Monte Carlo modified profile likelihood in models for clustered data

The main focus of the analysts who deal with clustered data is usually not on the clustering variables, and hence the group-specific parameters are treated as nuisance. If a fixed effects formulation is preferred and the total number of clusters is large relative to the single-group sizes, classical frequentist techniques relying on the profile likelihood are often misleading. The use of alternative tools, such as modifications to the profile likelihood or integrated likelihoods, for making accurate inference on a parameter of interest can be complicated by the presence of nonstandard modelling and/or sampling assumptions. We show here how to employ Monte Carlo simulation in order to approximate the modified profile likelihood in some of these unconventional frameworks. The proposed solution is widely applicable and is shown to retain the usual properties of the modified profile likelihood. The approach is examined in two instances particularly relevant in applications, i.e. missing-data models and survival models with unspecified censoring distribution. The effectiveness of the proposed solution is validated via simulation studies and two clinical trial applications

Doubly Robust Censoring Unbiased Transformations

We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. Additionally, we discuss how doubly robust censoring unbiased transformations can be utilized for regression with missing data, in causal inference problems, or with current status dat

Bayesian semiparametric inference for multivariate doubly-interval-censored data

Based on a data set obtained in a dental longitudinal study, conducted in Flanders (Belgium), the joint time to caries distribution of permanent first molars was modeled as a function of covariates. This involves an analysis of multivariate continuous doubly-interval-censored data since: (i) the emergence time of a tooth and the time it experiences caries were recorded yearly, and (ii) events on teeth of the same child are dependent. To model the joint distribution of the emergence times and the times to caries, we propose a dependent Bayesian semiparametric model. A major feature of the proposed approach is that survival curves can be estimated without imposing assumptions such as proportional hazards, additive hazards, proportional odds or accelerated failure time.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS368 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Saving Decisions of the Working Poor: Short-and Long-Term Horizons

We explore the predictive capacity of short-horizon time preference decisions for long-horizon investment decisions. We use experimental evidence from a sample of Canadian working poor. Each subject made a set of decisions trading off present and future amounts of money. Decisions involved both short and long time horizons, with stakes ranging up to six hundred dollars. Short horizon preference decisions do well in predicting the long-horizon investment decisions. These short horizon questions are much less expensive to administer but yield much higher estimated discount rates. We find no evidence that the present-biased preference measures generated from the short-horizon time preference decisions indicate any bias in long-term investment decisions. We also show that individuals are heterogeneous with respect to discount rates generated by short-horizon time preference decisions and long-horizon time preference decisions. Dans cet article, nous évaluons si les préférences exprimées pour le présent peuvent prédire les décisions d’investissement dans le long terme. L’article mobilise l’approche de l’économie expérimentale avec comme participants des travailleurs canadiens à faibles revenus. Chaque participant est invité à choisir entre une somme qu’il peut toucher à très court terme et un montant plus élevé, mais qui ne lui sera versé que plus tard dans le temps. Pour certains choix, les montants ne seront disponibles que dans 7 ans et peuvent atteindre jusqu’à 600 $. Nous trouvons que les décisions entre le présent et un horizon de court terme permettent de prédire les arbitrages réalisés par les participants entre le présent et des décisions à plus long terme. Ce résultat est important dans la mesure où il est plus difficile et coûteux d’étudier les décisions de long terme que celles de court terme. Nous observons également une forte hétérogénéité entre les participants relativement à leurs taux d’escompte de court et de long terme.intertemporal choice, field experiments, risk attitudes, choix intertemporels, économie expérimentale, attitudes vis-à-vis le risque

A Conjoint Analysis of Public Preferences for Agricultural Land Preservation

Public preferences for the nonmarket services of permanently preserved agricultural land are measured and compared using conjoint analysis. The results from a survey of 199 Delawareans suggest environmental and nonmarket-agricultural services are the most important preserved-land attributes. Results also suggest that open space associated with wetlands on farms is neither an amenity nor a disamenity. On the margin, preserved parcels with agricultural and environmental attributes provide net benefits, which may exceed $1,000,000 for a 1,000-acre parcel. Preserved forestland provides benefits per acre that are statistically equivalent to cropland, though forestland may be less expensive to preserve.Land Economics/Use,

Sample size and robust marginal methods for cluster-randomized trials with censored event times

This is the peer reviewed version of the following article: Zhong Yujie, and Cook Richard J. (2015), Sample size and robust marginal methods for cluster-randomized trials with censored event times, Statist. Med., 34, pages 901–923. doi: 10.1002/sim.6395, which has been published in final form at http://dx.doi.org/10.1002/sim.6395. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.In cluster-randomized trials, intervention effects are often formulated by specifying marginal models, fitting them under a working independence assumption, and using robust variance estimates to address the association in the responses within clusters. We develop sample size criteria within this framework, with analyses based on semiparametric Cox regression models fitted with event times subject to right censoring. At the design stage, copula models are specified to enable derivation of the asymptotic variance of estimators from a marginal Cox regression model and to compute the number of clusters necessary to satisfy power requirements. Simulation studies demonstrate the validity of the sample size formula in finite samples for a range of cluster sizes, censoring rates and degrees of within-cluster association among event times. The power and relative efficiency implications of copula misspecification is studied, as well as the effect of within-cluster dependence in the censoring times. Sample size criteria and other design issues are also addressed for the setting where the event status is only ascertained at periodic assessments and times are interval censored.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626