通过临床数据差异分析优化电子化逻辑检查表
发布时间：2018-08-06 14:08
【摘要】：在临床试验中,通常会收集很多不同的数据模块,比如不良事件,疾病历史,人口统计学等,当数据模块中的录入数据与数据标准有违背时,会产生相应的数据差异。目前大部分数据差异是通过电子化逻辑检查产生,其有助于及时发现和处理问题数据,但是电子化逻辑检查表仍有一定的不足之处,如重复逻辑检查,错误逻辑检查等.目的分析病例报告表中数据差异率比较高的数据模块,探讨优化逻辑检查程序。在保证数据一致性,完整性,准确性的前提下,减少不必要的电子化逻辑检查程序,提高数据管理员的工作效率。方法1.收集来自某大型跨国企业数据库中,完成年份在2011年到2013年间的113个临床试验数据。调查每个临床试验的基本情况：试验编号,临床期数,完成年份,治疗领域,病人总数,数据点总数。2.比较不同临床期数,不同完成年份和不同治疗领域下,各临床试验数据模块差异率的高低。3.分析数据差异率较高的模块上的逻辑检查程序和数据差异,通过以下三个方面：系统优化,程序简化以及电子化检查和人工检查的平衡,探讨改良电子化逻辑检查表的方法。4.针对每个数据模块提出的优化方法,抽取项目人数适中,数据模块差异率有代表性的临床试验,对其进行回顾性验证,分析应用以上优化方案后,所减少的逻辑检查数目,以及进而减少的不必要数据差异数目,验证其可行性。结果：1.比较发现不同临床试验完成年份,不同试验期数和不同治疗领域下,差异率高的数据模块主要都集中在不良事件数据模块,合并用药数据模块,实验室数据模块,服药记录数据模块。其中不良事件数据模块的差异率范围是4%-15%,合并用药数据模块的差异率范围是7.5%-27.6%,服药记录数据模块的差异率范围是1.3%-18.3%,实验室数据模块的差异率范围是0.6%-23.7%。2.对于这四个数据模块上设置的电子化逻辑检查程序,建议通过以下三个方面对其进行改善：①对病例报告表上设置的关联问题,叙述性文本和缺失数据,进行系统优化,在数据录入阶段进行控制,减少数据录入时可能发生的错误,从而减少数据清理时所需的逻辑检查②对于同一数据点上设置的多条逻辑检查,通过程序简化,去除逻辑重复或相近的检查。③数据库中设置的逻辑检查并不是越多越好,实际项目中,需考虑电子化检查和人工检查的平衡,对于实验室数据模块,建议将原本的电子化检查改为人工检查(Lab Review Tool)。3.对于不良事件模块,发现临床试验A通过上述优化方法可以减少22.45%的逻辑检查程序：对于合并用药模块,发现临床试验B通过上述优化方法可以减少26.3%的逻辑检查程序：对于服药记录模块,发现临床试验C通过上述优化方法可以减少21.1%的逻辑检查程序；对于实验室记录模块,发现临床试验D通过上述方法可以减少16.7%的逻辑检查程序。结论：1.临床数据差异率高的模块主要集中在不良事件模块,合并用药模块,服药记录模块和实验室数据模块,使今后临床试验数据管理计划的制定更有侧重性。2.优化后的电子化逻辑检查表,既减少了不必要的电子化逻辑检查程序,又在确保数据一致性,准确性,完整性的基础上减轻了数据管理人员的工作负担,为将来临床数据管理中电子化检查的标准化提供有价值的实验依据和参考。
[Abstract]:In clinical trials, many different data modules are usually collected, such as adverse events, disease history, demography, and so on. When the input data in the data module are contrary to the data standard, the corresponding data differences are generated. Most of the data differences are generated by electronic logic check, which helps to find and place the data in time. But the electronic logic check table still has some shortcomings, such as repeated logic check, error logic check and so on. Aim to analyze the data module with high data difference rate in the case report table, and discuss the optimization logic check program. In order to reduce the unnecessary electricity on the premise of ensuring the consistency, integrity and accuracy of the data. Methods 1. to improve the efficiency of data administrators. Method 1. collected from a large multinational enterprise database to complete 113 clinical trial data from 2011 to 2013. Investigate the basic situation of each clinical trial: test number, clinical period, completion year, treatment field, total number of patients, data points general .2. compared the number of different clinical periods, different years and different fields of treatment, the difference rate of each clinical trial data module,.3. analysis of the logic check program and data difference on the module with high difference rate of data, through the following three aspects: system optimization, program simplification, and the balance of electronic inspection and manual inspection. The method of improving the electronic logic Checklist (.4.), the optimization method proposed by each data module to extract the moderate number of project number and the representative clinical trial of the data module difference rate is reviewed, and the number of the reduced logic checks and the unnecessary data difference after the application of the above optimization are analyzed. Results: 1. the results are as follows: the data modules with high difference rate are mainly concentrated in the bad event data module, the combined drug data module, the laboratory data module, the drug recording data module, and the adverse event data module. The range of the difference rate is 4%-15%. The range of the difference rate of the combined drug data module is 7.5%-27.6%. The difference rate range of the drug recording data module is 1.3%-18.3%. The difference rate range of the laboratory data module is the electronic logic check program set on the four data modules by 0.6%-23.7%.2.. It is suggested to use the following three aspects. To improve: (1) the correlation problem set on the case report table, the narrative text and the missing data, the system optimization, the control in the data entry stage, the reduction of the possible errors when the data entry is reduced, thus reducing the logical check needed in the data cleaning. The program is simplified to remove logical repetition or close inspection. 3. The logical check set in the database is not the more the better. In the actual project, the balance between the electronic inspection and the manual inspection should be considered. For the laboratory data module, it is suggested to change the original electronic check into the Lab Review Tool.3. for the bad event module, It is found that the clinical trial A can reduce 22.45% of the logic check program through the above optimization: for the combined medication module, it is found that the clinical trial B can reduce the logic check program by the above optimization method: for the drug recording module, it is found that the clinical trial C can reduce the logical inspection process by 21.1% through the above optimization method. In the laboratory record module, it is found that the clinical trial D can reduce 16.7% of the logic check program through the above methods. Conclusion: 1. the modules with high clinical data difference rate are mainly concentrated in the adverse event module, the combined drug module, the medicine recording module and the laboratory data module, so that the future clinical trial data management plan is formulated. More emphasis is placed on the electronic logic checklist after.2. optimization, which reduces the unnecessary electronic logic check program, and reduces the work burden of data managers on the basis of ensuring data consistency, accuracy and integrity, and provides valuable experimental basis for standardization of electronic inspection in future clinical data management. Reference resources.
