Statistical considerations in long-term efficacy evaluation of anti-cancer therapies.

Ruobing Li, Jingyi Zhang, Jingzhao Wang, Jun Wang

Abstract readReview

In one paragraph

Review in Frontiers in pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ruobing Li *Office of Biostatistics and Clinical Pharmacology, Center for Drug Evaluation, National Medical Products Administration, Beijing, China.

Jingyi Zhang *Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.

Jingzhao WangOffice of Biostatistics and Clinical Pharmacology, Center for Drug Evaluation, National Medical Products Administration, Beijing, China.

Jun WangOffice of Biostatistics and Clinical Pharmacology, Center for Drug Evaluation, National Medical Products Administration, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anti-cancer therapy has been a significant focus of research. Developing and marketing various types and mechanisms of anti-cancer therapies benefit a variety of patients significantly. The long-term benefit to patients in evaluating the risk-benefit ratio of anti-cancer therapy has become a significant concern. This paper discusses the evaluation of long-term efficacy within the estimand framework and summarizes the various strategies for addressing potential intercurrent events. Non-proportional hazards of survival data may arise with novel anti-cancer therapies, leading to potential bias in conventional evaluation methods. This paper reviews statistical methods for addressing this issue, including novel endpoints, hypothesis testing, and efficacy estimation methods. We also discuss the influences of treatment switching. Although advanced methods have been developed to address the non-proportional hazard, they still have limitations that require continued collaborative efforts to resolve issues.

Indexed as

anti-cancer therapyestimandlong-term efficacy evaluationnon-proportional hazard assumptionpatient-reported outcomestreatment switching

Identifiers

PMID37854717

DOI10.3389/fphar.2023.1265953

PMCPMC10579585

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.