CDISC的SDTMIG，3.2版翻译和学习2---第⼆章SDTM基础
2 Fundamentals of the SDTM SDTM基础
2.1 Obrvations and Variables 观测（数据）和变量
The V3.x Submission Data Standards are bad 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 obrvations collected about subjects who participated in a clinical study. Each obrvation can be described by a ries of variables, corresponding to a row in a datat 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 obrvation and how it can be ud. 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 tho that identify the study, subject, domain, and quence 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龙回故乡
· Topic variables, which specify the focus of the obrvation (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 obrvation (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 resu
银行自我介绍lts or additional traits of the obrvation (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 t of Qualifier variables can be further categorized into
five sub-class: 修饰变量可进⼀步细分为五个⼦类别：
· Grouping Qualifiers are ud to group together a collection of obrvations 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 datat. They answer the question raid 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 obrvation. 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 obrvation 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 ud to further modify or describe a specific variable within an obrvation a
nd 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 obrvation, “Subject 101 had mild naua
starting on Study Day 6, “ the Topic variable value is the term for the adver 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 verity, the value for which is “MILD”. Additional Timing and Qualifier variables could be included to provide the necessary detail to adequately describe an obrvation.
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 Datats and Domains数据集和域
Obrvations about study subjects are normally collected for all
subjects in a ries of domains. A domain is defined as a collection of
logically related obrvations 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 reprented by a single datat. Each domain datat is distinguished by a unique, two-character code that should be ud consistently throughout the submission. This code, which is stored in the SDTM variable named DOMAIN, is ud in four ways:
你会在哪里>单位圆as the datat name,
the value of the DOMAIN variable in that datat,
as a prefix for most variable names in that datat,
and as a value in the RDOMAIN variable in relationship tables [Section8 - Reprenting 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 datats are structured as flat files with rows reprenting obrvations and columns reprenting variables. Each datat is described by metadata definitions that provide information ab
out the variables ud in the datat. The metadata are described in a data definition document named “define” that is submitted with the data to regulatory authorities. (See the Ca Report Tabulation Data Definition Specification [Define-XML], available at www.CDISC). Define-XML specifies ven 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 datat ·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 t of controlled terminology for the value or the prentation 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 [e 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 ud in the datat. For the V3.x domain models,Roles are ud to reprent 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 datats 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 t of brief CDISC guidelines that provide a general description for each variable ud for a general obrvation class. The domain datat models included in Section 5 – Models For Special-Purpo Domains and Section 6 – Domain Models Ba
d On The General Obrvation Class of this document provide additional information about Controlled Terms or Format, notes on proper usage, and examples. Controlled terminology (CT) is now reprented 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 who 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 u of controlled terminology parately.· 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-Purpo Datats
The SDTM includes three types of special-purpo datats: SDTM包含3类特殊⽤途的数据集：
·Domain datats, 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 obrvation class. The are described in Section 5 – Models For Special-Purpo 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) datats, such as Trial Arms (TA) and Trial Elements (TE), which reprent information about the study design but do not contain subject data. The are described in Section 7 – Trial Design Datats.
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 datats, which include the RELREC and SUPP-- datats described in Section 8 -Reprenting
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 Obrvation Class 通⽤观测数据类别
Most subject-level obrvations collected during the study should
be reprented according to one of the three SDTM general obrvation class: Interventions, Events, or Findings. The lists of variables allowed to be ud in each of the 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 asssment period (e.g., concomitant medications), or lf-administered by the subject (such as u 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 , adver 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 obrvations 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。
描写大自然In most cas, the choice of obrvation 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 respons to questions usually at specific visits or time points, will fit the Findings general obrvation class. Additional guidance on choosing the appropriate general obrvation class is provided in Section 8: 8.6.1, Guidelines For Determining The General Obrvation 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。强力定眩胶囊
General assumptions for u with all domain models and custom domains bad on the general obrvation class 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 parately 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-Purpo Domains (defined in Section 5 – Models For Special-Purpo 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 Obrvation 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 U (SU) 物质使⽤
· Procedures (PR) 操作
Events General Obrvation 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）：· Adver Events (AE) 不良事件
威海动物园
· 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
循序渐进近义词· Medical History (MH) 既往病史
Findings General Obrvation 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 Asssments (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
·
Dia Respon (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
