2 Fundamentals of the SDTM SDTM基础

2.1 Observations and Variables 观测（数据）和变量

The V3.x Submission Data Standards are based on the SDTM’s general framework for organizing clinical trials information that is to be submitted to the FDA. The SDTM is built around the concept of observations collected about subjects who participated in a clinical study. Each observation can be described by a series of variables, corresponding to a row in a dataset or table. Each variable can be classified according to its Role. A Role determines the type of information conveyed by the variable about each distinct observation and how it can be used. Variables can be classified into five major roles:

先前向FDA递交的一系列基于IG V3.X版本的数据格式都是基于SDTM的通用框架。SDTM是围绕受试者在临床试验中观测数据的一系列概念创建。每一条观测数据通过一系列的变量，即表格中的不同列，进行描述。每一个变量可以根据其角色类型进行分类。对于每一条不重复观测，变量的角色决定了变量如何传达信息和如何使用。变量可被归纳为以下5种主要角色：

• Identifier
variables, such as those that identify the study, subject, domain, and sequence number of the record
•标识符 (Identifier)变量：例如，用来标识研究本身、参与研究的受试者、域名以及记录序号等

• Topic variables, which specify the focus of the observation (such as the name of a lab test)
• 主题 (Topic)变量：指明观测记录的主要目的（例如，某一实验室检测的名称）

• Timing variables, which describe the timing of the observation (such as start date and end date)
• 时间 (Timing)变量：描述观测记录的时间（例如，开始时间和结束时间）

• Rule variables, which express an algorithm or executable method to define start, end, and branching or looping conditions in the Trial Design model
• 规则变量：在试验设计模型里，表达一种算法或可执行的方法，来定义其开始或结束，分流或循环等条件

• Qualifier variables, which include additional illustrative text or numeric values that describe the results or additional traits of the observation (such as units or descriptive adjectives)
• 修饰语 (Qualifier)变量：包括用来进一步描述结果的说明性文字或者数值，或观测记录的更多特征（例如，单位或描述性形容词）

The set of Qualifier variables can be further categorized into
five sub-classes: 修饰变量可进一步细分为五个子类别：

• Grouping Qualifiers are used to group together a collection of observations within the same domain. Examples include --CAT and --SCAT.
• 分组修饰语 (Grouping Qualifiers) ：对同一域中的数据分组。例如：–CAT 和 --SCAT

• Result Qualifiers describe the specific results associated with the topic variable in a Findings dataset. They answer the question raised by the topic variable. Result Qualifiers are --ORRES, --STRESC, and --STRESN.
• 结果修饰语 (Result Qualifiers) ：在发现类数据集中，用来描述与主题变量相关的特定的结果。它们回答了主题变量（Topic Variable）所要表达的问题。例如：ORRES，–STRESC和 –STRESN

• Synonym Qualifiers specify an alternative name for a particular variable in an observation. Examples include --MODIFY and --DECOD, which are equivalent terms for a --TRT or --TERM topic variable, --TEST and --LOINC which are equivalent terms for a --TESTCD.
• 同义词修饰语 (Synonym Qualifiers) ：指定了观测记录中某一特定变量的其他可用名称。例如：–MODIFY和–DECOD是主题变量–TRT或–TERM的同义词修饰语，–TEST和–LOINC则是–TESTCD的同义词修饰语

• Record Qualifiers define additional attributes of the observation record as a whole (rather than describing a particular variable within a record). Examples include --REASND, AESLIFE, and all other SAE flag variables in the AE domain; AGE, SEX, and RACE in the DM domain; and --BLFL,–POS, --LOC, --SPEC and --NAM in a Findings domain
• 记录修饰语 (Record Qualifiers) ：从记录水平（而不是变量水平）定义某一观测的附加属性。例如：–REASND，–AESLIFE以及不良事件域（AE）中所有其他严重不良事件（SAE）的标识变量；人口统计学域（DM）中的AGE，SEX和RACE变量；发现类域中的–BLFL，–POS，–LOC，–SPEC和–NAM

• Variable Qualifiers are used to further modify or describe a specific variable within an observation and are only meaningful in the context of the variable they qualify. Examples include --ORRESU, --ORNRHI, and --ORNRLO, all of which are Variable Qualifiers of --ORRES; and
–DOSU, which is a Variable Qualifier of --DOSE.
• 变量修饰语 (Variable Qualifiers):用来进一步修饰和描述某一观测的特定变量，只能结合它所修饰的变量使用才有意义。例如：–ORRESU，–ORNRHI和–ORNRLO都是–ORRES的变量修饰语； --DOSU是–DOSE的变量修饰语。

