A semiparametric empirical likelihood method for data from an outcome-dependent sampling scheme with a continuous outcome

Outcome-dependent sampling (ODS) schemes can be a cost effective way to enhance study efficiency. The case-control design has been widely used in epidemiologic studies. However, when the outcome is measured on a continuous scale, dichotomizing the outcome could lead to a loss of efficiency. Recent epidemiologic studies have used ODS sampling schemes where, in addition to an overall random sample, there are also a number of supplemental samples that are collected based on a continuous outcome variable. We consider a semiparametric empirical likelihood inference procedure in which the underlying distribution of covariates is treated as a nuisance parameter and is left unspecified. The proposed estimator has asymptotic normality properties. The likelihood ratio statistic using the semiparametric empirical likelihood function has Wilks-type properties in that, under the null, it follows a chi-square distribution asymptotically and is independent of the nuisance parameters. Our simulation results indicate that, for data obtained using an ODS design, the semiparametric empirical likelihood estimator is more efficient than conditional likelihood and probability weighted pseudolikelihood estimators and that ODS designs (along with the proposed estimator) can produce more efficient estimates than simple random sample designs of the same size. We apply the proposed method to analyze a data set from the Collaborative Perinatal Project (CPP), an ongoing environmental epidemiologic study, to assess the relationship between maternal polychlorinated biphenyl (PCB) level and children's IQ test performance.     

On the use of star-shaped genealogies in inference of coalescence times

Genealogies from rapidly growing populations have approximate "star" shapes. We study the degree to which this approximation holds in the context of estimating the time to the most recent common ancestor (T(MRCA)) of a set of lineages. In an exponential growth scenario, we find that unless the product of population size (N) and growth rate (r) is at least approximately 10(5), the "pairwise comparison estimator" of T(MRCA) that derives from the star genealogy assumption has bias of 10-50%. Thus, the estimator is appropriate only for large populations that have grown very rapidly. The "tree-length estimator" of T(MRCA) is more biased than the pairwise comparison estimator, having low bias only for extremely large values of Nr.     

Instrumental variable estimation of complier causal treatment effect with interval-censored data

Assessing causal treatment effect on a time-to-event outcome is of key interest in many scientific investigations. Instrumental variable (IV) is a useful tool to mitigate the impact of endogenous treatment selection to attain unbiased estimation of causal treatment effect. Existing development of IV methodology, however, has not attended to outcomes subject to interval censoring, which are ubiquitously present in studies with intermittent follow-up but are challenging to handle in terms of both theory and computation. In this work, we fill in this important gap by studying a general class of causal semiparametric transformation models with interval-censored data. We propose a nonparametric maximum likelihood estimator of the complier causal treatment effect. Moreover, we design a reliable and computationally stable expectation–maximization (EM) algorithm, which has a tractable objective function in the maximization step via the use of Poisson latent variables. The asymptotic properties of the proposed estimators, including the consistency, asymptotic normality, and semiparametric efficiency, are established with empirical process techniques. We conduct extensive simulation studies and an application to a colorectal cancer screening data set, showing satisfactory finite-sample performance of the proposed method as well as its prominent advantages over naive methods.     

A note on semiparametric efficient inference for two-stage outcome-dependent sampling with a continuous outcome

Outcome-dependent sampling designs have been shown to be a cost-effectiveway to enhance study efficiency. We show that the outcome-dependentsampling design with a continuous outcome can be viewed as anextension of the two-stage case-control designs to the continuous-outcomecase. We further show that the two-stage outcome-dependent samplinghas a natural link with the missing-data and biased-samplingframeworks. Through the use of semiparametric inference andmissing-data techniques, we show that a certain semiparametricmaximum-likelihood estimator is computationally convenient andachieves the semiparametric efficient information bound. Wedemonstrate this both theoretically and through simulation.     

Semiparametric maximum likelihood for measurement error model regression

This paper presents an EM algorithm for semiparametric likelihood analysis of linear, generalized linear, and nonlinear regression models with measurement errors in explanatory variables. A structural model is used in which probability distributions are specified for (a) the response and (b) the measurement error. A distribution is also assumed for the true explanatory variable but is left unspecified and is estimated by nonparametric maximum likelihood. For various types of extra information about the measurement error distribution, the proposed algorithm makes use of available routines that would be appropriate for likelihood analysis of (a) and (b) if the true x were available. Simulations suggest that the semiparametric maximum likelihood estimator retains a high degree of efficiency relative to the structural maximum likelihood estimator based on correct distributional assumptions and can outperform maximum likelihood based on an incorrect distributional assumption. The approach is illustrated on three examples with a variety of structures and types of extra information about the measurement error distribution.     

