Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models

In this paper we review an approach to estimating the causal effect of a time-varying treatment on time to some event of interest. This approach is designed for the situation where the treatment may have been repeatedly adapted to patient characteristics, which themselves may also be time-dependent. In this situation the effect of the treatment cannot simply be estimated by conditioning on the patient characteristics, as these may themselves be indicators of the treatment effect. This so-called time-dependent confounding is typical in observational studies. We discuss a new class of failure time models, structural nested failure time models, which can be used to estimate the causal effect of a time-varying treatment, and present methods for estimating and testing the parameters of these models

Treatment Effect Quantification for Time-to-event Endpoints -- Estimands, Analysis Strategies, and beyond

A draft addendum to ICH E9 has been released for public consultation in August 2017. The addendum focuses on two topics particularly relevant for randomized confirmatory clinical trials: estimands and sensitivity analyses. The need to amend ICH E9 grew out of the realization of a lack of alignment between the objectives of a clinical trial stated in the protocol and the accompanying quantification of the "treatment effect" reported in a regulatory submission. We embed time-to-event endpoints in the estimand framework, and discuss how the four estimand attributes described in the addendum apply to time-to-event endpoints. We point out that if the proportional hazards assumption is not met, the estimand targeted by the most prevalent methods used to analyze time-to-event endpoints, logrank test and Cox regression, depends on the censoring distribution. We discuss for a large randomized clinical trial how the analyses for the primary and secondary endpoints as well as the sensitivity analyses actually performed in the trial can be seen in the context of the addendum. To the best of our knowledge, this is the first attempt to do so for a trial with a time-to-event endpoint. Questions that remain open with the addendum for time-to-event endpoints and beyond are formulated, and recommendations for planning of future trials are given. We hope that this will provide a contribution to developing a common framework based on the final version of the addendum that can be applied to design, protocols, statistical analysis plans, and clinical study reports in the future.Comment: 37 page

Monitoring Frequency of Intra‐Fraction Patient Motion Using the ExacTrac System for LINAC‐based SRS Treatments

Purpose: The aim of this study was to investigate the intra‐fractional patient motion using the ExacTrac system in LINAC‐based stereotactic radiosurgery (SRS). Method: A retrospective analysis of 104 SRS patients with kilovoltage image‐guided setup (Brainlab ExacTrac) data was performed. Each patient was imaged pre‐treatment, and at two time points during treatment (1st and 2nd mid‐treatment), and bony anatomy of the skull was used to establish setup error at each time point. The datasets included the translational and rotational setup error, as well as the time period between image acquisitions. After each image acquisition, the patient was repositioned using the calculated shift to correct the setup error. Only translational errors were corrected due to the absence of a 6D treatment table. Setup time and directional shift values were analyzed to determine correlation between shift magnitudes as well as time between acquisitions. Results: The average magnitude translation was 0.64 ± 0.59 mm, 0.79 ± 0.45 mm, and 0.65 ± 0.35 mm for the pre‐treatment, 1st mid‐treatment, and 2nd mid‐treatment imaging time points. The average time from pre‐treatment image acquisition to 1st mid‐treatment image acquisition was 7.98 ± 0.45 min, from 1st to 2nd mid‐treatment image was 4.87 ± 1.96 min. The greatest translation was 3.64 mm, occurring in the pre‐treatment image. No patient had a 1st or 2nd mid‐treatment image with greater than 2 mm magnitude shifts. Conclusion: There was no correlation between patient motion over time, in direction or magnitude, and duration of treatment. The imaging frequency could be reduced to decrease imaging dose and treatment time without significant changes in patient position

Time to revise COPD treatment algorithm

Parallel approach; Treatable traits; ICSEnfoque paralelo; Rasgos tratables; ICSEnfocament paral·lel; Trets tractables; ICSIn 2017, a new two-step algorithm for the treatment of COPD was proposed. This algorithm was based on the severity of symptoms and phenotypes or treatable traits, and patient-specialised assessment targeting eosinophilic inflammation, chronic bronchitis, and frequent infections is recommended after exacerbation occurs despite maximal bronchodilation therapy. However, recent studies have revealed the clinical characteristics of patients who should have second controllers added, such as ICS. We again realized that treatable traits should be assessed and intervened for as early as possible. Moreover, the treatment algorithm is necessary to be adapted to the situation of clinical practice, taking into account the characteristics of the patients. The time to revise COPD treatment algorithm has come and we propose a new 3-step parallel approach for initial COPD treatment. After the diagnosis of COPD, the first assessment is to divide into two categories based on the usual clinical characteristics for patients with COPD and the specific clinical characteristics for each patient with concomitant disease. In the former, the assessment should be based on the level of dyspnea and the frequency of exacerbations. After the assessment, mono- or dual bronchodilator should be selected. In the latter, the assessment should be based on asthma characteristics, chronic bronchitis, and chronic heart failure. After the assessment, patients with asthmatic characteristics may consider treatment with ICS, while patients with chronic bronchitis may consider treatment with roflumilast and/or macrolide, while patients with chronic heart failure may consider treatment with selective β1-blocker. The 3-step parallel approach is completed by adding an additional therapy for patients with concomitant disease to essential therapy for patients with COPD. In addition, it is important to review the response around 4 weeks after the initial therapy. This COPD management proposal might be considered as an approach based on patients’ clinical characteristics and on personalized therapy

Statistical modeling of causal effects in continuous time

This article studies the estimation of the causal effect of a time-varying treatment on time-to-an-event or on some other continuously distributed outcome. The paper applies to the situation where treatment is repeatedly adapted to time-dependent patient characteristics. The treatment effect cannot be estimated by simply conditioning on these time-dependent patient characteristics, as they may themselves be indications of the treatment effect. This time-dependent confounding is common in observational studies. Robins [(1992) Biometrika 79 321--334, (1998b) Encyclopedia of Biostatistics 6 4372--4389] has proposed the so-called structural nested models to estimate treatment effects in the presence of time-dependent confounding. In this article we provide a conceptual framework and formalization for structural nested models in continuous time. We show that the resulting estimators are consistent and asymptotically normal. Moreover, as conjectured in Robins [(1998b) Encyclopedia of Biostatistics 6 4372--4389], a test for whether treatment affects the outcome of interest can be performed without specifying a model for treatment effect. We illustrate the ideas in this article with an example.Comment: Published in at http://dx.doi.org/10.1214/009053607000000820 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org