Parametric Assumptions Research Articles

Defining non-inferiority margins in randomised controlled surgical trials: a protocol for a systematic review

BackgroundThe reporting of randomised controlled non-inferiority (NI) drug trials is poor with less than 50% of published trials reporting a justification of the NI margin. This is despite the introduction...

On q-Painlevé VI and the geometry of Segre surfaces

In the context of q-Painlevé VI with generic parameter values, the Riemann–Hilbert correspondence induces a one-to-one mapping between solutions of the nonlinear equation and points on an affine Segre surface. Upon fixing a generic point on the surface, we give formulae for the function values of the corresponding solution near the critical points, in the form of complete, convergent, asymptotic expansions. These lead in particular to the solution of the nonlinear connection problem for the general solution of q-Painlevé VI. We further show that, when the point on the Segre surface is moved to one of the sixteen lines on the surface, one of the asymptotic expansions near the critical points truncates, under suitable parameter assumptions. At intersection points of lines, this then yields doubly truncated asymptotics at one of the critical points or simultaneous truncation at both.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BackgroundDescribing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.MethodsThis study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.ResultsWhen the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9).ConclusionThis study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

A NEW CONVERGENCE ANALYSIS FOR MINIMUM NORM SOLUTION OF SPLIT SYSTEM OF NONSMOOTH MINIMIZATION PROBLEMS

This work presents a novel self-adaptive steepest-descent type algorithm for solving the split system of minimization problem (SSMP) related to convex nonsmooth functions. The algorithm includes a self-adaptive step size mechanism, which uses a step size that does not need prior information about the operator norm. Under certain weakened assumptions of parameters, a strong convergence theorem is established and proved for the algorithm. Specifically, the sequence generated by this new algorithm strongly converges towards the minimum norm element of the SSPM. To assess the implementation of our algorithm, a numerical example is provided. According to the numerical results, our algorithm showcases effectiveness and simplicity in its implementation. Furthermore, the primary numerical experiment results indicate that our proposed algorithm surpasses some existing results in the literature in terms of CPU time and iteration count. Our result represents an extension and enhancement of recent findings in this area.

Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approach

To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.

Bootstrap Method as a Tool for Analyzing Data with Atypical Distributions Deviating from Parametric Assumptions: Critique and Effectiveness Evaluation

In today’s research environment characterized by exponential data growth and increasing complexity, the selection of appropriate statistical tests, tailored to research objectives and data distributions, is paramount for rigorous analysis and accurate interpretation. This article explores the growing prominence of bootstrapping, an advanced statistical technique for multiple comparisons analysis, offering flexibility and customization by estimating sample distributions without assuming population distributions, thus serving as a valuable alternative to traditional methods in various data scenarios. Computer simulations were conducted using data from cardiovascular disease patients. Two approaches, spontaneous partly controlled simulation and fully constrained simulation using self-written R scripts, were utilized to generate datasets with specified distributions and analyze the data using tests for comparing more than two groups. The utilization of the bootstrap method greatly improves statistical analysis, especially in overcoming the constraints of conventional parametric tests. Our research showcased its effectiveness in comparing multiple scenarios, yielding strong findings across diverse distributions, even with minor inflation in p values. Serving as a valuable substitute for parametric approaches, bootstrap promotes careful consideration when rejecting hypotheses, thus fostering a deeper understanding of statistical nuances and bolstering analytical rigor.

Using a monotonic density ratio model to increase the power of the goodness-of-fit test for logistic regression models with case-control data.

Logistic regression models are widely used in case-control data analysis, and testing the goodness-of-fit of their parametric model assumption is a fundamental research problem. In this article, we propose to enhance the power of the goodness-of-fit test by exploiting a monotonic density ratio model, in which the ratio of case and control densities is assumed to be a monotone function. We show that such a monotonic density ratio model is naturally induced by the retrospective case-control sampling design under the alternative hypothesis. The pool-adjacent-violator algorithm is adapted to solve for the constrained nonparametric maximum likelihood estimator under the alternative hypothesis. By measuring the discrepancy between this estimator and the semiparametric maximum likelihood estimator under the null hypothesis, we develop a new Kolmogorov-Smirnov-type statistic to test the goodness-of-fit for logistic regression models with case-control data. A bootstrap resampling procedure is suggested to approximate the -value of the proposed test. Simulation results show that the type I error of the proposed test is well controlled and the power improvement is substantial in many cases. Three real data applications are also included for illustration.

