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MISSING DATA

MISSING DATA. DSBS Meeting 28 May 2009 Kristian Windfeld, Genmab. ICH E9 (1998) on missing values. Try to avoid – by design (2.3) Frequency and type must be documented in the CTR (5.2) Imputation techniques (LOCF, …, complex math. models…) (5.2.1)

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MISSING DATA

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  1. MISSING DATA DSBS Meeting 28 May 2009 Kristian Windfeld, Genmab

  2. ICH E9 (1998) on missing values • Try to avoid – by design (2.3) • Frequency and type must be documented in the CTR (5.2) • Imputation techniques (LOCF, …, complex math. models…) (5.2.1) • Are considered protocol violations (5.2.2) • Assess pattern of occurrence among treatment groups (5.2.2) • Source of bias (5.3) • Pre-specify methods for handling in protocol (5.3) • Analyze sensitivity of trial results to missing value handling method (5.3)

  3. CPMP PtC on missing data (2001) Effect of missing values on analysis & interpretation: • Power/variability (smaller sample size => smaller power but plausibly less variability => more power) • Bias – unless not related to unobserved real value (which cannot be verified)

  4. CPMP PtC on missing data (2001) Handling of missing data • Complete case analysis • Exploratory trials or as supportive analysis • Imputation • LOCF (”widely used”…”likely to be accepted if measurements constant over time”…”may provide an acceptably conservative approach”) • best/worst case imputation • multiple imputation • mixed models

  5. CPMP PtC on missing data (2001) Recommendations • Avoid missing data • Pre-specify handling method in statistical section of protocol with justification • Approach must be conservative • Approach may be updated in a SAP if unforeseen problems occur • Analyze missing values (number, timing, pattern, reason)! • Analyze sensitivity of results to missing value handling method => Everybody continued to do ITT with LOCF…

  6. CHMP: Recommendation for revision of PtC on missing data (2007) • In many MAAs the handling of missing data is poor • Little or no discussion on missing data pattern… • Only one sensitivity analysis… or none with no justification • Misconception that LOCF is necessary and sufficient… • Need for cautionary note on mixed models and multiple imputation methods – their use still controversal

  7. New EMEA draft guideline • GUIDELINE ON MISSING DATA IN CONFIRMATORY CLINICAL TRIALS (CPMP/EWP/1776/99 Rev. 1) • Released for consultation 23 april – 31 oct 2009

  8. New EMEA draft guideline Not much news… but some additional points: • Missing values violate ITT principle (collect data regardsless of protocol compliance) • Missing value taxonomy (MCAR, MAR, MNAR) – but since unverifiable => use conservative method • Use methods not assuming MCAR/MAR, ”pattern mixture, selection, and shared parameter models” mentioned • Specific suggestions for sensitivity analyses

  9. Example: Missing ACR20 values from RA trial Is non-response imputation conservative?

  10. Key message We need to start taking missing values seriously!

  11. Imagine a news headline… ”Licensing application for obliviximab rejected because of the lack of analysis of the missing data…” Do you want to be responsible?

  12. Discussion questions • Why don’t we take the guidelines seriously when it comes to missing value handling? • How many sensitivity analyses are needed and should be planned? • When is the missing values influence so great that trial results become non-interpretable? • Is the potentially inflated precision resulting from single imputation methods a problem and if so, how may this be addressed?

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