测试负面设计和队列设计与明确目标试验模拟的比较,用于评估COVID-19疫苗的有效性
Guilin Li1,2, Hanna Gerlovin3, Michael J Figueroa Muñiz3,4
1From the CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA.
Epidemiology (Cambridge, Mass.)
|December 18, 2023
概括
通过比较COVID-19疫苗有效性的观察性研究设计,目标试验模拟的队列设计显示了与使用综合数据的测试负面设计类似的结果,但与有限的数据有所不同.
科学领域:
- 流行病学 流行病学
- 疫苗学 疫苗学 疫苗学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 实际的疫苗有效性 (VE) 估计依赖于观察性研究.
- 队列和测试负面设计是常见的 VE 估计方法.
- 将这些设计与COVID-19疫苗进行比较至关重要.
研究的目的:
- 为了比较队列和测试负面设计的性能,以估计BNT162b2疫苗对COVID-19的有效性.
- 评估数据丰富对设计性能的影响.
主要方法:
- 使用了来自美国退伍军人事务部的全国数据.
- 在队列设计中采用了明确的目标试验仿真.
- 实施并比较了案例控制抽样和测试负面设计特征.
- 在综合和有限数据集中对性能进行了评估.
主要成果:
- BNT162b2 VE估计在具有丰富数据的设计之间是相似的.
- 有限的数据集显示出不同的估计,队列设计偏向下方,测试负面设计偏向上方.
- 通过寻求医疗保健的行为观察到剩余的混.
结论:
- 当丰富的数据可用时,队列和测试负面设计会产生类似的 VE 估计.
- 设计性能与有限的数据有所分歧,突出了潜在的偏差.
- 目标试验模拟的队列设计在数据丰富的环境中可能更好,因为它的原则性方法和可解释性.
相关概念视频
Crossover Experiments
2.8K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
2.8K
Study Designs in Epidemiology
225
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
225
What is an Experiment?
11.6K
An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
11.6K
Controls in Experiments
7.8K
When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
7.8K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
130
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
130
Comparing the Survival Analysis of Two or More Groups
195
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
195


