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Abstract Background To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis. Methods A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases’ characteristics were conducted. Results The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values. Conclusions The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

Background: To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis. Methods: A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases' characteristics were conducted. Results: The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values. Conclusions: The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes. [ABSTRACT FROM AUTHOR]

In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach for covariate adjustment to gain credibility and efficiency while producing asymptotically valid inference even when the model is incorrect. In this article we present three considerations for better practice when model-assisted inference is applied to adjust for covariates under simple or covariate-adaptive randomized trials: (a) guaranteed efficiency gain: a model-assisted method should often gain but never hurt efficiency; (b) wide applicability: a valid procedure should be applicable, and preferably universally applicable, to all commonly used randomization schemes; (c) robust standard error: variance estimation should be robust to model misspecification and heteroscedasticity. To achieve these, we recommend a model-assisted estimator under an analysis of heterogeneous covariance working model that includes all covariates used in randomization. Our conclusions are based on an asymptotic theory that provides a clear picture of how covariate-adaptive randomization and regression adjustment alter statistical efficiency. Our theory is more general than the existing ones in terms of studying arbitrary functions of response means (including linear contrasts, ratios, and odds ratios), multiple arms, guaranteed efficiency gain, optimality, and universal applicability. for this article are available online. [ABSTRACT FROM AUTHOR]

To improve the precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.

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]

The composition and digestion of phospholipid-rich foods have important effects on the health of the body. Herein, a model-assisted liquid chromatography coupling mass spectrometry (LC-MS) method was established to analyze the phosphatidylcholine (PC) and lyso-phosphatidylcholine (LPC) species in krill oil before and after digestion. According to the confirmed PC and LPC species in the IDA (information dependent acquisition) results, three categories of mathematical models were set up, involving the retention time (RT), carbon number and unsaturation degree of the fatty acyl chain. All of the regression coefficient values (R2) were greater than 0.90, showing satisfactory fitting results. On this basis, using the computationally created precursor ion mass of PC and LPC species, 12 extra PC species and 4 LPC species were found in the SWATH (sequential windowed acquisition of all theoretical fragment ions) results. The PC and LPC compositions in the final digestive products had obvious differences among the different krill oils with different phospholipid content. Furthermore, more than half of the LPC species in the final digestive products were newly generated, indicating that LPC was one of basic constituents in the digestive products of krill oil. In conclusion, model-assisted hybrid IDA and SWATH acquisition has excellent detection performance, contributing to deep studies of the formations and functions of phospholipids. [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]

Background/Aims: In pediatric oncology, a Phase II trial often utilizes a safety run-in phase followed by an efficacy phase that enrolls at the dose level selected based on the safety run-in. Different from a Phase I trial, a Phase II safety run-in often assesses a very small number of dose levels. In the context of a safety run-in that assesses two or three dose levels, this article aims to compare three design methods, including the algorithm-based designs 3+3 and Rolling 6, and the model-assisted designs such as the Bayesian optimal interval design. Methods: Extensive simulations were conducted to evaluate and compare operating characteristics of the three design methods for a safety run-in with two or three dose levels, varying the starting dose level. Results: The performance of algorithm-based and model-assisted designs can be influenced by selection of the starting dose level, with trials starting at a lower dose level having a higher probability of selecting a low dose or considering all doses as toxic. The impact is larger for 3+3 and Rolling 6 but to a lesser extent for Bayesian optimal interval design. For a safety run-in with two dose levels, using 3+3 or Rolling 6 and starting at the higher dose often lead to similar performance to Bayesian optimal interval design. For safety run-in with three dose levels, starting at the middle dose with 3+3, Rolling 6 or Bayesian optimal interval design is a good compromise between improving correct dose selection and imposing a toxic dose to less patients. Conclusions: Despite being sensitive to the starting dose level, the 3+3, Rolling 6 and Bayesian optimal interval designs overall demonstrate reasonable performance, which can be further improved with wise selection of the starting dose level. The Rolling 6 design remains the recommended design method especially if pharmacokinetics is important or required with this design having the feature of treating six patients per dose level. When designing a safety run-in, selection of a design method or selection of a starting dose should consider both the performance of the design approaches with different choices of a starting dose level and the magnitude of safety concerns with the dose levels under investigation. [ABSTRACT FROM AUTHOR]

