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It s About Time The Time Component of Clinical Data Maria Reiss

Overview. Current representation of timing informationWindowing = slotting data into time intervalsCan apply time labels directly to the data Can use time labels to add actual treatment to dataCDISC SDTM specifications for timing informationTiming variables in CRF dataTrial Design Model. Met

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It s About Time The Time Component of Clinical Data Maria Reiss

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    1. It’s About Time! The Time Component of Clinical Data Maria Reiss My name is Maria Reiss. Thank you very much for coming to hear me speak. The topic of my presentation is the timing aspects of clinical data. The reason I chose this topic: As everyone here knows, the timing component of data from clinical trials is fundamental in analysis. Clinical trials are normally divided into phases such as placebo run-in periods, washout periods, double-blind treatment periods, followup periods, etc. To analyze clinical data, you must consider the time periods of the study, and you must analyze the data according to when it occurred. You can’t have change from baseline unless you have defined the baseline period and slotted data into the baseline period. First I will go briefly discuss the way that time information is currently represented, at least at Wyeth. At Wyeth, we follow a process of slotting data into time categories, a process that we call windowing. This is why I have a window graphic. Then I want to discuss how time information is handled in the CDISC SDTM model. At Wyeth we are currently transitioning to CDISC. I am now working on my first study with pure SDTM data. I will discuss how we are transitioning from our current method of windowing to using CDISC SDTM model to represent the time aspects of data. --------------------------------------------------------------------------------------------- My name is Maria Reiss. Thank you very much for coming to hear me speak. The topic of my presentation is the timing aspects of clinical data. The reason I chose this topic: As everyone here knows, the timing component of data from clinical trials is fundamental in analysis. Clinical trials are normally divided into phases such as placebo run-in periods, washout periods, double-blind treatment periods, followup periods, etc. To analyze clinical data, you must consider the time periods of the study, and you must analyze the data according to when it occurred. You can’t have change from baseline unless you have defined the baseline period and slotted data into the baseline period. First I will go briefly discuss the way that time information is currently represented, at least at Wyeth. At Wyeth, we follow a process of slotting data into time categories, a process that we call windowing. This is why I have a window graphic. Then I want to discuss how time information is handled in the CDISC SDTM model. At Wyeth we are currently transitioning to CDISC. I am now working on my first study with pure SDTM data. I will discuss how we are transitioning from our current method of windowing to using CDISC SDTM model to represent the time aspects of data. ---------------------------------------------------------------------------------------------

    2. Overview Current representation of timing information Windowing = slotting data into time intervals Can apply time labels directly to the data Can use time labels to add actual treatment to data CDISC SDTM specifications for timing information Timing variables in CRF data Trial Design Model First, I’m going to discuss current method of windowing clinical data. When I use the term windowing, I am referring to the process of categorizing data into time intervals and also the process of applying time labels directly to the data to make it easy to select records from different time periods. At Wyeth, we also use these time labels to add actual treatment used during that time interval to records. Actual treatment during a time interval may be different from assigned treatment (e.g. during baseline period or for crossover studies). I will also cover the CDISC SDTM specifications for timing information.First, I’m going to discuss current method of windowing clinical data. When I use the term windowing, I am referring to the process of categorizing data into time intervals and also the process of applying time labels directly to the data to make it easy to select records from different time periods. At Wyeth, we also use these time labels to add actual treatment used during that time interval to records. Actual treatment during a time interval may be different from assigned treatment (e.g. during baseline period or for crossover studies). I will also cover the CDISC SDTM specifications for timing information.

    3. Methods of Windowing Two ways of slotting data into time intervals: Visit Number Slots data according to planned schedule of data collection Actual time values Slots data according to actual schedule of data collection As we all know, clinical trials follow a predefined schedule of data collection. Planned schedule: Visit number of visit when data collected is used to slot data. Actual schedule: Slot data according to actual time values in the data. At Wyeth, we apply time labels to our data using both methods.As we all know, clinical trials follow a predefined schedule of data collection. Planned schedule: Visit number of visit when data collected is used to slot data. Actual schedule: Slot data according to actual time values in the data. At Wyeth, we apply time labels to our data using both methods.

