1 Handling of Missing Data. A regulatory view Ferran Torres, MD, PhD IDIBAPS. Hospital Clinic Barcelona Autonomous University of Barcelona (UAB)

2 Documentation Power Point presentation Direct links to guidelines List of selected relevant references

3 Disclaimer The opinions expressed today are my personal views and should not be understood or quoted as being made on behalf of any organization. – Regulatory Spanish Medicines Agency (AEMPS) European Medicines Agency (EMA) – Scientific Advice Working Party (SAWP) – Biostatistics Working Party (BSWP) – Hospital - Academic - Independent Research IDIBAPS. Hospital Clinic Barcelona Autonomous University of Barcelona (UAB) CAIBER. Spanish Clinical Trials Platform

Best way to deal with Missing Data?? Don’t have any!!! Methods for imputation: – Many techniques – No gold standard for every situation 4

5 Regulatory guidance concerning MD 1998:: ICHE9. Statistical Principles for Clinical Trials 2001:: PtC on Missing Data (rapporteurs: Gonzalo Calvo & Ferran Torres) Dec-2007: Recommendation for the Revision of the PtC on MD 2009:: Release for consultation 2010: Adopted new guideline ( rapporteurs: David Wright & Ferran Torres )

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: – 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 Key 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 analyses

9 Current status in 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)

10 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 5.1Theoretical Framework 5.2 Complete Case Analysis 5.3 Methods for Handling Missing Data 6.Sensitivity Analyses

Time (months) > Worse < Better Options after withdrawal

12 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 Imputation methods Analysing data as incomplete – Time to event analysis, direct estimation (likelihood methods )

13 Single imputation methods LOCF, BOCF, mean imputation 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

MCAR - missing completely at random – Neither observed or unobserved outcomes are related to dropout MAR - missing at random – Unobserved outcomes are not related to dropout, they can be predicted from the observed data MNAR - missing not at random – Drop-out is related to the missing outcome Rubin (1976) Missing Data Mechanisms 14

15 Mixed models & 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

16 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

17 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

18 MAR Estimate the treatment effect that would be seen if patients had continued on the study as planned....results could be seen as not fully compliant with the ITT principle

Combination of ≠ methods Imputation Using Drop-out Reason (IUDR) – Penalise treatment related drop-outs (i.e. lack of efficacy or/and adverse events) – Worst response // Placebo effect // expected effect (low percentile: P10, Median….) Example: 1) Retrieve data after withdrawal + 2) IUDR with Multiple Imputation (avoids deflation of variability) for lack of efficacy/Safety drop-outs + 3) Perform a Mixed Model for Repeated Measurements (MMRM) analysis 19

20 Key recommendations (1/4) Design – Assume that MD is probably biased – Avoidance of MD – 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

21 Key recommendations (2/4) Detailed description (numerical & 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

22 Key recommendations (3/4) Sensitivity Analyses 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

No universally best method Analysis must be tailored to the specific situation at hand Better methods than LOCF: But still useful for sensitivity analyses and as an anchor to compare with previous trials Methods: – MCAR: almost any method is valid but difficult to assume – MAR: More likely to occur Likelihood (Mixed Models MMRM, E-M) / weighted-GEE Multiple imputation – MNAR: model drop-out as well as response Theoretically more useful, in practice highly dependent on drop-out assumptions which are un-checkable For sensibility analysis. Key recommendations (4/4) 23

24 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

25