When estimation of model parameters is the target in survey data, the inference on the model can be based on a pure model-based approach or a model-assisted approach which is a hybrid approach combining design-based and model-based methods. This study aims to compare these methods for survival data that are gathered from a stratified random sampling design. The Accelerated Failure Time (AFT) model was fitted to describe the relationship between the censored response variable and the explanatory variables. Resampling methods with different sample sizes and different sampling designs from a real dataset were considered in the study to generate samples. The AFT models were fitted to each of the samples using, firstly a pure model-based method ignoring the survey design and weights, secondly a model-based method with survey weights, and finally a model-assisted method considering both survey design and weights. Squared bias, variance, and mean squared error (MSE) were used to compare the three approaches. The AFT model, with all covariates and the best AFT model with the best set of covariates and distribution, were analyzed. Even though it was challenging to select the best method for all cases, the second and third approaches worked better for small samples than the first approach. [ABSTRACT FROM AUTHOR]

This study assesses the performance of the model-assisted Design of Experiment (mDoE) software toolbox for the design of two microalgae bioprocesses. The mDoE-toolbox was applied to maximize biomass growth for Desmodesmus pseudocommunis in a photobioreactor by varying the light intensity and pH and for Chlorella vulgaris in shake flasks, by varying the light intensity and duration. For both case studies, a mathematical mechanistic model was applied. In the first study only one experiment was necessary to adapt the mathematical model and identify a combination of light intensity and pH that improved biomass yield, as confirmed experimentally. In the second study, no well-established model was available for the specific experimental arrangement. On the basis of the literature, a mathematical model was constructed and a first cycle of mDoE was performed, thus identifying the desired factor combinations. Experiments confirmed the high biomass yield but revealed shortcomings of the model. The model was improved and a second cycle of mDoE was performed. The recommended factor combinations from both cycles were comparable. The mDoE was found to be a time-saving, cost-effective and useful method enabling the identification of factor combinations leading to high biomass production for the design of two different microalgae bioprocesses with low experimental effort.

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]

This paper proposes a hybrid data-driven method for fast solutions of preventive security-constrained optimal power flow (SCOPF) of power systems. The proposed method formulates the SCOPF problem as constraints-satisfying training of a deep reinforcement learning (DRL) agent, where the action-value function of DRL is augmented by contingency security constraints. In the training process, the proposed method hybridizes the primal-dual deep deterministic policy gradient (PD-DDPG) and the classic SCOPF model. Instead of building reward critic networks and cost critic networks via interacting with the environment (i.e., power flow equations), the actor gradients are approximated by solving KKT conditions of the Lagrangian. Finally, with the formulated sparse Jacobians of constraints and sparse Hessians of Lagrangians, the interior point method is incorporated in PD-DDPG to derive the parameters updating rule of the DRL agent. Numerical tests are carried out on a modified IEEE 57-bus system and a modified IEEE 300-bus system for critical contingencies. The results show that the well-trained DRL agent can rapidly (real-time) obtain high-quality SCOPF solutions that satisfy the security constraints. [ABSTRACT FROM AUTHOR]

Sample designs are typically developed to estimate summary statistics such as means, proportions and prevalences. Analytical outputs may also be a priority but there are fewer methods and results on how to efficiently design samples for the fitting and estimation of statistical models. This paper develops a general approach for determining efficient sampling designs for probability‐weighted maximum likelihood estimators and considers application to generalized linear models. We allow for non‐ignorable sampling, including outcome‐dependent sampling. The new designs have probabilities of selection closely related to influence statistics such as dfbeta and Cook's distance. The new approach is shown to perform well in a simulation based on data from the New Zealand Health Survey. [ABSTRACT FROM AUTHOR]

• The traditional deep deterministic policy gradient algorithm is improved to adapt to the under-frequency load shedding problem. • A suitable reward function is designed for the under-frequency load shedding problem. • The dual experience pool structure is designed to speed up the convergence of the algorithm and increase the steadiness of the algorithm. • A novel under-frequency load shedding method is proposed, which integrates data-driven and model-driven ideas. Under-frequency load shedding control framework based on this proposed method is designed. With the access of a high percentage of new energy sources, power system frequency stability is challenged. Under-frequency load shedding (UFLS) is one of the primary measures to maintain frequency stability. Due to the mismatch between the amount of load shed by the traditional UFLS methods and the actual active power deficit, a new UFLS method needs to be designed. An approach utilizing a deep deterministic policy gradient (DDPG) algorithm for the problem is proposed. First, the DDPG algorithm is modified to adapt to the UFLS problem. Then, the idea of model-driven is introduced to improve the validity of the model. Thus, a novel UFLS method is proposed, which integrates data-driven and model-driven ideas. Furthermore, a structure called the dual experience pool is designed to accelerate training speed and improve stability. Based on the proposed method, a UFLS control framework is designed. Finally, the suggested methodology is validated using the IEEE-39 bus system and the Fujian power grid as test cases. [ABSTRACT FROM AUTHOR]