    4. Visit Number Methods of Windowing The first method is visit number slotting. I’ve worked with several CROs that use this method. It is very simple but it is not always accurate because people deviate from the schedule. At Wyeth we can not solely rely on this method because of unscheduled visits and diary data. In the database at Wyeth, the visit numbers of unscheduled data and diary data is meaningless. We can not use the visit numbers in analysis.The first method is visit number slotting. I’ve worked with several CROs that use this method. It is very simple but it is not always accurate because people deviate from the schedule. At Wyeth we can not solely rely on this method because of unscheduled visits and diary data. In the database at Wyeth, the visit numbers of unscheduled data and diary data is meaningless. We can not use the visit numbers in analysis.

    5. Actual Time Values Use a time value from data record to slot data into time intervals Can be either time of event (e.g. AE Start) Or time the data was collected (e.g. lab test) Methods of Windowing

    6. Actual Time Values To slot data based on actual time values: Create dataset with actual date/times of each subject’s milestone events. Create dataset with analysis time intervals for each subject. Join this dataset with the CRF data to apply time labels to each data record. Methods of Windowing This is the algorithm we follow at Wyeth. I am very curious how you handle this at GSK. 2. Dataset with analysis time intervals. For every subject, his or her time in the clinical trial is divided into intervals that are useful for analysis. Advantages: More accurate – if use time labels to add actual treatment during the time interval, this is important. Handles unscheduled visits and diary data. This is the algorithm we follow at Wyeth. I am very curious how you handle this at GSK. 2. Dataset with analysis time intervals. For every subject, his or her time in the clinical trial is divided into intervals that are useful for analysis. Advantages: More accurate – if use time labels to add actual treatment during the time interval, this is important. Handles unscheduled visits and diary data.

    7.   Milestone Dataset One record per subject per protocol milestone. Milestones in this table depend on the study design. Actual data for subjects in the trial that comes from case report forms. For example, therapy start is first dose of test article.One record per subject per protocol milestone. Milestones in this table depend on the study design. Actual data for subjects in the trial that comes from case report forms. For example, therapy start is first dose of test article.

    8.   Time Interval Dataset From the milestone dataset, we create a second dataset of the time intervals for each subject. One record per subject per time interval. Again, this dataset contains actual data from case report forms. We can define the time intervals any way that we want. For safety analysis, it might be sufficient to have double-blind treatment as one time interval. But for efficacy analysis, we might define smaller time intervals such as Week 1, Week 2, etc.From the milestone dataset, we create a second dataset of the time intervals for each subject. One record per subject per time interval. Again, this dataset contains actual data from case report forms. We can define the time intervals any way that we want. For safety analysis, it might be sufficient to have double-blind treatment as one time interval. But for efficacy analysis, we might define smaller time intervals such as Week 1, Week 2, etc.

    9. Join time interval dataset with the CRF data to apply time labels to each data record. proc sql; create table newadverse as select a.*, b.tmperiod, b.tmperord, b.regimen from adverse as a left join timeperiods as b on (a.subjid = b.subjid) and (b.tmstart <= a.startdt < b.tmstop); Applying Time Labels to Data Time interval dataset contains time interval name, time interval start, time interval stop, actual treatment during the time interval.Time interval dataset contains time interval name, time interval start, time interval stop, actual treatment during the time interval.

    10. Applying Time Labels to Data Join time interval dataset with the CRF data to apply time labels to each data record. Example Output Record We also add 2 additional variables to the data record when we apply the time label – study day and actual treatment during time interval. Study day is the number of days since the start of treatment based on the start of the adverse event. We also add 2 additional variables to the data record when we apply the time label – study day and actual treatment during time interval. Study day is the number of days since the start of treatment based on the start of the adverse event.

