Overstay2 scoring model for St. Boniface: Difference between revisions

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* OS = exp(Y) / (1 + exp(Y)

*The model gave a 0.028 optimum cut-off probability for OS GE 10 Days with sensitivity = 69.0%, the rate of identifying OS GE 10d out of the patients who actually have OS GE 10d and with specificity = 70.4 the rate of identifying OS LT 10d out of the patients who actually have OS LT 10d. This cut-off point of 0.~~076~~ will result to 29.6% rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d'''. Increasing the cut off predicted probability of (Overstay >= 10 days) to 0.039 resulted to sensitivity = 66.9%, thus reducing the rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d''' to 26.3%. Thus '''a predicted probability of (Overstay >= 10 days) of 0.039''' is going to be used to classify patients into two groups; Overstay >= 10 days and Overstay < 10 days. See more discussion on [[Overstay2 colour]].

*The model gave a 0.028 optimum cut-off probability for OS GE 10 Days with sensitivity = 69.0%, the rate of identifying OS GE 10d out of the patients who actually have OS GE 10d and with specificity = 70.4 the rate of identifying OS LT 10d out of the patients who actually have OS LT 10d. This cut-off point of 0.028 will result to 29.6% rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d'''. Increasing the cut off predicted probability of (Overstay >= 10 days) to 0.039 resulted to sensitivity = 66.9%, thus reducing the rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d''' to 26.3%. Thus '''a predicted probability of (Overstay >= 10 days) of 0.039''' is going to be used to classify patients into two groups; Overstay >= 10 days and Overstay < 10 days. See more discussion on [[Overstay2 colour]].

*The model has been validated using the data from the validation set of same period. The AUC C-index = 0.742, the Hosmer and Lemeshow Goodness of fit test Chi-Square value = 13.4047 (prob=0.099) and the optimum cut-off probability for OS = 0.028 with sensitivity = 67.1% and specificity = 71.6%.

@@ Line 38: / Line 38: @@
 * OS = exp(Y) / (1 + exp(Y)
-*The model gave a 0.028 optimum cut-off probability for OS GE 10 Days with sensitivity = 69.0%, the rate of identifying OS GE 10d out of the patients who actually have OS GE 10d  and with specificity = 70.4  the rate of identifying OS LT 10d out of the patients who actually have OS LT 10d. This  cut-off point of 0.076 will result to 29.6% rate of identifying '''OS GE 10d''' out of the patients  who actually have '''OS LT 10d'''.  Increasing the cut off predicted probability of (Overstay >= 10 days) to 0.039  resulted to sensitivity = 66.9%, thus reducing the rate of identifying '''OS GE 10d''' out of the patients  who actually have '''OS LT 10d''' to 26.3%.  Thus  '''a predicted probability of (Overstay >= 10 days) of 0.039'''  is going to be used to classify patients into two groups; Overstay >= 10 days and Overstay < 10 days. See more discussion on [[Overstay2 colour]].
+*The model gave a 0.028 optimum cut-off probability for OS GE 10 Days with sensitivity = 69.0%, the rate of identifying OS GE 10d out of the patients who actually have OS GE 10d  and with specificity = 70.4  the rate of identifying OS LT 10d out of the patients who actually have OS LT 10d. This  cut-off point of 0.028 will result to 29.6% rate of identifying '''OS GE 10d''' out of the patients  who actually have '''OS LT 10d'''.  Increasing the cut off predicted probability of (Overstay >= 10 days) to 0.039  resulted to sensitivity = 66.9%, thus reducing the rate of identifying '''OS GE 10d''' out of the patients  who actually have '''OS LT 10d''' to 26.3%.  Thus  '''a predicted probability of (Overstay >= 10 days) of 0.039'''  is going to be used to classify patients into two groups; Overstay >= 10 days and Overstay < 10 days. See more discussion on [[Overstay2 colour]].
 *The model has been validated using the data from the validation set of same period. The AUC C-index = 0.742, the Hosmer and Lemeshow Goodness of fit test Chi-Square value = 13.4047 (prob=0.099)  and the optimum cut-off probability for OS = 0.028 with sensitivity = 67.1% and specificity = 71.6%.