For example, in the observation, “Subject 101 had mild nausea
starting on Study Day 6, “ the Topic variable value is the term for the adverse event, “NAUSEA”. The Identifier variable is the subject identifier, “101”. The Timing variable is the study day of the start of the event, which captures the information, “starting on Study Day 6”, while an example of a Record Qualifier is the severity, the value for which is “MILD”. Additional Timing and Qualifier variables could be included to provide the necessary detail to adequately describe an observation.
例如，对“受试者101在研究的第六天开始出现轻度恶心症状”这一观测记录，其主题变量值是不良事件术语“恶心”。标识符变量则是该受试者编号“101” 。时间变量值是该不良事件出现时研究已开始的天数， “开始于研究第6天”。该事件严重程度可视为记录修饰语的示例，其值为“轻度”，其他时间和修饰变量可视情况加入，以提供必要的细节来对观测记录进行充分的描述。

2.2 Datasets and Domains数据集和域

Observations about study subjects are normally collected for all
subjects in a series of domains. A domain is defined as a collection of
logically related observations with a common topic. The logic of the relationship may pertain to the scientific subject matter of the data or to its role in the trial. Each domain is represented by a single dataset. Each domain dataset is distinguished by a unique, two-character code that should be used consistently throughout the submission. This code, which is stored in the SDTM variable named DOMAIN, is used in four ways:

as the dataset name,

the value of the DOMAIN variable in that dataset,

as a prefix for most variable names in that dataset,

and as a value in the RDOMAIN variable in relationship tables [Section8 - Representing Relationships and Data].
通常情况下，所有研究受试者的观测数据都会收录到一系列不同的域中。域（Domain）是一组具有共同主题并且逻辑相关的观测结果的集合。其内在关系逻辑可能基于数据的科学属性或者与其在试验中的角色。每个域通过对应的数据集进行呈现。

每个域都由两个英文字母组成的代码进行区别，该代码在整个数据递交过程中要保持始终一致。域代码储存在SDTM标准变量DOMAIN中，有以下四种应用方式：

• 作为数据集的名称；

• 作为数据集中变量DOMAIN的值；

• 作为数据集中大多数变量名的前缀；

• 作为关系型数据集中变量RDOMAIN的值（参见第8章）。

All datasets are structured as flat files with rows representing observations and columns representing variables. Each dataset is described by metadata definitions that provide information about the variables used in the dataset. The metadata are described in a data definition document named “define” that is submitted with the data to regulatory authorities. (See the Case Report Tabulation Data Definition Specification [Define-XML], available at www.CDISC.org). Define-XML specifies seven distinct metadata attributes to describe SDTM data:
所有的数据集都是二维结构，其中行代表观测，列为变量。每个数据集通过相应的元数据对其所属的变量进行描述。元数据即Define-XML
（参加CDISC网站的相关描述），通常与研究数据一并向监管机构进行提交。 Define-XML通过下列的一系列属性描述SDTM数据：

• The Variable Name (limited to 8 characters for compatibility with the SAS Transport format) • 变量名称（Variable Name）：考虑到SAS传输格式兼容性，最多8个英文字符长度

• A descriptive Variable Label, using up to 40 characters, which should be unique for each variable in the dataset •变量标签（Variable Label ）：数据集中每个变量的标签应当是唯一的，且长度不超过40个英文字符

• The data Type (e.g.,whether the variable value is a character or numeric) • 数据类型（Type）：例如字符型或数值型

• The set of controlled terminology for the value or the presentation format of the variable (Controlled Terms, Codelist, or Format) • 受控术语或数据显示格式：变量值通过术语或显示格式等进行呈现

• The Origin of each variable [see Section 4: 4.1.1.8, Origin Metadata] • 来源（Origin）：[参见章节4: 4.1.1.8]

• The Role of the variable, which determines how the variable is used in the dataset. For the V3.x domain models,Roles are used to represent the categories of variables such as Identifier, Topic, Timing, or the five types of Qualifiers. • 角色（Role）：决定了在相应数据集中如何使用该变量。对于V3.X域模型，角 色用于表示变量的分类，如标识变量，主题变量，时间变量或五类修饰变量。