    11. Planned Schedule For each CRF dataset: Planned visit number Planned time interval label Actual Schedule (Windowing) Milestone dataset Time interval dataset For each CRF dataset: Actual time interval label Actual study day Actual treatment during the time interval Current Representation of Time In summary, here is how we currently represent the time component of clinical data in our database at Wyeth. In practice, issues often arise when windowing dirty and incomplete data and with early terminators. We have techniques for dealing with this, but I don’t have time to go into detail about them right now.In summary, here is how we currently represent the time component of clinical data in our database at Wyeth. In practice, issues often arise when windowing dirty and incomplete data and with early terminators. We have techniques for dealing with this, but I don’t have time to go into detail about them right now.

    13. Time Representation in CDISC SDTM Clinical Data Interchange Standards Consortium (CDISC) www.cdisc.org Study Data Tabulation Model (SDTM) Disposition Domain – major study milestones Timing Variables Trial Design Model 7 Datasets – 5 datasets are about timing CDISC – a standard based on data modeling principles, rather than data management/operational database principles. At Wyeth, we are really just starting to use SDTM. I’m not sure how far along GSK with CDISC. I suspect that you are further along since you have in-house expertise. One of the developers of the Trial Design Model is Diane Wold of GSK. I have learned a lot from her presentations. SDTM has 3 parts that deal with time component of clinical data. Every domain has timing variables. There are lots of timing variables which shows how important the time aspect of clinical data is. Trial Design Model There are 7 trial design datasets. Trial Inclusion/Exclusion and Trial Summary do not involve timing, so I won’t talk about them. Current version of the Trial Design Model distinguishes between the planned design of the study and the actual conduct of the study. This is not true in the new draft version of the SDTM Implementation Guide V3.1.2 – in the new version, the information about the actual conduct of the study is moved out of the Trial Design Model. --------------------------------------------------------------------------------------------- CDISC – a standard based on data modeling principles, rather than data management/operational database principles. At Wyeth, we are really just starting to use SDTM. I’m not sure how far along GSK with CDISC. I suspect that you are further along since you have in-house expertise. One of the developers of the Trial Design Model is Diane Wold of GSK. I have learned a lot from her presentations. SDTM has 3 parts that deal with time component of clinical data. Every domain has timing variables. There are lots of timing variables which shows how important the time aspect of clinical data is. Trial Design Model There are 7 trial design datasets. Trial Inclusion/Exclusion and Trial Summary do not involve timing, so I won’t talk about them. Current version of the Trial Design Model distinguishes between the planned design of the study and the actual conduct of the study. This is not true in the new draft version of the SDTM Implementation Guide V3.1.2 – in the new version, the information about the actual conduct of the study is moved out of the Trial Design Model. ---------------------------------------------------------------------------------------------

    14. CDISC Disposition Domain One record per subject per disposition status or protocol milestone May include protocol milestones such as Randomization Start of Treatment End of Treatment Discontinuation for each phase or segment of the study including screening or post treatment follow-up Sponsors may choose which milestones to submit for a study.Sponsors may choose which milestones to submit for a study.

    15. CDISC Timing Variables SDTM defines timing variables in all domains Example - Demography Domain RFSTDTC – Reference Start Date/time (first dose of drug) RFENDTC – Reference Stop Date/time (last dose of drug or study stop) Timing variables are not required. They are permissable. There are lots of timing variables in the SDTM models which shows the importance of the time component of data. Permissable = should be used in a domain as appropriate when collected or derived. Domain = data typeTiming variables are not required. They are permissable. There are lots of timing variables in the SDTM models which shows the importance of the time component of data. Permissable = should be used in a domain as appropriate when collected or derived. Domain = data type

    16. CDISC Timing Variables Example - Concomitant Medications Domain CMSTDTC – Start date/time of medication CMENDTC – End date/time of medication CMSTDY – Study day of start of medication CMENDDY – Study day of end of medication CMDUR – Duration of medication (from CRF only) CMSTRF – Start relative to reference period (BEFORE, DURING, AFTER) CMENRF – End relative to reference period (BEFORE, DURING, AFTER) Concomitant medications are in the interventions domain. 3 domains are interventions, findings, and events.Concomitant medications are in the interventions domain. 3 domains are interventions, findings, and events.

