Statistics in Medicine
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2.37
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#1Zachary R. McCawH-Index: 8
#2Lu Tian (Stanford University)H-Index: 70
Last. Lee-Jen Wei (Harvard University)H-Index: 43
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#1Zhengyang Zhou (University of North Texas Health Science Center)H-Index: 9
#2Minge Xie (RU: Rutgers University)H-Index: 22
Last. Eun Young Mun (University of North Texas Health Science Center)H-Index: 25
view all 4 authors...
Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero-inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study-level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta-analysis. The current study proposes a novel statistical appro...
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#1Peihua Qiu (UF: University of Florida)H-Index: 33
#2Kai Yang (UF: University of Florida)H-Index: 5
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#1Yipeng Wang (FSU: Florida State University)
#2Lifeng Lin (FSU: Florida State University)H-Index: 16
Last. Haitao Chu (UMN: University of Minnesota)H-Index: 48
view all 4 authors...
Systematic reviews and meta-analyses are principal tools to synthesize evidence from multiple independent sources in many research fields. The assessment of heterogeneity among collected studies is a critical step when performing a meta-analysis, given its influence on model selection and conclusions about treatment effects. A common-effect (CE) model is conventionally used when the studies are deemed homogeneous, while a random-effects (RE) model is used for heterogeneous studies. However, both...
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#1Chixiang Chen (UMB: University of Maryland, Baltimore)
#2Peisong Han (UM: University of Michigan)H-Index: 10
Last. Fan He (PSU: Pennsylvania State University)
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In many clinical and observational studies, auxiliary data from the same subjects, such as repeated measurements or surrogate variables, will be collected in addition to the data of main interest. Not directly related to the main study, these auxiliary data in practice are rarely incorporated into the main analysis, though they may carry extra information that can help improve the estimation in the main analysis. Under the setting where part of or all subjects have auxiliary data available, we p...
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#1Kevin Doubleday (UA: University of Arizona)H-Index: 4
#2Jin Zhou (UCLA: University of California, Los Angeles)H-Index: 16
Last. Haoda Fu (Eli Lilly and Company)H-Index: 18
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Statistical methods generating individualized treatment rules (ITRs) often focus on maximizing expected benefit, but these rules may expose patients to excess risk. For instance, aggressive treatment of type 2 diabetes (T2D) with insulin therapies may result in an ITR which controls blood glucose levels but increases rates of hypoglycemia, diminishing the appeal of the ITR. This work proposes two methods to identify risk-controlled ITRs (rcITR), a class of ITR which maximizes a benefit while con...
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Confidence intervals for the mean of discrete exponential families are widely used in many applications. Since missing data are commonly encountered, the interval estimation for incomplete data is an important problem. The performances of the existing multiple imputation confidence intervals are unsatisfactory. We propose modified multiple imputation confidence intervals to improve the existing confidence intervals for the mean of the discrete exponential families with quadratic variance functio...
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#1Nicole M. Butera (GW: George Washington University)H-Index: 8
#2Donglin Zeng (UNC: University of North Carolina at Chapel Hill)H-Index: 50
Last. Jianwen Cai (UNC: University of North Carolina at Chapel Hill)H-Index: 64
view all 5 authors...
Missing data are common in longitudinal cohort studies and can lead to bias, particularly in studies with informative missingness. Many common methods for handling informatively missing data in survey samples require correctly specifying a model for missingness. Although doubly robust methods exist to provide unbiased regression coefficients in the presence of missing outcome data, these methods do not account for correlation due to clustering inherent in longitudinal or cluster-sampled studies....
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#1Jianrong Wu (UK: University of Kentucky)H-Index: 1
#2Jing Wei (UK: University of Kentucky)H-Index: 1
Immunotherapies are increasingly used for treating patients with advanced-stage cancers. However, cancer immunotherapy trials often present delayed treatment effects and long-term survivors which result nonproportional hazard models and challenge the immunotherapy trial designs. In this article, we proposed a general random delayed cure rate model for designing cancer immunotherapy trials. A sample size formula is derived for a weighted log-rank test. The accuracy of sample size estimation is as...
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#1Bosheng Li (CPU: China Pharmaceutical University)H-Index: 1
#2Liwen Su (CPU: China Pharmaceutical University)H-Index: 2
Last. Fangrong Yan (CPU: China Pharmaceutical University)H-Index: 14
view all 4 authors...
A random delayed treatment effect is expected in a confirmatory clinical trial for an immunotherapy due to the individual heterogeneity of physiological conditions. For this reason, the delay time will be assumed to follow a continuous distribution that is difficult to estimate accurately based on the early-phase data, which hinders the specification of the most powerful weighted log-rank test. Therefore, we propose a simulation-based maximum duration design with a robustly powerful Maxcombo tes...
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