Monitoring surgical performance using risk-adjusted cumulative sum charts

The cumulative sum (CUSUM) procedure is a graphical method that is widely used for quality monitoring in industrial settings. More recently it has been used to monitor surgical outcomes whereby it 'signals' if sufficient evidence has accumulated that there has been a change in the surgical failure rate. A limitation of the standard CUSUM procedure in this context is that since it is simply based on the observed surgical outcomes, it may signal as a result of changes in the referral pattern, such as an increased proportion of high-risk patients, rather than due to a change in the actual surgical performance. We describe a new CUSUM procedure that adjusts for each patient's pre-operative risk of surgical failure through the use of a likelihood-based scoring method. The procedure is therefore ideally suited for settings where there is a variable mix of patients over time.     

Marine bacterioplankton can increase evaporation and gas transfer by metabolizing insoluble surfactants from the air–seawater interface

Hydrophobic surfactants at the air–sea interface can retard evaporative and gaseous exchange between the atmosphere and the ocean. While numerous studies have examined the metabolic role of bacterioneuston at the air–sea interface, the interactions between hydrophobic surfactants and bacterioplankton are not well constrained. A novel experimental design was developed, using Vibrio natriegens and ³H-labelled hexadecanoic acid tracer, to determine how the bacterial metabolism of fatty acids affects evaporative fluxes. In abiotic systems, >92% of the added hexadecanoic acid remained at the air–water interface. In contrast, the presence of V. natriegens cells draws down insoluble hexadecanoic acid from the air–water interface as an exponential function of time. The exponents characterizing the removal of hexadecanoic acid from the interface co-vary with the concentration of V. natriegens cells in the underlying water, with the largest exponent corresponding to the highest cell abundance. Radiochemical budgets show that evaporative fluxes from the system are linearly proportional to the quantity of hexadecanoic acid at the interface. Thus, bacterioplankton could influence the rate of evaporation and gas transfer in the ocean through the metabolism of otherwise insoluble surfactants.     

Competing Risks and Time‐Dependent Covariates

Time‐dependent covariates are frequently encountered in regression analysis for event history data and competing risks. They are often essential predictors, which cannot be substituted by time‐fixed covariates. This study briefly recalls the different types of time‐dependent covariates, as classified by Kalbfleisch and Prentice [The Statistical Analysis of Failure Time Data, Wiley, New York, 2002] with the intent of clarifying their role and emphasizing the limitations in standard survival models and in the competing risks setting. If random (internal) time‐dependent covariates are to be included in the modeling process, then it is still possible to estimate cause‐specific hazards but prediction of the cumulative incidences and survival probabilities based on these is no longer feasible. This article aims at providing some possible strategies for dealing with these prediction problems. In a multi‐state framework, a first approach uses internal covariates to define additional (intermediate) transient states in the competing risks model. Another approach is to apply the landmark analysis as described by van Houwelingen [Scandinavian Journal of Statistics 2007, 34 , 70–85] in order to study cumulative incidences at different subintervals of the entire study period. The final strategy is to extend the competing risks model by considering all the possible combinations between internal covariate levels and cause‐specific events as final states. In all of those proposals, it is possible to estimate the changes/differences of the cumulative risks associated with simple internal covariates. An illustrative example based on bone marrow transplant data is presented in order to compare the different methods.     

Integrating Remote Sensing and Ground Methods to Estimate Evapotranspiration

Evapotranspiraton (ET) is the second largest term in the terrestrial water budget after precipitation, and ET is expected to increase with global warming. ET studies are relevant to the plant sciences because over 80% of terrestrial ET is due to transpiration by plants. Remote sensing is the only feasible means for projecting ET over large landscape units. In the past decade or so, new ground and remote sensing tools have dramatically increased our ability to measure ET at the plot scale and to scale it over larger regions. Moisture flux towers and micrometeorological stations have been deployed in numerous natural and agricultural biomes and provide continuous measurements of actual ET or potential ET with an accuracy or uncertainty of 10–30%. These measurements can be scaled to larger landscape units using remotely-sensed vegetation indices (VIs), Land Surface Temperature (LST), and other satellite data. Two types of methods have been developed. Empirical methods use time-series VIs and micrometeorological data to project ET measured on the ground to larger landscape units. Physically-based methods use remote sensing data to determine the components of the surface energy balance, including latent heat flux, which determines ET. Errors in predicting ET by both types of methods are within the error bounds of the flux towers by which they are calibrated or validated. However, the error bounds need to be reduced to 10% or less for applications that require precise wide-area ET estimates. The high fidelity between ET and VIs over agricultural fields and natural ecosystems where precise ground estimates of ET are available suggests that this might be an achievable goal if ground methods for measuring ET continue to improve.     

