Probabilistic analysis of cost-effectiveness models: choosing between treatment strategies for gastroesophageal reflux disease

When choosing between mutually exclusive treatment options, it is common to construct a cost-effectiveness frontier on the cost-effectiveness plane that represents efficient points from among the treatment choices. Treatment options internal to the frontier are considered inefficient and are excluded either by strict dominance or by appealing to the principle of extended dominance. However, when uncertainty is considered, options excluded under the baseline analysis may form part of the cost-effectiveness frontier. By adopting a Bayesian approach, where distributions for model parameters are specified, uncertainty in the decision concerning which treatment option should be implemented is addressed directly. The approach is illustrated using an example from a recently published cost-effectiveness analysis of different possible treatment strategies for gastroesophageal reflux disease.It is argued that probabilistic analyses should be encouraged because they have potential to quantify the strength of evidence in favor of particular treatment choices

Partial Likelihood Estimation of a Cox Model with Random Effects: an EM Algorithm based on Penalized Likelihood.

The aim of this paper is to present a general EM algorithm to estimate Mixed Proportional Hazard models including more than one random effect, through partial likelihood. We assume only that the mixing distributions admit Laplace transforms. We show how to transform inference in a single complicated model in the estimation of MPH models involving only a single frailty, which are easily manageable. We then face on gamma unobserved heterogeneity. This choice is a weak assumption as the heterogeneity distribution among survivors converges to a gamma distribution, often quickly, for many types of unobserved heterogeneity distributions. The proposed approach can thus be used to estimate a wide class of models. We describe how to use the penalized partial likelihood within the EM algorithm, to improve speed and stability. The behaviour of the estimator on different clusterings and sample sizes is assessed through a Monte Carlo study. We also provide an application on the ratiffcation of ILO conventions by developing countries over the period 1975-1995. Both the simulations and the empirical results indicate an important decrease in computing time. Furthermore, our procedure converges in settings where a standard EM algorithm does not.Random Effects, Duration analysis, Dynamic model

Bias in density estimations using strip transects in dry open-country environments in the Canary Islands

We studied bias in density estimations derived from strip transects in dry open-country in the Canary Islands. We also present some critical remarks on García-del-Rey’s (2005) paper regarding strip transects and the validity of comparisons based on population densities of birds in scrublands on Tenerife island using two different methods: territory mapping and strip transect sampling. Although strip transects with census belts of 25 m do not account for detectability, this method only slightly undervalues true density estimates, and allowed to detect more than 85% of birds present in poorly vegetated environments in the Canary Islands. Previously published works on distribution and abundance of terrestrial birds in the Canary Islands using the strip transect sampling with belts of 25 m on both sides of the observer, thus provide reliable information that only slightly underestimates true densities

Modeling Stochastic Crop Yield Expectations with a Limiting Beta Distribution

The use of plausible stochastic price processes in price risk analysis has allowed advances not seen in crop yield risk analysis. This study develops a stochastic process for yield modeling and risk management. The Pólya urn process is an internally consistent dynamic representation of yield expectations over a growing season that accommodates agronomic events such as growing degree days. The limiting distribution is the commonly used beta distribution. Binomial tree analysis of the process allows us to explore hedging decisions and crop valuation. The method is empirically flexible to accommodate alternative assumptions on the growing environment, such as intra-season input decisions.crop abandonment, crop insurance, derivative analysis, growing degree days, Pólya’s urn, stochastic process, Crop Production/Industries,