arXiv subjects

Frank Eriksson

Publications and source records attributed to Frank Eriksson.

5 recordsLinked to original sources

arXiv2025

Improving precision of cumulative incidence estimates in randomized controlled trials with external controls

Augmenting the control arm in clinical trials with external data can improve statistical power for demonstrating treatment effects. In many time-to-event outcome trials, participants are subject to truncation by death. Direct application of methods for competing risks analysis on the joint data may introduce bias, for example, due to covariate shifts between the populations. In this work, we consider transportability of the conditional cause-specific hazard of the event of interest under the control treatment. Under this assumption, we derive semiparametric efficiency bounds of causal cumulative incidences. This allows for quantification of the theoretical efficiency gain from incorporating the external controls. We propose triply robust estimators that can achieve the efficiency bounds, where the trial controls and external controls are made comparable through time-specific weights in a martingale integral. We conducted a simulation study to show the precision gain of the proposed fusion estimators compared to their counterparts without utilizing external controls. As a real data application, we used two cardiovascular outcome trials conducted to assess the safety of glucagon-like peptide-1 agonists. Incorporating the external controls from one trial into the other, we observed a decrease in the standard error of the treatment effects on adverse non-fatal cardiovascular events with all-cause death as the competing risk.

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arXiv2024

Efficient estimation of the target population average treatment effect from multi-source data

We consider estimation of the target population average treatment effect (TATE) when outcome information is unavailable. Instead, we observe the outcome in multiple source populations and wish to combine the treatment effects therein to make inference on the TATE. In contrast to existing works that assume transportability on the conditional distribution of potential outcomes or conditional treatment-specific means, we work under a weaker form of effect transportability. Following the framework for causally interpretable meta-analysis, we assume transportability of conditional average treatment effects across multiple populations, which may hold with fewer standardization variables. Under this assumption, we derive the semiparametric efficiency bound of the TATE and characterize a class of doubly robust and asymptotically linear estimators. Within this class, an efficient estimator assigns optimal weights to observations from different data sources. Additionally, we suggest estimators of a low-dimensional summary of effect heterogeneity in the target population. We illustrate the use of the proposed estimators on a multicentre weight management clinical trial for semaglutide, a glucagon-like peptide-1 receptor agonist, on overweight or obese patients. Using outcome information from other regions, we estimate the weight loss effect of semaglutide in the United States subgroup.

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arXiv2024

Proximal indirect comparison

We consider the problem of indirect comparison, where a treatment arm of interest is absent by design in one randomized controlled trial but available in the other. The former is the target trial, and the latter is the source trial. The identifiability of the target population average treatment effect often relies on conditional transportability assumptions. However, it is a common concern whether all relevant effect modifiers are measured and controlled for. We give a new proximal identification result in the presence of shifted, unobserved effect modifiers based on proxies: an adjustment proxy in both trials and an additional reweighting proxy in the source trial. We propose an estimator which is doubly-robust against misspecifications of the so-called bridge functions and asymptotically normal under mild consistency of estimators for the bridge functions. We use two weight management trials as a context to illustrate selection of proxies and apply our method to compare the weight loss effect of active treatments from these trials.

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arXiv2021

Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation

Targeted maximum likelihood estimation is a general methodology combining flexible ensemble learning and semiparametric efficiency theory in a two-step procedure for estimation of causal parameters. Proposed targeted maximum likelihood procedures for survival and competing risks analysis have so far focused on events taken values in discrete time. We here present a targeted maximum likelihood estimation procedure for event times that take values in R+. We focuson the estimation of intervention-specific mean outcomes with stochastic interventions on a time-fixed treatment. For data-adaptive estimation of nuisance parameters, we propose a new flexible highly adaptive lasso estimation method for continuous-time intensities that can be implemented with L1-penalized Poisson regression. In a simulation study the targeted maximum likelihood estimator based on the highly adaptive lasso estimator proves to be unbiased and achieve proper coverage in agreement with the asymptotic theory and further displays efficiency improvements relative to a Kaplan-Meier approach.

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arXiv2019

Semiparametric multi-parameter regression survival modelling

We consider a log-linear model for survival data, where both the location and scale parameters depend on covariates and the baseline hazard function is completely unspecified. This model provides the flexibility needed to capture many interesting features of survival data at a relatively low cost in model complexity. Estimation procedures are developed and asymptotic properties of the resulting estimators are derived using empirical process theory. Finally, a resampling procedure is developed to estimate the limiting variances of the estimators. The finite sample properties of the estimators are investigated by way of a simulation study, and a practical application to lung cancer data is illustrated.

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