Acetone Production as a Result of Sodium Bisulfate Preservation of Soil Samples

The use of sodium bisulfate for field preservation of soil samples collected for the analysis of volatile organics (VOCs) can result in the formation of acetone. The work was performed at Camp Edwards, Massachusetts, as part of a multi-year investigation of the effects of training activities on the local environment. Preservation with sodium bisulfate was initiated in 1999 under regulatory mandate. The frequency of acetone detection and the quantities reported increased immediately thereafter. Correlation of duplicate sample results and a side-by-side comparison of six sample handling methods involving two analytical laboratories suggest the interaction of sodium bisulfate with naturally occurring organic matter in samples from Camp Edwards cause the formation of acetone. The data also indicate that a higher content of natural organic matter results in a greater production of acetone, and more acetone is formed the longer the sodium bisulfate is in contact with the soil.     

Fitting Survival Data to a Piecewise Linear Hazard Rate in the Presence of Covariates

This paper extends the work of KODLIN (1967), who proposed a method for analyzing patient survival data wherein the hazard rate was linearly related to the survival time. The present paper extends Kodlin's model to permit maximum likelihood estimation of the parameters so that covariate effects are included and the slope and intercept parameters are allowed to change over fixed intervals of the time domain of study. An illustration of the method using multiple myeloma data is given and the results are compared with those of Kodlin's model, the Feigl-Zelen, Zippin-Armitage model, the exponential model, and Cox's proportional hazards model.     

Estimation of contemporaneous mortality factors

We present methods for solving and making statistical inferences about marginal attack rates based on observed death rates for contemporaneous mortality factors. The general method of solution involves solving a system of nonlinear equations which depend in part on competition coefficients that express the outcome when more than one agent attacks the same host individual. For two factors, we present a detailed analysis of the effect of varying this competition coefficient. Statistical inferences are illustrated using standard large sample approximations (the delta method) and the bootstrap, which is a resampling technique. We also extend the results to allow inferences for k-values.