Parametric regression on cumulative incidence function

We propose parametric regression analysis of cumulative incidence function with competing risks data. A simple form of Gompertz distribution is used for the improper baseline subdistribution of the event of interest. Maximum likelihood inferences on regression parameters and associated cumulative incidence function are developed for parametric models, including a flexible generalized odds rate model. Estimation of the long-term proportion of patients with cause-specific events is straightforward in the parametric setting. Simple goodness-of-fit tests are discussed for evaluating a fixed odds rate assumption. The parametric regression methods are compared with an existing semiparametric regression analysis on a breast cancer data set where the cumulative incidence of recurrence is of interest. The results demonstrate that the likelihood-based parametric analyses for the cumulative incidence function are a practically useful alternative to the semiparametric analyses.     

A note on including time-dependent covariate in regression model for competing risks data

Recently, regression analysis of the cumulative incidence function has gained interest in competing risks data analysis, through the model proposed by Fine and Gray (JASA 1999; 94: 496-509). In this note, we point out that inclusion of time-dependent covariates in this model can lead to serious bias. We illustrate the problems arising in such a context, using bone marrow transplant data as a working example and numerical simulations. Practical advices are given, preventing the misuse of this model.     

Estimating the cumulative risk of a false-positive test in a repeated screening program

The goal of screening tests for a chronic disease such as cancer is early detection and treatment with a consequent reduction in mortality from the disease. Screening tests, however, might produce false positive and false-negative results. With an increasing number of screening tests, it is clear that the risk of a false-positive screen, a finding with potentially significant emotional, financial, and health costs, also increases. Elmore et al. (1998, New England Journal of Medicine 338, 1089-1096), Christiansen et al. (2000, Journal of the National Cancer Institute 92, 1657-1666), and Gelfand and Wang (2000, Statistics in Medicine 19, 1865-1879) investigated this problem under the somewhat unrealistic assumption that the choice of making the decision to drop out at the kth screen does not depend upon the results of the earlier k - 1 screens. In this article we obtain sufficient and necessary conditions for their assumption to hold and use one of them to provide a method for testing the validity of the assumption. A new model which does not depend on their assumption is introduced. The maximum likelihood estimator of the cumulative risk of receiving a false-positive screen under the new model is derived and its asymptotic normality is proved. The extension of the new model by incorporating covariate information is also considered. We apply our testing method and the new model to data from the breast cancer screening trial of the Health Insurance Plan of Greater New York.     

Influence of aluminum tristearate and sucrose stearate as the dispersing agents on physical properties and release characteristics of Eudragit RS microspheres

The purpose of this research was to investigate the effects of different concentrations of polymer and sucrose stearate, aluminum tristearate as dispersing agents on microsphere properties and performance. The yield values of microspheres were over the 78%, and the encapsulation efficiencies were found to be ∼735. Particle sizes of microspheres prepared with aluminum tristearate were between 76 and 448 μm, while that of the microspheres containing sucrose stearate were between 521 and 2000 μm. Morphological and physicochemical properties of microspheres were investigated by scanning electron micrography and differential scanning calorimetry (DSC). DSC analysis indicated that verapamil hydrochloride formed a solid solution with acrylic polymers. In vitro release studies were performed using the flow-through cell method. While ∼80% of drug was released from the microspheres containing aluminum tristearate in 480 minutes, the same amount of drug was released from microspheres containing sucrose stearate in only 60 minutes. Chemical structures and concentrations of the dispersing agents were clearly effective on the physical properties of microspheres and their drug-release characteristics. Published: February 24, 2006