Inhomogeneous terminators on the exoplanet WASP-39 b

Transmission spectroscopy has been a workhorse technique used over the past two decades to constrain the physical and chemical properties of exoplanet atmospheres1–5. One of its classical key assumptions is that the portion of the atmosphere it probes—the terminator region—is homogeneous. Several works from the past decade, however, have put this into question for highly irradiated, hot (Teq ≳ 1,000 K) gas giant exoplanets, both empirically6–10 and through three-dimensional modelling11–17. While models have predicted clear differences between the evening (day-to-night) and morning (night-to-day) terminators, direct morning and evening transmission spectra in a wide wavelength range have not been reported for an exoplanet so far. Under the assumption of precise and accurate orbital parameters for the exoplanet WASP-39 b, here we report the detection of inhomogeneous terminators on WASP-39 b, which has allowed us to retrieve its morning and evening transmission spectra in the near-infrared (2–5 μm) using the James Webb Space Telescope. We have observed larger transit depths in the evening, which are, on average, 405 ± 88 ppm larger than the morning ones, and also have qualitatively larger features than the morning spectrum. The spectra are best explained by models in which the evening terminator is hotter than the morning terminator by 177−57+65\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$17{7}_{-57}^{+65}$$\\end{document} K, with both terminators having C/O ratios consistent with solar. General circulation models predict temperature differences broadly consistent with the above value and point towards a cloudy morning terminator and a clearer evening terminator.

Semiparametric efficiency gains from parametric restrictions on propensity scores

Abstract We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit representations of efficiency bounds, we propose a novel framework that enables us to see whether knowing a parametric model is valuable in terms of efficiency even when it is high dimensional. In addition to the intuitive fact that knowing the parametric model does not help much if it is sufficiently flexible, we discover that the efficiency gain can be nearly zero even though the parametric assumption significantly restricts the space of possible propensity scores.

Approximations and inference for envelopment estimators of production frontiers

Nonparametric methods have been commonly used to assess the performance of both private and public organizations. Among them, the most popular ones are envelopment estimators such as Free Disposal Hull (FDH) or Data Envelopment Analysis (DEA), which estimate the attainable sets and their efficient boundaries by enveloping the cloud of observed units in the appropriate input-output space. However, these nonparametric envelopment techniques do not provide estimates of marginal products and other coefficients of economic interest. This paper presents a new approach that provides local estimates of all the desired partial derivatives and economic coefficients, which complement and complete the analysis based on nonparametric envelopment estimators. We improve nonparametric estimators by estimating nonparametrically smoothed efficient boundaries and providing derivatives and other coefficients without having to assume any parametric structure for the frontier and the inefficiency distribution. Our approach offers several advantages, such as a flexible nonparametric adjustment of the efficient frontier based on local linear models; a general multivariate efficiency model based on directional distances where one can choose the desired benchmark direction; the possibility of assessing the impact of external-environmental variables; a bootstrap-based statistical inference for deriving confidence intervals on the estimated coefficients for nonparametric and robust frontier approximations; the possibility of including factors aggregating inputs or outputs and recovering the estimated coefficients in the original units. To demonstrate the usefulness of the proposed approach, we provide an illustration in the field of education, where economic coefficients are important but the parametric assumptions have been questioned.

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.

This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.

Efficacy of scrambler therapy in chronic neuropathic pain: pairwise and dose-response meta-analysis

