Teixeira-Pinto Armando, Normand Sharon-Lise
Serviço de Bioestatística e Informática Médica, CINTESIS, Faculdade de Medicina, Universidade do Porto, Portugal.
Revstat Stat J. 2011 Mar 1;9(1):37-55.
Biomedical research often involves the measurement of multiple outcomes in different scales (continuous, binary and ordinal). A common approach for the analysis of such data is to ignore the potential correlation among the outcomes and model each outcome separately. This can lead not only to loss of efficiency but also to biased estimates in the presence of missing data. We address the problem of missing data in the context of multiple non-commensurate outcomes. The consequences of missing data when using likelihood and quasi-likelihood methods are described, and an extension of these methods to the situation of missing observations in the outcomes is proposed. Two real data examples illustrate the methodology.
生物医学研究通常涉及对不同尺度（连续、二元和有序）的多个结果进行测量。分析此类数据的一种常见方法是忽略结果之间的潜在相关性，并分别对每个结果进行建模。这不仅会导致效率损失，而且在存在缺失数据的情况下还会导致估计偏差。我们在多个不可通约结果的背景下解决缺失数据问题。描述了使用似然和拟似然方法时缺失数据的后果，并提出了将这些方法扩展到结果中存在缺失观测值的情况。两个实际数据示例说明了该方法。
