Inference on Survival Data with Covariate Measurement Error - An Imputation-based Approach

We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerative distribution. Simulations indicate high efficiency and robustness. We consider the special case where error-prone replicates are available on the unobserved true covariates. As expected, increasing the number of replicate for the unobserved covariates increases efficiency and reduces bias. We illustrate the practical utility of the proposed method with an Eastern Cooperative Oncology Group clinical trial where a genetic marker, c-myc expression level, is subject to measurement error.

GEOSTATISTICAL INFERENCE UNDER PREFERENTIAL SAMPLING

Geostatistics involves the fitting of spatially continuous models to spatially discrete data (Chil`es and Delfiner, 1999). Preferential sampling arises when the process that determines the data-locations and the process being modelled are stochastically dependent. Conventional geostatistical methods assume, if only implicitly, that sampling is non-preferential. However, these methods are often used in situations where sampling is likely to be preferential. For example, in mineral exploration samples may be concentrated in areas thought likely to yield high-grade ore. We give a general expression for the likelihood function of preferentially sampled geostatistical data and describe how this can be evaluated approximately using Monte Carlo methods. We present a model for preferential sampling, and demonstrate through simulated examples that ignoring preferential sampling can lead to seriously misleading inferences. We describe an application of the model to a set of bio-monitoring data from Galicia, northern Spain, in which making allowance for preferential sampling materially changes the inferences.

Causal Inference in Observational Studies with Outcome-Dependent Sampling

In this paper, we consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals are drawn from a population. On these individuals, information is obtained on treatment, outcome, and a few low-dimensional confounders. These individuals are then stratified according to these factors. In the second phase, a random sub-sample of individuals are drawn from each stratum, with known, stratum-specific selection probabilities. On these individuals, a rich set of confounding factors are collected. In this setting, we introduce four estimators: (1) simple inverse weighted, (2) locally efficient, (3) doubly robust and (4)enriched inverse weighted. We evaluate the finite-sample performance of these estimators in a simulation study. We also use our methodology to estimate the causal effect of trauma care on in-hospital mortality using data from the National Study of Cost and Outcomes of Trauma.

Sperm-binding fibronectin type II-module proteins are genetically linked and functionally related

Fibronectin type II (Fn2) module-containing proteins in the male genital tract are characterized by different numbers of Fn2 modules. Predominantly two classes exist which are distinct by having either two or four Fn2 modules. Minor variants with three Fn2 modules were also found in the human and the porcine epididymis. To reveal their relationship, mRNAs and proteins of representatives of these classes were studied in human, in Sus scrofa, and in rodents. Adult boars expressed members of both classes, i.e. ELSPBP1 and pB1, in subsequent regions of the epididymis, and both were under androgenic control. Human and rodent epididymides, on the other hand, alternatively contained only representatives of one of these two classes, i.e. ELSPBP1 in the human and two different pB1-related counterparts in rodents. ELSPBP1 and pB1-related genomic sequences were closely linked in chromosomal regions HSA 19q and SSC 6 q11-q21; conserved synteny between these regions is well established. On the other hand, in a syntenic region on mouse chromosome 7, ELSPBP1-related sequences were lacking. Tight binding to the sperm membrane via a choline-mediated mechanism was a common feature of the two classes of Fn2-module proteins, suggesting related function(s). However, differences in their regionalized expression patterns along the male genital tract as well as in association sites on the sperm surface suggested a species-specific sequential order in sperm binding.

A pain assessment scale for population-based studies: Development and validation of the Pain Module of the Standard Evaluation Questionnaire

The objectives of this study were to develop and validate a tool for assessing pain in population-based observational studies and to develop three subscales for back/neck, upper extremity and lower extremity pain. Based on a literature review, items were extracted from validated questionnaires and reviewed by an expert panel. The initial questionnaire consisted of a pain manikin and 34 items relating to (i) intensity of pain in different body regions (7 items), (ii) pain during activities of daily living (18 items) and (iii) various pain modalities (9 items). Psychometric validation of the initial questionnaire was performed in a random sample of the German-speaking Swiss population. Analyses included tests for reliability, correlation analysis, principal components factor analysis, tests for internal consistency and validity. Overall, 16,634 of 23,763 eligible individuals participated (70%). Test-retest reliability coefficients ranged from 0.32 to 0.97, but only three coefficients were below 0.60. Subscales were constructed combining four items for each of the subscales. Item-total coefficients ranged from 0.76 to 0.86 and Cronbach's alpha were 0.75 or higher for all subscales. Correlation coefficients between subscales and three validated instruments (WOMAC, SPADI and Oswestry) ranged from 0.62 to 0.79. The final Pain Standard Evaluation Questionnaire (SEQ Pain) included 28 items and the pain manikin and accounted for the multidimensionality of pain by assessing pain location and intensity, pain during activity, triggers and time of onset of pain and frequency of pain medication. It was found to be reliable and valid for the assessment of pain in population-based observational studies.