Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study.
Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study.
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DOI：
10.1186/s12874-021-01364-0
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发表时间：
2021-08-28
影响因子：
4
通讯作者：
McKenzie JE
中科院分区：
文献类型：
--
作者：
Turner SL;Forbes AB;Karahalios A;Taljaard M;McKenzie JE
中断时间序列（ITS）研究经常用于评估人群水平干预或暴露的影响。然而，对这种设计的统计方法的性能的检验得到的关注相对较少。我们模拟了连续数据，比较了一套统计方法在不同水平和坡度变化、不同序列长度和lag-1自相关程度的情况下的性能。我们还检查了Durbin-Watson （DW）检测自相关的性能。所有方法都对所有情景下的水位和坡度变化进行了无偏估计。所有方法都低估了自相关的程度，然而，限制最大似然（REML）产生了最小偏差的估计。对自相关的低估导致标准误差过小，覆盖范围小于标称的95%。除了存在自相关的普通最小二乘（OLS）和高自相关值的newy - west外，所有方法在较长的时间序列上都表现较好。除了长序列和大的自相关外，DW检验表现不佳。从评价的方法来看，在小于12点的序列中，OLS是首选方法，而在较长的序列中，REML是首选方法。不应依赖DW检验来检测自相关性，除非序列很长。在解释所有方法的结果时需要小心，因为给定的置信区间通常太窄。需要进一步研究开发性能更好的ITS方法，特别是短序列。在线版本包含补充材料，可在10.1186/s12874-021-01364-0获得。
Interrupted time series (ITS) studies are frequently used to evaluate the effects of population-level interventions or exposures. However, examination of the performance of statistical methods for this design has received relatively little attention. We simulated continuous data to compare the performance of a set of statistical methods under a range of scenarios which included different level and slope changes, varying lengths of series and magnitudes of lag-1 autocorrelation. We also examined the performance of the Durbin-Watson (DW) test for detecting autocorrelation. All methods yielded unbiased estimates of the level and slope changes over all scenarios. The magnitude of autocorrelation was underestimated by all methods, however, restricted maximum likelihood (REML) yielded the least biased estimates. Underestimation of autocorrelation led to standard errors that were too small and coverage less than the nominal 95%. All methods performed better with longer time series, except for ordinary least squares (OLS) in the presence of autocorrelation and Newey-West for high values of autocorrelation. The DW test for the presence of autocorrelation performed poorly except for long series and large autocorrelation. From the methods evaluated, OLS was the preferred method in series with fewer than 12 points, while in longer series, REML was preferred. The DW test should not be relied upon to detect autocorrelation, except when the series is long. Care is needed when interpreting results from all methods, given confidence intervals will generally be too narrow. Further research is required to develop better performing methods for ITS, especially for short series. The online version contains supplementary material available at 10.1186/s12874-021-01364-0.
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影响因子：
6.1
作者：
GODFREY, LG
通讯作者：
GODFREY, LG
影响因子：
4.9
作者：
Hacek, Donna M.;Ogle, Anna Marie;Peterson, Lance R.
通讯作者：
Peterson, Lance R.
影响因子：
2.7
作者：
LJUNG, GM;BOX, GEP
通讯作者：
BOX, GEP
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作者：
BREUSCH, TS
通讯作者：
BREUSCH, TS
影响因子：
2.7
作者：
DURBIN, J;WATSON, GS
通讯作者：
WATSON, GS