• Comments or other relevant information about the variable or its data included by the sponsor as necessary to communicate information about the variable or its contents to a regulatory agency.Data stored in SDTM datasets include both raw (as originally collected) and derived values (e.g., converted into standard units, or computed on the basis of multiple values, such as an average). The SDTM lists only the name,label, and type, with a set of brief CDISC guidelines that provide a general description for each variable used for a general observation class. The domain dataset models included in Section 5 – Models For Special-Purpose Domains and Section 6 – Domain Models Based On The General Observation Classes of this document provide additional information about Controlled Terms or Format, notes on proper usage, and examples. Controlled terminology (CT) is now represented one of four ways:
• 注释（Comments）以及其它申办者用来与药物监管机构就该变量及其内容进 行交流的相关的必要信息。 储存在SDTM数据集中的数据既包括原始值（原始收集的），也包括衍生值（ 例如：转化为标准单位的值，或基于多个值算出的值，如均值）。SDTM只列 出变量名称、标签和数据类型，以及基于CDISC指导原则的，对该变量所属分 类的简单描述。 本文档第5章-特殊用途域模型和第6章-通用观测类域模型的相关数据提供了关于受控术语和数据显示格式的附加信息 ，也提供了关于如何正确使用注释说明和示例。受控术语（CT）当前有4种呈 现方式：

• A single asterisk when there is no specific CT available at the current time, but the SDS Team expects that sponsors may have their own CT and/or the CDISC Controlled Terminology Team may be developing CT.
•带星号（asterisk）的受控术语：表示当前无标准的受控术语可用，但是SDS 团队期望申办者有自定义的受控术语或者CDISC 受控术语团队可能会开发的新的受控术语。

• A list of controlled terms for the variable when values are not yet maintained externally• 受控术语清单：对外部没有维护，内部自己提供的受控术语，列出该变量的受 控术语清单。

• The name of an external codelist whose values can be found via the hyperlinks in either the domain or by accessing the CDISC Controlled Terminology as outlined in Appendix C – Controlled Terminology. •外部代码列表的名称，其值可以通过域中的超链接找到，也可以通过访问附录C中列出的CDISC控制术语来找到。

• A common format such as ISO 8601 The CDISC Controlled Terminology team will be publishing additional guidance on use of controlled terminology separately.• 通用数据格式，如ISO8601。 CDISC受控术语团队将单独出版关于受控术语使用的附加指南。

2.3 Special-Purpose Datasets

The SDTM includes three types of special-purpose datasets: SDTM包含3类特殊用途的数据集：

•Domain datasets, consisting of Demographics (DM), Comments (CO), Subject Elements(SE), and Subject Visits (SV) 1, all of which include subject-level data that do not conform to one of the three general observation classes. These are described in Section 5 – Models For Special-Purpose Domains. 域数据集，包括人口学信息（DM），注释（CO），受试者元素（SE）和受试者访视(SV) 1，上述数据集是基于受试者级别的，不属于3类观测类数据集的任何一类。章节5对这几个特殊用途数据集进行了详细阐述。（早期版本的SDTMIG中，SE和SV包含在试验设计部分里）

• Trial Design Model (TDM) datasets, such as Trial Arms (TA) and Trial Elements (TE), which represent information about the study design but do not contain subject data. These are described in Section 7 – Trial Design Datasets.
试验设计模型（TDM）数据集，比如：试验组（TA）和试验元素（TE）数据集包含试验设计的信息，但是不含任何受试者数据。这类数据集将在章节7－试验设计数据集中进行描述。

• Relationship datasets, which include the RELREC and SUPP-- datasets described in Section 8 -Representing Relationships and Data. 关联数据集，包含RELREC和SUPP --的数据集，将在章节8-描述关系和数据中描述。

2.4 The General Observation Classes 通用观测数据类别

Most subject-level observations collected during the study should
be represented according to one of the three SDTM general observation classes: Interventions, Events, or Findings. The lists of variables allowed to be used in each of these can be found in the SDTM. 大多数在试验过程中采集到的受试者级别的观测数据，都可以被归为干预（Interventions），事件（Events）和发现（Findings）三大类中的某一类。SDTM描述了每一观测数据类中被允许使用的变量列表。

• The Interventions class captures investigational, therapeutic and other treatments that are administered to the subject (with some actual or expected physiological effect) either as specified by the study protocol (e.g.,exposure to study drug), coincident with the study assessment period (e.g., concomitant medications), or self-administered by the subject (such as use of alcohol, tobacco, or caffeine). 干预类（Interventions）：获取施加于受试者身上的干预措施（伴随有实际的或预期的生理效应），包括研究性治疗、伴发疾病的治疗和其他治疗或干预等。这些措施包括，基于研究方案确定的（例如，暴露于某一研究药物），或与研究评估阶段同时发生的（例如，伴随用药），或由受试者自我给予的其他物质（例如，酒精、烟草或咖啡因等）。

