paalcry

• Janvin, Matias; Ryalen, Pål; Sarvet, Aaron L. & Stensrud, Mats Julius (2025). A positivity robust strategy to study effects of switching treatment. Biometrics. ISSN 0006-341X. 81(3). doi: 10.1093/biomtc/ujaf085. Full text in Research Archive Show summary

  In studies of medical treatments, individuals often experience post-treatment events that predict their future outcomes. In this work, we study how to use initial observations of a recurrent event—a type of post-treatment event—to offer updated treatment recommendations in settings where no, or few, individuals are observed to switch between treatment arms. Specifically, we formulate an estimand quantifying the average effect of switching treatment on subsequent events. We derive bounds on the value of this estimand under plausible conditions and propose non-parametric estimators of the bounds. Furthermore, we define a value and regret function for a dynamic treatment-switching regime, and use these to determine 3 types of optimal regimes under partial identification: the pessimist (maximin value), optimist (maximax value), and opportunist (minimax regret) regimes. The pessimist regime is guaranteed to perform at least as well as the standard of care. We apply our methods to data from the Systolic Blood Pressure Intervention Trial.

• Røysland, Kjetil; Ryalen, Pål Christie; Nygård, Mari & Didelez, Vanessa (2024). Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses. Journal of the Royal Statistical Society, Series B (Statistical Methodology). ISSN 1369-7412. 87(1), p. 74–97. doi: 10.1093/jrsssb/qkae056. Full text in Research Archive Show summary

  We consider continuous-time survival and event-history settings, where our aim is to graphically represent causal structures allowing us to characterize when a causal parameter is identified from observational data. This causal parameter is formalized as the effect on an outcome event of a (possibly hypothetical) intervention on the intensity of a treatment process. To establish identifiability, we propose novel graphical rules indicating whether the observed information is sufficient to obtain the desired causal effect by suitable reweighting. This requires a different type of graph than in discrete time. We formally define causal semantics for the corresponding dynamic graphs that represent local independence models for multivariate counting processes. Importantly, our work highlights that causal inference from censored data relies on subtle structural assumptions on the censoring process beyond independent censoring; these can be verified graphically. Put together, our results are the first to establish graphical rules for nonparametric causal identifiability in event processes in this generality for the continuous-time case, not relying on particular parametric survival models. We conclude with a data example on Human papillomavirus (HPV) testing for cervical cancer screening, where the assumptions are illustrated graphically and the desired effect is estimated by reweighted cumulative incidence curves.

• Berg, Erlend; Tegn, Nicolai Kloumann; Abdelnoor, Michael; Røysland, Kjetil; Ryalen, Pål Christie & Aaberge, Lars [Show all 14 contributors for this article] (2023). Long-Term Outcomes of Invasive vs Conservative Strategies for Older Patients With Non–ST-Segment Elevation Acute Coronary Syndromes. Journal of the American College of Cardiology. ISSN 0735-1097. 82(21), p. 2021–2030. doi: 10.1016/j.jacc.2023.09.809. Full text in Research Archive Show summary

  Background Non–ST-segment elevation acute coronary syndrome (NSTE-ACS) is a frequent cause of hospital admission in older people, but clinical trials targeting this population are scarce. Objectives The After Eighty Study assessed the effect of an invasive vs a conservative treatment strategy in a very old population with NSTE-ACS. Methods Between 2010 and 2014, the investigators randomized 457 patients with NSTE-ACS aged ≥80 years (mean age 85 years) to an invasive strategy involving early coronary angiography with immediate evaluation for revascularization and optimal medical therapy or to a conservative strategy (ie, optimal medical therapy). The primary endpoint was a composite of myocardial infarction, need for urgent revascularization, stroke, and death. The long-term outcomes are presented. Results After a median follow up of 5.3 years, the invasive strategy was superior to the conservative strategy in the reduction of the primary endpoint (incidence rate ratio: 0.76; 95% CI: 0.63-0.93; P = 0.0057). The invasive strategy demonstrated a significant gain in event-free survival of 276 days (95% CI: 151-400 days; P = 0.0001) at 5 years and 337 days (95% CI: 123-550 days; P = 0.0001) at 10 years. These results were consistent across subgroups of patients with respect to major cardiovascular prognostic factors. Conclusions In patients aged ≥80 years with NSTE-ACS, the invasive strategy was superior to the conservative strategy in the reduction of composite events and demonstrated a significant gain in event-free survival. (The After Eighty Study: a randomized controlled trial; NCT01255540)

• Janvin, Matias; Young, Jessica G.; Ryalen, Pål Christie & Stensrud, Mats Julius (2023). Causal inference with recurrent and competing events. Lifetime Data Analysis. ISSN 1380-7870. 30, p. 59–118. doi: 10.1007/s10985-023-09594-8. Full text in Research Archive Show summary

  Abstract Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in athletes. Competing events, such as death, complicate causal inference in studies of recurrent events because once a competing event occurs, an individual cannot have more recurrent events. Several statistical estimands have been studied in recurrent event settings, with and without competing events. However, the causal interpretations of these estimands, and the conditions that are required to identify these estimands from observed data, have yet to be formalized. Here we use a formal framework for causal inference to formulate several causal estimands in recurrent event settings, with and without competing events. When competing events exist, we clarify when commonly used classical statistical estimands can be interpreted as causal quantities from the causal mediation literature, such as (controlled) direct effects and total effects. Furthermore, we show that recent results on interventionist mediation estimands allow us to define new causal estimands with recurrent and competing events that may be of particular clinical relevance in many subject matter settings. We use causal directed acyclic graphs and single world intervention graphs to illustrate how to reason about identification conditions for the various causal estimands based on subject matter knowledge. Furthermore, using results on counting processes, we show that our causal estimands and their identification conditions, which are articulated in discrete time, converge to classical continuous time counterparts in the limit of fine discretizations of time. We propose estimators and establish their consistency for the various identifying functionals. Finally, we use the proposed estimators to compute the effect of blood pressure lowering treatment on the recurrence of acute kidney injury using data from the Systolic Blood Pressure Intervention Trial.

