巴雷特食道中的埃索梅普拉和阿司匹林 (AspECT):随机因子试验
Janusz A Z Jankowski1, John de Caestecker2, Sharon B Love3
1Gastroenterology Unit, Morecambe Bay University Hospitals NHS Trust, Lancaster, UK; National Institute for Health and Care Excellence, London, UK.
Lancet (London, England)
|July 31, 2018
概括
高剂量的质子抑制剂 (PPI) 和阿司匹林显著改善了巴雷特食道患者的结果. 高剂量的PPI与阿司匹林的结合显示出最强的保护作用,报告的不良反应最小.
科学领域:
- 胃肠病学
- 癌症学
- 临床药理学
背景情况:
- 消化管腺癌是全球癌症死亡的主要原因.
- 巴雷特的食道是这种癌症的主要危险因素.
- 评估化疗预防策略对于管理巴雷特食道至关重要.
研究的目的:
- 评估高剂量质子抑制剂 (PPI) 和阿司匹林在改善巴雷特食道患者的治疗结果方面的有效性.
- 在化疗预防试验中,将高剂量与低剂量PPI进行比较.
主要方法:
- 一个2x2因子随机对照试验,涉及85个中心的2557名患者.
- 患者接受高剂量 (每天两次40毫克) 或低剂量 (每天一次20毫克) PPI,与或没有阿司匹林 (每天300-325毫克) 至少8年.
- 主要终点:使用加速失效时间建模分析的全因死亡率,食道腺癌或高度发育不良.
主要成果:
- 高剂量的PPI优于低剂量的PPI (p=0. 038).
- 当非类固醇抗炎药物使用被审查时,阿司匹林显示出显著的益处 (p=0. 043).
- 高剂量的PPI与阿司匹林的结合显示出最强的保护作用 (p=0. 0068),报道的不良事件很少.
结论:
- 高剂量的PPI和阿司匹林化疗,特别是在组合中,显著改善了巴雷特食道患者的结果.
- 这种治疗方案被认为是安全的,只有1%的参与者报告了与研究相关的严重不良事件.
- 这些发现支持使用高剂量的PPI和阿司匹林来治疗巴雷特食道并预防食道腺癌.
相关概念视频
Factorial Design
13.1K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.1K
Introduction to Test of Independence
2.1K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.1K
Randomized Experiments
6.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
6.3K
Identifying Statistically Significant Differences: The F-Test
3.6K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
3.6K
Wald-Wolfowitz Runs Test II
659
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
659
McNemar's Test
1.0K
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
1.0K


