ArticlePharmaceutical statistics2022
Estimating survival parameters under conditionally independent left truncation.
Article in Pharmaceutical statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Role of ctDNA Tumor Fraction in Selecting Immunotherapy-Based Regimens in Advanced Non-Small Cell Lung Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- CBFB mutations predict endocrine therapy benefit in estrogen receptor-positive breast cancer.medRxiv : the preprint server for health sciences · 2026Article
- Mapping the heritability of disease: a nationwide study.Nature communications · 2026Article
- Individualized prediction of survival after brain metastasis development in patients with breast cancer.Journal of neuro-oncology · 2026Article
- Brief Report: Not Created Equal: Survival Differences byJTO clinical and research reports · 2025Article
- Biomarker Inference and the Timing of Next-Generation Sequencing in a Multi-Institutional, Cross-Cancer Clinicogenomic Data Set.JCO precision oncology · 2024Article
- Replication of Real-World Evidence in Oncology Using Electronic Health Record Data Extracted by Machine Learning.Cancers · 2023Article
- Elucidating Analytic Bias Due to Informative Cohort Entry in Cancer Clinico-genomic Datasets.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023Article
- Cancer survival: left truncation and comparison of results from hospital-based cancer registry and population-based cancer registry.Frontiers in oncology · 2023Article
- Estimating survival parameters under conditionally independent left truncation.Pharmaceutical statistics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Databases derived from electronic health records (EHRs) are commonly subject to left truncation, a type of selection bias that occurs when patients need to survive long enough to satisfy certain entry criteria. Standard methods to adjust for left truncation bias rely on an assumption of marginal independence between entry and survival times, which may not always be satisfied in practice. In this work, we examine how a weaker assumption of conditional independence can result in unbiased estimation of common statistical parameters. In particular, we show the estimability of conditional parameters in a truncated dataset, and of marginal parameters that leverage reference data containing non-truncated data on confounders. The latter is complementary to observational causal inference methodology applied to real-world external comparators, which is a common use case for real-world databases. We implement our proposed methods in simulation studies, demonstrating unbiased estimation and valid statistical inference. We also illustrate estimation of a survival distribution under conditionally independent left truncation in a real-world clinico-genomic database.
Indexed as
Identifiers
What OpenQuestion holds
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.