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Ecologists are increasingly relying on national forest inventories to address a wide variety of issues. The 'FIESTA' R package (Forest Inventory ESTimation and Analysis) is a tool that enables customized investigations using the extensive sample-based inventory data collected across all lands in the US by the US Dept of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program. To date, the complex nature of the FIA inventory constrains many users to conduct only limited analyses through existing tools with pre-specified geographic boundaries, timeframes, and auxiliary data under a single statistical estimation process. Yet, the rapid evolution of available remotely sensed data and statistical methods present the opportunity to conduct spatial and temporal analyses of forest attributes that are much more relevant to many pressing ecological, environmental, economic, and social issues in the US, The 'FIESTA' package was developed to augment the current set of available tools by providing a flexible platform that accommodates evolving technologies and leading-edge estimation techniques. The package contains a collection of functions that can query FIA databases, summarize sample-based inventory data, extract and aggregate auxiliary spatial data, and generate estimates with associated variances. [ABSTRACT FROM AUTHOR]

This short communication supports that rule‐based study designs such as the '3 + 3' study design are still being used in early phase oncology development programs despite their inferior performance to model‐based and model‐assisted designs. Statisticians have an opportunity to shape and improve early phase oncology drug development programs by introducing newer, more efficient study designs that estimate the Optimal Biological dose to their oncology trialist colleges. [ABSTRACT FROM AUTHOR]

An objective of phase I dose‐finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment frequently consists of multiple cycles of therapy. In many cases, the overall risk of toxicity for a given treatment is not fully encapsulated by observations from the first cycle, and hence it is advantageous to include toxicity outcomes from later cycles in phase I trials. Extending the follow up period in a trial naturally extends the total length of the trial which is undesirable. We present a comparison of eight methods that incorporate late onset toxicities while not extensively extending the trial length. We conduct simulation studies over a number of scenarios and in two settings; the first setting with minimal stopping rules and the second setting with a full set of standard stopping rules expected in such a dose finding study. We find that the model‐based approaches in general outperform the model‐assisted approaches, with an interval censored approach and a modified version of the time‐to‐event continual reassessment method giving the most promising overall performance in terms of correct selections and trial length. Further recommendations are made for the implementation of such methods. [ABSTRACT FROM AUTHOR]

Summary: Estimation with longitudinal Y having nonignorable dropout is considered when the joint distribution of Y and covariate X is nonparametric and the dropout propensity conditional on (Y,X) is parametric. We apply the generalised method of moments to estimate the parameters in the nonignorable dropout propensity based on estimating equations constructed using an instrument Z, which is part of X related to Y but unrelated to the dropout propensity conditioned on Y and other covariates. Population means and other parameters in the nonparametric distribution of Y can be estimated based on inverse propensity weighting with estimated propensity. To improve efficiency, we derive a model‐assisted regression estimator making use of information provided by the covariates and previously observed Y‐values in the longitudinal setting. The model‐assisted regression estimator is protected from model misspecification and is asymptotically normal and more efficient when the working models are correct and some other conditions are satisfied. The finite‐sample performance of the estimators is studied through simulation, and an application to the HIV‐CD4 data set is also presented as illustration. [ABSTRACT FROM AUTHOR]