Ventilatory effects of gap junction blockade in the RTN in awake rats

We tested the hypothesis that carbenoxolone, a pharmacological inhibitor of gap junctions, would reduce the ventilatory response to CO(2) when focally perfused within the retrotrapezoid nucleus (RTN). We tested this hypothesis by measuring minute ventilation (V(E)), tidal volume (V(T)), and respiratory frequency (F(R)) responses to increasing concentrations of inspired CO(2) (Fi(CO(2)) = 0-8%) in rats during wakefulness. We confirmed that the RTN was chemosensitive by perfusing the RTN unilaterally with either acetazolamide (AZ; 10 microM) or hypercapnic artificial cerebrospinal fluid equilibrated with 50% CO(2) (pH approximately 6.5). Focal perfusion of AZ or hypercapnic aCSF increased V(E), V(T), and F(R) during exposure to room air. Carbenoxolone (300 microM) focally perfused into the RTN decreased V(E) and V(T) in animals <11 wk of age, but V(E) and V(T) were increased in animals >12 wk of age. Glyzyrrhizic acid, a congener of carbenoxolone, did not change V(E), V(T), or F(R) when focally perfused into the RTN. Carbenoxolone binds to the mineralocorticoid receptor, but spironolactone (10 microM) did not block the disinhibition of V(E) or V(T) in older animals when combined with carbenoxolone. Thus the RTN is a CO(2) chemosensory site in all ages tested, but the function of gap junctions in the chemosensory process varies substantially among animals of different ages: gap junctions amplify the ventilatory response to CO(2) in younger animals, but appear to inhibit the ventilatory response to CO(2) in older animals.     

Time-dependent ROC curves for censored survival data and a diagnostic marker

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.     

Inverse Probability of Censoring Weighted Estimates of Kendall's τ for Gap Time Analyses

Summary In life history studies, interest often lies in the analysis of the interevent, or gap times and the association between event times. Gap time analyses are challenging however, even when the length of follow‐up is determined independently of the event process, because associations between gap times induce dependent censoring for second and subsequent gap times. This article discusses nonparametric estimation of the association between consecutive gap times based on Kendall's τ in the presence of this type of dependent censoring. A nonparametric estimator that uses inverse probability of censoring weights is provided. Estimates of conditional gap time distributions can be obtained following specification of a particular copula function. Simulation studies show the estimator performs well and compares favorably with an alternative estimator. Generalizations to a piecewise constant Clayton copula are given. Several simulation studies and illustrations with real data sets are also provided.     

Double‐Robust Semiparametric Estimator for Differences in Restricted Mean Lifetimes in Observational Studies

Summary Restricted mean lifetime is often of direct interest in epidemiologic studies involving censored survival times. Differences in this quantity can be used as a basis for comparing several groups. For example, transplant surgeons, nephrologists, and of course patients are interested in comparing posttransplant lifetimes among various types of kidney transplants to assist in clinical decision making. As the factor of interest is not randomized, covariate adjustment is needed to account for imbalances in confounding factors. In this report, we use semiparametric theory to develop an estimator for differences in restricted mean lifetimes although accounting for confounding factors. The proposed method involves building working models for the time‐to‐event and coarsening mechanism (i.e., group assignment and censoring). We show that the proposed estimator possesses the double robust property; i.e., when either the time‐to‐event or coarsening process is modeled correctly, the estimator is consistent and asymptotically normal. Simulation studies are conducted to assess its finite‐sample performance and the method is applied to national kidney transplant data.     

Nonparametric and semiparametric estimation with sequentially truncated survival data

In observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or exceeds another random time, that is, the truncation time. In more complex settings, observation may require a particular ordering of event times; we refer to this as sequential truncation. Estimators of the event time distribution have been developed for simple left-truncated or right-truncated data. However, these estimators may be inconsistent under sequential truncation. We propose nonparametric and semiparametric maximum likelihood estimators for the distribution of the event time of interest in the presence of sequential truncation, under two truncation models. We show the equivalence of an inverse probability weighted estimator and a product limit estimator under one of these models. We study the large sample properties of the proposed estimators and derive their asymptotic variance estimators. We evaluate the proposed methods through simulation studies and apply the methods to an Alzheimer's disease study. We have developed an R package, seqTrun , for implementation of our method.     