    17. CDISC Timing Variables Example – Laboratory Test Results Domain VISIT, VISITNUM, VISITDY LBDTC – Date/time of specimen collection LBENDTC – End date/time of specimen collection LBDY – Study day of specimen collection LBTPT – Planned time point name LBTPTNUM – Planned time point number LBELTM – Elapsed time from reference point LBTPTREF – Time point reference Examples: LBTPTREF – PREVIOUS DOSE, PREVIOUS MEAL LBELTM – P15M, P8H, -P1H Laboratory test results are in the findings domain.Examples: LBTPTREF – PREVIOUS DOSE, PREVIOUS MEAL LBELTM – P15M, P8H, -P1H Laboratory test results are in the findings domain.

    18. CDISC Trial Design Model Method of representing Planned Design of the Study Actual Conduct of the Study Trial design model is a way of representing the planned design of the study and the actual conduct of the study. You are supposed to store this information about a clinical trial in several datasets and provide them to the FDA. Purpose of Trial Design Model – Allow reviewers to Clearly and quickly grasp the design of a clinical trial Compare the designs of different trials Search a data warehouse for clinical trials with certain features Compare planned and actual treatments and visits for subjects in a clinical trial Version 2 of the Trial Design Model is being revised in light of comments received. A new release is planned for 2007. Trial design model is a way of representing the planned design of the study and the actual conduct of the study. You are supposed to store this information about a clinical trial in several datasets and provide them to the FDA. Purpose of Trial Design Model – Allow reviewers to Clearly and quickly grasp the design of a clinical trial Compare the designs of different trials Search a data warehouse for clinical trials with certain features Compare planned and actual treatments and visits for subjects in a clinical trial Version 2 of the Trial Design Model is being revised in light of comments received. A new release is planned for 2007.

    19. Terms for Planned Design of the Study Element – basic building block of time Arm – planned sequence of elements (a treatment group) Epoch – element in a blinded trial (phase of study) Visit – clinical encounter CDISC Trial Design Model Here are the CDISC terms for the planned design of a study. Concept of visit is easy for an outpatient study, but not as clear for studies of hospitalized subjects. Time might need to be divided into segments and called visits for hospital studies. Here are the CDISC terms for the planned design of a study. Concept of visit is easy for an outpatient study, but not as clear for studies of hospitalized subjects. Time might need to be divided into segments and called visits for hospital studies.

    20. CDISC Trial Design Model Here is a flowchart of a very simple clinical trial. Each block in the study flowchart is an element. Element is the most basic piece of study, the basic building block of time. Can be used for actual treatment that a subject received. Notice that there are 2 branches in this study – Drug A and Placebo. Every subject will follow one of the two branches. Subjects can be randomized to either branch. Each branch is called an “arm” in the CDISC Trial Design Model. An arm is a branch of the study – a treatment group. Arm is the planned sequence of elements for a subject.Here is a flowchart of a very simple clinical trial. Each block in the study flowchart is an element. Element is the most basic piece of study, the basic building block of time. Can be used for actual treatment that a subject received. Notice that there are 2 branches in this study – Drug A and Placebo. Every subject will follow one of the two branches. Subjects can be randomized to either branch. Each branch is called an “arm” in the CDISC Trial Design Model. An arm is a branch of the study – a treatment group. Arm is the planned sequence of elements for a subject.

    21. CDISC Trial Design Model During the course of a blinded trial, you won’t know which arm a subject is in. So you need a general term to refer to elements in a blinded trial. Epochs – an element in a blinded trial. Each column in the flow chart is an epoch. Epoch is the name for each period in the study. SDTM Guide recommends avoiding using the same names for elements and epochs. During the course of a blinded trial, you won’t know which arm a subject is in. So you need a general term to refer to elements in a blinded trial. Epochs – an element in a blinded trial. Each column in the flow chart is an epoch. Epoch is the name for each period in the study. SDTM Guide recommends avoiding using the same names for elements and epochs.

    22. CDISC Trial Design Model

    23. CDISC Trial Design Model

    24. CDISC Trial Design Model Planned Design of the Study Trial Elements Table Trial Arms Table Trial Visits Table These 3 tables describe the planned design of a study.These 3 tables describe the planned design of a study.

    25. CDISC Trial Design Model Trial Elements Table One record per planned element – this table is not subject-based. This table does not contain actual subject data. This table can be created early during the design of a study, at time of protocol is written. There are no gaps between elements. New type of variable – a “rule” variable. Value of a rule variable is the rule in English text. Trial Elements, Trial Arms, and Trial Visits tables contain rule variables instead of start and stop date variables. The SDTM Guide states “At some point in the future, it is expected that these will become executable code.” This is a neat idea – we will provide these tables to the FDA and they will programmatically process them. TESTRL – defines rule for the start of the element TEENRL – defines rule for when the element should end, not when it actually ends. Can have rule such as “After WBC values have recovered” for end of element TEDUR – defines the planned duration of the element in ISO 8601 format. P signifies a duration. --------------------------------------------------------------------------------------------- One record per planned element – this table is not subject-based. This table does not contain actual subject data. This table can be created early during the design of a study, at time of protocol is written. There are no gaps between elements. New type of variable – a “rule” variable. Value of a rule variable is the rule in English text. Trial Elements, Trial Arms, and Trial Visits tables contain rule variables instead of start and stop date variables. The SDTM Guide states “At some point in the future, it is expected that these will become executable code.” This is a neat idea – we will provide these tables to the FDA and they will programmatically process them. TESTRL – defines rule for the start of the element TEENRL – defines rule for when the element should end, not when it actually ends. Can have rule such as “After WBC values have recovered” for end of element TEDUR – defines the planned duration of the element in ISO 8601 format. P signifies a duration. ---------------------------------------------------------------------------------------------

    26. CDISC Trial Design Model Trial Arms Table Trial Arms table has one record per planned element per arm. Again there is no subject data. This table could be part of the setup of a study. In this example, I am showing one arm only – Drug A arm. TAETORD – defines order of the elements in an arm. TABRANCH – branching rule. My example study has 2 arms – patients are randomized to either Drug A or Placebo. Branching rule is simply which group the subject is randomized to. Implicit assumption is that a subject will proceed sequentially through the elements. If this is not true, then the rule for skipping elements must be entered into the TATRANS variable. Transition rules are used for choices within an Arm.Trial Arms table has one record per planned element per arm. Again there is no subject data. This table could be part of the setup of a study. In this example, I am showing one arm only – Drug A arm. TAETORD – defines order of the elements in an arm. TABRANCH – branching rule. My example study has 2 arms – patients are randomized to either Drug A or Placebo. Branching rule is simply which group the subject is randomized to. Implicit assumption is that a subject will proceed sequentially through the elements. If this is not true, then the rule for skipping elements must be entered into the TATRANS variable. Transition rules are used for choices within an Arm.

