Why do people use LOCF? Or why not?

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Why do people use LOCF? Or why not?

• Naitee Ting, Allison Brailey
• Pfizer Global RD
• CT Chapter Mini Conference

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Outline

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

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Observed data from each patient over time
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Complete Data
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Last-Observation-Carried-Forward
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LOCF

• Conservative? Or anti-conservative?
• Biased point estimate
• May underestimate variance

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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

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Simulated data

• ctr pid trt wk0 wk2
  wk4 wk6 wk8
• 501 1 1 103.9 102.0
  103.6 102.2 100.4
• 501 2 0 105.9 111.8
  112.5 115.0 117.0
• 501 5 0 93.8 98.4
  103.4 104.5 116.7
• 501 6 1 102.8 87.4
  72.8 60.9 48.5
• 501 11 0 109.4 105.3
  99.2 96.9 89.7
• 501 15 0 93.9 81.6
  66.1 50.5 40.3
• 501 16 1 92.4 83.6
  71.7 66.2 56.5
• 501 18 0 99.3 99.0
  101.9 102.5 103.2
• 502 1 0 105.8 102.7
  87.5 84.9 78.8
• 502 4 1 102.0 100.3
  101.1 95.7 .
• 502 5 1 110.3 116.8
  120.6 132.7 136.8
• 502 8 0 125.6 121.7
  116.1 110.0 108.5
• 502 9 1 92.9 91.4
  82.1 . .
• 502 12 0 123.7 121.7
  118.3 122.0 120.3
• 502 13 0 107.7 121.4
  141.5 154.7 168.9
• 502 16 1 112.1 109.6
  103.6 103.3 104.2

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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

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ANCOVA on LOCF data

• Source df MS
  F p-Value
• TREATMENT 1 2441.0
  4.13 0.0444
• CENTER 8 765.8
  1.30 0.2523
• BASELINE 1 318.4
  0.54 0.4644
• ERROR 119 591.1
• Statistic Test Drug
  Placebo
• Raw Mean -9.40 -0.54
• Adj Mean -8.93 -0.26
• Std Error 3.08
  3.01
• N 65
  65

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Analysis of completed cases

• Source df MS
  F p-Value
• TREATMENT 1 1963.6
  3.32 0.0713
• CENTER 8 1007.2
  1.70 0.1060
• BASELINE 1 73.2
  0.12 0.7258
• ERROR 109 592.0
• Statistic Test Drug
  Placebo
• Raw Mean -10.40 -1.72
• Adj Mean -10.23 -2.11
• Std Error 3.27
  3.14
• N 60
  60

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Naive interpretation

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

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Mixed model analysis

• For demonstration purposes, only repeated measure
  results are presented
• proc mixed methodreml where weekgt0
• class pid trt week ctr
• model ywk0 trt ctr week trtweek/solution
• repeated week / typecs subjectpid r rcorr
• estimate 'trt dif at week 8' trt -1 1 trtweek 0
  0 0 -1 0 0 0 1 / cl alpha0.05

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Results from PROC MIXED

• Num Den
• Effect DF DF F Value Pr gt F
• Baseline 1 456 3.03 0.0826
• Treatment 1 16 5.57 0.0313
• Center 8 85 5.43 lt.0001
• Week 3 108 2.46 0.0662
• Trtweek 3 46 1.22 0.3132
• Standard
• Label Estimate Error DF t Value Pr gt
  t
• week 8 dif 7.3739 3.0127 46 2.45
  0.0183

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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

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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?

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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?

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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

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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

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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

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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

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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?

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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

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Modeling approaches

• Assumptions
• Can be complicated
• Difficult to explain to end users
• George Box All models are wrong, some are
  useful

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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

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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

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