A Dynamical Modeling Approach for Analysis of Longitudinal Clinical Trials in the Presence of Missing Endpoints.

H T Banks, Shuhua Hu, Eric Rosenberg

Abstract read

In one paragraph

Article in Applied mathematics letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Remarks on the design and analyses of longitudinal studies for cancer patients with anorexia and weight loss.Journal of cachexia, sarcopenia and muscle · 2019
Review

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.

H T BanksCenter for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212.

Shuhua HuCenter for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212; Certara, Inc., Cary, NC 27518.

Eric RosenbergCenter for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212; Departments of Pathology and Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114.

Funding

Mathematical and Statistical Modeling to Inform Design of HIV Clinical TrialsR01AI071915 · NIAID · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI BANKS, H. THOMAS · 2006 to 2012

$4.6M

NIAID NIH HHS R01 AI071915

6 · The paper itself

Abstract

Randomized longitudinal clinical trials are the gold standard to evaluate the effectiveness of interventions among different patient treatment groups. However, analysis of such clinical trials becomes difficult in the presence of missing data, especially in the case where the study endpoints become difficult to measure because of subject dropout rates or/and the time to discontinue the assigned interventions are different among the patient groups. Here we report on using a validated mathematical model combined with an inverse problem approach to predict the values for the missing endpoints. A small randomized HIV clinical trial where endpoints for most of patients are missing is used to demonstrate this approach.

Indexed as

HIVhypothesis testinginverse problemsordinary differential equation

Identifiers

PMID28344385

DOI10.1016/j.aml.2016.07.002

PMCPMC5363994

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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.