【学位授予单位】：复旦大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.13;R95
本文编号：2167969
[Abstract]:In clinical trials, many different data modules are usually collected, such as adverse events, disease history, demography, and so on. When the input data in the data module are contrary to the data standard, the corresponding data differences are generated. Most of the data differences are generated by electronic logic check, which helps to find and place the data in time. But the electronic logic check table still has some shortcomings, such as repeated logic check, error logic check and so on. Aim to analyze the data module with high data difference rate in the case report table, and discuss the optimization logic check program. In order to reduce the unnecessary electricity on the premise of ensuring the consistency, integrity and accuracy of the data. Methods 1. to improve the efficiency of data administrators. Method 1. collected from a large multinational enterprise database to complete 113 clinical trial data from 2011 to 2013. Investigate the basic situation of each clinical trial: test number, clinical period, completion year, treatment field, total number of patients, data points general .2. compared the number of different clinical periods, different years and different fields of treatment, the difference rate of each clinical trial data module,.3. analysis of the logic check program and data difference on the module with high difference rate of data, through the following three aspects: system optimization, program simplification, and the balance of electronic inspection and manual inspection. The method of improving the electronic logic Checklist (.4.), the optimization method proposed by each data module to extract the moderate number of project number and the representative clinical trial of the data module difference rate is reviewed, and the number of the reduced logic checks and the unnecessary data difference after the application of the above optimization are analyzed. Results: 1. the results are as follows: the data modules with high difference rate are mainly concentrated in the bad event data module, the combined drug data module, the laboratory data module, the drug recording data module, and the adverse event data module. The range of the difference rate is 4%-15%. The range of the difference rate of the combined drug data module is 7.5%-27.6%. The difference rate range of the drug recording data module is 1.3%-18.3%. The difference rate range of the laboratory data module is the electronic logic check program set on the four data modules by 0.6%-23.7%.2.. It is suggested to use the following three aspects. To improve: (1) the correlation problem set on the case report table, the narrative text and the missing data, the system optimization, the control in the data entry stage, the reduction of the possible errors when the data entry is reduced, thus reducing the logical check needed in the data cleaning. The program is simplified to remove logical repetition or close inspection. 3. The logical check set in the database is not the more the better. In the actual project, the balance between the electronic inspection and the manual inspection should be considered. For the laboratory data module, it is suggested to change the original electronic check into the Lab Review Tool.3. for the bad event module, It is found that the clinical trial A can reduce 22.45% of the logic check program through the above optimization: for the combined medication module, it is found that the clinical trial B can reduce the logic check program by the above optimization method: for the drug recording module, it is found that the clinical trial C can reduce the logical inspection process by 21.1% through the above optimization method. In the laboratory record module, it is found that the clinical trial D can reduce 16.7% of the logic check program through the above methods. Conclusion: 1. the modules with high clinical data difference rate are mainly concentrated in the adverse event module, the combined drug module, the medicine recording module and the laboratory data module, so that the future clinical trial data management plan is formulated. More emphasis is placed on the electronic logic checklist after.2. optimization, which reduces the unnecessary electronic logic check program, and reduces the work burden of data managers on the basis of ensuring data consistency, accuracy and integrity, and provides valuable experimental basis for standardization of electronic inspection in future clinical data management. Reference resources.
【学位授予单位】：复旦大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.13;R95
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