STUDY CONDUCT

Patient —➤site—➤assessments–➤enter edc Company project manager or clinical operation manager send SHIPPING MANIFEST to lab(vendor) 🠟 lab entry data in acquisition form Lab—➤send test tubes—➤site                                          🠟                                        Pk samples–➤lab—➤analyze—➤send data to data manager         Lab 2 types  Central   Local  CENTRAL:   Reference range values: lab maintain their own lab range values Set  up:  acquisition form =site data in vendor database LOCAL:  lab specific, site specific, sponsor specific Set up:   they do not maintain acquisition form          Data transfer specifications  Version Table of contents Contact details Purpose Data file structure  Data file transfer Frequency = weekly or monthly Data transfers will be cumulative or incremental Date of file File naming conventions=blinded,unblinded Data deliver method       email,sftp(secure file portal) like passwords or client web portal  Test transfer Data quality  Data receipt Data file contents Mapping tables =visit dates, codes           Data transfers: Mock data from vendors, IRT, SAE               Data transfer agreement: https://clinicalda.blogspot.com/2024/03/dtadata-transfer-agreement.html        Purpose        Type of data          File naming conventions          Data transfer mode           Data transfer type          Frequency/Transfer frequency /schedule Vendor agreements Company agreements Reconciliation :External data Vendor reconciliation : reconcile lab data eg:Pk, ECG etc.. IRT reconciliation : https://clinicalda.blogspot.com/2024/04/irt-reconiliation.html Reconcile header data eg: subjectid,site id etc… IRT=interactive response or randomization Technology IRT helps clinical trial sponsors & site for manage the patient & drug supply logistics—-->because =ability to offer control & flexibility while increasing efficiency                                                                                                                                   IRT also helps to maintain blinding by making sure that specific roles in a study do not know the treatment that each patient is receiving CDM reconcile below list:                                                                                        Screening date Re-screening date Randomization number SAE reconciliation: reconcile SAE data: Adverse event : Any effect/disease Adverse event maybe : MILD,MODERATE,SEVERE Serious adverse event : https://clinicalda.blogspot.com/2024/03/sae-reconciliation.html SAE →collected from→clinical trial & marketed products Reporting containing SAE —>case—->goes to safety department—>case of adverse effect During clinical trial=SAE information also received through CRF or EDC—>stored in ADVERSE EVENT—>DMS Stored in clinical & safety database Important to match = SAE with data management system—------->SAE reconciliation               During information following information are checked: Cases found in the SAE system but not in the CDM system—->(case of adverse effect)safety system.   Cases found in the CDM system but not in the SAE system —>(adverse event)database system. Death —>any case—>found only one system–>.(cases of adverse effect) adverse event           Need of SAE reconciliation=compare SAE with CDM     SAE CDM Less organized/defined more organized/defined data: eg: Subject & investigate ID subject,investigate well defined in study data such as age, sex information | | Collected information but collect only events collected separately adverse event are collected event by event passociate each problem                             Depends                                                                 for   Companies—---->reports & manual comparison—-->SAEreconclliation                                On When the reporting system has access to the underlying database  it might be possible to do --------------------------------> initial match on some information such as study ID,Sub ID etc…     Underlying database present in SAE system and CDM system.  Depends                                                                 for                                                            Reconciliation issues: Ex: un schedule visit                MRI assessments                Accession ID          Query management:        Query management. In terms of clinical trials, a query is a request for clarification from trial sponsors to researchers. Such requests are made during data review, before database lock, and aimed at resolving errors and inconsistencies. The query management feature facilitates communication among data managers,sponsors, and other stakeholders. It helps faster resolve all questions. Open queries : when site coordinator open query( seen the query) Closed queries: when site coordinator opened ,answered query and closed query Answered queries: when site coordinator opened and answered query Canceled queries: when DM/biostatician/medical monitor/clinical coder enter   query in EDC and it can be canceled by these people Queries  prepare  in excel sheet which discrepancy is present in data(EDC) Query posting         DM of CRO will provide weekly metrics          Sponsor DM will review meetrics          Metrics: can be open query by–DM,CRA,medical reviewer          Queries like eg:subject part,unconfirmed missed date etc.., Automated query—query populate in EDC when discrepancy is entered by site coordinate–edit checks         Manual data listing  SAS programme: \ CRO will provide data listing-sponsor will review programme listing  If not possible sponsor will prepare programme listing from EDC EDC listing: EDC–installed modules–reporter–Data listing  STUDY - prod–submit==production clinical review —-from:ad,cm,medical—run–DATA–download file Patient identifier J-review Spot fire illuminate         Manual data review Manual checks  Ex : Compare adverse events with cocomident medication  Apply concatenate and vlookup Create Unique ID : SUB ID  Concatenate:  Formula: Unique ID + the variable we want to compare (Record number) Create 2 concatenate records in both the listings Vlook up:  Formula:vlookup(first concatenate cell no 1,second concatenate cell no 2 in different listing:$ columns name of the variable we want to compare $ total no of rows/records,1,0). Review coding dictionary MEDRA and WHO drug.