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Model selection in high-dimensional settings has received substantial attention in recent years, however, similar advancements in the low-dimensional setting have been lacking. In this article, we introduce a new variable selection procedure for low to moderate scale regressions (n>p). This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model. In an extensive simulation study, we show that the proposed variable selection technique enjoys superior performance compared with candidate methods (backward elimination via repeated data splitting, univariate screening at 0.05 level, adaptive LASSO, SCAD), being amongst those with the lowest inclusion of noisy predictors while having the highest power to detect the correct model and being unaffected by correlations among the predictors. We illustrate the methods by applying them to a cohort of patients undergoing hepatectomy at our institution., (© 2020 John Wiley & Sons, Ltd.)

Background: Although rare in the United States, gallbladder cancer (GBCA) is a common cause of cancer death in some parts of the world. To investigate regional differences in pathogenesis and outcomes for GBCA, tumor mutations were analyzed from a sampling of specimens., Methods: Primary tumors from patients with GBCA who were treated in Chile, Japan, and the United States between 1999 and 2016 underwent targeted sequencing of known cancer-associated genes. Fisher exact and Kruskal-Wallis tests assessed differences in clinicopathologic and genetic factors. Kaplan-Meier methods evaluated differences in overall survival from the time of surgery between mutations., Results: A total of 81 patients were included. Japanese patients (11 patients) were older (median age, 72 years [range, 54-81 years]) compared with patients from Chile (21 patients; median age, 59 years [range, 32-73 years]) and the United States (49 patients; median age, 66 years [range, 46-87 years]) (P = .002) and had more well-differentiated tumors (46% vs 0% for Chile/United States; P < .001) and fewer gallstone-associated cancers (36% vs 67% for Chile and 69% for the United States; P = .13). Japanese patients had a median mutation burden of 6 (range, 1-23) compared with Chile (median mutation burden, 7 [range, 3-20]) and the United States (median mutation burden, 4 [range, 0-27]) (P = .006). Tumors from Japanese patients lacked AT-rich interaction domain 1A (ARID1A) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutations, whereas Chilean tumors lacked Erb-B2 receptor tyrosine kinase 3 (ERBB3) and AT-rich interaction domain 2 (ARID2) mutations. SMAD family member 4 (SMAD4) was found to be mutated similarly across centers (38% in Chile, 36% in Japan, and 27% in the United States; P = .68) and was univariately associated with worse overall survival (median, 10 months vs 25 months; P = .039). At least one potentially actionable gene was found to be altered in 80% of tumors., Conclusions: Differences in clinicopathologic variables suggest the possibility of distinct GBCA pathogenesis in Japanese patients, which may be supported by differences in mutation pattern. Among all centers, SMAD4 mutations were detected in approximately one-third of patients and may represent a converging factor associated with worse survival. The majority of patients carried mutations in actionable gene targets, which may inform the design of future trials., (© 2018 American Cancer Society.)

