Use of standards: can we really be analysis ready?

Transcription

1 Standards in analysis & reporting Use of standards: can we really be analysis ready?

2 Analysis ready? Why? Reducing time to market/approval Reducing time to delivery Reducing re work Designing subsequent studies deliver reproducible results Responding to regulatory queries What? Analysis of clinical trial studies for CSR Post text TLFs In text tables / figures Case Report Tabulation Support for monitoring boards/committees Analysis of multiple l studies (ISS/E, SCS/E) Exploratory analysis How? Standardize

3 Standardization What can we do? Process Approach to analysis of end points Including safety end points Develop detailed analysis plans Including Master/Project analysis plans Develop standard analyses Wherever possible (across all domains/modules) Completion of programmingduring study conduct Identify which subject/visits are clean Identification of potential changes towards DBL Perform planned testing of programs Perform planned testing of programs Identifying what requires change from planned analysis Plan for it

4 CROSS FUNCTIONAL SYNERGIES

5 Data Management Draft protocol Development Synergies at Study Setup process Ethics Committee Meeting CRF build Draft Data Management Plan Data Extraction Database Build Data Management Plan FSI SAP developed conjointly, immediately after finalisation of study protocol & CRF Reviews and inputs received from the MW and Regulatory teams Stats / Programming Draft SAP SAP including detailed programming specifications Medical Writing Review Skeleton TLGs Clinical teams Review Skeleton TLGs Regulatory / Clinical teams providing feedback on TLGs Abbreviations: SAP: Statistical Analysis Plan; TLG: Tables Listings & Graphs

6 Synergies at Study conduct Data Management Stats / rogramming Early review of draft TLGs FSI 25% 50% 95% MW& Regulatory review of TLGs Additional Analysis determination & SAP updation CSR Skeleton finalization Data Management Recommendation on anyadditional additional checks based on Data review Start Programming Draft TLGs LSLV Database Lock activities Clean File DBL Final Data Extraction Unblind P Additional Analysis Draft TLGs M edical Writing Skeleton CSR Clinical Teams Request for additional data based on other study results results of the current study Review TLGs Review Skeleton CSR BDRs as per project plan Abbreviations: TLG: Tables Listings & Graphs; CSR: Clinical Study Report; BDR: Blinded Data Review

7 Synergies at Study Closure DBL Parallel Processing of CSR, along with the Final TLG preparation CSR Finalization Study Closure Data a Managem ment Stats / Program mming Final Data Extraction Unblind Key results published Other reports published Final TLGs prepared Creation of ectd format (if required) Medical Writing / Publishing Draft CSR Draft CSR review Final CSR preparation Collation Collation of of appendix appendix items items for for CSR CSR Additional Analysis reports Finalization of CSR Client teams Review Final BDRs Stats Review Review MW deliverables CSR Publishing Signoff on Final CSR Abbreviations: TLG: Tables Listings & Graphs; CSR: Clinical Study Report; BDR: Blinded Data Review

8 Data Standards Data collection instruments Standardize CRFs CDASH Data Transformation CDISC SDTM ADaM SDTM as part of the process rather than submission TLFs Standardize At various levels At various levels 1) Organization 2) TA/DA 3) Project

9 Recommended Methodologies for Creating Data Collection Instruments CDASH Methodology Necessary Data Only CRFs should avoid collecting redundant should instead focus on collecting needed ddto answer the protocol questions provide adequate safety data. Rationale Usually, only data that will be used for analysis should be collected on the CRF due to the cost and time associated itdwith collecting data. dt Data that is collected should generally be reviewed and cleaned. When available, the Statistical Analysis Plan (SAP) needs to be reviewed to ensure that the parameters needed for analysis are collected and can be easilyanalyzed. The Statistician is responsible for confirming that the CRF collects all of the correct data.

10 Recommended Methodologies for Creating Data Collection Instruments CDASH Methodology Adequate Review The team that designs the data collection instruments for a study needs to be involved din the development of the protocol and should have appropriate expertise represented on the CRF design team (e.g., statistics, programming, datamanagement, clinical operations, science,regulatory, pharmacovigilance). Statisticians should review the CRF against their planned analyses to make sure all required data will be collected in an appropriate form for those analyses. Rationale The CRF design team should perform an adequate review of the CRF to ensure that the CRF captures all of the data needed ddfor analysis. Furthermore, the team needs to ensure that the data are collected in a manner consistent with the sponsor s processes and should also be easy for the site to complete.

11 Recommended Methodologies for Creating Data Collection Instruments CDASH Methodology Employ Standards Within the data collection environment, standards should be employed to collect consistent it tdt data across compounds and TAs. CDASH standards should be used wherever possible and sponsor standards developed as needed. Rationale Using data collection standards across compounds and TAs saves time and money at every step of drugdevelopment. Ui Using standards: d reduces production time for CRF design reduces site re training and queries and p improves compliance and data quality at first collection. enables easy reuse and integration of data across studies and facilitates data mining and the production of integrated summaries. reduces the need for new clinical and statistical programming with each new study.

12 Standard Modules CDISC Special Purpose Domains: Demographics DM Comments CO Interventions: Concomitant Medications CM Exposure EX Substance Use SU Events: Adverse Events AE Disposition DS Medical History MH *Protocol Deviations DV Findings: *Drug Accountability DA ECG Tests EG Inclusion/Exclusion Exceptions IE Laboratory Tests LB *Microbiology MB Questionnaires QS *Pharmacokinetics Concentrations PC *Pharmacokinetics Parameters PP Physical Examinations PE Subject Characteristics SC Vital Signs VS Trial Design Domains: Trial Elements TE Trial Arms TA Trial Visits TV Subject Elements SE Subject Visits SV Trial Inclusion/Exclusion Criteria. TI Trial Summary TS Special Purpose Relationship Datasets: Supplemental Qualifiers SUPPQUAL Relate Records RELREC

13 CREATING A NEW DOMAIN CDISC First, ensure that there is a definite need to create a new domain. Verify that there are no existing domain models Choose the general observation class (Interventions, Events, or Findings) that best fits the data as follows: a. Identify the topic of the observation and determine which of the three general observation classes it most closely resembles. If the new domain shares both the same topicality and general observation class as an existing domain in the submission, the existing domain should be used. b. Look for other existing domain models that may serve as a relevant prototype (most domains will follow the Findings model). c. Determine if the chosen general observation class has most of the required and expected qualifiers for the new domain. d. Select the variables that apply to the new domain once you have selected the general observation class.

14 CDISC creating new variables

15 Analysis datasets Key principles Analysis datasets should: be analysis ready Contain all information needed for analysis results A variety of sources are possible for analysis datasets. One source could be the SDTM datasets submitted as part of a regulatory submission. In all cases, the data sources should be clearly described in the metadata and the analysis dataset creation documentation

16 Analysis datasets Must include a subject level analysis dataset named ADSL consist of the optimum number of analysis datasets needed to allow analysis and review with little or no programming or data processing. maintain SDTM variable attributes if the identical variable also exists in an SDTM dataset. be named using the convention ADxxxxxx. follow naming conventions for datasets dt t and variables ibl that t are sponsor defined and applied consistently across a given submission or multiple submissions for a product, yet use published SDTM naming fragments for variables where feasible.

17 Analysis Data Flow Diagram

18 Programming Standards Creating analysis datasets or standard reports Module macros Performing routine tasks across all modules OR Create variations Component tmacros

19 Governance Develop a standards committee Responsible for approving standards Responsible for adherence to standards Develop support teams SMEs for various roles Programming, Biostatistics Functions domains Creation of standards program/macros Cross functional support

20 Summary Developing standards: Reduce the time and cost of the drug development process by supporting the use of company standards and the sharing/re use of user programs Automate and facilitate company validation efforts Increase drug development knowledge by integrating multiple company data sources Transform data to information Increase knowledge of our drugs