cancel
Showing results for 
Search instead for 
Did you mean: 
Reply
Highlighted
Route217 Member
Member

TRICKY DAX Calculated Column Measures

Hi Experts

 

Need to add the following as calcaluated column measure into my pbix file (see images below) i am trying to populate columns I, J and k into my pbix file. The formulas used in excel are shown below.

 

Sample file. 

link: https://www.dropbox.com/s/47fyhkj5euzw02q/Kaplan_Miers_v1.2.pbix?dl=0

 

Capture.PNG

 

1 ACCEPTED SOLUTION

Accepted Solutions
Super User
Super User

Re: TRICKY DAX Calculated Column Measures

Hey @Route217 , I received a request regarding confidence intervals with Kaplan Meier survival curves from @Venusura and so I was working on it and came up with the following answer:

 

OK, following along from this:

http://www.real-statistics.com/survival-analysis/kaplan-meier-procedure/confidence-interval-for-the-...

 

And if you are following my article here:

https://community.powerbi.com/t5/Community-Blog/Kaplan-Meier-Survival-Curves-with-Power-BI-Part-2/ba...

 

Then in your KMDeptDaysAll table, create this measure:

 

ci_alpha = 0.05

This is your confidence interval, in this case 95%

 

And then create these columns:

 

ci_S(t) = 
VAR __table = FILTER(ALL(KMDeptDaysAll),[Department] = EARLIER([Department]) && [Days] <= EARLIER([Days]))
RETURN
PRODUCTX(__table,[1-e_i/d_i])

ci_se_1 = 
VAR __calc = [e_i] / ([d_i] * ([d_i] - [e_i]))
RETURN
IF(ISERROR(SQRT(__calc)),BLANK(),[ci_S(t)] * SQRT(__calc))

ci_se = 
VAR __table = FILTER(ALL(KMDeptDaysAll),[Department] = EARLIER([Department]) && [Days] <= EARLIER([Days]))
VAR __sum = SUMX(__table,[ci_se_1])
RETURN
[ci_S(t)] * __sum

ci_lower = 
[ci_S(t)] ^ IF(ISERROR(EXP(NORM.S.INV([ci_alpha]/2)/IF(ISERROR(LN([ci_S(t)])),BLANK(),LN([ci_S(t)])*[ci_se]/[ci_S(t)]))),BLANK(),EXP(NORM.S.INV([ci_alpha]/2)/IF(ISERROR(LN([ci_S(t)])),BLANK(),LN([ci_S(t)])*[ci_se]/[ci_S(t)])))

ci_upper = 
[ci_S(t)] ^ IF(ISERROR(EXP(-NORM.S.INV([ci_alpha]/2)/IF(ISERROR(LN([ci_S(t)])),BLANK(),LN([ci_S(t)])*[ci_se]/[ci_S(t)]))),BLANK(),EXP(-NORM.S.INV([ci_alpha]/2)/IF(ISERROR(LN([ci_S(t)])),BLANK(),LN([ci_S(t)])*[ci_se]/[ci_S(t)])))

Can't be certain this is all 100% correct as the original article I was working from didn't have confidence intervals. Will try to do some verification work on this.


I have book! Learn Power BI from Packt


Did I answer your question? Mark my post as a solution!

Proud to be a Datanaut!

2 REPLIES 2
Community Support Team
Community Support Team

Re: TRICKY DAX Calculated Column Measures

Hi @Route217 ,

In your sample, I found  there are some values like negative number and ∞. They cause that the SQRT function returns an error. You could use the function of IF to handle them firstly. 

 

Best Regards,

Xue Ding

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

Super User
Super User

Re: TRICKY DAX Calculated Column Measures

Hey @Route217 , I received a request regarding confidence intervals with Kaplan Meier survival curves from @Venusura and so I was working on it and came up with the following answer:

 

OK, following along from this:

http://www.real-statistics.com/survival-analysis/kaplan-meier-procedure/confidence-interval-for-the-...

 

And if you are following my article here:

https://community.powerbi.com/t5/Community-Blog/Kaplan-Meier-Survival-Curves-with-Power-BI-Part-2/ba...

 

Then in your KMDeptDaysAll table, create this measure:

 

ci_alpha = 0.05

This is your confidence interval, in this case 95%

 

And then create these columns:

 

ci_S(t) = 
VAR __table = FILTER(ALL(KMDeptDaysAll),[Department] = EARLIER([Department]) && [Days] <= EARLIER([Days]))
RETURN
PRODUCTX(__table,[1-e_i/d_i])

ci_se_1 = 
VAR __calc = [e_i] / ([d_i] * ([d_i] - [e_i]))
RETURN
IF(ISERROR(SQRT(__calc)),BLANK(),[ci_S(t)] * SQRT(__calc))

ci_se = 
VAR __table = FILTER(ALL(KMDeptDaysAll),[Department] = EARLIER([Department]) && [Days] <= EARLIER([Days]))
VAR __sum = SUMX(__table,[ci_se_1])
RETURN
[ci_S(t)] * __sum

ci_lower = 
[ci_S(t)] ^ IF(ISERROR(EXP(NORM.S.INV([ci_alpha]/2)/IF(ISERROR(LN([ci_S(t)])),BLANK(),LN([ci_S(t)])*[ci_se]/[ci_S(t)]))),BLANK(),EXP(NORM.S.INV([ci_alpha]/2)/IF(ISERROR(LN([ci_S(t)])),BLANK(),LN([ci_S(t)])*[ci_se]/[ci_S(t)])))

ci_upper = 
[ci_S(t)] ^ IF(ISERROR(EXP(-NORM.S.INV([ci_alpha]/2)/IF(ISERROR(LN([ci_S(t)])),BLANK(),LN([ci_S(t)])*[ci_se]/[ci_S(t)]))),BLANK(),EXP(-NORM.S.INV([ci_alpha]/2)/IF(ISERROR(LN([ci_S(t)])),BLANK(),LN([ci_S(t)])*[ci_se]/[ci_S(t)])))

Can't be certain this is all 100% correct as the original article I was working from didn't have confidence intervals. Will try to do some verification work on this.


I have book! Learn Power BI from Packt


Did I answer your question? Mark my post as a solution!

Proud to be a Datanaut!

Helpful resources

Announcements
Community Highlights

Community Highlights

Find out what's new in the Power BI Community!

Summit North America

Power Platform Summit North America

Register by September 5 to save $200

Virtual Launch Event

Microsoft Business Applications Virtual Launch Event

Watch the event on demand for an in-depth look at the new innovations across Dynamics 365 and the Microsoft Power Platform.

MBAS Gallery

Watch Sessions On Demand!

Continue your learning in our online communities.

Users Online
Currently online: 315 members 3,368 guests
Please welcome our newest community members: