Question
Assignment 8 Objective: To test examine the probability of a post-operative complication following an appendectomy. Dataset: Appendicitis.sas7bdat Package: SASStudio B. Particulars: 1. The dataset is,
Assignment 8 Objective: To test examine the probability of a post-operative complication following an appendectomy. Dataset: Appendicitis.sas7bdat Package: SASStudio B. Particulars: 1. The dataset is, as before, appendicitis.sas7bdat. 2. Use the "proc logistic" procedure to estimate the probability of a post-operative complication using the same model as in project 7. The dependent variable is defined as the occurrence of any post-operative complication. 3. Restrict the model to non-elderly adult patients, ages 21 to 64 years of age. The explanatory variables are the same as those you used in the previous exercise. 4. A short video clip is provided on canvas showing the use of the proc logistic procedure. 5. Your focus is "by how much do the odds of a post-operative complication increase for patients who are admitted with complicated appendicitis?" C. Turn in a structured abstract (no more than 300 words, not including the title and section headers, so 307 in total) with the format shown below. a. A point will be deducted if you exceed the word limit. Adhering to the word limit is essential as it forces you to get to the point; at the same time you have to be careful not to omit anything of importance. i. Do not include tables or graphs in the abstract (that would go in the main text or presentation - if you were to do one)! Determine which values from your test(s) are most important to the reader and report those in the abstract. ii. Since the word limit applies to everyone equally, no exceptions will be made!!! Abstract Objective: This contains a brief statement describing the general objective of the analysis. For example "The objective of this analysis is to examine ..." Data and methods: This contains a brief description of the particular dataset used to test the hypothesis. For example, concerning the dataset briefly mention the unit of measurement, whether the data is publicly available, and what types of variables it includes. Results: This contains a brief layman's description of the least squares results and whether the variables were statistically significant. In general there will be one "focus" variable that the story focuses on and is discussed in slightly more detail (this is still an abstract). Select one of the model variables with a coefficient estimate that is statistically different from zero, which you believe is the most interesting from an information stand-point, as your focus variable. Be sure to indicate how the result associated with this variable is interpreted. Conclusions: A very brief statement indicating your conclusions pertaining to the hypothesis - did your results reject the null hypothesis or not? Discussion: This is perhaps the most important section. Here you tell the story of why you believe the analysis was important - why is it "information" and not just "data"? Why would anyone within related professional or policy circles find your analysis and results interesting? How could your analysis help such individuals in terms of decision making? As part of the discussion, indicate whether you would have liked to test the influence of additional variables if they had been available. D. What is important or relevant information is by definition, at least partially, subjective. However, there are certain rules that have to be followed. i. For example, in the results section you should definitely discuss the significance of your test-statistic (at minimum you should provide the pvalue associated with the treatment variable). If you do not, at least one point will be deducted (yes, this is a big oversight). Given the word limit, you may want to group the other variables and report as such. ii. In the methods section, provide a rationalization for using the specific method
************************Data output of SAS FOR LINEAR REGRESSION **************************
Dependent Variable is Operating room Total charges
The REG Procedure
Model: MODEL1
Dependent Variable: TCHGS TCHGS
Number of Observations Read | 47460 |
Number of Observations Used | 47460 |
Analysis of Variance | |||||
---|---|---|---|---|---|
Source | DF | Sum of Squares | Mean Square | F Value | Pr>F |
Model | 8 | 1.842725E13 | 2.303406E12 | 2473.72 | <.0001 |
Error | 47451 | 4.418411E13 | 931152403 | ||
Corrected Total | 47459 | 6.261136E13 |
Root MSE | 30515 | R-Square | 0.2943 |
Dependent Mean | 50896 | Adj R-Sq | 0.2942 |
Coeff Var | 59.95464 |
Parameter Estimates | ||||||
---|---|---|---|---|---|---|
Variable | Label | DF | Parameter Estimate | Standard Error | tValue | Pr>|t| |
Intercept | Intercept | 1 | 271956 | 2177.14211 | 124.91 | <.0001 |
AGE | AGE | 1 | 91.20939 | 7.26987 | 12.55 | <.0001 |
black | 1 | 5684.95352 | 470.91645 | 12.07 | <.0001 | |
otherNW | 1 | 6813.95711 | 425.26911 | 16.02 | <.0001 | |
female | 1 | 311.73308 | 284.31449 | 1.10 | 0.2729 | |
hispanic | 1 | 2457.95591 | 342.49490 | 7.18 | <.0001 | |
uninsured | 1 | 1136.03849 | 366.21885 | 3.10 | 0.0019 | |
complicated | complicated | 1 | 11983 | 313.34904 | 38.24 | <.0001 |
SeverityOverall | SeverityOverall | 1 | -237142 | 2110.40017 | -112.37 | <.0001 |
Logistic regression with Dependent variable =Emergency
The LOGISTIC Procedure
Model Information | ||
---|---|---|
Data Set | WORK.APPEND | |
Response Variable | postOperative | postOperative |
Number of Response Levels | 2 | |
Model | binary logit | |
Optimization Technique | Fisher's scoring |
Number of Observations Read | 28736 |
Number of Observations Used | 28736 |
Response Profile | ||
---|---|---|
Ordered Value | postOperative | Total Frequency |
1 | 1 | 1848 |
2 | 0 | 26888 |
Probability modeled is postOperative='1'.