The landscape for early phase cancer clinical trials is changing dramatically because of the advent of targeted therapy. Increasingly, new drugs are designed to work against a target such as the presence of a specific tumor mutation. Because typically only a small proportion of cancer patients will possess the mutational target, but the mutation is present in many different cancers, a new class of basket trials is emerging, whereby the drug is tested simultaneously in different baskets, that is, subgroups of different tumor types. Investigators desire not only to test whether the drug works but also to determine which types of tumors are sensitive to the drug. A natural strategy is to conduct parallel trials, with the drug 's effectiveness being tested separately, using for example, the popular Simon two-stage design independently in each basket. The work presented is motivated by the premise that the efficiency of this strategy can be improved by assessing the homogeneity of the baskets ' response rates at an interim analysis and aggregating the baskets in the second stage if the results suggest the drug might be effective in all or most baskets. Via simulations, we assess the relative efficiencies of the two strategies. Because the operating characteristics depend on how many tumor types are sensitive to the drug, there is no uniformly efficient strategy. However, our investigation demonstrates that substantial efficiencies are possible if the drug works in most or all baskets, at the cost of modest losses of power if the drug works in only a single basket. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Concordance measures are frequently used for assessing the discriminative ability of risk prediction models. The interpretation of estimated concordance at external validation is difficult if the case-mix differs from the model development setting. We aimed to develop a concordance measure that provides insight into the influence of case-mix heterogeneity and is robust to censoring of time-to-event data. We first derived a model-based concordance (mbc) measure that allows for quantification of the influence of case-mix heterogeneity on discriminative ability of proportional hazards and logistic regression models. This mbc can also be calculated including a regression slope that calibrates the predictions at external validation (c-mbc), hence assessing the influence of overall regression coefficient validity on discriminative ability. We derived variance formulas for both mbc and c-mbc. We compared the mbc and the c-mbc with commonly used concordance measures in a simulation study and in two external validation settings. The mbc was asymptotically equivalent to a previously proposed resampling-based case-mix corrected c-index. The c-mbc remained stable at the true value with increasing proportions of censoring, while Harrell's c-index and to a lesser extent Uno's concordance measure increased unfavorably. Variance estimates of mbc and c-mbc were well in agreement with the simulated empirical variances. We conclude that the mbc is an attractive closed-form measure that allows for a straightforward quantification of the expected change in a model's discriminative ability due to case-mix heterogeneity. The c-mbc also reflects regression coefficient validity and is a censoring-robust alternative for the c-index when the proportional hazards assumption holds. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Background: Intrahepatic cholangiocarcinoma (ICC) is associated with poor survival. This study compared the outcomes of patients with unresectable ICC treated with hepatic arterial infusion (HAI) plus systemic chemotherapy (SYS) with the outcomes of patients treated with SYS alone., Methods: Consecutive patients with ICC were retrospectively reviewed. Clinicopathologic data were reviewed. Survival rates were compared by Kaplan-Meier analysis and log-rank testing., Results: Between January 2000 and August 2012, 525 patients with ICC were evaluated at Memorial Sloan Kettering Cancer Center, and 236 patients with unresectable tumors (locally advanced or metastatic) were analyzed. Disease was confined to the liver in 104 patients, who underwent treatment with combined HAI and SYS (n = 78 or 75%) or SYS alone (n = 26 or 25%). The response rate in the combined group was better than the rate in the group receiving SYS alone, although this did not reach statistical significance (59% vs 39%, P = .11). Overall survival for the combined group was longer than overall survival for the patients who received SYS alone (30.8 vs 18.4 months, P < .001), and this difference was maintained when patients with portal lymph node disease were included in the survival analysis (29.6 months with HAI and SYS [n = 93] vs 15.9 months with SYS [n = 74], P < .001). Eight patients who initially presented with unresectable tumors responded enough to undergo complete resection and had a median overall survival of 37 months (range, 10.4-92.3 months)., Conclusions: In patients with unresectable ICC confined to the liver or with limited regional nodal disease, a combination of SYS and HAI chemotherapy is associated with greater survival than SYS alone. Cancer 2016;122:758-765. © 2015 American Cancer Society., (© 2015 American Cancer Society.)

Two main classes of methodology have been developed for addressing the analytical intractability of generalized linear mixed models: likelihood-based methods and Bayesian methods. Likelihood-based methods such as the penalized quasi-likelihood approach have been shown to produce biased estimates especially for binary clustered data with small clusters sizes. More recent methods using adaptive Gaussian quadrature perform well but can be overwhelmed by problems with large numbers of random effects, and efficient algorithms to better handle these situations have not yet been integrated in standard statistical packages. Bayesian methods, although they have good frequentist properties when the model is correct, are known to be computationally intensive and also require specialized code, limiting their use in practice. In this article, we introduce a modification of the hybrid approach of Capanu and Begg, 2011, Biometrics 67, 371-380, as a bridge between the likelihood-based and Bayesian approaches by employing Bayesian estimation for the variance components followed by Laplacian estimation for the regression coefficients. We investigate its performance as well as that of several likelihood-based methods in the setting of generalized linear mixed models with binary outcomes. We apply the methods to three datasets and conduct simulations to illustrate their properties. Simulation results indicate that for moderate to large numbers of observations per random effect, adaptive Gaussian quadrature and the Laplacian approximation are very accurate, with adaptive Gaussian quadrature preferable as the number of observations per random effect increases. The hybrid approach is overall similar to the Laplace method, and it can be superior for data with very sparse random effects., (Copyright © 2013 John Wiley & Sons, Ltd.)

One of the most basic biostatistical problems is the comparison of two binary diagnostic tests. Commonly, one test will have greater sensitivity, and the other greater specificity. In this case, the choice of the optimal test generally requires a qualitative judgment as to whether gains in sensitivity are offset by losses in specificity. Here, we propose a simple decision analytic solution in which sensitivity and specificity are weighted by an intuitive parameter, the threshold probability of disease at which a patient will opt for treatment. This gives a net benefit that can be used to determine which of two diagnostic tests will give better clinical results at a given threshold probability and whether either is superior to the strategy of assuming that all or no patients have disease. We derive a simple formula for the relative diagnostic value, which is the difference in sensitivities of two tests divided by the difference in the specificities. We show that multiplying relative diagnostic value by the odds at the prevalence gives the odds of the threshold probability below which the more sensitive test is preferable and above which the more specific test should be chosen. The methodology is easily extended to incorporate combinations of tests and the risk or side effects of a test., (Copyright © 2012 John Wiley & Sons, Ltd.)

In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding Wald test from the underlying regression model is significant. A recent article showed using simulations that the widely used ROC area test produces exceptionally conservative test size and extremely low power. In this article, we demonstrate that both the test statistic and its estimated variance are seriously biased when predictions from nested regression models are used as data inputs for the test, and we examine in detail the reasons for these problems. Although it is possible to create a test reference distribution by resampling that removes these biases, Wald or likelihood ratio tests remain the preferred approach for testing the incremental contribution of a new marker., (Copyright © 2012 John Wiley & Sons, Ltd.)

