Proschan, Brittain, and Kammerman made a very interesting observation that for some examples of the unequal allocation minimization, the mean of the unconditional randomization distribution is shifted away from 0. Kuznetsova and Tymofyeyev linked this phenomenon to the variations in the allocation ratio from allocation to allocation in the examples considered in the paper by Proschan et al. and advocated the use of unequal allocation procedures that preserve the allocation ratio at every step. In this paper, we show that the shift phenomenon extends to very common settings: using conditional randomization test in a study with equal allocation. This phenomenon has the same cause: variations in the allocation ratio among the allocation sequences in the conditional reference set, not previously noted. We consider two kinds of conditional randomization tests. The first kind is the often used randomization test that conditions on the treatment group totals; we describe the variations in the conditional allocation ratio with this test on examples of permuted block randomization and biased coin randomization. The second kind is the randomization test proposed by Zheng and Zelen for a multicenter trial with permuted block central allocation that conditions on the within-center treatment totals. On the basis of the sequence of conditional allocation ratios, we derive the value of the shift in the conditional randomization distribution for specific vector of responses and the expected value of the shift when responses are independent identically distributed random variables. We discuss the asymptotic behavior of the shift for the two types of tests.
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http://dx.doi.org/10.1002/pst.1556 | DOI Listing |
Front Plant Sci
January 2025
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between the vegetative canopy and the surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a prevalent method for large-scale LAI estimation. However, existing machine learning models have exhibited various flaws, hindering the accurate estimation of LAI.
View Article and Find Full Text PDFBioinformatics
January 2025
Department of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
Motivation: Numerous microbiome studies have revealed significant associations between the microbiome and human health and disease. These findings have motivated researchers to explore the causal role of the microbiome in human complex traits and diseases. However, the complexities of microbiome data pose challenges for statistical analysis and interpretation of causal effects.
View Article and Find Full Text PDFEur J Surg Oncol
January 2025
Division of Hepatobiliary and Pancreatic Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan. Electronic address:
Background: The efficacy of local control for pancreatic cancer liver metastases (PCLM), including surgical treatment, remains controversial, with no consensus on the management and clinical significance of disappearing liver metastases (DLMs). This study aimed to evaluate the clinical implications of DLMs in treating PCLM after multi-agent chemotherapy, utilizing contrast-enhanced imaging modalities.
Methods: A retrospective analysis was conducted on patients who underwent curative resection for pancreatic cancer with synchronous or metachronous liver metastases between 2014 and 2023.
Expert Rev Pharmacoecon Outcomes Res
January 2025
Merck & Co. Inc, Rahway, NJ, USA.
Background: We evaluated UK nurses' preferences for pediatric hexavalent vaccine attributes.
Research Design And Methods: In a discrete-choice experiment study, 150 nurses chose between 2 hypothetical pediatric hexavalent vaccines with varying attribute levels (device type, plastic in packaging, time on the market, and time the vaccine can stay safely at room temperature) in a series of choice questions. Using random-parameters logit-model estimates, conditional relative attribute importance (CRAI) and odds ratios (ORs) were calculated.
Ann Surg Oncol
January 2025
Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
Patients And Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) were analyzed and split into training and validation groups (7:3 ratio). CS profiles for patients with GNEC were examined in the full cohort.
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