The composition and digestion of phospholipid-rich foods have important effects on the health of the body. Herein, a model-assisted liquid chromatography coupling mass spectrometry (LC-MS) method was established to analyze the phosphatidylcholine (PC) and lyso-phosphatidylcholine (LPC) species in krill oil before and after digestion. According to the confirmed PC and LPC species in the IDA (information dependent acquisition) results, three categories of mathematical models were set up, involving the retention time (RT), carbon number and unsaturation degree of the fatty acyl chain. All of the regression coefficient values (R2) were greater than 0.90, showing satisfactory fitting results. On this basis, using the computationally created precursor ion mass of PC and LPC species, 12 extra PC species and 4 LPC species were found in the SWATH (sequential windowed acquisition of all theoretical fragment ions) results. The PC and LPC compositions in the final digestive products had obvious differences among the different krill oils with different phospholipid content. Furthermore, more than half of the LPC species in the final digestive products were newly generated, indicating that LPC was one of basic constituents in the digestive products of krill oil. In conclusion, model-assisted hybrid IDA and SWATH acquisition has excellent detection performance, contributing to deep studies of the formations and functions of phospholipids.

Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into an estimation of the population total of a survey variable in order to improve the estimation precision. We consider a generalized regression estimator formulated under a model-assisted framework, in which a regression model is used for the available covariates, and the estimator retains the basic design-based properties. The generalized regression estimator is shown to improve the efficiency of the design-based Horvitz-Thompson estimator when the number of covariates is fixed. We investigate the performance of the generalized regression estimator when the number of covariates p is allowed to diverge as the sample size n increases. We examine two approaches. First, the model parameter is estimated using the weighted least squares method when p < n. Second, the Lasso method is employed when the model parameter is sparse. We show that under an assisted model and certain conditions on the joint distribution of the covariates, as well as the divergence rates of n and p, the generalized regression estimator is asymptotically more efficient than the Horvitz-Thompson estimator, and is robust against a model misspecification. We also study the consistency of the variance estimation for the generalized regression estimator. Our theoretical results are corroborated by simulation studies and an example. [ABSTRACT FROM AUTHOR]

Nonparametric model-assisted estimators have been proposed to improve estimates of finite population parameters. Flexible nonparametric models provide more reliable estimators when a parametric model is misspecified. In this article, we propose an information criterion to select appropriate auxiliary variables to use in an additive model-assisted method. We approximate the additive nonparametric components using polynomial splines and extend the Bayesian Information Criterion (BIC) for finite populations. By removing irrelevant auxiliary variables, our method reduces model complexity and decreases estimator variance. We establish that the proposed BIC is asymptotically consistent in selecting the important explanatory variables when the true model is additive without interactions, a result supported by our numerical study. Our proposed method is easier to implement and better justified theoretically than the existing method proposed in the literature. [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]

Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables.

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurements of AGB may be a challenge because of their remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund Oceanium project that monitors 10,000 ha of mangrove plantations. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric support vector regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2, and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m, respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in young mangrove plantations.

Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha − 1 with a standard error of 11.17 Mgha − 1 (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha − 1 with a slightly smaller standard error of 10.57 Mgha − 1 (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha − 1 . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precision.

Background/aims: In pediatric oncology, a Phase II trial often utilizes a safety run-in phase followed by an efficacy phase that enrolls at the dose level selected based on the safety run-in. Different from a Phase I trial, a Phase II safety run-in often assesses a very small number of dose levels. In the context of a safety run-in that assesses two or three dose levels, this article aims to compare three design methods, including the algorithm-based designs 3 + 3 and Rolling 6, and the model-assisted designs such as the Bayesian optimal interval design., Methods: Extensive simulations were conducted to evaluate and compare operating characteristics of the three design methods for a safety run-in with two or three dose levels, varying the starting dose level., Results: The performance of algorithm-based and model-assisted designs can be influenced by selection of the starting dose level, with trials starting at a lower dose level having a higher probability of selecting a low dose or considering all doses as toxic. The impact is larger for 3 + 3 and Rolling 6 but to a lesser extent for Bayesian optimal interval design. For a safety run-in with two dose levels, using 3 + 3 or Rolling 6 and starting at the higher dose often lead to similar performance to Bayesian optimal interval design. For safety run-in with three dose levels, starting at the middle dose with 3 + 3, Rolling 6 or Bayesian optimal interval design is a good compromise between improving correct dose selection and imposing a toxic dose to less patients., Conclusions: Despite being sensitive to the starting dose level, the 3 + 3, Rolling 6 and Bayesian optimal interval designs overall demonstrate reasonable performance, which can be further improved with wise selection of the starting dose level. The Rolling 6 design remains the recommended design method especially if pharmacokinetics is important or required with this design having the feature of treating six patients per dose level. When designing a safety run-in, selection of a design method or selection of a starting dose should consider both the performance of the design approaches with different choices of a starting dose level and the magnitude of safety concerns with the dose levels under investigation.