Non-inferiority trials, which aim to demonstrate that a test product is not worse than a competitor by more than a pre-specified small amount, are of great importance to the pharmaceutical community. As a result, methodology for designing and analyzing such trials is required, and developing new methods for such analysis is an important area of statistical research. The three-arm trial consists of a placebo, a reference and an experimental treatment, and simultaneously tests the superiority of the reference over the placebo along with comparing this reference to an experimental treatment. In this paper, we consider the analysis of non-inferiority trials using Bayesian methods which incorporate both parametric as well as semi-parametric models. The resulting testing approach is both flexible and robust. The benefit of the proposed Bayesian methods is assessed via simulation, based on a study examining home-based blood pressure interventions., (Copyright © 2011 John Wiley & Sons, Ltd.)

Bioequivalence trials are usually conducted to compare two or more formulations of a drug. Simultaneous assessment of bioequivalence on multiple endpoints is called multivariate bioequivalence. Despite the fact that some tests for multivariate bioequivalence are suggested, current practice usually involves univariate bioequivalence assessments ignoring the correlations between the endpoints such as AUC and C(max). In this paper we develop a semiparametric Bayesian test for bioequivalence under multiple endpoints. Specifically, we show how the correlation between the endpoints can be incorporated in the analysis and how this correlation affects the inference. Resulting estimates and posterior probabilities 'borrow strength' from one another where the amount and the direction of the strength borrowed are determined by the prior correlations. The method developed is illustrated using a real data set., ((c) 2007 John Wiley & Sons, Ltd.)

Background: Gastrointestinal stromal tumor (GIST) is the most frequent sarcoma of the intestinal tract and often shows constitutive activation of either the KIT or PDGFRA receptor tyrosine kinases because of gain-of-function mutation. Although the efficacy of tyrosine kinase inhibitors in metastatic GIST depends on tumor mutation status, there have been conflicting reports on the prognostic importance of KIT mutation in primary GIST., Methods: A total of 127 patients were studied who presented to our institution from 1983 to 2002 with localized primary GIST and underwent complete gross surgical resection of disease. The majority of tumors originated in the stomach (58%) or small intestine (28%). By using polymerase chain reaction (PCR) and direct sequencing, a KIT mutation was found in 71% of patients and a PDGFRA mutation in 6%., Results: After a median follow-up of 4.7 years, recurrence-free survival was 83%, 75%, and 63% at 1, 2, and 5 years, respectively. On multivariate analysis recurrence was predicted by > or =5 mitoses/50 high-power fields, tumor size > or =10 cm, and tumor location (with patients having small bowel GIST doing the worst). In particular, a high mitotic rate conferred a hazard rate of 14.6 (95% confidence interval, 6.5-32.4). Specific KIT mutations had prognostic importance by univariate but not multivariate analysis. Patients with KIT exon 11 point mutations and insertions had a favorable prognosis. Those with KIT exon 9 mutations or KIT exon 11 deletions involving amino acid W557 and/or K558 had a higher rate of recurrence, whereas patients without a tyrosine kinase mutation had intermediate outcome., Conclusions: In the absence of therapy with tyrosine kinase inhibitors, recurrence in completely resected primary GIST is independently predicted by mitotic rate, tumor size, and tumor location.

This article is concerned with a dose-response study where the doses are subject-specific. The motivating example is a study on the use of radioiodine in metastatic thyroid cancer where the dose is individualized for each subject based on pharmacokinetic models of clearance of the agent. The goal is to design a study that will estimate the probability of response within a specified precision. This setup does not fit into well-studied dose-response designs primarily because doses are subject-specific. Here the dose-response relationship is modelled using logistic regression and a second-order approximation to the asymptotic variance of the model parameters is developed. The resulting procedure is simple to apply and requires minimal assumptions regarding the distribution of the dose levels. Simulation studies establish that, for a reasonable range of parameter values, the approximation is reasonably accurate. A Monte Carlo procedure is developed as well for cases when the approximation performs poorly. The proposed method is applied to the thyroid cancer study, including elicitation of parameters and a sensitivity analysis. Computer code in SAS and R are provided in the Appendix., (Copyright (c) 2005 John Wiley & Sons, Ltd.)

McNemar's test is used to compare the distribution of two paired binary random variables. When the data are clustered adjustment is needed to ensure that it is still a valid test. This article presents two approximations for calculating the power and sample size for the adjusted McNemar's test for clustered data, working with a particular adjustment. A simulation study is conducted to demonstrate the accuracy of these approximations. The method is also applied to the design of a study involving positron emission tomography in detecting metastatic colorectal cancer and sensitivity of sample size computations to the design parameters are explored in this context., (Copyright 2004 John Wiley & Sons, Ltd.)