Missing Data - much ado about nothing. Michael Hannah, Trial Database Manager & Petra Rauchhaus, Clinical Trials Statistician
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1 Missing Data - much ado about nothing Michael Hannah, Trial Database Manager & Petra Rauchhaus, Clinical Trials Statistician
2 Missing data - introduction 1. The data management team 2. The stats team 3. Today is just a very quick overview of dealing with missing data and other data queries more questions than answers 4. Raising the profile of data management
3 Missing data 1. Who cares? 2. What do we mean by missing 3. Prevention and treatment Prevention by Designing out discrepancies Treatment by analysis
4 Who cares? Data Management is nothing but a nit-picking department that makes everything difficult.
5 Who cares? Actually.the MHRA / FDA do! A cautionary tale. Cancer trial in about 20 sites across several countries typical AE form instructions given to add pages as required
6 A cautionary tale In a cancer trial over months there are typically MANY AEs No-one noticed that in one centre, participants never had more than 10 AEs ie the number of lines on a single AE log page someone hadn t realized they needed to add pages to the log
7 A cautionary tale In this case the FDA accused the company of under-reporting AEs to make the drug seem more tolerable. The error was resolved but the entire US market could have been closed to the company as a result of this.
8 A cautionary tale
9 But surely this is only relevant for commercial trials...ctimps Bigger journals are increasingly insisting on protocols and SAPs to be published before accepting a results paper Initiatives like CONSORT ( and SPIRIT ( adding pressure for higher standards
10 What do we mean by missing? 1. Value got lost in transcription 2. Procedure didn t get done 3. Samples got lost 4. Participant refused / forgot to answer (or wanted to answer differently) 5. Participant couldn t answer or N/A 6. Participant withdrew from trial 7. 0 vs NULL
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13 Prevention and Treatment 1. Prevention - designing out missingness 2. Treatment - statistical methods for dealing with missingness
14 Prevention 1. Protocol - address the issues early 2. pcrf / ecrf design 3. UAT / guidance / training 4. Timely data cleaning procedures
15 Prevention Protocol - address the issues early Recommendation 6: Study sponsors should explicitly anticipate potential problems of missing data. In particular, the trial protocol should contain a section that addresses missing data issues, including the anticipated amount of missing data, and steps taken in trial design and trial conduct to monitor and limit the impact of missing data. The Prevention and Treatment of Missing Data in Clinical Trials
16 Prevention pcrf / ecrf design Start early and take time Take care about its size Use it to anticipate / resolve problems It can be an iterative process
17 Prevention pcrf / ecrf design Some examples
18 Prevention UAT / guidance / training Testing systems and getting feedback Training for data entry / research nurses Providing good documentation for data entry
19 Prevention Timely data cleaning procedures Case Report Forms (CRFs) Data calculations Data checking Data cleansing Data Entry Export and Analysis Paper Data Management system
20 Prevention Timely data cleaning procedures Identifying systematic problems early Resolving queries quickly Having robust processes in place from the start Think about data entry models Do not underestimate the time this takes!!!
21 Treatment statistical methods 1. Replacement by the worst case 2. Replacement by the best case 3. Replacement by the mean of the rest of the scores 4. Normalization 5. Multiple imputation
22 Treatment statistical methods 1. The degree of missingness is important in determining how reliable these methods will be 2. MAR vs MNAR
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24 Conclusions 1. Prevention is better than treatment 2. Think about the different ways data might be missing in your study and plan for it early on 3. Use the CRF design to minimize missing data 4. Take time to train all relevant staff in data entry 5. Recognize the time and effort that has to go into data cleaning and budget for it
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