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Cluster-randomized trials (CRTs) are widely used to evaluate interventions delivered at the clinic, practice, or community level. Although standard analyses typically target average treatment effects, such summaries mask potentially…

统计方法学 · 统计学 2026-04-16 Changjun Li , Xi Fang , Michael O. Harhay , Andrew B. Forbes , F. Perry Wilson , Guangyu Tong , Fan Li

A dynamic treatment regimen (DTR) is a pre-specified sequence of decision rules which maps baseline or time-varying measurements on an individual to a recommended intervention or set of interventions. Sequential multiple assignment…

统计方法学 · 统计学 2019-10-23 Brook Luers , Min Qian , Inbal Nahum-Shani , Connie Kasari , Daniel Almirall

Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large. Most investigators estimate such personalized treatment effects by regressing the outcome of a…

机器学习 · 统计学 2021-09-02 Eric V. Strobl , Shyam Visweswaran

Attrition is a common occurrence in cluster randomised trials (CRTs) which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance…

统计方法学 · 统计学 2016-03-15 Anower Hossain , Karla Diaz-Ordaz , Jonathan W. Bartlett

Considerable interest has recently been focused on studying multiple phenotypes simultaneously in both epidemiological and genomic studies, either to capture the multidimensionality of complex disorders or to understand shared etiology of…

统计方法学 · 统计学 2015-11-26 Denis Agniel , Katherine P. Liao , Tianxi Cai

Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average…

统计方法学 · 统计学 2022-02-09 Kellie Ottoboni , Jason Poulos

A randomized controlled trial (RCT) is widely regarded as the gold standard for assessing the causal effect of a treatment or intervention, assuming perfect implementation. In practice, however, randomization can be compromised for various…

统计方法学 · 统计学 2026-04-21 Yin Tang , Yanyuan Ma , Jiwei Zhao

Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes. A completely randomized design is usually used to randomly assign treatment levels to…

统计方法学 · 统计学 2026-05-12 Yiou Li , Lulu Kang , Xiao Huang

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary…

统计方法学 · 统计学 2021-08-20 J Hoogland , J IntHout , M Belias , MM Rovers , RD Riley , FE Harrell , KGM Moons , TPA Debray , JB Reitsma

Objective: Randomised controlled trials (RCTs) are widely considered as gold standard for assessing the effectiveness of new health interventions. When treatment non-compliance is present in RCTs, the treatment effect in the subgroup of…

应用统计 · 统计学 2025-03-25 Theodosios Papazoglou , Ed Waddingham , Alastair Young

With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding)…

In Randomised Controlled Trials (RCT) with treatment non-compliance, instrumental variable approaches are used to estimate complier average causal effects. We extend these approaches to cost-effectiveness analyses, where methods need to…

统计方法学 · 统计学 2016-12-02 Karla DiazOrdaz , Angelo Franchini , Richard Grieve

Although randomized controlled trials have long been regarded as the ``gold standard'' for evaluating treatment effects, there is no natural prevention from post-treatment events. For example, non-compliance makes the actual treatment…

统计方法学 · 统计学 2025-04-25 Qinqing Liu , Xiang Peng , Tao Zhang , Yuhao Deng

The randomization inference literature studying randomized controlled trials (RCTs) assumes that units' potential outcomes are deterministic. This assumption is unlikely to hold, as stochastic shocks may take place during the experiment. In…

计量经济学 · 经济学 2022-12-15 Antoine Deeb , Clément de Chaisemartin

In this paper we present tools for applied researchers that re-purpose off-the-shelf methods from the computer-science field of machine learning to create a "discovery engine" for data from randomized controlled trials (RCTs). The applied…

机器学习 · 统计学 2019-05-13 Jens Ludwig , Sendhil Mullainathan , Jann Spiess

In randomized controlled trials (RCTs) that focus on time-to-event outcomes, intercurrent events can arise in two ways: as semi-competing events, which modify the hazard of the primary outcome events, or as competing events, which make the…

统计方法学 · 统计学 2026-05-26 Yuhao Deng , Shasha Han , Xiao-Hua Zhou

Clustering and dependence are common in trials. For example, in some cluster randomized trials (CRTs), pre-existing clusters are enrolled, randomized, and serve as the basis of intervention delivery. Such CRTs are "fully clustered":…

Randomised controlled trials aim to assess the impact of one (or more) health interventions relative to other standard interventions. RCTs sometimes use an ordinal outcome, which is an endpoint that comprises of multiple, monotonically…

统计方法学 · 统计学 2022-08-15 Chris J. Selman , Katherine J. Lee , Robert K. Mahar

Disruptions in clinical trials may be due to external events like pandemics, warfare, and natural disasters. Resulting complications may lead to unforeseen intercurrent events (events that occur after treatment initiation and affect the…

应用统计 · 统计学 2024-08-20 Rachael V. Phillips , Mark J. van der Laan

Randomized controlled trials (RCTs) are the standard for evaluating the effectiveness of clinical interventions. To address the limitations of RCTs on real-world populations, we developed a methodology that uses a large observational…

机器学习 · 计算机科学 2024-10-21 Panayiotis Petousis , David Gordon , Susanne B. Nicholas , Alex A. T. Bui