An efficient estimation approach to joint modeling of longitudinal and survival data.

The joint models for longitudinal and survival data have recently received significant attention in medical and epidemiological studies. Joint models typically combine linear mixed effects models for repeated measurement data and Cox models for survival time. When we are jointly modeling the longitudinal and survival data, variable selection and efficient estimation of parameters are especially important for performing reliable statistical analyzes, both of which are currently lacking in the literature. In this paper we discuss the pretest and shrinkage estimation methods for jointly modeling longitudinal data and survival time data when some of the covariates in both longitudinal and survival components may not be relevant for predicting survival times. In this situation, we fit two models: the full model that contains all the covariates and the subset model that contains a reduced number of covariates. We combine the full model estimators and the estimators that are restricted by a linear hypothesis to define pretest and shrinkage estimators. We provide their numerical mean squared errors (MSE) and relative MSE. We show that if the shrinkage dimension exceeds two, the risk of the shrinkage estimators is strictly less than that of the full model estimators. Our proposed methods are illustrated by extensive simulation studies and a real-data example.

Longitudinal Changes in Prevalence of Colorectal Cancer in Farm and Non-Farm Residents of Saskatchewan.

OBJECTIVE: To determine predictors associated with longitudinal changes in colorectal cancer (CRC) prevalence in farm and non-farm rural residents in Saskatchewan, Canada. METHODS: Data from the Saskatchewan Rural Health Study were collected from 8261 individuals nested within 4624 households at baseline survey in 2010 and 4867 individuals (2797 households) at follow-up survey in 2014. The study sample consists of 5599 individuals (baseline) and 3933 at (follow-up) (more than or equal to 50 years). RESULTS: The prevalence of CRC increased over time among rural farm (baseline: 0.8%; follow-up: 1.3%, P < 0.05) and non-farm (baseline: 1.4%; follow-up: 2.0%, P > 0.05) residents. Longitudinal predictors of CRC prevalence were: quadrant, location of home, mother ever had cancer, age, body mass index (BMI), sex, radiation, natural gas. CONCLUSIONS: Longitudinal changes in prevalence of CRC among farm and non-farm residents appear to depend on a complex combination of individual and contextual factors.

Exponentiated Weibull regression for time-to-event data.

The Weibull, log-logistic and log-normal distributions are extensively used to model time-to-event data. The Weibull family accommodates only monotone hazard rates, whereas the log-logistic and log-normal are widely used to model unimodal hazard functions. The increasing availability of lifetime data with a wide range of characteristics motivate us to develop more flexible models that accommodate both monotone and nonmonotone hazard functions. One such model is the exponentiated Weibull distribution which not only accommodates monotone hazard functions but also allows for unimodal and bathtub shape hazard rates. This distribution has demonstrated considerable potential in univariate analysis of time-to-event data. However, the primary focus of many studies is rather on understanding the relationship between the time to the occurrence of an event and one or more covariates. This leads to a consideration of regression models that can be formulated in different ways in survival analysis. One such strategy involves formulating models for the accelerated failure time family of distributions. The most commonly used distributions serving this purpose are the Weibull, log-logistic and log-normal distributions. In this study, we show that the exponentiated Weibull distribution is closed under the accelerated failure time family. We then formulate a regression model based on the exponentiated Weibull distribution, and develop large sample theory for statistical inference. We also describe a Bayesian approach for inference. Two comparative studies based on real and simulated data sets reveal that the exponentiated Weibull regression can be valuable in adequately describing different types of time-to-event data.

A comparison of methods for the analysis of recurrent health outcome data with environmental covariates.

Recurrent events such as repeated hospital admissions for the same health outcome occur frequently in environmental health studies. Dewanji and Moolgavkar proposed a flexible parametric model and a conditional likelihood analysis for recurrent events based on a Poisson process formulation. In this paper, we examine the statistical properties of the Dewanji-Moolgavkar (DM) estimator of the risk of an adverse health outcome associated with environmental exposures based on recurrent event data using computer simulation. We also compare the DM approach with both case-crossover analysis for multiple observations and time series analysis when there are no subject-specific covariates. When using a correctly specified model, the DM method produced better estimates with respect to relative mean square error when each subject had constant or curved baseline intensity functions than it did when baseline intensities were increasing or decreasing in a linear fashion. For under-specified models, the DM method outperformed case-crossover analysis for decreasing straight line intensity functions, was outperformed by case-crossover analysis for increasing straight line intensity functions, and was roughly equivalent to case-crossover analysis for constant and curved intensity functions. Case-crossover analysis produced superior risk estimates more frequently than the other two methods in the cases considered here, especially for linear representations of the baseline intensities.

