Brenner H
Department of Epidemiology, University of Ulm, Germany.
Am J Epidemiol. 1995 Dec 1;142(11):1236-45. doi: 10.1093/oxfordjournals.aje.a117583.
Estimates of prevalence in epidemiologic surveys are prone to bias due to selective response. Therefore, much effort is devoted to reduce the number of nonrespondents. For example, individuals who do not respond in the first round of recruitment in mail surveys are usually contacted a second (or even third or fourth) time yielding consecutive waves of responses. Yet this sequence of waves is often neglected in epidemiologic analyses in that prevalence is simply estimated as the proportion of trait-positive individuals among the total group of respondents. This paper investigates alternative estimates of prevalence that might be used in surveys with two waves of respondents. The estimates are based on different assumptions on the relation of response rates with the trait of interest. As this relation is likely to vary from survey to survey depending on the specific circumstances under which the recruitment of participants is conducted, none of the estimates is universally preferable. The performance of the different estimates is assessed in a variety of hypothetical and empirical examples, and strategies are discussed to make the best use of the different estimates in the analysis of epidemiologic studies.
由于选择性应答，流行病学调查中的患病率估计容易出现偏差。因此，人们投入了大量精力来减少无应答者的数量。例如，在邮寄调查中第一轮招募未应答的个体通常会被再次（甚至第三次或第四次）联系，从而产生连续几轮的应答。然而，在流行病学分析中，这几轮应答序列常常被忽视，因为患病率只是简单地估计为应答者总数中具有该特征个体的比例。本文研究了在有两轮应答者的调查中可能使用的患病率替代估计方法。这些估计基于对应答率与感兴趣特征之间关系的不同假设。由于这种关系可能因调查而异，具体取决于招募参与者时的特定情况，所以没有一种估计方法是普遍更可取的。在各种假设和实证例子中评估了不同估计方法的性能，并讨论了在流行病学研究分析中充分利用不同估计方法的策略。
