ArticleJournal of the American Statistical Association2014
Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial.
Article in Journal of the American Statistical Association, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Development and Validation of a Novel Conditional Event-Free Survival Tool in Diffuse Large B-Cell Lymphoma.American journal of hematology · 2026Article
- Efficacy of contrast versus non-contrast CT surveillance among patients surviving two years without recurrence after surgery for stage I lung cancer.Scientific reports · 2025Article
- Article
- Evaluating US Multiple Listing Practices in Lung Transplantation: Unveiling Hidden Disparities.Chest · 2024Article
- Tackling dynamic prediction of death in patients with recurrent cardiovascular events.Statistics in medicine · 2023Article
- Semi-supervised calibration of noisy event risk (SCANER) with electronic health records.Journal of biomedical informatics · 2023Article
- More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study.Trials · 2023Article
- Regression of nonalcoholic fatty liver disease is associated with reduced risk of incident diabetes: A longitudinal cohort study.PloS one · 2023Article
- Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Evaluation of Surrogate Endpoints Using Information-Theoretic Measure of Association Based on Havrda and Charvat Entropy.Mathematics (Basel, Switzerland) · 2022Article
- Evaluating multiple surrogate markers with censored data.Biometrics · 2021Article
- Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes.Biometrics · 2021Article
- Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions.Proceedings of machine learning research · 2021Article
- Dynamic landmark prediction for mixture data.Biostatistics (Oxford, England) · 2021Article
- Dynamic Survival Analysis with Individualized Truncated Parametric Distributions.Proceedings of machine learning research · 2021Article
- Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes.medRxiv : the preprint server for health sciences · 2020Article
- Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On-Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data.BioMed research international · 2020Article
- Using a surrogate marker for early testing of a treatment effect.Biometrics · 2019Article
- Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards.Lifetime data analysis · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In many studies with a survival outcome, it is often not feasible to fully observe the primary event of interest. This often leads to heavy censoring and thus, difficulty in efficiently estimating survival or comparing survival rates between two groups. In certain diseases, baseline covariates and the event time of non-fatal intermediate events may be associated with overall survival. In these settings, incorporating such additional information may lead to gains in efficiency in estimation of survival and testing for a difference in survival between two treatment groups. If gains in efficiency can be achieved, it may then be possible to decrease the sample size of patients required for a study to achieve a particular power level or decrease the duration of the study. Most existing methods for incorporating intermediate events and covariates to predict survival focus on estimation of relative risk parameters and/or the joint distribution of events under semiparametric models. However, in practice, these model assumptions may not hold and hence may lead to biased estimates of the marginal survival. In this paper, we propose a semi-nonparametric two-stage procedure to estimate and compare
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.