Overstay2 scoring model for HSC: Difference between revisions

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== Data elements and assigned coefficients ==

{{~~Discuss~~|z

The binary logistic regression Y (Overstay GE 10 Days) which gave the AUC C-index =0.789 implying better predicted model and Hosmer and Lemeshow Goodness of fit test Chi-Square value of 10.5443

* ~~once we~~ have ~~a model~~, ~~add~~ the ~~content for~~ the ~~chosen model here~~, ~~with~~ [[~~template:OSDD~~]] ~~links }}~~

(prob=0.2289) is :

LN(OS/1-OS) = Y

Y = Intercept Estimate + sum of all the rest of (the Parameter values multiplied by the Estimate) listed below:

{| class="wikitable notsortable"

!Parameter !! Estimate !! Prob > ChiSq

|-

| Intercept ||-6.628750489 || <.0001

|-

| {{OSDD|Age}} || 0.020816662 || <.0001

|-

| {{OSDD|Homeless}} || 0.73542220 || 0.0048

|-

| {{OSDD|MB_Winnipeg}} || 0.462297834 || 0.0009

|-

| {{OSDD|Dementia}} || 0.444548081 || 0.0472

|-

| {{OSDD|Trach}} || 1.439221519 || 0.0003

|-

| {{OSDD|ADL_bath}} || 0.407661477 || <.0001

|-

| {{OSDD|ADL_feed}} ||0.066130137 || 0.0521

|-

| {{OSDD|ADL_continence}} ||0.09033481 || 0.0074

|-

| {{OSDD|ADL_Adlmean_nh}} ||-0.095015407 || 0.0024

|-

| {{OSDD|ADL_Adlmean_age}} || -0.000470209 || 0.019

|}

Where OS is the likelihood to overstay 10 days and beyond.

How to calculate OS:

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

*The model gave a 0.043 optimum cut-off probability for OS GE 10 Days with sensitivity = 71.0%, the rate of identifying OS GE 10d out of the patients who actually have OS GE 10d and with specificity = 71.7%, the rate of identifying OS LT 10d out of the patients who actually have OS LT 10d. This cut-off point of 0.043 will result to 28.3% 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 0.054 resulted to sensitivity = 66.6%, thus reducing the rate of identifying '''OS GE 10d''' out of the patients who actually have '''OS LT 10d''' to 24.2%. Thus '''a predicted probability of (Overstay >= 10 days) of 0.054''' 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 = 0.815, the Hosmer and Lemeshow Goodness of fit test Chi-Square value = 8.4318 (prob=0.4002) and the optimum cut-off probability for OS = 0.079 with sensitivity = 74.4% and specificity = 74.5%.

== Log ==

@@ Line 3: / Line 3: @@
 == Data elements and assigned coefficients ==
-{{Discuss|z
+The binary logistic regression  Y  (Overstay GE 10 Days)  which gave the AUC C-index =0.789 implying better predicted model and Hosmer and Lemeshow Goodness of fit test Chi-Square value of 10.5443
-* once we have a model, add the content for the chosen model here, with [[template:OSDD]] links }}
+ (prob=0.2289) is :
+LN(OS/1-OS) = Y
+Y = Intercept Estimate + sum of all the rest of (the Parameter values multiplied by the Estimate)   listed below:
+{| class="wikitable notsortable"
+ !Parameter !! Estimate !! Prob > ChiSq
+|-
+| Intercept  ||-6.628750489 || <.0001
+|-
+| {{OSDD|Age}}  || 0.020816662 || <.0001
+|-
+| {{OSDD|Homeless}} || 0.73542220 || 0.0048
+|-
+| {{OSDD|MB_Winnipeg}}  || 0.462297834  || 0.0009
+|-
+| {{OSDD|Dementia}} || 0.444548081 || 0.0472
+|-
+| {{OSDD|Trach}} || 1.439221519  || 0.0003
+|-
+| {{OSDD|ADL_bath}} || 0.407661477  || <.0001
+|-
+| {{OSDD|ADL_feed}}  ||0.066130137  || 0.0521
+|-
+| {{OSDD|ADL_continence}}  ||0.09033481  || 0.0074
+|-
+| {{OSDD|ADL_Adlmean_nh}}  ||-0.095015407 || 0.0024
+|-
+| {{OSDD|ADL_Adlmean_age}}  || -0.000470209 || 0.019
+|}
+Where OS is the likelihood to overstay 10 days and beyond.
+How to calculate OS:
+* OS = exp(Y) / (1 + exp(Y)
+*The model gave a 0.043 optimum cut-off probability for OS GE 10 Days with sensitivity = 71.0%, the rate of identifying OS GE 10d out of the patients who actually have OS GE 10d  and with specificity = 71.7%, the rate of identifying OS LT 10d out of the patients who actually have OS LT 10d. This  cut-off point of 0.043 will result to 28.3% 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 0.054  resulted to sensitivity = 66.6%, thus reducing the rate of identifying '''OS GE 10d''' out of the patients  who actually have '''OS LT 10d''' to 24.2%.  Thus  '''a predicted probability of (Overstay >= 10 days) of 0.054'''  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 = 0.815, the Hosmer and Lemeshow Goodness of fit test Chi-Square value = 8.4318 (prob=0.4002)  and the optimum cut-off probability for OS = 0.079 with sensitivity = 74.4% and specificity = 74.5%.
 == Log ==