Evidence map›Paper›PMID 22114687›Full record

Trial reportPloS one2011

Antiretroviral treatment cohort analysis using time-updated CD4 counts: assessment of bias with different analytic methods.

Katharina Kranzer, James J Lewis, Richard G White, Judith R Glynn, Stephen D Lawn, Keren Middelkoop, Linda-Gail Bekker, Robin Wood

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in PloS one, 2011. 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
0.3field-weighted citation impact, top 41% of its field
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, 4 citations in OpenAlex.

  1. 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

8 authors at 3 institutions in 2 countries.

Katharina KranzerDepartment of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom. katharina.kranzer@lshtm.ac.uk
James J Lewis
Richard G White
Judith R Glynn
Stephen D Lawn
Keren Middelkoop
Linda-Gail Bekker
Robin Wood
London School of Hygiene & Tropical Medicine · GBDesmond Tutu HIV Foundation · ZAUniversity of Cape Town · ZA

Funding

Observational Antiretroviral Studies In Southern Africa (OASIS) CollaborationU01AI069924 · NIAID · UNIVERSITAT BERN · PI Cleophas Chimbetete, Mary-Ann Davies · 2006 to 2026
$55.9M
The effect of ART on rates and transmission of TBU19AI053217 · NIAID · WITS HEALTH CONSORTIUM (PTY), LTD · PI MCINTYRE, JAMES ALASDAIR · 2002 to 2011
$44.9M
Optimizing HIV Care in Less Developed CountriesR01AI058736 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI Kenneth Alan Freedberg, Emily Parker Hyle · 2004 to 2026
$21.5M
Medical Research Council G0802414NIAID NIH HHS 1U19AI53217NIAID NIH HHS 1U19AI53217-01NIAID NIH HHS 5 R01 AI058736-02NIAID NIH HHS 5U01AI069924-02NIAID NIH HHS IU19AI53217-07NIAID NIH HHS R01 AI058736NIAID NIH HHS U01 AI069924NIAID NIH HHS U19 AI053217Wellcome TrustWellcome Trust 087262/Z/08/ZWellcome Trust 088590/Z/09/ZWellcome Trust WT084323MA
6 · The paper itself

Abstract

backgroundSurvival analysis using time-updated CD4+ counts during antiretroviral therapy is frequently employed to determine risk of clinical events. The time-point when the CD4+ count is assumed to change potentially biases effect estimates but methods used to estimate this are infrequently reported.

methodsThis study examined the effect of three different estimation methods: assuming i) a constant CD4+ count from date of measurement until the date of next measurement, ii) a constant CD4+ count from the midpoint of the preceding interval until the midpoint of the subsequent interval and iii) a linear interpolation between consecutive CD4+ measurements to provide additional midpoint measurements. Person-time, tuberculosis rates and hazard ratios by CD4+ stratum were compared using all available CD4+ counts (measurement frequency 1-3 months) and 6 monthly measurements from a clinical cohort. Simulated data were used to compare the extent of bias introduced by these methods.

resultsThe midpoint method gave the closest fit to person-time spent with low CD4+ counts and for hazard ratios for outcomes both in the clinical dataset and the simulated data.

conclusionThe midpoint method presents a simple option to reduce bias in time-updated CD4+ analysis, particularly at low CD4 cell counts and rapidly increasing counts after ART initiation.

Indexed as

Antiretroviral Therapy, Highly ActiveAnti-HIV AgentsBiasCD4 Lymphocyte CountCD4-Positive T-LymphocytesCohort StudiesComputer SimulationHIV InfectionsHumansSouth AfricaTreatment OutcomeTuberculosisViral LoadAnti-HIV Agents

Identifiers

PMID22114687
PMCPMC3219676
OpenAlexW2088364443

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.