Chronic neuropathic pain (CNP) affects 7% of the world’s population and is challenging to control since existing medications are inadequate and have negative effects. Electrocutaneous devices, such as scrambler therapy (ST), have emerged as a possible option and have shown promising results in multiple randomized controlled trials (RCTs). However, the long-term efficacy of ST remains unknown. We aimed to evaluate the efficacy of ST in CNP reduction over time. We used the data sources including PubMed, Embase, Scopus, and Cochrane Central Register of Controlled Trials from inception to September 2023. Five placebo and three routine-care controlled RCTs were selected among the screened abstracts. Two authors independently extracted the data. Data was pooled using a model under the common parameters assumption. The studies were evaluated for methodological quality using the MethodologicAl STandard for Epidemiological Research (MASTER) scale. The primary outcome measure was pain reduction; pain was converted to a common 0 to 10 scale, and a weighted mean difference of more than 2 points on a 10-point pain scale was considered clinically important. Eight RCTs that evaluated the effect of ST on CNP were included, with a total sample size of 350 participants. None of the participants withdrew in all these trials owing to adverse events or lack of efficacy. There was high-quality evidence that ST reduced pain in the short term, with a mean difference of –3 points. The dose-response meta-analysis demonstrated a significant reduction in pain scores post-treatment, with a peak reduction at day 40. The effect of ST remained below the baseline values for 90 days, although with limited certainty. This study is the first dose-response meta-analysis to assess the duration of efficacy of ST in the treatment of CNP. The results demonstrated a clinically significant and more sustained reduction in pain created by ST compared to conventional treatments. Our findings indicate that ST could be used as a safe and effective alternative for managing CNP.

PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLAR SINGAPURA, BAHT, DAN PESO MENGGUNAKAN METODE GSTAR

The Generalized Space-Time Autoregressive (GSTAR) model is an extension of the Space-Time Autoregressive (STAR) model. The difference between the two models lies in the parameter assumptions. In the STAR model, the parameters are assumed to be independent of location, so this model is only suitable for data with homogeneous locations. Meanwhile in the GSTAR model, the parameters are assumed to change for each different location. This research aims to develop the best model for forecasting the Rupiah exchange rate against the Singapore Dollar, Thai Baht, and Philippine Peso. The appropriate model used for the Rupiah exchange rate data is the GSTAR(51)I(1) model. The weights used in this study are uniform location weights and inverse distance. The modeling results show that the best model is the model with inverse distance weighting, which has an MSE value of 371.8907 with MAPE values for each of the Rupiah exchange rate data against the Singapore Dollar, Thai Baht, and Philippine Peso of 0.3154214%, 0.8369436%, and 0.6237245%, respectively.

Late time acceleration in Bianchi type-V dark energy cosmological models with linear deceleration parameter

We present a Bianchi type-V cosmological model with deceleration parameter as a simple linear function of the Hubble parameter interacting with a perfect fluid in the general theory of relativity. For Bianchi type-V space-time, the available solutions of Einstein field equations have been procure under the assumption of linear deceleration parameter, q = A + BH, where H is the Hubble parameter. Also,during the evolution of the Bianchi-V universe, dark energy(DE) with a variable EoS parameters has been studied in detail and w slowly varies around w = –0.33 for a 10 GYr. lapse, which involves an accelerated expansion, through an expansion not compatible with current. In this article, we discuss the physical and geometrical facets of the cosmological parameter of the model. Several elements of the model universe are explained by the jerk parameter and energy conditions, including the recent cosmological expansion and singularity theories. We also deliberated state-finder parameters, which forecast that the Universe created from Einstein’s static era rallies towards ΛCDM model (r = 1, s = 0).

Effects of Non-Invasive Ventilation with different modalities in patients undergoing heart surgery: Protocol for a randomized controlled clinical trial.

