Common Problems in Writing Statistical Plan of Clinical Trial Protocol

1
Common Problems in Writing Statistical Plan of
Clinical Trial Protocol

• Liying XU
• CCTER
• CUHK

2
The importance of statistical planning

• International Conference on Harmonization (ICH)
  E9 Guideline on Statistical Principles for
  Clinical Trials

3
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

4
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.

5
Factors Affecting Statistical Methods Used

• The nature of the variables
• The number of treatment compared
• The experimental design
• Additional factors taken into account for
  analysis (e.g. baseline)

6
Failure to define the analysis sets

• Full analysis setThe 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.

7
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.

8
Criteria to exclude randomized subjects from full
analysis set

• Failure to satisfy major entry criteria
• Failure to take at least one does of trial
  medication
• The lack of any data post randomization

9
Per Protocol Setvalid 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.

10
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

11
An ExampleProtocol 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.

12
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.

13
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

14
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.

15
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).

16
Failure to define other derived variables

• Change from baseline
• Percentage change from baseline
• AUC of repeated measures
• Ratio of two different variables

17
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

18
Failure to consider the adjustment of
multiplicity

• Significance
• Confidence levels

19
Multiplicity and Method to Reduce Multiplicity
20
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

21
Covariant

• Definition
• Efficacy variables or treatment responses are
  often influenced by or related to factors other
  than treatment.

22
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.

23
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

24
Data Type and Adjustment Procedure
25
Failure to model outcomes adequately

• Treating ordered categorical data in a way that
  ignores the ordering

26
Relationship Between Frequency of Caesarian
Section(CS) and Maternal Shoe Size
27
Snoring Behavior in Relation to Presence or
Absence of Heart Disease
28
Fail to reflect the trial structure

• Carry out a multicentre trial and not fitting
  centre the centre effect.