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• W2999649832 abstract "Quality control is important in many fields, especially industrial production. Major research has been developed with regard to industrial quality control to ensure reliable and consistent products. We adapt and develop methodology in quality control to monitor data collection in epidemiologic studies. There are no procedures currently used by epidemiologists to evaluate quality control during the actual process of data collection; methods are implemented only after the data have been collected. We focus on procedures that can be used during data collection: instrument calibration and population sampling. For the first, we propose methods utilizing Shewhart control charts and Westgard stopping rules. For evaluating population sampling, we present methods utilizing regression analysis. We provide a motivating example to highlight the utility of these methods. The proposed methodology may help investigators to identify data quality problems that can be corrected while data are still being collected, and also to identify biases in data collection that might be adjusted later. Data collection for epidemiologic investigations is a dynamic process susceptible to variations in quality. Lack of quality control can be detrimental to analysis and conclusions in unexpected ways. Epidemiologic tools have been developed to evaluate data either a priori (through the use of pilot studies), or post hoc through data cleaning and basic data analysis (eg, comparison of the characteristics of participants vs. nonparticipants, stratified analysis, whisker plots). Little has been written about methods to evaluate quality control during the actual process of data collection. This concept has been applied extensively in industrial production and has led to industrial standards for quality control to ensure a reliable and consistent product. 1 An extension of this concept to epidemiologic research would allow investigators to monitor ongoing data collection to assess data quality. There are 2 main processes to be evaluated. The first is calibration of data collection instruments, including study questionnaires and biomarker measurements. For example, are incomplete responses in the questionnaires missing at random? Are there systematic variations in measurements obtained by different interviewers? The second consideration is whether study participants are an unbiased sample of the target population. Examining these 2 processes during real-time data collection allows investigators to assess the quality of data they are collecting and, if necessary, to adjust low quality data that has already been collected. In this paper, we attempt to define the 2 main processes affecting quality control and to propose quality control methods for use during actual data collection. These methods allow real-time monitoring of data quality, assure that the sample is representative of the target population, and suggest clear stopping rules in case of bias. These methods can be used in most types of epidemiologic studies, including case-control studies, cohort studies, and clinical trials. In the next section we introduce sources of variability that require quality control. We subsequently" @default.
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• W2999649832 date "2009-01-01" @default.
• W2999649832 modified "2023-09-27" @default.
• W2999649832 title "Monitoring Quality Control" @default.
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