The thoracic surgical procedure leads to a reduction in respiratory muscle strength. To restore it, certain strategies must be employed. Physiotherapy utilizes resources and techniques such as deep breathing stimulation, cough stimulation, use of incentive spirometers, mobilization, and ambulation. However, at times these resources and techniques may prove insufficient, and additional measures, such as Non-Invasive Ventilation (NIV), are employed Pieczkoski (2017). Non-Invasive Positive Pressure Ventilation (NPPV) has been utilized to expedite pulmonary function recovery as well as to prevent and treat postoperative pulmonary complications Nasrala 2018. NIV diminishes the risk of ventilator-associated complications due to its non-invasive nature. Consequently, NIV has been adopted to avert post-extubation complications in postoperative patients Liu 2020. The objective of this study is to conduct a randomized clinical trial and assess the efficacy of NIV in comparison to conventional physiotherapy in terms of pulmonary function among patients undergoing cardiac surgery at a selected hospital in Campina Grande, Paraíba, Brazil. This randomized, controlled, double-blind (patient and analyst) clinical trial will be conducted at Hospital João XXIII in Campina Grande, Paraíba, Brazil. Patients do not know which group they are allocated to. Those in the group that use CPAP or BIPAP will not be able to distinguish one from the other. The data analyst at the end of the collections will also be blinded. Only the health professional who will be applying the protocol cannot be blinded. The sample size, determined via sample calculation, yielded a total of 21 patients per group (63 patients). The patients will be allocated into 3 groups (CPAP group - CPAP + standard physiotherapy, BiPAP group - BiPAP + standard physiotherapy, and Control group - standard physiotherapy) in a 1:1:1 allocation ratio. The control group will receive the usual physiotherapeutic treatment as per the kinesiotherapy protocol. The treatment will be administered twice daily, starting in the ICU and progressing to the ward. In the CPAP group, nasal CPAP at 10cmH2O will be administered for 1 hour, twice daily, using an approved device. In the BiPAP group, nasal BiPAP with an IPAP of 13cmH2O and EPAP of 8cmH2O will be administered for 1 hour, twice daily, using an approved device. The NIV sessions will be conducted over the course of 5 days of hospitalization, both in the ICU and the ward. Assessments will be conducted at two time points: on day 1 preoperatively and on day 5 postoperatively. The following measures will be evaluated: pulmonary function, length of hospital stay, presence of postoperative pulmonary complications, score of the Minnesota Living with Heart Failure Questionnaire (MLHFQ) in its Portuguese version, functional capacity, the Global Perception of Change Scale, and the Functional Independence Measure (MIF). The normality of variables will be assessed using the Shapiro-Wilk test. IBM SPSS Statistics Base 25.0, using the Shapiro-Wilk test for normality and paired Student's t-test for pre-post intervention comparison. They will use linear mixed effects models for longitudinal analysis and GLMMs to compare NIV effects over time between groups. They will employ ITT for missing data, INAR models for time dependence, fixed effects models for endogeneity, and Cohen's d for effect sizes. Parametric model assumptions will be checked, and various models will be considered for data characteristics. Pulmonary function, Length of hospital stay. Score of the Minnesota Living with Heart Failure Questionnaire (MLHFQ) in Portuguese version, Funcional capacity, The global perception of change scale, The functional independence measure (MIF), pO2 (partial pressure of oxygen), pCO2 (partial pressure of carbon dioxide), HCO3 (bicarbonate), Arterial Oxygen Saturation (SaO2), Base Excess (BE), Presence of lung complications. Duration of cardiopulmonary bypass, type of surgery, personal history, preoperative ejection fraction, previous respiratory complications, body mass index (BMI), gender and age. Trial register number NCT05966337.

Conditional mean dimension reduction for tensor time series

The dimension reduction problem for a stationary tensor time series is addressed. The goal is to remove linear combinations of the tensor time series that are mean independent of the past, without imposing any parametric models or distributional assumptions. To achieve this goal, a new metric called cumulative tensor martingale difference divergence is introduced and its theoretical properties are studied. Unlike existing methods, the proposed approach achieves dimension reduction by estimating a distinctive subspace that can fully retain the conditional mean information. By focusing on the conditional mean, the proposed dimension reduction method is potentially more accurate in prediction. The method can be viewed as a factor model-based approach that extends the existing techniques for estimating central subspace or central mean subspace in vector time series. The effectiveness of the proposed method is illustrated by extensive simulations and two real-world data applications.

Evidence on an endogenous growth model with public R&D

ABSTRACT The empirical investigation of properties of an endogenous growth model by Huang, Lai, and Peretto ([2023]. “Public R&D, Private R&D and Growth: A Schumpeterian Approach.” public.econ.duke.edu mimeo) in this paper confirms important assumptions and results of the model for OECD countries. Labour-augmenting technical change is enhanced through private and public R&D stocks in fully modified and dynamic OLS mean-group and pooled mean-group estimations, also when adding the number of enterprises. The CES spillover functions in the growth model’s functions for R&D stock dynamics are supported through nonlinear estimation under the assumptions of identical or different spillover parameters for private and public R&D. We suggest strong public-to-private spillovers and weak private-to-public spillovers as well as high elasticities of substitution for private–public R&D stocks for private R&D processes and low CES for public R&D processes. We confirm the existence of private–public researcher interaction effects in the private R&D knowledge growth function and provide tentative evidence for the linear model relation between public researchers and firm-level R&D and the hump-shaped relation between public and private researchers (both as % labour force). A vector-autoregressive (VAR) panel model in growth rates produces results, which are in accordance with the impact of public R&D cuts on the steady state and the transitional dynamics of the HLP model.