• The Events class captures planned protocol milestones such as randomization and study completion, and occurrences, conditions, or
incidents independent of planned study evaluations occurring during the trial(e.g., adverse events) or prior to the trial (e.g., medical history). 事件类（Event）：获取包括方案中计划的每一个重要里程碑事件，例如受试者随机化或受试者试验结束；也包括在试验期间发生的独立于计划研究评估的事件或者状况（例如，不良事件）；也包括试验前发生的事件或者状况（例如，既往病史）。

• The Findings class captures the observations resulting from planned evaluations to address specific tests or questions such as laboratory tests, ECG testing, and questions listed on questionnaires. 发现类（Findings）：获取临床计划中的评估类观测数据，通常包括用特定的检验指标或相关问题类条目，例如来自实验室检查，心电图检查和调查量表上问题。

In most cases, the choice of observation class appropriate to a specific collection of data can be easily determined according to the descriptions provided above. The majority of data, which typically consists of measurements or responses to questions usually at specific visits or time points, will fit the Findings general observation class. Additional guidance on choosing the appropriate general observation class is provided in Section 8: 8.6.1, Guidelines For Determining The General Observation Class. 大多数情况下，根据上述描述，比较容易即可将所采集数据归入相对应的某一观测数据类中。大多数记录属于发现类观测数据，该类数据通常描述在某一特定访视时间的对某一问题的观测结果或回答。相关选择准则，可以参考章节8.61。

General assumptions for use with all domain models and custom domains based on the general observation classes are described in Section 4 - Assumptions For Domain Models of this document; specific assumptions for individual domains are included with the domain models. 基于通用观测数据类别的所有域模型和自定义域使用的一般假设，在本文档章节4有详细描述。各个域的特定假设将在该域模型中加以阐述。

1 SE and SV were included as part of the Trial Design Model in earlier versions of the SDTMIG. 在早期版本的SDTMIG中，SE和SV被作为试验设计模型的一部分。

2.5 The SDTM Standard Domain Models

The following standard domains, listed in alphabetical order by Domain Code, with their respective domain codes have been defined or referenced by the CDISC SDS Team in this document. Note that other domain models may be posted separately for comment after this publication. 以下按照域模型代码的字母顺序陈列的标准模型，包括相应的代码，是CDISC SDS团队定义或推荐使用的。其他域模型有可能在本文档正式发布后，单独发布并征求意见。

Special-Purpose Domains (defined in Section 5 – Models For Special-Purpose Domains): 特殊目的域（定义在章节5 –特殊目的域模型）:

• Comments (CO) 注释

• Demographics (DM) 人口学数据

• Subject Elements (SE) 受试者元素

• Subject Visits (SV)受试者访视

Interventions General Observation Class (defined in Section 6.1 - Interventions): 干预通用类观测数据类别 （定义在章节6.1 –干预类）：

• Concomitant Medications (CM) 伴随用药

• Exposure as Collected (EC) 收集的暴露

• Exposure (EX) 暴露

• Substance Use (SU) 物质使用

• Procedures (PR) 操作

Events General Observation Class (defined in Section 6.2 - Events): 事件通用类观测数据类别（定义在章节6.2 –事件类）：

• Adverse Events (AE) 不良事件

• Clinical Events (CE) 临床事件

• Disposition (DS) 处置（实施情况）

• Protocol Deviations (DV) 方案偏离

• Healthcare Encounters (HO) 医疗护理

• Medical History (MH) 既往病史

Findings General Observation Class (defined in Section 6.3 - Findings): 发现通用类观测数据类别（定义在章节6.3 –发现类）：

