percent of opportunities were opportunities opportunities that were missed Jan 2018 00 1454 484 32.7% Feb 2018 10 1857 506 33.8% Mar 2018 20 1538 529 34.4% Apr 2018 30 1557 540 34.7% May 2018 40 1566 545 34.8% Jun 2018 (program started) 50 1584 530 33.5% Jul 2018 61 1583 481 30.4% Aug 2018 72 1612 442 27.4% Sep 2018 83 1001 392 24.5% Oct 2018 1631 378 23.26 A logistic regression model is fit to the data in which the dependent variable is MO and the time trends before and after June 2018 are modeled using a piecewise linear spline. The piecewise line spline is parameterized using the two variables and max(-5,0). The model is ()- +++ The estimates obtained when fitting this model are shown on the next page. Use them to answer parts (a)-(c) of the problem Logistic regression model estimates: | Coef. Std. Err. Z P>|z| [95% Conf. Interval] tl .0040216 .0117921 s | -.149222 .0243438 0.34 -6.13 0.000 0.733 -.0190905 .0271337 -.196935 -.101509 sex | intercept | .0712193 -.715349 .0347585 2.05 0.040 .0030939 .1393447 .0425485 -16.81 0.000 -.7987425 -.6319555 (a) Provide the odds ratio for sex, along with a brief interpretation. (b) According to the model results, what is the probability that a visit by a male patient in March 2018 was a missed opportunity? (c) Is there evidence from the logistic modeling results that the probability of a missed oppor- tunity decreased after the program was implemented? Support your answer with a careful interpretation of the coefficients of t and s and their signs and significance. Explain how you could formally test whether the rate of missed opportunities has been decreasing over the period from June 2018 to October 2018. (d) Now suppose that instead of using t and s as continous predictors, a model is fit treating t as a categorical variable. The corresponding dummy variables will be constructed so that t=0 is the reference category and d; = 1 if t = j and d; = 0 otherwise, for j = 1,2...,9. The corresponding model is Pi log ... + Badg,i+ B10sexi Bo+Bidi,i+4 Explain how to test whether the average log odds of a missed opportunity for months February through May 2018 (t = 1 to 4) is equal to the average log odds of a missed opportunity for the months July through October 2018 (t = 6 to 9), using this model and a linear contrast. Explain what a significant test would tell you about the success of the program and how this differs conceptually from part (c). A pediatric clinic is interested in reducing the rate of missed opportunities to vaccinate its patients for info. An opportunity is defined as a clinic visit by a patient who has not been vaccinated for influenza. An opportunity is missed if the unvaccinated patient leaves the clinic without receiv ing the vaccination and not missed if the patient receives the vaccination. In June of 2018, the clinic implemented a new program designed to decrease the rate of mid opportunities. To evaluate whether the program has had an effect, the clinic administration has assembled a dates that contains the following information for each patient visit that was an op- portunity to vaccinate, covering the 5 months before and the 5 months after the start of the progra Mouth of visit: Jan 2018 through Oct 2018, coded as through t Sex: 1 male, female MO: 1-missed opportunity and not. Assume that no patient appears in the data set more than once and that each observation independent. The overall rates of missed opportunities by month are as follow Month Number of visits that Number of missed Percent of opportunities were opportunities opportunities that were missed Jan 2018 00 1454 484 32.7% Feb 2018 10 1857 506 33.8% Mar 2018 20 1538 529 34.4% Apr 2018 30 1557 540 34.7% May 2018 40 1566 545 34.8% Jun 2018 (program started) 50 1584 530 33.5% Jul 2018 61 1583 481 30.4% Aug 2018 72 1612 442 27.4% Sep 2018 83 1001 392 24.5% Oct 2018 1631 378 23.26 A logistic regression model is fit to the data in which the dependent variable is MO and the time trends before and after June 2018 are modeled using a piecewise linear spline. The piecewise line spline is parameterized using the two variables and max(-5,0). The model is ()- +++ The estimates obtained when fitting this model are shown on the next page. Use them to answer parts (a)-(c) of the problem Logistic regression model estimates: | Coef. Std. Err. Z P>|z| [95% Conf. Interval] tl .0040216 .0117921 s | -.149222 .0243438 0.34 -6.13 0.000 0.733 -.0190905 .0271337 -.196935 -.101509 sex | intercept | .0712193 -.715349 .0347585 2.05 0.040 .0030939 .1393447 .0425485 -16.81 0.000 -.7987425 -.6319555 (a) Provide the odds ratio for sex, along with a brief interpretation. (b) According to the model results, what is the probability that a visit by a male patient in March 2018 was a missed opportunity? (c) Is there evidence from the logistic modeling results that the probability of a missed oppor- tunity decreased after the program was implemented? Support your answer with a careful interpretation of the coefficients of t and s and their signs and significance. Explain how you could formally test whether the rate of missed opportunities has been decreasing over the period from June 2018 to October 2018. (d) Now suppose that instead of using t and s as continous predictors, a model is fit treating t as a categorical variable. The corresponding dummy variables will be constructed so that t=0 is the reference category and d; = 1 if t = j and d; = 0 otherwise, for j = 1,2...,9. The corresponding model is Pi log ... + Badg,i+ B10sexi Bo+Bidi,i+4 Explain how to test whether the average log odds of a missed opportunity for months February through May 2018 (t = 1 to 4) is equal to the average log odds of a missed opportunity for the months July through October 2018 (t = 6 to 9), using this model and a linear contrast. Explain what a significant test would tell you about the success of the program and how this differs conceptually from part (c). A pediatric clinic is interested in reducing the rate of missed opportunities to vaccinate its patients for info. An opportunity is defined as a clinic visit by a patient who has not been vaccinated for influenza. An opportunity is missed if the unvaccinated patient leaves the clinic without receiv ing the vaccination and not missed if the patient receives the vaccination. In June of 2018, the clinic implemented a new program designed to decrease the rate of mid opportunities. To evaluate whether the program has had an effect, the clinic administration has assembled a dates that contains the following information for each patient visit that was an op- portunity to vaccinate, covering the 5 months before and the 5 months after the start of the progra Mouth of visit: Jan 2018 through Oct 2018, coded as through t Sex: 1 male, female MO: 1-missed opportunity and not. Assume that no patient appears in the data set more than once and that each observation independent. The overall rates of missed opportunities by month are as follow Month Number of visits that Number of missed Percent of opportunities were opportunities opportunities that were missed Jan 2018 00 1454 484 32.7% Feb 2018 10 1857 506 33.8% Mar 2018 20 1538 529 34.4% Apr 2018 30 1557 540 34.7% May 2018 40 1566 545 34.8% Jun 2018 (program started) 50 1584 530 33.5% Jul 2018 61 1583 481 30.4% Aug 2018 72 1612 442 27.4% Sep 2018 83 1001 392 24.5% Oct 2018 1631 378 23.26 A logistic regression model is fit to the data in which the dependent variable is MO and the time trends before and after June 2018 are modeled using a piecewise linear spline. The piecewise line spline is parameterized using the two variables and max(-5,0). The model is ()- +++ The estimates obtained when fitting this model are shown on the next page. Use them to answer parts (a)-(c) of the problem Logistic regression model estimates: | Coef. Std. Err. Z P>|z| [95% Conf. Interval] tl .0040216 .0117921 s | -.149222 .0243438 0.34 -6.13 0.000 0.733 -.0190905 .0271337 -.196935 -.101509 sex | intercept | .0712193 -.715349 .0347585 2.05 0.040 .0030939 .1393447 .0425485 -16.81 0.000 -.7987425 -.6319555 (a) Provide the odds ratio for sex, along with a brief interpretation. (b) According to the model results, what is the probability that a visit by a male patient in March 2018 was a missed opportunity? (c) Is there evidence from the logistic modeling results that the probability of a missed oppor- tunity decreased after the program was implemented? Support your answer with a careful interpretation of the coefficients of t and s and their signs and significance. Explain how you could formally test whether the rate of missed opportunities has been decreasing over the period from June 2018 to October 2018. (d) Now suppose that instead of using t and s as continous predictors, a model is fit treating