Cerrito P B, Cerrito J C
Department of Mathematics, University of Louisville, Kentucky 40292.
J Biopharm Stat. 1991;1(2):221-35. doi: 10.1080/10543409108835020.
We need to find some way to assess the probability of risk for individuals taking medications for a prolonged period of time either singly or, as is usually more likely, in combinations. It is not possible to perform detailed, controlled studies for all combinations of drugs, not even those most commonly administered. Therefore, we must rely primarily on data from various case studies of attending physicians. Since this data is not collected in a controlled setting, we require a statistical technique that can do the following: be modified to handle data sequentially as information continues to accumulate, be very robust for dependent data, make minimal assumptions concerning the underlying distribution of the data, and be used for multivariate data. A statistical technique--nonparametric density estimation--is available that will satisfy all of the above requirements. We will demonstrate its use in postmarketing surveillance.
我们需要找到某种方法来评估长期单独或(更常见的是)联合用药的个体的风险概率。对于所有药物组合,甚至是最常用的组合,都不可能进行详细的对照研究。因此,我们必须主要依靠主治医生的各种病例研究数据。由于这些数据不是在受控环境中收集的,我们需要一种能做到以下几点的统计技术:随着信息不断积累,能够进行修改以顺序处理数据;对相关数据具有很强的稳健性;对数据的潜在分布做出最少假设;并用于多变量数据。有一种统计技术——非参数密度估计——可以满足上述所有要求。我们将展示其在上市后监测中的应用。