• Drug Accountability (DA) 药物分发记录

• Death Details (DD) 死亡细节

• ECG Test Results (EG) 心电图

• Inclusion/Exclusion Criterion Not Met (IE) 不符合入排标准

• Immunogenicity Specimen Assessments (IS) 免疫原性评估

• Laboratory Test Results (LB) 实验室检查

• Microbiology Specimen (MB) 微生物样本

• Microscopic Findings (MI) 微观发现

• Morphology (MO) 形态学发现

• Microbiology Susceptibility Test (MS) 微生物敏感度分析

• PK Concentrations (PC) 药代动力学浓度

• PK Parameters (PP) 药代动力学参数

• Physical Examination (PE) 体格检查

• Questionnaires (QS) 调查量表

• Reproductive System Findings(RP) 生殖系统检查

• Disease Response (RS) 疾病反应

• Subject Characteristics (SC) 受试者特征

• Subject Status (SS) 受试者状态

• Tumor Identification (TU) 肿瘤鉴定

• Tumor Results (TR) 肿瘤结果

• Vital Signs (VS) 生命体征

Findings About (defined in Section 6.4 - FA Domain) 相关发现（定义在章节6.4 – FA 域）：

• Findings About (FA) 相关观测发现

• Skin Response (SR) 皮肤反应

Trial Design Domains (defined in Section 7 - Trial Design Datasets): 试验设计域（定义在章节7 –试验设计数据集）：

• Trial Arms (TA) 试验分组

• Trial Disease Assessment (TD) 试验疾病评估

• Trial Elements (TE) 试验元素

• Trial Visits (TV) 试验访视

• Trial Inclusion/Exclusion Criteria(TI) 试验入排标准

• Trial Summary (TS) 试验概要

Relationship Datasets (defined in Section 8 - Representing Relationships and Data): 关联数据（定义在章节8 –描述关系和数据）：

• Supplemental Qualifiers (SUPP-- datasets) 补充修饰语数据集 (SUPP–数据集)

• Related Records (RELREC) 关联记录数据集

A sponsor should only submit domain datasets that were actually
collected (or directly derived from the collected data) for a given study. Decisions on what data to collect should be based on the scientific objectives of the study, rather than the SDTM. Note that any data that was collected and will be submitted in an analysis dataset must also appear in a tabulation dataset. 实际研究中，申办者只需递交该研究实际采集的域数据（或从采集数据中衍生出来的数据），具体采集哪些数据应当由研究的具体科学目的所决定，而不是基于SDTM。需要特别说明的是，任何收集并出现在分析数据集中的数据必须被包括在SDTM制表数据集中。

The collected data for a given study may use some or all of the SDS standard domains as well as additional custom domains based on the three general observation classes. A list of standard domain codes for many commonly used domains is provided in . Additional standard domain models will be published by CDISC as they are developed, and sponsors are encouraged to check the CDISC website for updates. 实际研究中，一个研究中采集到的数据，可能会用到基于3个通用观测数据类别的一部分或全部SDTM标准域，以及另外的自定义域。很多常用的标准域编码可参见列表。CDISC将不定期发布其它已开发标准域，申办者应当定期查看CDISC网站以获取相关最新信息。

These general rules apply when determining which variables to include in a domain: 在决定变量应当被包含在哪一类具体域时，下面是可参考的一般规则：

• The Identifier variables, STUDYID, USUBJID, DOMAIN, and --SEQ are
required in all domains based on the general observation classes. Other Identifiers may be added as needed. 标识变量，STUDYID，USUBJID，DOMAIN，和–SEQ在基于通用观测数据类别的所有域中是必须的。其它标识变量可根据需要添加。

• Any Timing variables are permissible for use in any submission dataset based on a general observation class except where restricted by specific domain assumptions. 除某些域特定限制外，相关时间变量可被允许使用在基于通用观测数据类别的域数据集中。

• Any additional Qualifier variables from the same general observation class may be added to a domain model except where restricted by specific domain assumptions. 除某些域特定限制外，来自同一观测数据类别的任意修饰变量可以被添加到该域模型中。

• Sponsors may not add any other variables than those described in the preceding three bullets. The addition of non-standard variables will compromise the FDA’s abilities to populate the data repository and to use standard tools. The SDTM allows for the inclusion of the sponsors non-SDTM variables using the Supplemental Qualifiers special-purpose dataset structure, described in Section 8: 8.4, Relating Non-Standard Variables Values To A Parent Domain. As the SDTM continues to evolve over time, certain additional standard variables may be added to the general observation classes. Therefore, Sponsors wishing to nominate such variables for future consideration should provide a rationale and description of the proposed variable(s) along with representative examples to the CDISC Public Discussion Forum. 申办者不可以在标准域中添加除了上述三点提到的任何别的变量。添加非标准变量会妨碍FDA将数据加载到数据库和使用标准工具。SDTM允许申办者在SUPPQUAL补充数据集中包含非标准的SDTM变量，这些会在章节8.4—一个父域相关的非标准变量值中阐述。考虑到SDTM会随着时间演化，一些新的标准变量将来可能会被加入到通用观测数据类别中来。申办者如果想建议将某些变量加入将来标准变量考虑之列，可向CDISC公共讨论论坛提供其正当理由、有关提议变量的描述及有代表性的示例等。