t as a categorical variable. The corresponding dummy variables will be constructed so that t=0 is the reference category and d; = 1 if t = j and d; = 0 otherwise, for j = 1,2...,9. The corresponding model is Pi log ... + Badg,i+ B10sexi Bo+Bidi,i+4 Explain how to test whether the average log odds of a missed opportunity for months February through May 2018 (t = 1 to 4) is equal to the average log odds of a missed opportunity for the months July through October 2018 (t = 6 to 9), using this model and a linear contrast. Explain what a significant test would tell you about the success of the program and how this differs conceptually from part (c). A pediatric clinic is interested in reducing the rate of missed opportunities to vaccinate its patients for info. An opportunity is defined as a clinic visit by a patient who has not been vaccinated for influenza. An opportunity is missed if the unvaccinated patient leaves the clinic without receiv ing the vaccination and not missed if the patient receives the vaccination. In June of 2018, the clinic implemented a new program designed to decrease the rate of mid opportunities. To evaluate whether the program has had an effect, the clinic administration has assembled a dates that contains the following information for each patient visit that was an op- portunity to vaccinate, covering the 5 months before and the 5 months after the start of the progra Mouth of visit: Jan 2018 through Oct 2018, coded as through t Sex: 1 male, female MO: 1-missed opportunity and not. Assume that no patient appears in the data set more than once and that each observation independent. The overall rates of missed opportunities by month are as follow Month Number of visits that Number of missed Percent of opportunities were opportunities opportunities that were missed Jan 2018 00 1454 484 32.7% Feb 2018 10 1857 506 33.8% Mar 2018 20 1538 529 34.4% Apr 2018 30 1557 540 34.7% May 2018 40 1566 545 34.8% Jun 2018 (program started) 50 1584 530 33.5% Jul 2018 61 1583 481 30.4% Aug 2018 72 1612 442 27.4% Sep 2018 83 1001 392 24.5% Oct 2018 1631 378 23.26 A logistic regression model is fit to the data in which the dependent variable is MO and the time trends before and after June 2018 are modeled using a piecewise linear spline. The piecewise line spline is parameterized using the two variables and max(-5,0). The model is ()- +++ The estimates obtained when fitting this model are shown on the next page. Use them to answer parts (a)-(c) of the problem Logistic regression model estimates: | Coef. Std. Err. Z P>|z| [95% Conf. Interval] tl .0040216 .0117921 s | -.149222 .0243438 0.34 -6.13 0.000 0.733 -.0190905 .0271337 -.196935 -.101509 sex | intercept | .0712193 -.715349 .0347585 2.05 0.040 .0030939 .1393447 .0425485 -16.81 0.000 -.7987425 -.6319555 (a) Provide the odds ratio for sex, along with a brief interpretation. (b) According to the model results, what is the probability that a visit by a male patient in March 2018 was a missed opportunity? (c) Is there evidence from the logistic modeling results that the probability of a missed oppor- tunity decreased after the program was implemented? Support your answer with a careful interpretation of the coefficients of t and s and their signs and significance. Explain how you could formally test whether the rate of missed opportunities has been decreasing over the period from June 2018 to October 2018. (d) Now suppose that instead of using t and s as continous predictors, a model is fit treating t as a categorical variable. The corresponding dummy variables will be constructed so that t=0 is the reference category and d; = 1 if t = j and d; = 0 otherwise, for j = 1,2...,9. The corresponding model is Pi log ... + Badg,i+ B10sexi Bo+Bidi,i+4 Explain how to test whether the average log odds of a missed opportunity for months February through May 2018 (t = 1 to 4) is equal to the average log odds of a missed opportunity for the months July through October 2018 (t = 6 to 9), using this model and a linear contrast. Explain what a significant test would tell you about the success of the program and how this differs conceptually from part (c).

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According to the information provided in the image the company is interested in analyzing the rate of missed opportunities to improve efficiency The d... View the full answer