Evidence map›Paper›PMID 35096402›Full record

ReviewNeuro-oncology practice2022

Missing repeated measures data in clinical trials.

Stephanie L Pugh, Paul D Brown, Danielle Enserro

Abstract readReview
In one paragraph

Review in Neuro-oncology practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Addressing Mental Health Symptoms Among COVID-19 Healthcare Workers: A Heart Rate Variability Biofeedback Pilot Study.Stress and health : journal of the International Society for the Investigation of Stress · 2024
    Trial
  4. Article
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  12. Challenges to and considerations of designing cancer prevention trials.Journal of the National Cancer Institute. Monographs · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Quantifying bias due to missing data in quality of life surveys of advanced-stage cancer patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
    Article
  19. Article
  20. Article
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

3 authors.

Stephanie L PughDepartment of Statistics, American College of Radiology, NRG Oncology Statistics and Data Management Center, Philadelphia, Pennsylvania, USA.
Paul D BrownDepartment of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA.
Danielle EnserroRoswell Park Comprehensive Cancer Center, NRG Oncology Statistics and Data Management Center, Buffalo, New York, USA.

Funding

Statistics CoreU10CA180822 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI James J. Dignam, GREG YOTHERS · 2014 to 2026
$146.4M
NCI NIH HHS U10 CA180822
6 · The paper itself

Abstract

Clinical trials typically collect longitudinal data, data that are collected repeated over time, such as laboratories, scans, or patient-reported outcomes. Due to a variety of reasons, this data can be missing, whether a patient stops attending clinical visits (ie, dropout) or misses assessments intermittently. Understanding the reasons for missing data as well as predictors of missing data can aid in determination of the missing data mechanism. The analysis methods used are dependent on the missing data mechanism and may make certain assumptions about the missing data itself. Methods for nonignorable missing data, which assumes that the missing data depend on the missing data itself, make stronger assumptions and include pattern-mixture models and shared parameter models. Missing data that are ignorable after adjusting for other covariates can be analyzed using methods that adjust for covariates, such as mixed-effects models or multiple imputation. Missing data that are ignorable can be analyzed using standard approaches that require complete case data, such as change from baseline or proportion of patients who declined at a specified time point. In clinical trials, truly ignorable data are rare, resulting in additional analysis methods required for proper interpretation of the results. Conducting several analyses under different assumptions, called sensitivity analyses, can determine the extent of the impact of the missing data.

Indexed as

longitudinal analysismissing datapatient-reported outcomes

Identifiers

PMID35096402
PMCPMC8789297

What OpenQuestion holds

Textmetadata
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.