79 research outputs found
A Fault Location Method Based on Electromagnetic Transient Convolution Considering Frequency-Dependent Parameters and Lossy Ground
As the capacity of power systems grows, the need for quick and precise
short-circuit fault location becomes increasingly vital for ensuring the safe
and continuous supply of power. In this paper, we propose a fault location
method that utilizes electromagnetic transient convolution (EMTC). We assess
the performance of a naive EMTC implementation in multi-phase power lines by
using frequency-dependent parameters in real fault simulation, while using
constant parameters in pre-calculation. Our results show that the location
error increases as the distance between the fault location and the measurement
location increases. Therefore, we adopt the aerial mode transients after
phase-mode transformation to perform the convolution, which reduces the
influence of frequency-dependence and ground loss. We conduct numerical
experiments in a 3-phase 100-km transmission line, a radial distribution
network and IEEE 9-bus system under different fault conditions. Our results
show that the proposed method achieves tolerable location errors and operates
efficiently through direct convolution of the real fault-generated transient
signals and the pre-stored calculated transient signals
Non-collapsibility and Built-in Selection Bias of Hazard Ratio in Randomized Controlled Trials
Background: The hazard ratio of the Cox proportional hazards model is widely
used in randomized controlled trials to assess treatment effects. However, two
properties of the hazard ratio including the non-collapsibility and built-in
selection bias need to be further investigated. Methods: We conduct simulations
to differentiate the non-collapsibility effect and built-in selection bias from
the difference between the marginal and the conditional hazard ratio.
Meanwhile, we explore the performance of the Cox model with inverse probability
of treatment weighting for covariate adjustment when estimating the marginal
hazard ratio. The built-in selection bias is further assessed in the
period-specific hazard ratio. Results: The conditional hazard ratio is a biased
estimate of the marginal effect due to the non-collapsibility property. In
contrast, the hazard ratio estimated from the inverse probability of treatment
weighting Cox model provides an unbiased estimate of the true marginal hazard
ratio. The built-in selection bias only manifests in the period-specific hazard
ratios even when the proportional hazards assumption is satisfied. The Cox
model with inverse probability of treatment weighting can be used to account
for confounding bias and provide an unbiased effect under the randomized
controlled trials setting when the parameter of interest is the marginal
effect. Conclusions: We propose that the period-specific hazard ratios should
always be avoided due to the profound effects of built-in selection bias.Comment: 17 pages, 2 figure
Efficient estimation of subgroup treatment effects using multi-source data
Investigators often use multi-source data (e.g., multi-center trials,
meta-analyses of randomized trials, pooled analyses of observational cohorts)
to learn about the effects of interventions in subgroups of some well-defined
target population. Such a target population can correspond to one of the data
sources of the multi-source data or an external population in which the
treatment and outcome information may not be available. We develop and evaluate
methods for using multi-source data to estimate subgroup potential outcome
means and treatment effects in a target population. We consider identifiability
conditions and propose doubly robust estimators that, under mild conditions,
are non-parametrically efficient and allow for nuisance functions to be
estimated using flexible data-adaptive methods (e.g., machine learning
techniques). We also show how to construct confidence intervals and
simultaneous confidence bands for the estimated subgroup treatment effects. We
examine the properties of the proposed estimators in simulation studies and
compare performance against alternative estimators. We also conclude that our
methods work well when the sample size of the target population is much larger
than the sample size of the multi-source data. We illustrate the proposed
methods in a meta-analysis of randomized trials for schizophrenia
Extending Inferences from Randomized Clinical Trials to Target Populations: A Scoping Review of Transportability Methods
Objective: Randomized controlled trial (RCT) results often inform clinical
decision-making, but the highly curated populations of trials and the care
provided during the trial are often not reflective of real-world practice. The
objective of this scoping review is to identify the ability of methods to
transport findings from RCTs to target populations. Study design: A scoping
review was conducted on the literature focusing on the transportability of the
results from RCTs to observational cohorts. Each study was assessed based on
the methodology used for transportability and the extent to which the treatment
effect from the RCT was estimated in the target population in observational
data. Results: A total of 15 published papers were included. The research
topics include cardiovascular diseases, infectious diseases, psychiatry,
oncology, orthopedics, anesthesiology, and hematology. These studies show that
the findings from RCTs could be translated to real-world settings, with varying
degrees of effect size and precision. In some cases, the estimated treatment
effect for the target population were statistically significantly different
from those in RCTs. Conclusion: Despite variations in the magnitude of effects
between RCTs and real-world studies, transportability methods play an important
role in effectively bridging the RCTs and real-world care delivery, offering
valuable insights for evidence-based medicine
Causal inference with multiple concurrent medications: a comparison of methods and an application in multidrug-resistant tuberculosis
Simulation study code available at https://github.com/arman817/Simulation-Codes-for-Causal-Inference-for-polypharmacyThis paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most prevalent regimens, we compare the results obtained using these methods in a simulation study with four potentially concurrent medications. We perform a second simulation study in which some combinations of medications may occur rarely or not occur at all in the dataset. Finally, we apply the methods explored to contrast the probability of patient treatment success for the most prevalent regimens of antimicrobial agents for patients with multidrug-resistant pulmonary tuberculosis
Structured Learning in Time-dependent Cox Models
Cox models with time-dependent coefficients and covariates are widely used in
survival analysis. In high-dimensional settings, sparse regularization
techniques are employed for variable selection, but existing methods for
time-dependent Cox models lack flexibility in enforcing specific sparsity
patterns (i.e., covariate structures). We propose a flexible framework for
variable selection in time-dependent Cox models, accommodating complex
selection rules. Our method can adapt to arbitrary grouping structures,
including interaction selection, temporal, spatial, tree, and directed acyclic
graph structures. It achieves accurate estimation with low false alarm rates.
