The number and timing of time -dependent covariate measurements for the Cox regression model

The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^

The two -state model of receptor activation: The agonist and the efficacy

The Two State model describes how drugs activate receptors by inducing or supporting a conformational change in the receptor from “off” to “on”. The beta 2 adrenergic receptor system is the model system which was used to formalize the concept of two states, and the mechanism of hormone agonist stimulation of this receptor is similar to ligand activation of other seven transmembrane receptors. Hormone binding to beta 2 adrenergic receptors stimulates the intracellular production of cyclic adenosine monophosphate (cAMP), which is mediated through the stimulatory guanyl nucleotide binding protein (Gs) interacting with the membrane bound enzyme adenylylcyclase (AC). ^ The effects of cAMP include protein phosphorylation, metabolic regulation and transcriptional regulation. The beta 2 adrenergic receptor system is the most well known of its family of G protein coupled receptors. Ligands have been scrutinized extensively in search of more effective therapeutic agents at this receptor as well as for insight into the biochemical mechanism of receptor activation. Hormone binding to receptor is thought to induce a conformational change in the receptor that increases its affinity for inactive Gs, catalyzes the release of GDP and subsequent binding of GTP and activation of Gs. ^ However, some beta 2 ligands are more efficient at this transformation than others, and the underlying mechanism for this drug specificity is not fully understood. The central problem in pharmacology is the characterization of drugs in their effect on physiological systems, and consequently, the search for a rational scale of drug effectiveness has been the effort of many investigators, which continues to the present time as models are proposed, tested and modified. ^ The major results of this thesis show that for many b2 -adrenergic ligands, the Two State model is quite adequate to explain their activity, but dobutamine (+/−3,4-dihydroxy-N-[3-(4-hydroxyphenyl)-1-methylpropyl]- b -phenethylamine) fails to conform to the predictions of the Two State model. It is a weak partial agonist, but it forms a large amount of high affinity complexes, and these complexes are formed at low concentrations much better than at higher concentrations. Finally, dobutamine causes the beta 2 adrenergic receptor to form high affinity complexes at a much faster rate than can be accounted for by its low efficiency activating AC. Because the Two State model fails to predict the activity of dobutamine in three different ways, it has been disproven in its strictest form. ^

Calcareous nannofossil datums and control points for the TCMS age model of ODP Site 175-1085

An astronomically calibrated age model for the Pliocene section of Ocean Drilling Program Leg 175 Cape Basin Site 1085 based on magnetic susceptibility data was developed using shipboard biostratigraphic datums. The composite core magnetic susceptibility record was compiled using shipboard correlations between Holes 1085A and 1085B and then tuned to the record of orbital variations in eccentricity to generate an orbitally tuned age model. Magnetic susceptibility apparently records climate variations in the Cape Basin. Strong power spectra values at the 100- and 400-k.y. frequency suggest an orbital control on the beat of Pliocene climate change in the Cape Basin.

Simulated Bromoform emission and concentration using the ocean biogeochemistry model MPIOM/HAMOCC forced by 6-hourly NCEP data

Bromoform (CHBr3) is one important precursor of atmospheric reactive bromine species that are involved in ozone depletion in the troposphere and stratosphere. In the open ocean bromoform production is linked to phytoplankton that contains the enzyme bromoperoxidase. Coastal sources of bromoform are higher than open ocean sources. However, open ocean emissions are important because the transfer of tracers into higher altitude in the air, i.e. into the ozone layer, strongly depends on the location of emissions. For example, emissions in the tropics are more rapidly transported into the upper atmosphere than emissions from higher latitudes. Global spatio-temporal features of bromoform emissions are poorly constrained. Here, a global three-dimensional ocean biogeochemistry model (MPIOM-HAMOCC) is used to simulate bromoform cycling in the ocean and emissions into the atmosphere using recently published data of global atmospheric concentrations (Ziska et al., 2013) as upper boundary conditions. Our simulated surface concentrations of CHBr3 match the observations well. Simulated global annual emissions based on monthly mean model output are lower than previous estimates, including the estimate by Ziska et al. (2013), because the gas exchange reverses when less bromoform is produced in non-blooming seasons. This is the case for higher latitudes, i.e. the polar regions and northern North Atlantic. Further model experiments show that future model studies may need to distinguish different bromoform-producing phytoplankton species and reveal that the transport of CHBr3 from the coast considerably alters open ocean bromoform concentrations, in particular in the northern sub-polar and polar regions.

Simulated Methyl iodide emission and concentration using the ocean biogeochemistry model MPIOM/HAMOCC forced by OMIP data

Production pathways of the prominent volatile organic halogen compound methyl iodide (CH3I) are not fully understood. Based on observations, production of CH3I via photochemical degradation of organic material or via phytoplankton production has been proposed. Additional insights could not be gained from correlations between observed biological and environmental variables or from biogeochemical modeling to identify unambiguously the source of methyl iodide. In this study, we aim to address this question of source mechanisms with a three-dimensional global ocean general circulation model including biogeochemistry (MPIOM-HAMOCC (MPIOM - Max Planck Institute Ocean Model HAMOCC - HAMburg Ocean Carbon Cycle model)) by carrying out a series of sensitivity experiments. The simulated fields are compared with a newly available global data set. Simulated distribution patterns and emissions of CH3I differ largely for the two different production pathways. The evaluation of our model results with observations shows that, on the global scale, observed surface concentrations of CH3I can be best explained by the photochemical production pathway. Our results further emphasize that correlations between CH3I and abiotic or biotic factors do not necessarily provide meaningful insights concerning the source of origin. Overall, we find a net global annual CH3I air-sea flux that ranges between 70 and 260 Gg/yr. On the global scale, the ocean acts as a net source of methyl iodide for the atmosphere, though in some regions in boreal winter, fluxes are of the opposite direction (from the atmosphere to the ocean).