Missing data is common in clinical trials and can bias results if not addressed properly. There are three types of missing data mechanisms: missing completely at random, missing at random, and missing not at random. Common reasons for missing data include patient withdrawal, loss to follow up, and non-compliance. Methods to handle missing data include complete case analysis, single imputation, multiple imputation, and mixed models. Multiple imputation and mixed models are preferred over single imputation or complete case analysis as they help reduce bias and maintain statistical power.

1. IMPUTATION OF MISSING DATA
IN CLINICAL TRIALS
Seema Ahirwar

2. Flow of presentation
• Introduction
• Reasons
• Effect on Data Analysis and Interpretation
• Mechanism of Missing data
• Types of methods for imputation of missing
data

3. Missing data
Definition
• Missing data are defined as values that are not
available and that would be meaningful for analysis if
they were observed
Principle for dealing with missing data
 Analysing only the available data
 Imputing the missing data with replacement values

4. Reasons For Missing Data
• Patient refusal to continue in the study
• Patient withdrawals due to treatment failure
• Adverse events
• Patients moving
• Data is unreported or uncollected
• Missing Data Due to Death

5. The Effect of Missing Values on Data
Analysis and Interpretation
 Bias
 Loss of efficiency
 Power and variability

6. Three Scenarios for Missing Data
 Missing completely at random
 Data are missing at random
 Data are missing not at random

7. Missing completely at random
Missing does not depend on observed or
unobserved measurements then the observation
is Missing Completely At Random (MCAR)
Example – Patient moving to another city for
non health reasons
- Dropped test tube in a lab or equipment
failure

8. Data are missing at random
 missing depends only on observed measurements
then the observation is Missing At Random (MAR)
Example – when a patient drop’s out due to lack of
efficacy reflected by a series of poor efficacy
outcomes

9. Data are missing not at random
 missing depends on unobserved measurements
Example – After a series of visits with good outcome a
patient drop out due to lack of efficacy

10. Types of methods for imputation of
missing data
 Complete-case analysis
 Single imputation methods
 Multiple imputation
 Mixed models and generalised estimating equations

11. Complete-case analysis
• Participants with missing data are simply excluded from the
analysis it involves only complete cases
• Complete case analysis cannot be use as the primary analysis
Disadvantages
 reduces sample size
 reduced statistical efficiency of estimates
 violates the intention to treat principle and is subject to bias

12. Single imputation methods
 Single value is filled in for each missing value
by means of methods such as
• Last observation carried forward
• Baseline observation carried forward
• Worst observation carried forward

13. last observation carried forward
 This analysis imputes the last measured value
Advantages
It generates a complete data set
Easy to implement
Disadvantages
some time it provide inappropriate estimate of
the treatment effect and biased in favour of the test
product

14. Baseline observation carried forward
 The baseline-observation-carried-forward (BOCF) is
method to handle missing data from early treatment
discontinuation
 Example, a chronic pain trial where a patient
withdraws from treatment it is assume that pain return
to its baseline level

15. Worst observation carried forward
• Impute the worst possible value of the
outcome to dropouts for a negative reason like
treatment failure and adverse effect

16. Multiple imputation Method
• Multiple imputation methods generate multiple
copies of the original dataset by replacing
missing values
• Application of the technique requires three
steps imputation, analysis and pooling

17. Cont…..
• Imputation: Impute the missing entries of the
incomplete data sets, not once, but m’ times
Imputed values are different for each missing
entry this step results in “m’ complete data sets.
• Analysis: Analyze each of the m’ completed data
sets
• Pooling: Integrate the m’ analysis results into a
final result

18. Mixed models and generalised
estimating equations
 These are some statistical approaches for handling
missing data
• Mixed-effect models for repeated measures (MMRM)
• Generalized estimating equations(GEE)
• Generalized linear mixed models (GLMM)