Sensitivity of a Lumped-Capacitance Building Thermal Modelling Approach for Energy-Market-Scale Flexibility Studies

Despite all the literature on building energy management, building-stock-scale models depicting its impact for energy-market-scale optimisation models are lacking. To address this shortcoming, an open-source tool called ArchetypeBuildingModel.jl has been developed for aggregating building-stock-level data into simplified lumped-capacitance thermal models compatible with existing open-source energy-system modelling frameworks. This paper aims to demonstrate the feasibility of these simplified thermal models by comparing their performance against dedicated building simulation software, as well as examining their sensitivity to key modelling and parameter assumptions. Modelling and parameter assumptions comparable to the existing literature achieved an acceptable performance according to ASHRAE Guideline 14 across all tested buildings and nodal configurations. The most robust performance was achieved with a period of variations above 13 days and interior node depth between 0.1 and 0.2 for structural thermal mass calibrations, and with external shading coefficients between 0.6 and 1.0 and solar heat gain convective fractions between 0.4 and 0.6 for solar heat gain calibrations. Furthermore, three-plus-node lumped-capacitance thermal models are recommended when modelling buildings with structures varying in terms of thermal mass. Nevertheless, the ArchetypeBuildingModel.jl performance was found to be robust against uncertain key parameter assumptions, making it plausible for energy-market-scale applications.

A nonparametrically corrected likelihood for Bayesian spectral analysis of multivariate time series

A novel approach to Bayesian nonparametric spectral analysis of stationary multivariate time series is presented. Starting with a parametric vector-autoregressive model, the parametric likelihood is nonparametrically adjusted in the frequency domain to account for potential deviations from parametric assumptions. A proof of mutual contiguity of the nonparametrically corrected likelihood, the multivariate Whittle likelihood approximation and the exact likelihood for Gaussian time series is given. A multivariate extension of the nonparametric Bernstein-Dirichlet process prior for univariate spectral densities to the space of Hermitian positive definite spectral density matrices is specified directly on the correction matrices. An infinite series representation of this prior is then used to develop a Markov chain Monte Carlo algorithm to sample from the posterior distribution. The code is made publicly available for ease of use and reproducibility. With this novel approach, a generalisation of the multivariate Whittle-likelihood-based method of Meier et al. (2020) as well as an extension of the nonparametrically corrected likelihood for univariate stationary time series of Kirch et al. (2019) to the multivariate case is presented. It is demonstrated that the nonparametrically corrected likelihood combines the efficiencies of a parametric with the robustness of a nonparametric model. Its numerical accuracy is illustrated in a comprehensive simulation study. Its practical advantages are illustrated by a spectral analysis of two environmental time series data sets: a bivariate time series of the Southern Oscillation Index and fish recruitment and a multivariate time series of windspeed data at six locations in California.

Semiparametric g-computation for survival outcomes with time-fixed exposures: An illustration

PurposeGeneralized (g-) computation is a useful tool for causal inference in epidemiology. However, in settings when the outcome is a survival time subject to right censoring, the standard pooled logistic regression approach to g-computation requires arbitrary discretization of time, parametric modeling of the baseline hazard function, and the need to expand one’s dataset. We illustrate a semiparametric Breslow estimator for g-computation with time-fixed treatments and survival outcomes that is not subject to these limitations. MethodsWe compare performance of the Breslow g-computation estimator to the pooled logistic g-computation estimator in simulations and illustrate both approaches to estimate the effect of a 3-drug vs 2-drug antiretroviral therapy regimen among people with HIV. ResultsIn simulations, both approaches performed well at the end of follow-up. The pooled logistic approach was biased at times between the endpoints of the discrete time intervals used, while the Breslow approach was not. In the example, both approaches estimated a 1-year risk difference of about 6 % in favor of the 3-drug regimen, but the shape of the survival curves differed. ConclusionsThe Breslow g-computation estimator of counterfactual risk functions does not rely on strong parametric assumptions about the time-to-event distribution or onerous dataset expansions.