• Ryalen, Pål Christie; Møller, Bjørn; Laache, Christoffer Haug; Stensrud, Mats Julius & Røysland, Kjetil (2021). Prognosis of cancer survivors: estimation based on differential equations. Biostatistics. ISSN 1465-4644. 24(2), p. 345–357. doi: 10.1093/biostatistics/kxab009. Full text in Research Archive Show summary

  Summary We present a method for estimating several prognosis parameters for cancer survivors. The method utilizes the fact that these parameters solve differential equations driven by cumulative hazards. By expressing the parameters as solutions to differential equations, we develop generic estimators that are easy to implement with standard statistical software. We explicitly describe the estimators for prognosis parameters that are often employed in practice, but also for parameters that, to our knowledge, have not been used to evaluate prognosis. We then apply these parameters to assess the prognosis of five common cancers in Norway.

• Ryalen, Pål Christie; Stensrud, Mats Julius; Fosså, Sophie Dorothea & Røysland, Kjetil (2020). Causal inference in continuous time: an example on prostate cancer therapy. Biostatistics. ISSN 1465-4644. 21(1), p. 172–185. doi: 10.1093/biostatistics/kxy036. Full text in Research Archive
• Stensrud, Mats Julius; Røysland, Kjetil & Ryalen, Pål Christie (2019). On null hypotheses in survival analysis. Biometrics. ISSN 0006-341X. 75(4), p. 1276–1287. doi: 10.1111/biom.13102. Full text in Research Archive
• Stensrud, Mats Julius; Ryalen, Pål Christie & Røysland, Kjetil (2019). Sufficient Cause Interaction for Time-to-event Outcomes. Epidemiology. ISSN 1044-3983. 30(2), p. 189–196. doi: 10.1097/EDE.0000000000000958. Full text in Research Archive
• Ryalen, Pål Christie; Stensrud, Mats Julius & Røysland, Kjetil (2019). The additive hazard estimator is consistent for continuous-time marginal structural models. Lifetime Data Analysis. ISSN 1380-7870. 25(4), p. 611–638. doi: 10.1007/s10985-019-09468-y. Full text in Research Archive
• Ryalen, Pål Christie; Stensrud, Mats Julius & Røysland, Kjetil (2018). Transforming cumulative hazard estimates. Biometrika. ISSN 0006-3444. 105(4), p. 905–916. doi: 10.1093/biomet/asy035. Full text in Research Archive

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• Ryalen, Pål (2024). Mediering, moderering, interaksjon. Full text in Research Archive Show summary

  Bakgrunnen for dette seminaret å få klarhet i bruken av mediasjon, moderering, og interaksjon med utgangspunkt i artikler fra Morrow og andre fra 2022, Cashin og andre fra 2023, og Mellor og andre fra 2022. Mitt hovedfokus vil være å belyse hvilke forskningsspørsmål som er sentrale i denne sammenhengen.

• Ryalen, Pål (2024). On the role of martingales in continuous-time causal inference. Full text in Research Archive Show summary

  We define dynamic regimes and associated potential outcomes for data described by marked point processes (MPPs). These definitions motivate MPP analogues of the commonly used consistency, exchangeability, and positivity conditions that prove to be sufficient for identifying effects in MPP data structures. The conditions are formulated based on martingale theory, which allows us to derive explicit conditions for data described by stochastic processes. The definitions and conditions align with well-established discrete-time conditions in the special cases where they are expected to be similar. Thus, this work bridges the vast literatures on survival (event history) analysis with counting processes in continuous time and causal inference with variables in discrete-time. After formulating a set of identification conditions, we derive and characterize marginal $g$-formulas. The $g$-formulas are generally different from those studied in related works. We relate our findings to previous work on causal inference with (counting) processes, the classical survival literature, and the discrete-time causal inference literature.

• Ryalen, Pål Christie (2024). A framework for dynamic regimes, counterfactuals, and the identification of effects in marked point process models. Full text in Research Archive
• Ryalen, Pål Christie (2024). The role of martingales in identifying causal effects in survival analysis. Full text in Research Archive
• Ryalen, Pål Christie (2024). Mediation, moderation, interaction. Full text in Research Archive
• Ryalen, Pål Christie (2023). A counterfactual causal model for marked point processes. Full text in Research Archive
• Ryalen, Pål Christie (2023). A framework for counterfactuals in marked point process models. Full text in Research Archive
• Orvedal, Kjartan & Ryalen, Pål Christie (2015). Flom og stormflo. Full text in Research Archive Show summary

  Måleserier for havnivå og flom er analysert for å se om hendelsene flom og stormflo er statistisk koblede hendelser. Resultatet fra analysen tyder på at det er en svak kobling mellom flom og metrologisk bidrag til endret havnivå. Videre tyder resultatene på at sammenhengen mellom flom og stormflo sannsynligvis er forskjellig fra vassdrag til vassdrag og at måleseriene flom og stormflo ikke er uavhengige hendelser.

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