• Standard variables must not be renamed or modified for novel usage. Their metadata should not be changed. 标准变量不能被重命名或修改以用作其它用途。有关它们的元原数据描述亦不能有更改。

• As long as no data was collected for Permissible variables, a sponsor is free to drop them and the corresponding descriptions from the Define-XML. •对于许可标准变量，如果没有对应数据采集，申办者可以从数据集及define.xml相关描述中剔除。

2.6 Creating a New Domain创建自定义域

This section describes the overall process for creating a custom domain, which must be based on one of the three SDTM general observation classes. The number of domains submitted should be based on the specific requirements of the study. Follow the process below to create a custom domain: 此章节描述了如何创建一个新的CDISC SDTM自定义域的全过程，自定义新域必须基于SDTM现有的3个通用观测数据类别中某一类别来创建。递交数据域的个数应基于研究的具体要求。遵循下面的流程以创建一个新的自定义域：

1. Confirm that none of the existing published domains will fit the need. A custom domain may only be created if the data are different in nature and do not fit into an existing published domain. 确定现有标准域不能满足需求。只有当数据有本质的不同且不能应用现有标准域时，才能创建自定义新域。

• Establish
a domain of a common topic (i.e., where the nature of the data is the same), rather than by a specific method of collection (e.g. electrocardiogram - EG). Group and separate data within the domain using --CAT, --SCAT, --METHOD, --SPEC, --LOC, etc. as appropriate. Examples of different topics are: microbiology, tumor measurements, pathology/histology, vital signs, and physical exam results. 基于相似主题来建立新域（也就是，数据本质是相同的），而不是基于特定的数据采集方法（比如说，心电图-EG）。在域内恰当的运用分组、修饰变量如-CAT，-- SCAT，–METHOD，–SPEC，–LOC等归组或分离数据。不同主题的示例包括：微生物学，肿瘤测定，病理/组织学，生命体征，和体格检查等。

• Do not create separate domains based on time, rather represent both prior and current observations in a domain (e.g., CM for all non-study medications). Note that AE and MH are an exception to this best practice because of regulatory reporting needs. 不要基于时间将域进行拆分，即确保试验前与试验中的观测数据均放在一个域里会更合适(比如，域CM包含所有非研究用药信息)。注意，出于监管部门报表需要，域AE和MH 是个例外，可不受此限制。

• How collected data are used (e.g., to support analyses and/or efficacy endpoints) must not result in the creation of a custom domain. For example, if blood pressure measurements are endpoints in a hypertension study, they must still be represented in the VS (Vital Signs) domain as opposed to a custom “efficacy” domain. Similarly, if liver function test results are of special interest, they must still be represented in the LB (Laboratory Tests) domain. 不能基于采集的数据如何使用来创建自定义新域（例如，为了支持分析或疗效终点）。例如，即使血压测量是一个高血压研究的终点，它们还必需保留在生命体征域VS(Vital Sign)里，而不是去创建一个自定义疗效域。类似的，即使肝功能测试结果是研究重点，它们也必须保留在实验室检查LB(Laboratory Tests)域里。

• Data that were collected on separate CRF modules or pages may fit into an existing domain (such as separate questionnaires into the QS domain, or prior and concomitant medications in the CM domain). CRF单独模块或页采集的数据可以对应于一个现有的域（比如，单独的问卷调查对应QS域，先前的和伴随的药物治疗对应CM域等）。

• If it is necessary to represent relationships between data that are hierarchical in nature (e.g., a parent record must be observed before child records), then establish a domain pair (e.g., MB/MS, PC/PP). Note, domain pairs have been modeled for microbiology data (MB/MS domains) and PK data (PC/PP domains) to enable dataset-level relationships to be described using RELREC. The domain pair uses DOMAIN as an Identifier to group parent records (e.g., MB) from child records (e.g., MS) and enables a dataset-level relationship to be described in RELREC. Without using DOMAIN to facilitate description of the data relationships, RELREC, as currently defined could not be used without introducing a variable that would group data like DOMAIN. 如果有必要反映等级数据关系（比如，一个父系记录必须在子系记录之前观测），就要建立一个配对域（比如，MB/MS, PC/PP）。注意，微生物数据(MB/MS域) 和PK 数据（PC/PP域）的配对域已经被建立用来保证其数据间关系能在RELREC中得到描述。配对域通过变量DOMAIN作为标识符来区别父记录（例如，MB）与子记录（例如，MS），使得它们之间的关系在RELREC得以描述。如果不运用DOMAIN来帮助描述其数据关系，则必须引入一个类似DOMAIN的变量，否则不能再RELREC中使用。