To improve precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.

Abstract: The United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (UN REDD) was launched with the aim of contributing to the development of capacity for reducing emissions from loss of forest carbon in developing countries. It is understood that REDD mechanisms must be supported by forest assessment programs that can monitor the carbon stocks by carbon pools and human activities. Reporting at a national level will be required but many countries are likely to benefit from more local monitoring programs within the countries as well, gauging the effects of national policies and local financial mechanisms aimed at reaching goals for emission control for the nation as a whole. Field-based forest sample surveys are typically used as support for national reporting purposes. However, monitoring within the countries will require huge investments in field surveys to provide reliable change estimates with high spatial and temporal resolution. Airborne scanning LiDAR has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass. The aim of this study was to demonstrate how “wall-to-wall” LiDAR data can be used for change estimation. Estimators for areal changes of categories representing human activities such as “deforestation”, “degradation” and “untouched” were presented. Corresponding estimators for variance were also provided. Furthermore, it was shown how net change in biomass for the defined activity categories and for the entire area of interest can be estimated from a field sample survey with and without support of LiDAR remote sensing data and how the uncertainty can be quantified by corresponding variance estimates. In a case study in a small boreal forest area in southeastern Norway (852.6ha) a probability sample of 176 field sample plots distributed according to a stratified systematic design was measured twice over an 11year period. Corresponding multi-temporal scanning LiDAR data were also available. A multinomial logistic regression model was used to predict change category for every LiDAR grid cell in the area, and areal changes were estimated from the pure field sample and with the support of the LiDAR data applying model-assisted estimators. The standard errors of the areal change estimates were reduced by 43–75% by adding LiDAR data to the estimation. The change categories were used as post-strata in a subsequent estimation of net change in biomass. The standard errors of the biomass change estimates for the respective change categories were reduced by 18–84% compared to the pure field survey when using LiDAR data as auxiliary information in a model-assisted estimation procedure, which translates to a need for 1.5–38.7 times as many field plots when relying only on the field data. For the entire area of interest, the standard error of the overall net change in biomass was reduced by 57% compared to the uncertainty reported from the pure field survey. [Copyright &y& Elsevier]

Previous studies have demonstrated that light detection and ranging (LiDAR)-derived variables can be used to model forest yield variables, such as biomass, volume, and number of stems. However, the next step is underrepresented in the literature: estimation of forest yield with appropriate confidence intervals. It is of great importance that the procedures required for conducting forest inventory with LiDAR and the estimation precision of such procedures are sufficiently documented to enable their evaluation and implementation by land managers. In this study, we demonstrated the regression estimator, a model-assisted estimator (approximately design-unbiased), using LiDAR-derived variables for estimation of total forest yield. The LiDAR-derived variables are statistics associated with vegetation height and cover. The estimation procedure requires complete coverage of the forest with LiDAR and a random sample of precisely georeferenced field measurement plots. Regression estimation relies on sample-based ordinary least squares (0LS) regression models relating forest yield and LiDAR-derived variables. Estimation was performed using the OLS models and LiDAR-derived variables for the entire population. Regression estimates of basal area, volume, stand density, and biomass were much more precise than simple random sampling estimates (design effects were 0.25, 0.24, 0.44, and 0.27, respectively). [ABSTRACT FROM AUTHOR]

We review the essential characteristics of the three different approaches to specifying a sampling strategy; the design-based approach, the model-assisted approach and the model-based approach. We then describe a unified framework for survey design and estimation that incorporates all three approaches, allowing us to contrast them in terms of their concepts of efficiency as well as their robustness to assumptions about the characteristics of the finite population. Our conclusion is that although no one approach delivers both efficiency and robustness, the model-based approach seems to achieve the best compromise between these typically conflicting objectives. [ABSTRACT FROM AUTHOR]

