评估临床研究中的季节性。
Assessing seasonality in clinical research.
机构信息
European Interuniversity College Pharmaceutical Medicine, Lyon, France.
出版信息
Clin Chem Lab Med. 2012 Dec;50(12):2163-9. doi: 10.1515/cclm-2012-0295.
BACKGROUND
Seasonal patterns are assumed in many fields of medicine. However, biological processes are full of variations and the possibility of chance findings can often not be ruled out.
METHODS
Using simulated data we assess whether auto correlation is helpful to minimize chance findings and test to support the presence of seasonality.
RESULTS
Autocorrelation required to cut time curves into pieces. These pieces were compared with one another using linear regression analysis. Four examples with imperfect data are given. In spite of substantial differences in the data between the first and second year of observation, and in spite of otherwise inconsistent patterns, significant positive autocorrelations were constantly demonstrated with correlation coefficients around 0.40 (SE 0.14).
CONCLUSIONS
Our data suggest that autocorrelation is helpful to support the presence of seasonality of disease, and that it does so even with imperfect data.
背景
在医学的许多领域中都假设存在季节性模式。然而，生物过程充满了变化，并且常常不能排除偶然发现的可能性。
方法
我们使用模拟数据来评估自相关是否有助于最小化偶然发现，并检验是否存在季节性。
结果
自相关需要将时间曲线切成片段。使用线性回归分析比较这些片段。给出了四个具有不完美数据的示例。尽管在观察的第一年和第二年之间的数据存在实质性差异，并且模式不一致，但始终表现出显著的正自相关，相关系数约为 0.40（SE 0.14）。
结论
我们的数据表明，自相关有助于支持疾病季节性的存在，即使数据不完美也是如此。
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