Kiely J L
Gertrude H. Sergievsky Center, Faculty of Medicine, Columbia University, New York, New York.
Paediatr Perinat Epidemiol. 1991 Jul;5(3):243-57. doi: 10.1111/j.1365-3016.1991.tb00707.x.
Biostatisticians, epidemiologists and other researchers in maternal and child health have often used multivariable analysis to investigate associations with perinatal and infant mortality. A review of reports of such multivariable analyses published over the last 35 years revealed four problems that occur repeatedly. (1) Variables that are intermediate in the causal pathway between some study variables and perinatal death ('intervening variables') are controlled as though they were confounders. (2) When birthweight is analysed as an intervening variable, it is entered into the analysis in a few large categories, such as above and below 2500 g. This is not an adequate way of controlling for birthweight. (3) Social and demographic variables often interact statistically with birthweight in their effects on perinatal mortality, but these interactions have not been analysed in most multivariable studies. (4) Highly intercorrelated variables that represent similar theoretical constructs are entered simultaneously into one regression analysis. Solutions to these problems are suggested. Analytical approaches in which investigators use knowledge of biological and medical subject matter to make judgements about confounding and causal inference are encouraged.
生物统计学家、流行病学家以及其他母婴健康领域的研究人员经常使用多变量分析来研究与围产期和婴儿死亡率的关联。对过去35年发表的此类多变量分析报告的回顾揭示了四个反复出现的问题。(1)在某些研究变量与围产期死亡之间的因果路径中处于中间位置的变量(“干预变量”)被当作混杂因素进行控制。(2)当将出生体重作为干预变量进行分析时,它被归入几个大类别(如2500克及以上和以下)纳入分析。这不是控制出生体重的充分方法。(3)社会和人口统计学变量在对围产期死亡率的影响方面通常与出生体重存在统计学上的相互作用,但在大多数多变量研究中并未对这些相互作用进行分析。(4)代表相似理论结构的高度相互关联的变量被同时纳入一个回归分析中。文中提出了这些问题的解决方案。鼓励采用这样的分析方法,即研究人员利用生物和医学主题知识来判断混杂因素和因果推断。