Article type
Year
Abstract
Background: IPD meta-analysis is suggested as the least biased method, which might provide more detailed and reliable conclusions than conventional meta-analysis. Since the original data from individual studies can be re-categorized, IPD meta-analysis can provide more consistent results. Before initiating an IPD analysis, establishing comprehensive data standards is essential to the development of a database that enables the pooling of data from different sources. The Clinical Data Interchange Standards Consortium (CDISC) standards are preferred by regulators, industry, and other research organizations as a means of facilitating regulatory review, aggregation, and querying of data, sharing data between entities, and streamlining the acquisition and analysis of data.
Objectives: We aimed to develop a unified database to standardize and collect data from various sources.
Methods: Study Data Tabulation Model (SDTM) standard is a foundational dataset for data management procedures. SDTM is suited for collecting data of various types and storing it in a relatively small number of observation classes. We examined the data structures of three epidemiology investigations, and then developed new data domains for storing reproductive-specific data. With the standards in place, patient-level data from the three studies were remapped and used to construct the database. Logical mapping and programmatic transformation were applied in the whole process. Validating and reconciling were made following related standard operating procedures.
Results: To date, the unified database contains six domains (DM, SC, CM, MH, VS, QS) and has received data on a total of 23600 subjects from three remapped studies of reproductive health submitted by two researchers in the National Research Institute for Family Planning.
Conclusions: Developing data standards is essential for IPD meta-analysis. A unified database would facilitate the pooling of data from legacy studies. In addition, it is also a data reservoir for prospectively collecting data in new trials without the need for remapping.
Objectives: We aimed to develop a unified database to standardize and collect data from various sources.
Methods: Study Data Tabulation Model (SDTM) standard is a foundational dataset for data management procedures. SDTM is suited for collecting data of various types and storing it in a relatively small number of observation classes. We examined the data structures of three epidemiology investigations, and then developed new data domains for storing reproductive-specific data. With the standards in place, patient-level data from the three studies were remapped and used to construct the database. Logical mapping and programmatic transformation were applied in the whole process. Validating and reconciling were made following related standard operating procedures.
Results: To date, the unified database contains six domains (DM, SC, CM, MH, VS, QS) and has received data on a total of 23600 subjects from three remapped studies of reproductive health submitted by two researchers in the National Research Institute for Family Planning.
Conclusions: Developing data standards is essential for IPD meta-analysis. A unified database would facilitate the pooling of data from legacy studies. In addition, it is also a data reservoir for prospectively collecting data in new trials without the need for remapping.