Showing papers by "Donald B. Rubin published in 1973"
TL;DR: In this article, several matching methods that match all of one sample from another larger sample on a continuous matching variable are compared with respect to their ability to remove the bias of the matching variable.
Abstract: Several matching methods that match all of one sample from another larger sample on a continuous matching variable are compared with respect to their ability to remove the bias of the matching variable. One method is a simple mean-matching method and three are nearest available pair-matching methods. The methods' abilities to remove bias are also compared with the theoretical maximum given fixed distributions and fixed sample sizes. A summary of advice to an investigator is included.
867 citations
TL;DR: In this paper, the ability of matched sampling and linear regression adjustment to reduce the bias of an estimate of the treatment eff ect in two sample observational studies is investigated for a simple matching method and five simple estimates.
Abstract: The ability of matched sampling and linear regression adjustment to reduce the bias of an estimate of the treatment eff ect in two sample observational studies is investigated for a simple matching method and five simple estimates. Monte Carlo results are given for moderately linear exponential response surfaces and analytic results are presented for quadratic response surfaces. The conclusions are (1) in general both matched sampling and regression adjustment can be expected to reduce bias, (2) in some cases when the variance of the matching variable differs in the two populations both matching and regression adjustment can increase bias, (3) when the variance of the matching variable is the same in the two populations and the distributions of the matching variable are symmetric the usual covariance adjusted estimate based on random samples is almost unbiased, and (4) the combination of regression adjustment in matched samples generally produces the least biased estimate.
574 citations
TL;DR: In this article, the concept of missing at random is defined, several examples are discussed, and two simple conditions are given which are sufficient to assure that the missing data are not at random.
Abstract: Most articles on missing values assume the missing data are “missing at random” and ignore the process that “caused” the missing values. The condition under which this procedure is justified is explored here: the concept of missing at random is precisely defined, several examples are discussed, and two simple conditions are given which are sufficient to assure that the missing data are missing at random.
4 citations
01 Aug 1973
4 citations
TL;DR: Pyrenochaeta terrestris, the causal agent of pink root disease of onions, has recently become established in Israel and no method is yet known for direct isolation and identification of the pathogen from soil.
1 citations