Abstract：
Drug utilization researchers are often interested in evaluating prescribing and medication use patterns and trends over a specified period of time. Joinpoint regression is a useful methodology to identify any deviations in secular trends without a preconceived notion of where these break points might occur. This article provides a tutorial on the use of joinpoint regression, within Joinpoint software, for the analysis of drug utilization data.
The statistical considerations for whether a joinpoint regression analytical technique is a suitable approach are discussed. Then, we offer a tutorial as an introduction on conducting joinpoint regression (within Joinpoint software) through a step-by-step application, which is a case study developed using opioid prescribing data from the United States. Data were obtained from public files available through the Centers for Disease Control and Prevention from 2006 to 2018. The tutorial provides parameters and sample data needed to replicate the case study and it concludes with general considerations for the reporting of results using joinpoint regression in drug utilization research.
The case study evaluated the trend of opioid prescribing in the United States from 2006 to 2018, where time points of significant variation (one in 2012 and another in 2016) are detected and interpreted.
Joinpoint regression is a helpful methodology for drug utilization for the purposes of conducting descriptive analyses. This tool also assists with corroborating assumptions and identifying parameters for fitting other models such as interrupted time series. The technique and accompanying software are user-friendly; however, researchers interested in using joinpoint regression should exercise caution and follow best practices for correct measurement of drug utilization.
The statistical considerations for whether a joinpoint regression analytical technique is a suitable approach are discussed. Then, we offer a tutorial as an introduction on conducting joinpoint regression (within Joinpoint software) through a step-by-step application, which is a case study developed using opioid prescribing data from the United States. Data were obtained from public files available through the Centers for Disease Control and Prevention from 2006 to 2018. The tutorial provides parameters and sample data needed to replicate the case study and it concludes with general considerations for the reporting of results using joinpoint regression in drug utilization research.
The case study evaluated the trend of opioid prescribing in the United States from 2006 to 2018, where time points of significant variation (one in 2012 and another in 2016) are detected and interpreted.
Joinpoint regression is a helpful methodology for drug utilization for the purposes of conducting descriptive analyses. This tool also assists with corroborating assumptions and identifying parameters for fitting other models such as interrupted time series. The technique and accompanying software are user-friendly; however, researchers interested in using joinpoint regression should exercise caution and follow best practices for correct measurement of drug utilization.
摘要：
目的：药物利用研究人员通常对评估特定时间段内的处方和药物使用模式和趋势感兴趣。Joinpoint回归是一种有用的方法，可以识别长期趋势中的任何偏差，而无需对这些断点可能发生的位置有先入为主的概念。本文提供了一个关于使用连接点回归的教程，在Joinpoint软件中，用于分析药物利用数据。
方法：讨论了有关连接点回归分析技术是否是合适方法的统计考虑因素。然后，我们提供了一个教程，作为通过分步应用程序进行连接点回归（在Joinpoint软件内）的介绍，这是一项使用美国阿片类药物处方数据开发的案例研究。数据来自2006年至2018年疾病控制和预防中心提供的公共文件。本教程提供了复制案例研究所需的参数和样本数据，并在药物利用研究中使用连接点回归报告结果的一般考虑因素。
结果：案例研究评估了2006年至2018年美国阿片类药物处方的趋势，其中检测并解释了显着变化的时间点（一个在2012年，另一个在2016年）。
结论：Joinpoint回归是用于进行描述性分析的药物利用的有用方法。该工具还有助于证实假设并识别用于拟合其他模型(如中断时间序列)的参数。该技术和随附的软件是用户友好的;然而，有兴趣使用联合点回归的研究人员应谨慎行事,并遵循最佳实践,以正确测量药物利用率.本文受版权保护。保留所有权利。
方法：讨论了有关连接点回归分析技术是否是合适方法的统计考虑因素。
结果：案例研究评估了2006年至2018年美国阿片类药物处方的趋势，其中检测并解释了显着变化的时间点（一个在2012年，另一个在2016年）。
结论：Joinpoint回归是用于进行描述性分析的药物利用的有用方法。
