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Вид документа : Статья из журнала

РЖ ВИНИТИ 34 (BI38) 96.08-04А3.688

Автор(ы) : Choi S.C., Lu I.L.
Заглавие : Effect of non-random missing data mechanisms in clinical trials : Pap. 15th Int. Meet. Int. Soc. Clin. Biostatist., Basel, 25-29 July, 1994
Источник статьи : Statist. Med. - 1995. - Vol. 14, N 24. - С. 2675-2684
Аннотация: A simple form of non-ignorable missing data mechanisms based on two parameters is used to characterize the amount of missing data and the severity of non-randomness in clinical trials. Based on the formulation, the effect of non-randomly missing data on simple analyses which ignore the missing data is studied for binary and normally distributed response variables. In general, the effect of the non-randomly missing data on the bias and the power increases with the severity of non-randomness. The bias can be positive or negative and the power can be less than or greater than when the data are missing at random. The results of the analysis, ignoring the missing data, can be seriously flawed if the non-randomness is severe, even when only a small proportion of the sample is missing. The problem is more pronounced in the case of normally distributed response variables with unequal variances. США, Dep. of Biostatistics, Medical College of Virgina, Virginia Commonwealth Univ. Box 980032, Richmond, Virginia 23298-0032. Ил. 1. Табл. 1. Библ. 11
ГРНТИ : 34.05.25
Предметные рубрики: БИОМЕТРИЯ
ОТСУТСТВУЮЩИЕ ДАННЫЕ
КЛИНИЧЕСКИЕ ИСПЫТАНИЯ
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Вид документа : Статья из журнала

РЖ ВИНИТИ 34 (BI38) 97.03-04А3.391

Автор(ы) : Mendoza-Blanco Jose R., Tu Xin M., Iyengar, Satish
Заглавие : Bayesian inference on prevalence using a missing-data approach with simulation-based techniques: Applications to HIV screening
Источник статьи : Statist. Med. - 1996. - Vol. 15, N 20. - С. 2161-2176
Аннотация: Health departments and other health-related authorities seek accurate assessment of the spread of human immunodeficiency virus (HIV) among populations. Although screening for HIV provides a direct means for estimating its prevalence, it is complicated by the heterogeneity of available diagnostic tests and the degree to which they can diagnose HIV accurately. To integrate the limited precision of screening tests with prior results, Bayesian inference becomes a method of choice. Current Bayesian methods, however, have limited applications and do not readily generalize for complicated sampling designs and for modelling needs, particularly those that relate to HIV screening. By utilizing recent developments in the theories of missing-data analysis and simulation-based techniques, we develop an approach to Bayesian analysis of prevalence. This methodology is quite general for a variety of sampling schemes and sufficiently flexible to accommodate various practical considerations that arise from HIV screening. We illustrate the methodology with real as well as simulated data sets. Further, by utilizing methodology, we performed simulations to demonstrate that pooled testing provides a cost-effective means to improve the precision of estimates of prevalence under the currently limited screening technology. США, Dep. of Mathematics and Statistics, Univ. of Pittsburgh, Pittsburgh, PA 15260. Ил. 3. Библ. 47
ГРНТИ : 34.05.25
Предметные рубрики: БИОМЕТРИЯ
БАЙЕСОВСКИЙ ВЫВОД
ОТСУТСТВУЮЩИЕ ДАННЫЕ
ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ
ЗАБОЛЕВАНИЯ
СПИД
СКРИНИНГ
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