Data Management & Statistical Analysis in clinical trialsJamalludin Ab Rahman MD MPHDepartment of Community MedicineKulliyyah of Medicine

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Important notes Data management is not mention specifically Statistics is described is some sections of some

guidelines Covers mainly design and analysis of clinical trials

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Data Management in GCP

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Data management in ICH Under E2A: Clinical Safety Data Management Only specific for definition & terminology for reporting

clinical safety

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Ohmann, C., Kuchinke, W., Canham, S., Lauritsen, J., Salas, N., Schade-Brittinger, C., ... & ECRIN Working Group on Data Centres. (2011). Standard requirements for GCP-compliant data management in multinational clinical trials. Trials, 12(1), 85.

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European Clinical Research Infrastructures Network (ECRIN) IT & Data Management (DM) Proposed 115 IT requirements (in 15 sections) &

107 DM requirements (in 12 sections)

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Ohmann, C., Kuchinke, W., Canham, S., Lauritsen, J., Salas, N., Schade-Brittinger, C., ... & ECRIN Working Group on Data Centres. (2011). Standard requirements for GCP-compliant data management in multinational clinical trials. Trials, 12(1), 85.

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Example

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Statistics in GCP

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Statistical guidelines in ICH E1A: The Extent of Population Exposure to Assess Clinical Safety E2A: Clinical Safety Data Management: Definitions and Standards for Expedited Reporting E2B: Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports E2C: Clinical Safety Data Management: Periodic Safety Update Reports for Marketed Drugs E3: Structure and Content of Clinical Study Reports E4: Dose-Response Information to Support Drug Registration E5: Ethnic Factors in the Acceptability of Foreign Clinical Data E6: Good Clinical Practice: Consolidated Guideline E7: Studies in Support of Special Populations: Geriatrics E8: General Considerations for Clinical Trials E9: Statistical Principles For Clinical Trials E10: Choice of Control Group in Clinical Trials M1: Standardisation of Medical Terminology for Regulatory Purposes M3: Non-Clinical Safety Studies for the Conduct of Human Clinical Trials for Pharmaceuticals.

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Section 6.9 E6R1 ICH1. A description of the statistical methods to be employed, including timing of any planned interim

analysis(ses).

2. The number of subjects planned to be enrolled. In multicentre trials, the numbers of enrolled subjects projected for each trial site should be specified. Reason for choice of sample size, including reflections on (or calculations of) the power of the trial and clinical justification.

3. The level of significance to be used.

4. Criteria for the termination of the trial.

5. Procedure for accounting for missing, unused, and spurious data.

6. Procedures for reporting any deviation(s) from the original statistical plan (any deviation(s) from the original statistical plan should be described and justified in protocol and/or in the final report, as appropriate).

7. The selection of subjects to be included in the analyses (e.g. all randomized subjects, all dosed subjects, all eligible subjects, evaluable subjects).

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Statistical guidelines in Malaysia GCP Chapter 6: Clinical

Trial Protocol & Protocol Amendment(s); Subchapter 6.9: Statistics(exactly like Section 6.9 E6R1 ICH)

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Statistical guidelines in WHO GCP No specific chapter Principle 2 - Research involving humans should be

scientifically justified and described in a clear, detailed protocol

A protocol “describes the objective(s), design, methodology, statistical considerations, and organization of a trial. The protocol usually also gives the background and rationale for the trial, but these could be provided in other protocol referenced documents.” (ICH E6, Section 1.44)

A protocol “provides the background, rationale, and objective(s) of a biomedical research project and describes its design, methodology, and organization, including ethical and statistical considerations. Some of these considerations may be provided in other documents referred to in the protocol.” (WHO Operational Guidelines for Ethics Committees that Review Biomedical Research, Glossary)

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Target audience – Sponsors, scientific experts (e.g. trial statistician)

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Scope & Direction Responsibility of the Trial Statistician - A statistician who has a

combination of education/training and experience sufficient to implement the principles in this guidance and who is responsible for the statistical aspects of the trial

Planned (SAP – Statistical Analysis Plan) & approved BEFORE the trial begins

The guidelines specifically for later phases of trial i.e. confirmatory of efficacy (hypothesis testing)

Purpose to minimise bias, maximise precision & evaluating robustness

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MethodologyType of trial Exploratory vs. Confirmatory (with control - hypothesis testing – proof efficacy)

Population Early phase - very specific subgroup, later phase – resemble target population

Variables Primary variable – primary end point (should be only one) Secondary variable – related to primary objective Composite variable – combination of few variables Global assessment variable – quantifying subjective measurements e.g.

depression Surrogate variable – proxy

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Methodology..Control bias

Blinding (masking) – prevent identification of treatment, single, double blind; double dummy; open-label; breaking blind; blind review

Randomisation – unrestricted vs. block (fix or variable length) randomisation; centralised randomisation centre

Designs

Parallel – most common

Crossover – when sample is small; carryover effect; usually use for bio-equivalence study

Factorial – combination of treatments

Multicentre

Two reasons – (1) more samples (at all phases), (2) better generalisation (usually later phases)

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Hypothesis1. Showing superiority (A > B)

2. Showing equivalence (A = B)

3. Showing non-inferiority (A≥B)

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Sample size Should be based on the primary objective & the expected outcomes Must state clearly if otherwise e.g. safety reason To fulfil secondary objective, may require larger size Other information determine sample size:

1. Ho (superiority, equivalence, non-inferiority)

2. P value

3. Power

4. Statistical test

5. Anticipated treatment withdrawal

Must state the formula used

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Data collection & data quality Paper based Case Record Form Electronic form Missing value vs. not applicable data specified Refer ICH E6 Section 5

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Trial conduct1. Trial quality

Adherence to protocol, data collection quality, response rate, retention of subjects, adherence to selection criteria, accrual rate (drop out rate), sample size adjustment

All modifications must be justified & documented

2. Interim analysis Ongoing treatment effect monitoring & the possibility of un-

blinding

IDMC, DSMB & DMC

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Data analysis Pre-specification - Statistical Analysis Plan Analysis set – ideally should analyse ALL – intention-to-treat = Full

Analysis Set; if included only those follow the protocol = Per Protocol Set Missing values – best to avoid; manage missing value (e.g. multiple

imputation plus sensitivity analysis) Transformation done best if already anticipated in SAP Estimation include precision (e.g. 95%CI) & significance (P-value) Adjustment for precision & significance Adjusted for covariates & interaction

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Evaluation of safety Determine relevant variables that measure safety e.g.

side effects, complications Safety Analysis Set Analysis usually descriptive, occasionally analytical (P-

value)

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Reporting Follow SAP Report variation from SAP Describe the subjects Reasons for exclusion, describe lost & withdrawal Summarise all variables Report missing values Precise values e.g. P=0.032 rather than P<0.05 Statistical judgement in overall results of clinical trial