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How to clean up dirty data in Patient reported outcomes?

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Presentation on theme: "How to clean up dirty data in Patient reported outcomes?"— Presentation transcript:

1 How to clean up dirty data in Patient reported outcomes?
14 May 2019 How to clean up dirty data in Patient reported outcomes? Knut Mueller Senior Statistical Programmer UCB Monheim 14 May 2019

2 Regulatory Aspects / Guidelines / Rules Approach Process Description
Introduction Regulatory Aspects / Guidelines / Rules Approach Process Description Summary and Conclusions 14 May 2019

3 Introduction Patient Reported Outcomes Unclean Data Traceability
Keep valuable information 14 May 2019

4 Guidelines and Rules FDA Guidance for Industry – Patient-Reported Outcome Measures Counting Rules set up by Health Outcomes department No on-site transcription of ambiguous data track and report original answers of the subject 14 May 2019

5 Approach standard macro OC Database PRO SDTM datasets ADaM datasets
TFLs Data Entry Stat Prog Stat Prog Stat Prog DM standard macro 14 May 2019

6 Approach Development of a validated Standard macro
can be used similar to CALL routine number of paramters <6 Identification, invar, outvar, values, most severe value, tracking variable User friendliness 14 May 2019

7 Process description - overview
STEP 1 missing values? YES STEP 2 NO correct values? YES NO STEP 3B STEP 3A more than one "correct" answer? adjacent multiple answers? take most severe YES YES NO STEP 4 correct answer + our of range answer? remove out of range value YES NO STEP 5 correct answer + comment? remove comment YES NO Missing value correct value 14 May 2019

8 Process description – step 1
Missing value or missing code? NA ND UN IF YES  missing value . . . . IF NO  GO TO STEP 2 14 May 2019

9 Process description – step 2
Correct value? "2" "3" "1" "4" IF YES  take this value 2 3 1 4 IF NO  GO TO STEP 3A 14 May 2019

10 Process description – step 3A
multiple answer, all answers inside the range, no more than maximum? "2/3" "3/5" "1/2" "1/4" IF YES  GO TO STEP 3B IF NO  GO TO STEP 4 14 May 2019

11 Process description – step 3B
All values adjacent ? "2/3" "3/5" "1/2" "1/4" IF YES  take the most severe value 2 1 IF NO  missing value . . 14 May 2019

12 Process description – step 4
Correct value plus out of range answer? "2/6" "5/7" "0/1" "5/6" IF YES  remove the out of range value 2 5 1 5 IF NO  GO TO STEP 5 14 May 2019

13 Process description – step 5
Correct value plus comment? "2?" "5++" "+/-1" "3/4/5" IF YES  remove the comment 2 5 1 IF NO  missing value . 14 May 2019

14 Caveats All decisions have to be carefully tracked and checked
especially step 5 handles cases that aren't as closely defined than the others unexpected cases 14 May 2019

15 e PROs What about e PROs? need to be validated
technical equipment necessary probably the solution for the future 14 May 2019

16 Conclusions Compliance with FDA Guidance
use of validated standard macro saves programming time the CALL routine approach still provides a considerable amount of control over the process Necessity of close quality checks 14 May 2019


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