2. Check the Submission Data Standards area of the CDISC website (Hhttp://www.cdisc.org/) for models added after the last publication of the SDTMIG. 查询CDISC网站(http://www.cdisc.org/)数据申报标准专区以获得自上次SDTMIG发布以来新添加的域模型。

3. Look for an existing, relevant domain model to serve as a prototype. If no existing model seems appropriate, choose the general observation class (Interventions, Events, or Findings) that best fits the data by considering the topic of the observation The general approach for selecting variables for a custom domain is as follows (also see Figure 2.6, Creating A New Domain below) 寻找一个现有的，相关的域模型来作为原型参考。如果没有合适的现有模型供参考，选择最符合观测数据主题的通用观测数据类别（干预类、事件类和发现类）。为自定义新域选择变量的一般方法包括如下几个方面（参见图2.6）：

a. Select and include the required Identifier variables (e.g., STUDYID, DOMAIN, USUBJID, --SEQ) and any permissible Identifier variables from SDTM: Table 2.2.4. 确定必需的标识变量（STUDYID，DOMAIN USUBJID，和 --SEQ)和任何其他允许的标识变量（参见SDTM表2.2.4）。

b. Include the Topic variable from the identified general observation class (e.g., --TESTCD for Findings) [SDTM: Tables 2.2.1, 2.2.2, or 2.2.3]. 通过选定的通用观测数据类确定相关主题变量（例如，观测发现类的主题变量–TESTCD）（参见SDTM 表2.2.1, 2.2.2, 或2.2.3）。

c. Select and include the relevant Qualifier variables from the identified general observation class [SDTM: Tables 2.2.1, 2.2.2, or 2.2.3]. Variables belonging to other general observation classes must not be added. 通过选定的通用观测数据类确定相关修饰变量（参加SDTM表2.2.1, 2.2.2,或2.2.3），不能添加属于其它通用观测数据类别的变量。

d. Select and include the applicable Timing variables [see SDTM: Table 2.2.5]. Determine the domain code. Check the CDISC Controlled Terminology [see Appendix C – Controlled Terminology]
for reserved two-character domain identifiers or abbreviations. If one
has not been assigned by CDISC, then the sponsor may select the unique two- character domain code to be used consistently throughout the submission. 确定适用的时间变量（参见SDTM表2.2.5)。确定域代码。查阅CDISC受控术语（请参阅附录C），确保自定义域代码没有被CDISC分配或作为保留词使用，申办者可选择唯一的两字符长度的域代码并在整个申报过程中保持其一致性。

e. Apply the two-character domain code to the appropriate variables in the domain. Replace all variable prefixes (shown in the models as two hyphens “–“) with the domain code. If no domain code exists in the CDISC Controlled Terminology [see Appendix C – Controlled
Terminology] for this data and if it desired to have this domain code
as part of CDISC controlled terminology then submit a request to add the new domain via the CDISC website. 将自定义域代码应用于该域中的合适变量。用域代码代替所有适用变量的前缀（即在域模型里显示为两连字符 “–”）。如果申办者希望将该自定义域代码作为将来CDISC的标准受控术语，可通过CDISC官方网站来提交该申请。

Requests for new domain codes must include: 申请内容必须包括：

1. Two-letter domain code and description 两英文字符长度的域代码和描述

2. Rationale for domain code 域代码的基本原理

3. Domain model with assumptions 有关该域模型的假设

4. Examples 应用实例

Upon receipt, the SDS Domain Code Subteam will review the package. If accepted, then the proposal will be submitted to the SDS Team for review. Upon approval, a response will be sent to the requestor and package processing will begin (i.e., prepare for inclusion in a next release of the SDTM and SDTMIG, mapping concepts to BRIDG, and posting an update to the CDISC website). If declined, then the Domain Code Subteam will draft a response for SDS Team review. Upon agreement, the response will be sent to the requestor and also posted to the CDISC website. 接到申请后，数据申报标准SDS域代码评审组会审阅该申请文件。如果接受的话，该提议会提交给数据申报标准SDS评审组进一步审阅。一旦获得批准，申请者将会收到告知，相关的处理流程也会同时开始（例如，准备在下一版本的SDTM和SDTMIG中发布，建立与BRIDG的对应关系，在CDISC网站上发布更新等）。如果没有获得批准,域代码评审组会起草回复提交给SDS评审组审阅。在得到同意的情况下，会将回复发送给申请者，同时将结果发布在CDISC的网站上。

