Transcript Common Problems in Writing Statistical Plan

Common Problems in Writing Statistical
Plan of Clinical Trial Protocol
Liying XU
CCTER
CUHK
The importance of statistical planning
• International Conference on Harmonization
(ICH) E9 Guideline on Statistical Principles
for Clinical Trials
Statistical quality
• Any high quality trial will include a detailed
analysis plan as part of (or an appendix to)
the protocol.
• Statistical quality
– Professional competence
– Professional responsibility
Pre-specification of the analysis
• Statistical section of the protocol should
include all the principal features of the
proposed confirmatory analysis of the
primary variable(s) and the way in which
anticipated analysis problems will be
handled.
Factors Affecting Statistical Methods Used
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The nature of the variables
The number of treatment compared
The experimental design
Additional factors taken into account for
analysis (e.g. baseline)
Failure to define the analysis sets
• Full analysis set
The set of subjects that is as close as
possible to the ideal implied by the ITT
principle. It is derived from the set of all
randomized subjects by minimal and
justified elimination of subjects.
Intent to Treat Principle (ITT)
• The patients data should be analyzed in their
assigned treatment group after they have been
randomized regardless the treatment they are
actual received.
• Fundamental point
– Excluding participants or observed outcomes
from analysis and sub-grouping on the basis of
outcome or response variables can lead to
biased results of unknown magnitude or
direction.
Criteria to exclude randomized subjects
from full analysis set
1. Failure to satisfy major entry criteria
2. Failure to take at least one does of trial
medication
3. The lack of any data post randomization
Per Protocol Set
‘valid cases’, ‘efficacy sample’ or the
‘evaluable subjects’.
• The set of data generated by the subsets
who complied with the protocol
sufficiently to ensure that these data would
be likely to exhibit the effect of treatment,
according to the underlying scientific model.
Criteria of defining Per Protocol Set
• The completion of a certain pre-specified
minimal exposure to the treatment regimen
• The availability of measurements of the
primary variable(s)
• The absence of any major protocol
violations including the violation of entry
criteria
An Example:
Protocol criteria for patients
included in evaluable and ITT analysis
• Patients who complete all of the visits
without violation or major deviations and
are at least 80% compliant in taking
medication, will be analyzed in the per
protocol analysis. All patients taking at
least one does of study medication will be
included in the intention to treat analysis.
Testing the baseline imbalance
• This is a common procedure which has no
justification in statistical theory
• Baseline imbalance can not justify the
integrity of randomization process.
• Randomization does not guarantee the
balanced of baseline
• Baseline will be adjusted in the analysis.
Fail to specify the policy on
missing values and outliers
• Imputation techniques to compensate for
missing data:
– Carry forward of the last observation
– Complex mathematical models
– Defer detailed policy on irregularity until
the blind review of the data at the end of
the trial
Blind review
• The checking and assessment of data during
the period of time between trial completion
(the last observation on the last subject) and
the breaking of the blind, for the purpose of
finalizing the planned analysis.
Failure to specify data transformation
• Transformation (e.g. square root, logarithm)
should be specified in the protocol and a
rational provided. Especially for the primary
variable(s).
Failure to define other derived variables
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Change from baseline
Percentage change from baseline
AUC of repeated measures
Ratio of two different variables
Excessive emphasis on p-values
• Confidence Intervals are much more
informative
• Justification for one sided test
• Type I error
• Statistical model and the assumptions
underlying such models
• Parametric and non parametric
Failure to consider the adjustment of
multiplicity
• Significance
• Confidence levels
Multiplicity and Method to Reduce Multiplicity
Multiple comparisons of
treatments
Choosing the critical
treatment to contrast
Multiple endpoints
Identifying the key variable
Repeated evaluation and/or Using a summary measure
interim analysis
Inappropriate (or insufficient) use of
covariate information
• Using change from baseline rather than
fitting baseline as a covariate.
– The inference based on the covariance
adjustment is generally more precise than that
based on the change adjustment ( Patel
(1983,1986) Kenward and Jones (1987)
• To adjust the main analysis for covariates
measured after randomization
Covariant
• Definition
– Efficacy variables or treatment responses
are often influenced by or related to
factors other than treatment.
Covariant Adjustment
• Randomization can not guarantee the
comparability or the balance of all the
covariates especially in smaller studies.
• In order to obtain a valid and more precise
inference of the treatment effect, it is
necessary to adjust for covariates that are
statistically correlated with the clinical
endpoints.
Possible Covariates (Confounding
factors, Prognostic factors, risk factors)
• Demographic
– age, gender and race
• Patient characteristics
– disease severity, concomitant medication and
medical history
• Centre in a multicentre study
Data Type and Adjustment Procedure
Data Type
Continuous efficacy and Discreet covariate
Adjustment Procedure
ANOVA
Both efficacy and covariate are discreet
Sratified analysis (MentelHaeszel)
Discreet efficacy and continuous covariate
Multiple Logistic
Regression
Analysis of covariance
Change from baseline
Multiple regression
Survival Analysis
Cox regression
Both efficacy and covariate are continuous
Time to an event as efficacy
Failure to model outcomes adequately
• Treating ordered categorical data in a way
that ignores the ordering
Relationship Between Frequency of
Caesarian Section(CS) and Maternal
Shoe Size
Shoe size
CS
<4
4
4 1/2 5
5 1/2 6+
Total
Yes
5
7
6
7
8
10
No
17
28
36
41
46
140
308
Total 22
35
42
48
54
150
351
43
Snoring Behavior in Relation to
Presence or Absence of Heart Disease
Heart
disease
Nonsnorers
Yes
24(1.7%)
No
1355
Total
1379
Occasi.
snorers
Snore
nearly
every
night
21(9.9%)
Snore
every
night
603
192
224
2374
638
213
254
2484
35(5.5%)
Total
30(11.8%) 110(4.2%)
Fail to reflect the trial structure
• Carry out a multicentre trial and not fitting
centre the centre effect.