To be able to better manage the length of stay (LOS) of patients undergoing laparoscopic appendectomy, clinical researchers built a predictive (regression) model. The estimated model parameters are summarized in the table below: Variable Intercept Pre-operative LOS Presence of complications Complicating diagnosis Gender (Female vs. Male) Age Presence of comorbidities Heart disease Diabetes Hypertension Obesity Peritonitis B 7.542 0.941 SE p-value 0.760 <.001 0.066 <.001 -3.949 0.573 <.001 -0.863 0.234 <.001 -0.160 0.230 0.487 0.024 0.007 0.001 0.740 0.346 0.033 0.237 0.871 0.786 -1.861 0.972 0.057 1.053 0.563 0.064 -0.911 0.954 0.341 -0.649 0.856 0.449 -1.998 1.480 0.178 Cancer 1. What is the predicted length of stay for a 50-year-old male with no complicating diagnosis and no comorbidity (i.e., that also means no heart disease, diabetes, hypertension, obesity, peritonitis, or cancer), who had a 1-day pre-operative stay, and there were no complications during the surgery? 2. Interpret the estimated parameters for pre-operative LOS and the presence of complications. 3. What strikes you about (the values of) the estimated model parameters? What phenomenon may explain some of these puzzling results?
To be able to better manage the length of stay (LOS) of patients undergoing laparoscopic appendectomy, clinical researchers built a predictive (regression) model. The estimated model parameters are summarized in the table below: Variable Intercept Pre-operative LOS Presence of complications Complicating diagnosis Gender (Female vs. Male) Age Presence of comorbidities Heart disease Diabetes Hypertension Obesity Peritonitis B 7.542 0.941 SE p-value 0.760 <.001 0.066 <.001 -3.949 0.573 <.001 -0.863 0.234 <.001 -0.160 0.230 0.487 0.024 0.007 0.001 0.740 0.346 0.033 0.237 0.871 0.786 -1.861 0.972 0.057 1.053 0.563 0.064 -0.911 0.954 0.341 -0.649 0.856 0.449 -1.998 1.480 0.178 Cancer 1. What is the predicted length of stay for a 50-year-old male with no complicating diagnosis and no comorbidity (i.e., that also means no heart disease, diabetes, hypertension, obesity, peritonitis, or cancer), who had a 1-day pre-operative stay, and there were no complications during the surgery? 2. Interpret the estimated parameters for pre-operative LOS and the presence of complications. 3. What strikes you about (the values of) the estimated model parameters? What phenomenon may explain some of these puzzling results?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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- What is the predicted length of stay for a 50-year-old male with no complicating diagnosis and no comorbidity (i.e., that also means no heart disease, diabetes, hypertension, obesity, peritonitis, or cancer), who had a 1-day pre-operative stay, and there were no complications during the surgery?
- Interpret the estimated parameters for pre-operative LOS and the presence of complications.
- What strikes you about (the values of) the estimated model parameters? What phenomenon may explain some of these puzzling results?
Transcribed Image Text:To be able to better manage the length of stay (LOS) of patients undergoing laparoscopic appendectomy, clinical researchers built a
predictive (regression) model. The estimated model parameters are summarized in the table below:
В
SE
p-value
Intercept
7.542
0.760
<.001
Pre-operative LOS
0.941
0.066
<.001
Presence of complications
- 3.949
0.573
<.001
Complicating diagnosis
-0.863 0.234
<.001
Gender (Female vs. Male)
-0.160 0.230 0.487
Age
0.024
0.007
0.001
Presence of comorbidities
0.740
0.346
0.033
Heart disease
0.237
0.871
0.786
Diabetes
-1.861
0.972
0.057
Hypertension
1.053
0.563
0.064
Obesity
-0.911
0.954
0.341
-0.649
0.856 0.449
Peritonitis
Cancer
- 1.998 1.480 0.178
1. What is the predicted length of stay for a 50-year-old male with no complicating diagnosis and no comorbidity (i.e., that also means no
heart disease, diabetes, hypertension, obesity, peritonitis, or cancer), who had a 1-day pre-operative stay, and there were no
complications during the surgery?
2. Interpret the estimated parameters for pre-operative LOS and the presence of complications.
3. What strikes you about (the values of) the estimated model parameters? What phenomenon may explain some of these puzzling
results?
Variable
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