Numerical equivalence of imputing scores and weighted estimators in regression analysis with missing covariates

Imputation, weighting, direct likelihood, and direct Bayesian inference (Rubin, 1976) are important approaches for missing data regression. Many useful semiparametric estimators have been developed for regression analysis of data with missing covariates or outcomes. It has been established that some semiparametric estimators are asymptotically equivalent, but it has not been shown that many are numerically the same. We applied some existing methods to a bladder cancer case-control study and noted that they were the same numerically when the observed covariates and outcomes are categorical. To understand the analytical background of this finding, we further show that when observed covariates and outcomes are categorical, some estimators are not only asymptotically equivalent but also actually numerically identical. That is, although their estimating equations are different, they lead numerically to exactly the same root. This includes a simple weighted estimator, an augmented weighted estimator, and a mean-score estimator. The numerical equivalence may elucidate the relationship between imputing scores and weighted estimation procedures.     

Experimental design for analysis of complex kinetics using surface plasmon resonance

Using BIAcore surface plasmon resonance technology, we found that the real-time association kinetics of Fabs specific for hen egg-white lysozyme did not conform to a 1:1 Langmuir association model. Heterogeneity of the components is not the source of the complex kinetics. Informed by independent structural data suggesting conformational flexibility differences among these antibodies, we chose global mathematical analysis based on a two-phase model, consistent with the encounter-docking view of protein-protein associations. Experimental association times (T(a)) from 2 to 250 min revealed that initial dissociation rates decreased with increasing T(a), confirming a multiphasic association. The relationship between observed dissociation rate and T(a) is characteristic of each antibody-antigen complex. We define a new parameter, T(50), the time at which the encounter and final complexes are of equimolar concentration. The observed T(50) is a function of analyte concentration and the encounter and docking rate constants. Simulations showed that when the ligand is saturated at high analyte concentrations, T(50) reaches a minimum value, T(50)(MIN), which can be used to compare antigen-antibody complexes. For high-affinity complexes with rapid rearrangement to a stable complex, T(50)(MIN) approaches T(1/2) of the rearrangement forward rate constant. We conclude that experiments with a range of T(a) are essential to assess the nature of the kinetics, regardless of whether a two-state or 1:1 model is applicable. We suggest this strategy because each T(a) potentially reveals a different distribution of molecular states; for two-step analysis, a range of T(a) that brackets T(50) is optimal.     

Efficient estimation of the distribution of quality-adjusted survival time

Quality of life is an important aspect in evaluation of clinical trials of chronic diseases, such as cancer and AIDS. Quality-adjusted survival analysis is a method that combines both the quantity and quality of a patient's life into one single measure. In this paper, we discuss the efficiency of weighted estimators for the distribution of quality-adjusted survival time. Using the general representation theorem for missing data processes, we are able to derive an estimator that is more efficient than the one proposed in Zhao and Tsiatis (1997, Biometrika 84, 339-348). Simulation experiments are conducted to assess the small sample properties of this estimator and to compare it with the semiparametric efficiency bound. The value of this estimator is demonstrated from an application of the method to a data set obtained from a breast cancer clinical trial.     

The Semiparametric Case‐Only Estimator

Summary We propose a semiparametric case‐only estimator of multiplicative gene–environment or gene–gene interactions, under the assumption of conditional independence of the two factors given a vector of potential confounding variables. Our estimator yields valid inferences on the interaction function if either but not necessarily both of two unknown baseline functions of the confounders is correctly modeled. Furthermore, when both models are correct, our estimator has the smallest possible asymptotic variance for estimating the interaction parameter in a semiparametric model that assumes that at least one but not necessarily both baseline models are correct.     

Use of relaxation-edited one-dimensional and two dimensional nuclear magnetic resonance spectroscopy to improve detection of small metabolites in blood plasma

The 1H nuclear magnetic resonance (NMR) spectra of biological samples, such as blood plasma and tissues, are information rich but data complex owing to superposition of the resonances from a multitude of different chemical entities in multiple-phase compartments, hampering detection and subsequent resonance assignments. To overcome these problems, several spectral-editing NMR experiments are described here, combining spin-relaxation filters (based on T(1), T(rho), and T(2)) with both one-dimensional and two-dimensional (2D) NMR spectroscopy. These techniques enable the separation of NMR resonances based on their relaxation times and allow simplification of the complex spectra. In this paper, the approach is exemplified using a control human blood plasma, which is a complex mixture of proteins, lipoproteins, and small-molecule metabolites. In the case of T(1rho)- and T(2)-edited 2D NMR experiments, a "flip-back" pulse was introduced after the relaxation editing to make the phase cycling of the "relaxation filter" and the 2D NMR part independent, thus enabling easy implementation of the phase-sensitive 2D NMR experiments. These methods also permit much higher receiver gains to be used to reduce digitization error, in particular, for the small resonances, which are sometimes vitally important for metabonomics studies. Both pulse sequences and experimental results are discussed for T(1)-, T(1rho)-, and T(2)-filtered COSY, T(2)-filtered phase-sensitive DQF-COSY, and T(1), T(1rho)-, and T(2)-filtered TOCSY NMR.     

