1. Why do people use LOCF? Or why not?
Naitee Ting, Allison Brailey
Pfizer Global R&D
CT Chapter Mini Conference

2. Outline Last Observation Carried Forward (LOCF) Data set description
Modeling approaches
Concerns in clinical Trials
SAP concerns
Why or why not use LOCF

3. Observed data from each patient over time

4. Complete Data

5. Last-Observation-Carried-Forward

6. LOCF Conservative? Or anti-conservative? Biased point estimate
May underestimate variance

9. Data set Simulated - standing diastolic BP
Eight week study of test drug vs placebo
Clinic visit every 2 weeks
Primary endpoint – change in standing BP from baseline to week 8
Patients completed the study or dropped out at various time points
Missing completely at random

10. Simulated data ctr pid trt wk0 wk2 wk4 wk6 wk8

11. Modeling approaches Many proposals to deal with dropouts
Mixed model approach
Repeated measures
Random intercept, random slope
Single imputation
Multiple imputation
Imputation model
Analysis model

12. ANCOVA on LOCF data TREATMENT | 1 2441.0 4.13 0.0444
Source | df MS F p-Value
TREATMENT |
CENTER |
BASELINE |
ERROR |
Statistic Test Drug Placebo
Raw Mean
Adj Mean
Std Error N

13. Analysis of completed cases
Source | df MS F p-Value
TREATMENT |
CENTER |
BASELINE |
ERROR |
Statistic Test Drug Placebo
Raw Mean
Adj Mean
Std Error N

14. Naive interpretation If LOCF provides statistical significance
If completer analysis supports LOCF
True story may lie between the two
Clinical conclusion can be made

15. Mixed model analysis
For demonstration purposes, only repeated measure results are presented
proc mixed method=reml ; where week>0 ;
class pid trt week ctr ;
model y=wk0 trt ctr week trt*week/solution ;
repeated week / type=cs subject=pid r rcorr ;
estimate 'trt dif at week 8' trt -1 1 trt*week / cl alpha=0.05 ;

16. Results from PROC MIXED
Num Den
Effect DF DF F Value Pr > F
Baseline
Treatment
Center <.0001
Week
Trt*week
Standard
Label Estimate Error DF t Value Pr > |t|
week 8 dif

17. Single or multiple imputation
Mixed model can be considered as single imputation
For imputation, we can use the same model for imputation and analysis
However, one model can be used for imputation, but a different one is for analysis

18. Should LOCF be used?
After the modeling approaches became available, use of LOCF have been discouraged
Models are developed with assumptions
More complicated models require more assumptions
Are these assumptions justified?

19. Should LOCF be used?
LOCF is a model and there are simple assumptions behind it
In New Drug Applications (NDA), LOCF is still widely used
Why?

20. Different phases in clinical trials
Phase I, II, III, IV
Phase I – How often?
Phase II – How much?
Phase III – Confirm
Phase IV – Post-Market

21. DOES THE DRUG WORK?
Double-blind, placebo controlled, randomized clinical trial
Test hypothesis - does the drug work?
Null hypothesis (H0) - no difference between test drug and placebo
Alternative hypothesis (Ha) - there is a difference

22. TYPES OF ERRORS
Regulatory agencies focus on the control of Type I error
Probability of making a Type I error is not greater than a
In general, a = 0.05; i.e., 1 in 20
Avoid inflation of this error
Changing the method of analysis to fit data will inflate a

23. MULTIPLE COMPARISONS
For 20 independent variables (clinical endpoints), one significant at random
For 20 independent treatment comparisons, one significant at random
Subgroup analyses can also potentially inflate a
Multiple comparison adjustment

24. Report all data Scientific experiments generate data
Outliers may be observed
Delete outlier?
Clinical trials generate data
A wonder drug cures 9,999 patients of 10,000
One died – outlier – delete?

25. Statistical Analysis Plan (SAP)
Pre-specification of analysis
Prior to breaking blind
Internal agreement within project team
Binding document to communicate with regulatory authorities
Use of LOCF or modeling approach need to be pre-specified in SAP

26. Modeling approaches Assumptions Can be complicated
Difficult to explain to end users
George Box – “All models are wrong, some are useful”

27. Why LOCF? Or why not? Easy to understand
Easy to communicate between statisticians and clinicians, and between sponsor and regulators
Lots of prior examples
Biased point estimate, biased variance

28. Recommendations Understand the disease Understand data to be collected
Understand the dropout issues
Make use of Phase II results
Encourage use of statistical models
LOCF may still be considered as supportive