We develop the sox package, implementing a network flow algorithm for
efficiently solving models with complex covariate structures. Sox offers a
user-friendly interface for specifying grouping structures and delivers fast
computation. Through examples, including a case study on identifying predictors
of time to all-cause death in atrial fibrillation patients, we demonstrate the
practical application of our method with specific selection rules.Comment: 49 pages (with 19 pages of appendix),9 tables, 3 figure
Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes
Effect modification occurs when the impact of the treatment on an outcome
varies based on the levels of other covariates known as effect modifiers.
Modeling of these effect differences is important for etiological goals and for
purposes of optimizing treatment. Structural nested mean models (SNMMs) are
useful causal models for estimating the potentially heterogeneous effect of a
time-varying exposure on the mean of an outcome in the presence of time-varying
confounding. A data-driven approach for selecting the effect modifiers of an
exposure may be necessary if these effect modifiers are a priori unknown and
need to be identified. Although variable selection techniques are available in
the context of estimating conditional average treatment effects using marginal
structural models, or in the context of estimating optimal dynamic treatment
regimens, all of these methods consider an outcome measured at a single point
in time. In the context of an SNMM for repeated outcomes, we propose a doubly
robust penalized G-estimator for the causal effect of a time-varying exposure
with a simultaneous selection of effect modifiers and use this estimator to
analyze the effect modification in a study of hemodiafiltration. We prove the
oracle property of our estimator, and conduct a simulation study for evaluation
of its performance in finite samples and for verification of its
double-robustness property. Our work is motivated by and applied to the study
of hemodiafiltration for treating patients with end-stage renal disease at the
Centre Hospitalier de l'Universit\'e de Montr\'eal. We apply the proposed
method to investigate the effect heterogeneity of dialysis facility on the
repeated session-specific hemodiafiltration outcomes
Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patient data meta-analysis
Effect modification occurs while the effect of the treatment is not homogeneous across the different strata of patient characteristics. When the effect of treatment may vary from individual to individual, precision medicine can be improved by identifying patient covariates to estimate the size and direction of the effect at the individual level. However, this task is statistically challenging and typically requires large amounts of data. Investigators may be interested in using the individual patient data (IPD) from multiple studies to estimate these treatment effect models. Our data arise from a systematic review of observational studies contrasting different treatments for multidrug-resistant tuberculosis (MDR-TB), where multiple antimicrobial agents are taken concurrently to cure the infection. We propose a marginal structural model (MSM) for effect modification by different patient characteristics and co-medications in a meta-analysis of observational IPD. We develop, evaluate, and apply a targeted maximum likelihood estimator (TMLE) for the doubly robust estimation of the parameters of the proposed MSM in this context. In particular, we allow for differential availability of treatments across studies, measured confounding within and across studies, and random effects by study
Evaluating hybrid controls methodology in early-phase oncology trials: a simulation study based on the MORPHEUS-UC trial
Phase Ib/II oncology trials, despite their small sample sizes, aim to provide
information for optimal internal company decision-making concerning novel drug
development. Hybrid controls (a combination of the current control arm and
controls from one or more sources of historical trial data [HTD]) can be used
to increase the statistical precision. Here we assess combining two sources of
Roche HTD to construct a hybrid control in targeted therapy for decision-making
via an extensive simulation study. Our simulations are based on the real data
of one of the experimental arms and the control arm of the MORPHEUS-UC Phase
Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider
potential complications such as model misspecification, unmeasured confounding,
different sample sizes of current treatment groups, and heterogeneity among the
three trials. We evaluate two frequentist methods (with both Cox and Weibull
accelerated failure time [AFT] models) and three different priors in Bayesian
dynamic borrowing (with a Weibull AFT model), and modifications within each of
those, when estimating the effect of treatment on survival outcomes and
measures of effect such as marginal hazard ratios. We assess the performance of
these methods in different settings and potential of generalizations to
supplement decisions in early-phase oncology trials. The results show that the
proposed joint frequentist methods and noninformative priors within Bayesian
dynamic borrowing with no adjustment on covariates are preferred, especially
when treatment effects across the three trials are heterogeneous. For
generalization of hybrid control methods in such settings we recommend more
simulation studies.Comment: 34 pages, 3 figures, 5 tables. To be appear in Pharmaceutical
Statistic
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