Bootstrap choice of estimators in parametric and semiparametric families: an extension of EIC

Ishiguro, Sakamoto, and Kitagawa (1997, Annals of the Institute of Statistical Mathematics 49, 411-434) proposed EIC as an extension of Akaike criterion (AIC); the idea leading to EIC is to correct the bias of the log-likelihood, considered as an estimator of the Kullback-Leibler information, using bootstrap. We develop this criterion for its use in multivariate semiparametric situations, and argue that it can be used for choosing among parametric and semiparametric estimators. A simulation study based on aregression model shows that EIC is better than its competitors although likelihood cross-validation performs nearly as well except for small sample size. Its use is illustrated by estimating the mean evolution of viral RNA levels in a group of infants infected by HIV.     

Semiparametric regression analysis of interval-censored data

We propose a semiparametric approach to the proportional hazards regression analysis of interval-censored data. An EM algorithm based on an approximate likelihood leads to an M-step that involves maximizing a standard Cox partial likelihood to estimate regression coefficients and then using the Breslow estimator for the unknown baseline hazards. The E-step takes a particularly simple form because all incomplete data appear as linear terms in the complete-data log likelihood. The algorithm of Turnbull (1976, Journal of the Royal Statistical Society, Series B 38, 290-295) is used to determine times at which the hazard can take positive mass. We found multiple imputation to yield an easily computed variance estimate that appears to be more reliable than asymptotic methods with small to moderately sized data sets. In the right-censored survival setting, the approach reduces to the standard Cox proportional hazards analysis, while the algorithm reduces to the one suggested by Clayton and Cuzick (1985, Applied Statistics 34, 148-156). The method is illustrated on data from the breast cancer cosmetics trial, previously analyzed by Finkelstein (1986, Biometrics 42, 845-854) and several subsequent authors.     

DISTINGUISHING PLANT TISSUES WITH MAGNETIC RESONANCE MICROSCOPY

Using magnetic resonance microscopy at 7 Tesla, spin-lattice relaxation times (Tl), spinspin relaxation times (T2), and spin densities (N(H)) of live squash stem tissues were measured in order to gain an understanding of live tissue water relations and to improve imaging protocols that allow the clear distinction of tissues. T1 and N(H) differences among tissues were found to be much greater than T2 differences. Most tissues can be distinguished with high resolution T1 weighted images. Sclerenchyma and vessel elements are more easily seen with N(H) weighted images because of the relatively low water content of sclerenchyma and high water content of vessel elements. This work demonstrates further improvements in the ability of MR microscopy to distinguish tissues and individual cells and also to make measurements regarding water status at the tissue and cellular level of live plants in a nondestructive manner.     

Random effects logistic regression analysis with auxiliary covariates

We study a semiparametric estimation method for the random effects logistic regression when there is auxiliary covariate information about the main exposure variable. We extend the semiparametric estimator of Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the random effects model using the best linear unbiased prediction approach of Henderson (1975, Biometrics 31, 423-448). The method can be used to handle the missing covariate or mismeasured covariate data problems in a variety of real applications. Simulation study results show that the proposed method outperforms the existing methods. We analyzed a data set from the Collaborative Perinatal Project using the proposed method and found that the use of DDT increases the risk of preterm births among U.S. children.     

A semiparametric estimate of treatment effects with censored data

A semiparametric estimate of an average regression effect with right-censored failure time data has recently been proposed under the Cox-type model where the regression effect beta(t) is allowed to vary with time. In this article, we derive a simple algebraic relationship between this average regression effect and a measurement of group differences in k-sample transformation models when the random error belongs to the G(rho) family of Harrington and Fleming (1982, Biometrika 69, 553-566), the latter being equivalent to the conditional regression effect in a gamma frailty model. The models considered here are suitable for the attenuating hazard ratios that often arise in practice. The results reveal an interesting connection among the above three classes of models as alternatives to the proportional hazards assumption and add to our understanding of the behavior of the partial likelihood estimate under nonproportional hazards. The algebraic relationship provides a simple estimator under the transformation model. We develop a variance estimator based on the empirical influence function that is much easier to compute than the previously suggested resampling methods. When there is truncation in the right tail of the failure times, we propose a method of bias correction to improve the coverage properties of the confidence intervals. The estimate, its estimated variance, and the bias correction term can all be calculated with minor modifications to standard software for proportional hazards regression.