f. Set the order of variables consistent with the order defined in SDTM: Tables 2.2.1, 2.2.2, or 2.2.3, depending upon the general observation class the custom domain is based on. 设置变量顺序，确保自定义新域中的变量顺序与最相似域模型的中变量顺序相一致(参见SDTM表2.2.1，2.2.2，或2.2.3)。

g. Adjust the labels of the variables only as appropriate to properly convey the meaning in the context of the data being submitted in the newly created domain. Use title case for all labels (title case means to capitalize the first letter of every word except for articles, prepositions, and conjunctions). 只有在必要情况下，调整变量标签以更恰当的表达其在自定义域中含义。所有标签采用单词首字母大写方式(除了冠词、介词和连词)。

h. Ensure that appropriate standard variables are being properly applied by comparing the use of variables in standard domains. 通过与标准域中变量比较，确保标准变量在自定义新域中被正确使用。

i. Describe the dataset within the define.xml document [see Section 3: 3.2, Using The CDISC Domain Models In Regulatory Submissions - Dataset Metadata]. 在define.xml文档中描述该数据集（参见章节3.2）。

j. Place any non-standard (SDTM) variables in a Supplemental Qualifier dataset. Mechanisms for representing additional non-standard Qualifier variables not described in the general observation classes and for defining relationships between separate datasets or records are described in Section 8: 8.4, Relating Non-Standard Variables Values To A Parent Domain of this document. 所有非标准的SDTM变量将被放到SUPPQUAL补充数据集中。非标准修饰变量的使用并没有在通用观测数据类型中加以描述，其与如何定义数据集或数据之间关系一道，将在章节8.4—将非标准变量值与父域关联进行描述。

2.7 SDTM variables Allowed in SDTMIG SDTMIG允许的SDTM变量

This section identifies those SDTM variables that either 1) should not be used in SDTM-compliant data tabulations of clinical trials data or 2) have not yet been evaluated for use in human clinical trials. 本节确定以下2种SDTM变量：禁止在临床试验数据的SDTM标准数据表格使用；尚未评估是否可用于人体临床试验数据。

The following SDTM variables, defined for use in non-clinical studies (SEND), must NEVER be used in the submission of SDTM-based data for human clinical trials: 下列定义在非临床研究数据（SEND）中的SDTM变量，禁止用于人体临床试验的SDTM数据递交中。

• --DTHREL
(Findings)

• --EXCLFL
(Findings)

• --REASEX
(Findings)

• --DETECT
(Findings)

The following variables can be used for non-clinical studies (SEND) but must NEVER be used in the Demographics domain for human clinical trials. However, the use of these variables is currently being evaluated in Findings general observation class domains being developed for use in the tabulations of virology data: 下列变量可用于非临床研究数据（SEND）中，但禁止用于人体临床试验的人口统计学域（DM）。然而，在属于发现观测类域的病毒学相关的数据域，下列的这些变量正在被评估：

• SPECIES
(Demographics)

• STRAIN
(Demographics)

• SBSTRAIN
(Demographics)

The following variables have not been evaluated for use in human clinical trials and must therefore be used with extreme caution: 下列用于人体临床试验的变量从未被评估过，因此需非常慎重地使用这些变量

• --ANTREG (Findings)

• SETCD (Demographics)

[Note: The use of SETCD additionally requires the use of the Trials Sets domain] [注意：SETCD需要与试验集(Trials Sets)域配套使用]

The following identifier variable can be used for non-clinical studies (SEND), and may be used in human clinical trials when appropriate: 以下可作为非临床研究数据(SEND)的标识变量可酌情的用于人体临床试验。

• POOLID [Note: The use of POOLID additionally requires the use of the Pool Definition dataset]

Other variables defined in the SDTM are allowed for use as defined in this SDTMIG except when explicitly stated. Custom domains, created
following the guidance in Section 2.6, Creating A New Domain, may utilize any appropriate Qualifier variables from the selected general observation class. [注意：POOLID需要与池定义（Pool Definition）数据集配套使用]
除非明确地指出，其他SDTM中定义的变量允许按照本SDTMIG的规则使用。按照2.6节的指导创建的自定义域可从一般观测类中选取适当的修饰变量加以使用。

第2章（完）

本系列不再更新，网上有翻译好的中文版。