    27. CDISC Trial Design Model Trial Visits Table Trial Visits table has one record per planned visit per arm. VISITNUM – visit number TVSTRL – rule describing when a visit should occur (in relation to elements) TVENRL – rule describing when a visit should end (in relation to elements) If the timing of visits for a trial depends on which ARM a subject is in, then the variables ARM and ARMCD must be populated. Summarize – these are the 3 tables showing the planned events in a clinical trial. Potential Uses of TDM datasets (from Diane Wold presentation) Establish “time slicing” (elements, visits, windows, reference timepoints) Facilitate creation of the Statistical Analysis Plan Automate creation of data collection instruments Automate creation of clinical trial databases Automate creation of SDTM metadata --------------------------------------------------------------------------------------------- Trial Visits table has one record per planned visit per arm. VISITNUM – visit number TVSTRL – rule describing when a visit should occur (in relation to elements) TVENRL – rule describing when a visit should end (in relation to elements) If the timing of visits for a trial depends on which ARM a subject is in, then the variables ARM and ARMCD must be populated. Summarize – these are the 3 tables showing the planned events in a clinical trial. Potential Uses of TDM datasets (from Diane Wold presentation) Establish “time slicing” (elements, visits, windows, reference timepoints) Facilitate creation of the Statistical Analysis Plan Automate creation of data collection instruments Automate creation of clinical trial databases Automate creation of SDTM metadata ---------------------------------------------------------------------------------------------

    28. CDISC Trial Design Model Actual Conduct of the Study Subject Elements table Subject Visits table Current version of the CDISC SDTM Trial Design Model contains two tables that contain actual subject data. These tables contain data about a subject’s actual schedule in the trial. Trial Elements, Trial Arms, and Trial Visits tables contain rule variables instead of start and stop date variables. Subject tables contain actual start and stop date values.Current version of the CDISC SDTM Trial Design Model contains two tables that contain actual subject data. These tables contain data about a subject’s actual schedule in the trial. Trial Elements, Trial Arms, and Trial Visits tables contain rule variables instead of start and stop date variables. Subject tables contain actual start and stop date values.

    29. CDISC Trial Design Model Subject Elements Table Subject Elements – one record per actual element per subject. Contains start and stop dates instead of rules. Dates are character values in ISO 8601 representation. Gaps are not allowed between subject elements. “Deviations from planned schedule require judgement and interpretation.” Can merge with Trial Elements to add TAETORD and EPOCH (page 116).Subject Elements – one record per actual element per subject. Contains start and stop dates instead of rules. Dates are character values in ISO 8601 representation. Gaps are not allowed between subject elements. “Deviations from planned schedule require judgement and interpretation.” Can merge with Trial Elements to add TAETORD and EPOCH (page 116).

    30. CDISC Trial Design Model Subject Visits Table Subject Visits – one record per subject per actual visits. Contain start and stop dates instead of rules. Start and Stop dates are collected values. Include records for unplanned visits – Use visit numbers such as 2.1 for visit between visit 2 and 3. Many studies, especially outpatient studies, only collect a single date value for visit. In this case, store the single value in the SVSTDTC variable. Pharmacology studies may collect both start and stop dates.Subject Visits – one record per subject per actual visits. Contain start and stop dates instead of rules. Start and Stop dates are collected values. Include records for unplanned visits – Use visit numbers such as 2.1 for visit between visit 2 and 3. Many studies, especially outpatient studies, only collect a single date value for visit. In this case, store the single value in the SVSTDTC variable. Pharmacology studies may collect both start and stop dates.

    31. Interesting Clinical Trial Designs Crossover Trials with multiple branch rules Trials with dissimilar arms Trials with repeating cycles of treatment and rest Can have no upper limit on number of cycles Cycles with varying number of repeats Cycles of different lengths SDTM Implementation Guide V3.1.1 and V3.1.2 have examples of how to represent these types of trial designs. Future enhancements Better handling of cyclical chemotherapy treatments. Better representation of schedule of assessments and planned interventions. SDTM Implementation Guide V3.1.1 and V3.1.2 have examples of how to represent these types of trial designs. Future enhancements Better handling of cyclical chemotherapy treatments. Better representation of schedule of assessments and planned interventions.

