Masoud Asgharian

A reevaluation of the duration of survival after the onset of dementia.

BACKGROUND Dementia shortens life expectancy; estimates of median survival after the onset of dementia have ranged from 5 to 9.3 years. Previous studies of people with existing dementia, however, may have underestimated the deleterious effects of dementia on survival by failing to consider persons with rapidly progressive illness who died before they could be included in a study (referred to as length bias). METHODS We used data from the Canadian Study of Health and Aging to estimate survival from the onset of symptoms of dementia; the estimate was adjusted for length bias. A random sample of 10,263 subjects 65 years old or older from throughout Canada was screened for cognitive impairment. For those with dementia, we ascertained the date of onset and conducted follow-up for five years. RESULTS We analyzed data on 821 subjects, of whom 396 had probable Alzheimers disease, 252 had possible Alzheimers disease, and 173 had vascular dementia. For the group as a whole, the unadjusted median survival was 6.6 years (95 percent confidence interval, 6.2 to 7.1). After adjustment for length bias, the estimated median survival was 3.3 years (95 percent confidence interval, 2.7 to 4.0). The median survival was 3.1 years for subjects with probable Alzheimers disease, 3.5 years for subjects with possible Alzheimers disease, and 3.3 years for subjects with vascular dementia. CONCLUSIONS Median survival after the onset of dementia is much shorter than has previously been estimated.

Length-Biased Sampling With Right Censoring: An Unconditional Approach

When survival data arise from prevalent cases ascertained through a cross-sectional study, it is well known that the survivor function corresponding to these data is length biased and different from the survivor function derived from incident cases. Length-biased data have been treated both unconditionally and conditionally in the literature. In the latter case, where length bias is viewed as being induced by random left truncation of the survival times, the truncating distribution is assumed to be unknown. Conditioning on the observed truncation times hence causes very little loss of information. In many instances, however, it can be supposed that the truncating distribution is uniform, and it has been pointed out that under these circumstances, an unconditional analysis will be more informative. There are no results in the current literature that give the asymptotic properties of the unconditional nonparametric maximum likelihood estimator (NPMLE) of the unbiased survivor function in the presence of censoring. This article fills that gap by giving this NPMLE and its accompanying asymptotic properties when the data are purely length biased. An example of survival with dementia is presented in which the conditional and unconditional estimators are compared.

An input-oriented super-efficiency measure in stochastic data envelopment analysis: Evaluating chief executive officers of US public banks and thrifts

We develop an input-oriented super-efficiency measure in stochastic data envelopment analysis. A deterministic equivalent of the stochastic super-efficiency model is given. It is shown that this deterministic model can be converted to a quadratic program. Sensitivity analysis of the proposed super-efficiency model is discussed. The SNL Executive Compensation database is used to illustrate the methods developed in this article.

Covariate Bias Induced by Length-Biased Sampling of Failure Times

Although many authors have proposed different approaches to the analysis of length-biased survival data, a number of issues have not been fully addressed. The most important among these issues is perhaps that regarding inclusion of covariates into the analysis of length-biased lifetime data collected through cross-sectional sampling of a population. One aspect of this problem, which appears to have been neglected in the literature, concerns the effect of length bias on the sampling distribution of the covariates. In most regression analyses, it is conventional to condition on the observed covariate values; however, certain covariate values could be preferentially selected into the sample, being linked to the long-term survivors, who themselves are favored by the sampling mechanism. This observation raises two questions: (1) Does the conditional analysis of covariates lead to biased estimators of regression coefficients?; and (2) does inference through the joint l likelihood of covariates and failure times yield more efficient estimators of the regression parameters? We present a joint likelihood approach and study the large-sample behavior of the resulting maximum likelihood estimators (MLEs). We find that these MLEs are more efficient than their conditional counterparts even though the two MLEs are asymptotically equal. Our results are illustrated using data on survival with dementia, collected as part of the Canadian Study of Health and Aging.

Trace elements can influence the physical properties of tooth enamel

In previous studies, we showed that the size of apatite nanocrystals in tooth enamel can influence its physical properties. This important discovery raised a new question; which factors are regulating the size of these nanocrystals? Trace elements can affect crystallographic properties of synthetic apatite, therefore this study was designed to investigate how trace elements influence enamel’s crystallographic properties and ultimately its physical properties.The concentration of trace elements in tooth enamel was determined for 38 extracted human teeth using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The following trace elements were detected: Al, K, Mg, S, Na, Zn, Si, B, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sb, Se and Ti. Simple and stepwise multiple regression was used to identify the correlations between trace elements concentration in enamel and its crystallographic structure, hardness, resistance to crack propagation, shade lightness and carbonate content. The presence of some trace elements in enamel was correlated with the size (Pb, Ti, Mn) and lattice parameters (Se, Cr, Ni) of apatite nanocrystals. Some trace elements such as Ti was significantly correlated with tooth crystallographic structure and consequently with hardness and shade lightness. We conclude that the presence of trace elements in enamel could influence its physical properties.

Large-sample study of the kernel density estimators under multiplicative censoring

The multiplicative censoring model introduced in Vardi [Biometrika 76 (1989) 751--761] is an incomplete data problem whereby two independent samples from the lifetime distribution

Covariates in multipath change-point problems: Modelling and consistency of the MLE†

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Characterizing and Finding Full Dimensional Efficient Facets in DEA: A Variable Returns to Scale Specification

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On characterizing full dimensional weak facets in DEA with variable returns to scale technology

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Empirical likelihood-based confidence intervals for length-biased data

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