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Updates on Regulatory Requirements for Missing Data

Updates on Regulatory Requirements for Missing Data. Ferran Torres, MD, PhD Hospital Clinic Barcelona Universitat Autònoma de Barcelona. Documentation. http://ferran.torres.name/edu/dia. Power Point presentation Direct links to guidelines List of selected relevant references. Disclaimer.

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Updates on Regulatory Requirements for Missing Data

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  1. Updates on Regulatory Requirements for Missing Data Ferran Torres, MD, PhD Hospital Clinic Barcelona Universitat Autònoma de Barcelona

  2. Documentation http://ferran.torres.name/edu/dia • Power Point presentation • Direct links to guidelines • List of selected relevant references

  3. Disclaimer • The views expressed here are those of the author and may not necessary reflect those of any of the following institutions he is related to: • Spanish Medical Agency - AEMPS • EMEA (SAWP; EWP) • Hospital Clinic Barcelona • Autonomous University of Barcelona

  4. Regulatory guidance concerning MD • 1998: ICHE9. Statistical Principles for Clinical Trials • 2001: PtC on Missing Data • Dec-2007-2008: Recommendation for the Revision of the PtC on MD • 2009: Release for consultation

  5. ICH-E9 (3,6) • Key points: • Potential source of bias • Common in Clinical Trials • Avoiding MD • Importance of the methods • Pre-specification • Lack of universally accepted method for handling • Sensitivity analysis • Identification and description of missingness

  6. Status in early 2000s • In general, MD was not seen as a source of bias: • considered mostly as a loss of power issue • little efforts in avoiding MD • Importance of the methods for dealing with: • Available Data Only • Handling of missingness: Mostly LOCF, Worst Case

  7. Status in early 2000s • Very few information on the handling of MD in protocols and SAP (little pre-specification) • Lack of Sensitivity analysis, or only one, and no justification • Lack (little) identification and description of missingness in reports

  8. PtC on MD Structure • Introduction • The effect of MD on data analysis • Handling of MD • General recommendations

  9. Main Points • Avoidance of MD • Bias: specially when MD was related to the outcome • Methods: • Warning on the LOCF • Open the door to other methods: • Multiple imputation, Mixed Models… • Sensitivity analysis

  10. Current status in 2008-9 Missing data remains a problem in protocols and final reports: • Little or no critical discussion on pattern of MD data and withdrawals • None / only one sensitivity analysis • Methods: • Inappropriate methods for the handling of MD • LOCF: Still used as a general approach for too many situations • Methods with very little use in early 2000 are now common (Mixed Models)

  11. New Draft PtC 1. Executive Summary 2. Introduction 3. The Effect of MD on the Analysis & the Interpretation 4. General Recommendations 4.1 Avoidance of Missing Data 4.2 Design of the Study. Relevance Of Predefinition 4.3 Final Report 5. Handling of Missing Data 5.1 Theoretical Framework 5.2 Complete Case Analysis 5.3 Methods for Handling Missing Data 6. Sensitivity Analyses

  12. Statistical framework • applicability of methods based on a classification according to missingness generation mechanisms: • missing completely at random (MCAR) • missing at random (MAR) • missing not at random (MNAR)

  13. Options after withdrawal > Worse 36 32 28 24 20 16 12 8 4 < Better 0 2 4 6 8 10 12 14 16 18 Time (months)

  14. Options after withdrawal • Ignore that information completely: Available Data Only approach • To “force” data retrieval?: • “Pure” estimates valid only when no treatment alternatives are available • Otherwise the effect will be contaminated by the effect of other treatments • Single Imputation methods • MAR methods: • Mixed-effect models for repeated measures (MMRM) • MNAR methods

  15. Single imputation methods • LOCF, BOCF and others • Many problems described in the previous PtC • Their potential for bias depends on many factors • including true evolutions after dropout • Time, reason for withdrawal and proportion of missingness in the treatment arm • they do not necessarily yield a conservative estimation of the treatment effect • The imputation may distort the variance and the correlations between variables

  16. MMRM (and others MAR) • MAR assumption • MD depends on the observed data • the behaviour of the post drop-out observations can be predicted with the observed data • It seems reasonable and it is not a strong assumption, at least a priori • In RCT, the reasons for withdrawal are known • Other assumptions seem stronger and more arbitrary

  17. However… • It is reasonable to consider that the treatment effect will somehow cease/attenuate after withdrawal • If there is a good response, MAR will not “predict” a bad response • =>MAR assumption not suitable for early drop-outs because of safety issues • In this context MAR seems likely to be anti-conservative

  18. The main analysis: What should reflect ? A) The “pure” treatment effect: • Estimation using the “on treatment” effect after withdrawal • Ignore effects (changes) after treatment discontinuation • Does not mix up efficacy and safety B) The expected treatment effect in “usual clinical practice” conditions

  19. MAR • MMRM aims to estimate the treatment effect that would be seen if all patients had continued on the study as planned. • In that sense MMRM results could be seen as not fully compliant with the ITT principle • Regulatory assessment is focused on what could be expected "on average" in a population, where not all patients have complied with the assigned treatment  for the full duration of the trial

  20. Description of MD Detailed description (numerical and graphical): • Pattern of MD • Rate and time of withdrawal • By reason, time/visit and treatment • Some withdrawals will occur between visits: use survival methods • Outcome • By reason of withdrawal and also for completers

  21. General recommendations • Sensitivity analysis (there is a new separate section) • Avoidance of MD • Design • Relevance of predefinition (avoid data-driven methods ) • detailed description • and justification of absence of bias in favour of experimental treatment • Final Report • Detailed description of the planned and amendments of the predefined methods

  22. Sensitivity Analyses • One specific section • a set of analyses showing the influence of different methods of handling missing data on the study results • Pre-defined and designed to assess the repercussion on the results of the particular assumptions made in the handling of missingness • Responder analysis • Sensitivity analyses may give robustness to the conclusions

  23. Concluding Remarks • Avoid and foresee MD • Sensitivity analyses • Methods for handling: • No gold standard for every situation • In principle, “almost any method may be valid”: • =>But their appropriateness has to be justified

  24. http://ferran.torres.name/edu/dia

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