    32. Transition to CDISC Timing Planned Schedule of Clinical Trial On the next 2 slides, I show our current method of windowing data beside its counterpart in the CDISC SDTM. VISITNUM is required when data are collected more than once per subject or at a discrete time point (e.g. labs, ECG, Vital Signs or other domains with multiple assessment points). VISITNUM is not required in Adverse Events, Concomitant Medication, or Medical History.On the next 2 slides, I show our current method of windowing data beside its counterpart in the CDISC SDTM. VISITNUM is required when data are collected more than once per subject or at a discrete time point (e.g. labs, ECG, Vital Signs or other domains with multiple assessment points). VISITNUM is not required in Adverse Events, Concomitant Medication, or Medical History.

    33. Transition to CDISC Timing Actual Schedule of Clinical Trial Actual time interval – we are applying both epoch (for a broad time interval) and another time interval label to the data with smaller intervals (from the efficacy time interval dataset). Time interval label = Analysis Time Period at Wyeth. Comparisons of different treatments would group data by element, while tests for period effect would group data by epoch (page 112 of SDTM Implementation Guide). Assigned treatment group = Arm Actual time interval – we are applying both epoch (for a broad time interval) and another time interval label to the data with smaller intervals (from the efficacy time interval dataset). Time interval label = Analysis Time Period at Wyeth. Comparisons of different treatments would group data by element, while tests for period effect would group data by epoch (page 112 of SDTM Implementation Guide). Assigned treatment group = Arm

    34. Transition to CDISC Timing Store major milestones in DS domain Create Trial Elements, Trial Arms, and Trial Visits tables at time of study set-up Derive Subject Elements and Subject Visits tables Derive Subject Elements and Subject Visits as data from the trial comes in. These tables will be updated continuously as data comes in. They won’t be final until the end of the study.Derive Subject Elements and Subject Visits as data from the trial comes in. These tables will be updated continuously as data comes in. They won’t be final until the end of the study.

    35. Transition to CDISC Timing Derive Time Interval dataset for efficacy analysis Similar to Subject Elements Smaller intervals Create analysis datasets with time labels and actual treatment Join Subject Element or Time Interval datasets with CRF datasets. Create working analysis datasets. Still want time labels on the domain data records for ease of analysis. SDTM Implementation Guide recommends joining domain data with Trial Design and Subject Elements tables to apply element, arm, and epoch labels.Create working analysis datasets. Still want time labels on the domain data records for ease of analysis. SDTM Implementation Guide recommends joining domain data with Trial Design and Subject Elements tables to apply element, arm, and epoch labels.

    36. “Life is too unpredictable to live by a schedule.” - Unknown

    37. References CDISC Submission Data Standards Team. 2005. Study Data Tabulation Model Implementation Guide: Human Clinical Trials (Version 3.1.1). Clinical Data Interchange Standards Consortium. CDISC Submission Data Standards Team. 2007. Study Data Tabulation Model Implementation Guide: Human Clinical Trials (Version 3.1.2). Clinical Data Interchange Standards Consortium. Fridsma, D. The BRIDG model: a shared domain analysis model of regulated clinical research. 2006. Cancer Biomedical Informatics Grid. www.cancerinformatics.org.uk/Documents/DSCTworkshop/NCRI/2006%20NCRI%20presentation%20v2.ppt

    38. References Kubick, W. Data Management: The Clinical Research and Regulatory Perspective. June 21, 2004. NIH BECON/BISTIC Symposium. Wold, D. Trial Design Enhancements: Schedule of Activities. September 21, 2005. Clinical Data Interchange Standards Consortium. Wood, F. and Guinter, T. Evolution and Implementation of the CDISC Study Data Tabulation Model (SDTM). 2007. PharmaSUG Conference. www.lexjansen.com/pharmasug/2007/fc/fc08.pdf

    39. Acknowledgements SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are registered trademarks or trademarks of their respective companies.

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