Model Convergence Status |
---|
Convergence criterion (GCONV=1E-8) satisfied. |
Model Fit Statistics | ||
---|---|---|
Criterion | Intercept Only | Intercept and Covariates |
AIC | 13718.520 | 10969.309 |
SC | 13726.786 | 11043.702 |
-2 Log L | 13716.520 | 10951.309 |
Testing Global Null Hypothesis: BETA=0 | |||
---|---|---|---|
Test | Chi-Square | DF | Pr>ChiSq |
Likelihood Ratio | 2765.2111 | 8 | <.0001 |
Score | 4864.3455 | 8 | <.0001 |
Wald | 1982.3999 | 8 | <.0001 |
Analysis of Maximum Likelihood Estimates | |||||
---|---|---|---|---|---|
Parameter | DF | Estimate | Standard Error | Wald Chi-Square | Pr>ChiSq |
Intercept | 1 | 7.5077 | 0.3847 | 380.8706 | <.0001 |
AGE | 1 | 0.0101 | 0.00227 | 19.9878 | <.0001 |
black | 1 | 0.1945 | 0.0808 | 5.7930 | 0.0161 |
otherNW | 1 | -0.1207 | 0.0913 | 1.7481 | 0.1861 |
female | 1 | -0.4945 | 0.0558 | 78.4029 | <.0001 |
hispanic | 1 | -0.1497 | 0.0716 | 4.3635 | 0.0367 |
uninsured | 1 | -0.0698 | 0.0639 | 1.1918 | 0.2750 |
SeverityOverall | 1 | -11.3226 | 0.3626 | 975.2230 | <.0001 |
complicated | 1 | 1.1893 | 0.0551 | 466.5174 | <.0001 |
Odds Ratio Estimates | |||
---|---|---|---|
Effect | Point Estimate | 95% Wald Confidence Limits | |
AGE | 1.010 | 1.006 | 1.015 |
black | 1.215 | 1.037 | 1.423 |
otherNW | 0.886 | 0.741 | 1.060 |
female | 0.610 | 0.547 | 0.680 |
hispanic | 0.861 | 0.748 | 0.991 |
uninsured | 0.933 | 0.823 | 1.057 |
SeverityOverall | <0.001 | <0.001 | <0.001 |
complicated | 3.285 | 2.949 | 3.659 |
Association of Predicted Probabilities and Observed Responses | |||
---|---|---|---|
Percent Concordant | 85.6 | Somers' D | 0.712 |
Percent Discordant | 14.4 | Gamma | 0.712 |
Percent Tied | 0.0 | Tau-a | 0.086 |
Pairs | 49689024 | c | 0.856 |
Frequency distribution of complicated versus uncomplicated Appendicitis
The FREQ Procedure
complicated | ||||
---|---|---|---|---|
complicated | Frequency | Percent | Cumulative Frequency | Cumulative Percent |
0 | 33068 | 69.68 | 33068 | 69.68 |
1 | 14392 | 30.32 | 47460 | 100.00 |
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