1,721,090 research outputs found
Is the Maximum Partial Likelihood Estimator for Cox’s Proportional Hazards Model Also a General Least Squares Estimator?
No full textThe equivalences in estimation between the maximum likelihood approach (e.g., the
usual maximum likelihood estimator) and the least squares approach (e.g., the ordinary,
weighted, generalized, and iterative reweighted least squares estimators) have been estab-lished
for many well-known classes of statistical regression models such as linear regression
model, logistic regression model, and generalized linear models (GLMs). However, no such
connection has been discovered yet for the maximum partial likelihood estimator (MPLE) of
the regression coefficients in Cox’s proportional hazards model (Cox 1972, 1975). In this
study, by choosing an appropriate ”moment condition” of generalized method of moments
(GMM) estimation, we find that with the ”asymmetric orthogonal expected information ap-proach”
of adaptive estimation, the optimal martingale estimating function obtained from the
minimization of the corresponding GMM quadratic form for a consistent estimator of the
regression coefficients reduces to the partial score function of the Cox’s proportional hazards
model, which implies that the well-behaved MPLE is also a general least squares estimator.
This finding is not only very interesting in its own rights, but it provides us with an oppor-tunity
to develop GLMs-type regression models locally for stochastic processes and to apply
some powerful GMM-related estimating techniques such as the instrumental variables method
to deal with several known statistical modeling problems including measurement error and
simultaneous-equations bias in analysis of survival or time-to-event data
Pediatric Evaluation of Disability Inventory: A cross-cultural comparison of daily function between Taiwanese and American children
No full textESTIMATION OF THE CAUSAL EFFECT OF A TIME-VARYING EXPOSURE ON THE MARGINAL MEAN OF A REPEATED BINARY OUTCOME
No full textWe provide sufficient conditions for estimating from
longitudinal data the causal effect of a time-dependent
exposure or treatment on the marginal probability of
response for a dichotomous outcome. We then show how one can
estimate this effect under these conditions using the g-
computation algorithm of Robins. We also derive the
conditions under which some current approaches to the
analysis of longitudinal data, such as the generalized
estimating equations (GEE) approach of Zeger and Liang, the
feedback model techniques of Liang and Zeger, and within-
subject conditional methods, can provide valid tests and
estimates of causal effects. We use our methods to estimate
the causal effect of maternal stress on the marginal
probability of a child's illness from the Mothers' Stress
and Children's Morbidity data and compare our results with
those previously obtained by Zeger and Liang using a GEE
approach
Internal consistency and factor structure of the Eating Disorder Inventory among clinical and non-clinical subjects in Taiwan.
No full textThe Development of Cost-Effectiveness Indices with Equity Implications for the Economic Evaluation of Health Care
No full textPMC10 THE DEVELOPMENT OF COST-EFFECTIVENESS INDICES WITH EQUITY IMPLICATIONS FOR THE ECONOMIC EVALUATION OF HEALTH CARE
No full text
- …