Early in the twentieth century, Fisher and Neyman demonstrated how to infer effects of agricultural interventions using only the very weakest of assumptions, by randomly varying which plots were to be manipulated. Although the methods permitted uncontrolled variation between experimental units, they required strict control over assignment of interventions; this hindered their application to field studies with human subjects, who ordinarily could not be compelled to comply with experimenters' instructions. In 1996, however, Angrist, Imbens, and Rubin showed that inferences from randomized studies could accommodate noncompliance without significant strengthening of assumptions. Political scientists A. Gerber and D. Green responded quickly, fielding a randomized study of voter turnout campaigns in the November 1998 general election. Noncontacts and refusals were frequent, but Gerber and Green analyzed their data in the style of Angrist et al., avoiding the need to model nonresponse. They did use models for other purposes: to address complexities of the randomization scheme; to permit heterogeneity among voters and campaigners; to account for deviations from experimental protocol; and to take advantage of highly informative covariates. Although the added assumptions seemed straightforward and unassailable, a later analysis by Irnai found them to be at odds with Gerber and Green's data. Using a different model, he reaches the very opposite of Gerber and Green's central conclusion about getting out the vote. This article shows that neither of the models are necessary, addressing all of the complications of Gerber and Green's study using methods in the tradition of Fisher and Neyman. To do this, it merges recent developments in randomization-based inference for comparative studies with somewhat older developments in design-based analysis of sample surveys. The method involves regression, but large-sample analysis and simulations demonstrate its lack of dependence on regression assumptions. Its substantive results have consequences both for the design of campaigns to increase voter participation and for theories of political behavior more generally. [ABSTRACT FROM AUTHOR]

In this article we study a well-known response missing-data problem. Missing data is an ubiquitous problem in medical and social science studies. Imputation is one of the most popular methods for dealing with missing data. The most commonly used imputation that makes use of covariates is regression imputation, in which the regression model can be parametric, semiparametric, or nonparametric. Parametric regression imputation is efficient but is not robust against misspecification of the regression model. Although nonparametric regression imputation (such as nearest-neighbor imputation and kernel regression imputation) is model-free, it is not efficient, especially if the dimension of covariate vector is high (the well-known problem of curse of dimensionality). Semiparametric regression imputation (such as partially linear regression imputation) can reduce the dimension of the covariate in nonparametric regression fitting but is not robust against misspecification of the linear component in the regression. Assuming that the missing mechanism is covariate-dependent and that the propensity function can be specified correctly, we propose a regression imputation method that has good efficiency and is robust against regression model misspecification. Furthermore, our method is valid as long as either the regression model or the propensity model is correct, a property known as the double-robustness property. We show that asymptotically the sample mean based on our imputation achieves the semiparametric efficient lower bound if both regression and propensity models are specified correctly: Our simulation results demonstrate that the proposed method outperforms many existing methods for handling missing data, especially when the regression model is misspecified. As an illustration, an economic observational data set is analyzed. [ABSTRACT FROM AUTHOR]

Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into estimation of the population total of a survey variable to improve the estimation precision. We consider the generalized regression estimator formulated under the model-assisted framework in which a regression model is utilized to make use of the available covariates while the estimator still has basic design-based properties. The generalized regression estimator has been shown to improve the efficiency of the design-based Horvitz-Thompson estimator when the number of covariates is fixed. In this study, we investigate the performance of the generalized regression estimator when the number of covariates p is allowed to diverge as the sample size n increases. We examine two approaches where the model parameter is estimated using the weighted least squares method when p < n and the LASSO method when the model parameter is sparse. We show that under an assisted model and certain conditions on the joint distribution of the covariates as well as the divergence rates of n and p , the generalized regression estimator is asymptotically more efficient than the Horvitz-Thompson estimator, and is robust against model misspecification. We also study the consistency of variance estimation for the generalized regression estimator. Our theoretical results are corroborated by simulation studies and an example.

Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables. [ABSTRACT FROM AUTHOR]

The traditional 3 + 3 design continues to be commonly used for Phase I dose-finding oncology trials, despite increasing criticisms and development of innovative methods. Unfortunately, it is a challenge to convince principal investigators to use novel designs. The goal of this paper is to persuade researchers to break away from 3 + 3 design and provide potential solutions to better designs and implementation strategy. We reviewed the statistical methods for adaptive Phase I designs. The barriers among all the major components of the implementation team have been emphasized and potential solutions have been discussed. Institutional support to the principal investigators and statistician, as well as to other team members is essential to design and implement adaptive trials in academic medical institutions., Competing Interests: Financial & competing interests disclosure L Wei is funded by the NIH (grant numbers: P30CA016058, UM1CA76576, UM1CA186712, R01AG050801, UL1TR002733 and R21AI130485). The authors were partially supported by National Institutes of Health/National center for Advancing Translational Sciences grant UL1TR002733. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript., (© 2019 Lai Wei.)

Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha − 1 with a standard error of 11.17 Mgha − 1 (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha − 1 with a slightly smaller standard error of 10.57 Mgha − 1 (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha − 1 . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precision. [ABSTRACT FROM AUTHOR]