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РЖ ВИНИТИ 34 (BI38) 96.09-04А3.456

Neuhaus, John M.
A geometric approach to assess bias due to omitted covariates in generalized linear models [Text] / John M. Neuhaus, Nicholas P. Jewell // Biometrika. - 1993. - Vol. 80, N 4. - P807-815 . - ISSN 0006-3444
Перевод заглавия: Геометрический подход к оценке смещения, обусловленной опущением ковариат в обобщенных линейных моделях
Аннотация: Misspecifying generalized linear models by omitting covariates associated with the response can result in seriously biased estimates of the effects of the included covariates. For certain models, such bias can occur even when the omitted covariate is independent of the included covariates, as in studies with randomized treatment assignment. This paper presents a geometric approach to assess the direction of the bias resulting from omitted covariates and develop intuition about the effects of omitted covariates. We show that the direction of the bias depends simply on geometric properties of the link function. We present and expression for the magnitude of the bias and the results of simulations which corroborate these findings. We also consider an extension of the methods to assess the effects of omitted covariates which are correlated with those included. США, Dep. of Epidemiology and Biostatistics, Univ. of California, San Francisco, CA 94143-0560. Табл. 4. Библ. 6

ГРНТИ	34.05.25

ВИНИТИ 341.05.25.09.09
Рубрики: БИОМЕТРИЯ
ЗАТУХАНИЕ
ФУНКЦИЯ СВЯЗИ

Доп.точки доступа:
Jewell, Nicholas P.

РЖ ВИНИТИ 34 (BI38) 96.10-04А3.451

Lin, D.
Checking the Cox model with cumulative sums of martingale-based residuals [Text] / D. Lin, L. J. Wei, Z. Ying // Biometrika. - 1993. - Vol. 80, N 3. - P557-572 . - ISSN 0006-3444
Перевод заглавия: Проверка модели Кокса с кумулятивными суммами основанных на мартингалах остатков
Аннотация: This paper presents a new class of graphical and numerical methods for checking the adequacy of the Cox regression model. The procedures are derived from cumulative sums of martingale-based residuals over follow-up time and/or covariate values. The distributions of these stochastie processes under the assumed model can be approximated by zero-mean Gaussian processes. Each observed process can then be compared, both visually and analytically, with a number of simulated realizations from the approximate null distribution. These comparisons enable the data analyst to assess objectively how unusual the observed residual patterns are. Special attention is given to checking the functional form of a covariate, the form of the link function, and the validity of the proportional hazards assumption. An omnibus test, consistent against any model misspecification, is also studied. The proposed techniques are illustrated with two real data sets. США, Dep. of Biostatisties, SC-32, Univ. of Washington, Seattle, Washington 98195. Ил. 4. Библ. 21

ГРНТИ	34.05.25

ВИНИТИ 341.05.25.09.13
Рубрики: БИОМЕТРИЯ
ПОДГОНКА
ФУНКЦИЯ СВЯЗИ
ПРОПОРЦИОНАЛЬНЫЕ РИСКИ
ДИАГНОСТИЧЕСКИЕ РЕГРЕССИОННЫЕ МОДЕЛИ
ДАННЫЕ О ВЫЖИВАЕМОСТИ

Доп.точки доступа:
Wei, L.J.; Ying, Z.

"Электронные каталоги и базы данных библиотек СО РАН"

© Международная Ассоциация пользователей и разработчиков электронных библиотек и новых информационных технологий
(Ассоциация ЭБНИТ)

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