Condensation and validation of a 4-item index of the Acne-QoL.

BACKGROUND: The 19-item Acne-QoL is a validated psychometric instrument designed for use in clinical trials. OBJECTIVE: The purpose of this study was to develop a condensed version of the Acne-QoL as an alternative measure of psychosocial impact for use in routine clinical practice. METHODS: Four condensed versions of the Acne-QoL were evaluated by regression analysis in a subset of 434 patients. The version with four items most broadly representative of acne-specific quality of life combined with a high level of correlation to Acne-QoL total score was selected for further validation in two different patient samples. RESULTS: The patient ranked condensation, representing items from each domain of greatest patient-perceived importance and relevance was selected for further analysis (Acne-Q(4)). The Acne-Q(4) explained 98.5% of the variation in total score of Acne-QoL. Validation of the Acne-Q(4) in a follow-up group and an independent subset of patients resulted in Pearson correlation coefficients of 0.967 (p<0.001). Construct validity of the Acne-Q(4) was confirmed by demonstrating a similar relationship as the Acne-QoL to acne severity. LIMITATIONS: There is less breadth and detail of QoL information in the Acne-Q(4) than in the Acne-QoL. The analysis relied on a total score for Acne-QoL, which the original developers did not validate. CONCLUSIONS: The Acne-Q(4) is accurately reflective of the parent instrument and may facilitate the psychometric evaluation of the impact of facial acne on patients in routine clinical practice by its practicality.

Association between air pollution and multiple respiratory hospitalizations among the elderly in Vancouver, Canada.

Recurrent events, such as repeated hospital admissions for the same health outcome, occur frequently in environmental health studies. In this study, we conducted an analysis of data on repeated respiratory hospitalizations among the elderly in Vancouver, Canada, for the period of June 1, 1995, to March 31, 1999, using a new method proposed by (Dewanji and Moolgavkar 2000, 2002) for recurrent events, and compared it with some traditional methods. In particular, we assessed the impact of ambient gaseous (SO2, NO2, CO, and O3) and particulate pollutants (PM10, PM2.5, and PM10-2.5) as well as the coefficient of haze (CoH) on recurrent respiratory hospital admissions. Using the new procedure, significant associations were found between admissions and 3-day, 5-day, and 7-day moving averages of the ambient SO2 concentrations, with the strongest association observed at the 7-day lag (RR = 1.044, 95% CI: 1.018-1.070). We also found PM10-2.5 for 3-day and 5-day lag to be significant, with the strongest association at 5-day lag (RR = 1.020, 95% CI: 1.001-1.039). No significant associations with admission were found with current day exposure.

The impact of 9/11 on the association of ambient air pollution with daily respiratory hospital admissions in a Canada-US border city, Windsor, Ontario.

The 11 September 2001 (9/11) terrorist attacks in the United States resulted in long lines of trucks at the border crossing in Windsor, Ontario. Public concern about the potential impact of these trucks spewing toxic pollutants into the air drew attention to the need to investigate the impact of 9/11 on the daily levels of air pollutants and respiratory hospitalization. In this study, significant increases in respiratory admissions were found one month and 6 months post-9/11. Mean daily respiratory admission was also significantly higher than the same period one year earlier and one year later. SO(2) and CO concentration levels were found to be generally higher after 9/11 than one year before and immediately before. Relative risk estimates of respiratory hospitalization after 9/11 showed that SO(2) (RR̂ = 1.15 for two-day, RR̂ = 1.18 for three-day, and RR̂ = 1.21 for five-day averages), NO(2) (RR̂ = 1.10 for current day), and COH (RR̂ = 1.09 for current day, RR̂ = 1.10 for two-day average) had the most significant effects after 9/11. These results suggest the need for more stringent regulatory efforts in air quality in the region in response to the changing transportation dynamics at this Canada-US border crossing.