Evidence map›Paper›PMID 24498313›Full record

ReviewPloS one2014

A review of the study designs and statistical methods used in the determination of predictors of all-cause mortality in HIV-infected cohorts: 2002-2011.

Kennedy N Otwombe, Max Petzold, Neil Martinson, Tobias Chirwa

Abstract readReview
In one paragraph

Review in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
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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.

Kennedy N OtwombePerinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa ; School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
Max PetzoldCentre for Applied Biostatistics, Occupational and Environmental Medicine, University of Gothenburg, Gothenburg, Sweden.
Neil MartinsonPerinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa ; Johns Hopkins University, Center for TB Research, Baltimore, Maryland, United States of America.
Tobias ChirwaSchool of Public Health, University of the Witwatersrand, Johannesburg, South Africa.

Funding

South Africa Tuberculosis AIDS Training (SATBAT)U2RTW007370 · FIC · WITS HEALTH CONSORTIUM (PTY), LTD · PI MARTINSON, NEIL A · 2007 to 2011
$1.9M
FIC NIH HHS TW007370/3Wellcome TrustWellcome Trust 087547/Z/08/Z
6 · The paper itself

Abstract

backgroundResearch in the predictors of all-cause mortality in HIV-infected people has widely been reported in literature. Making an informed decision requires understanding the methods used.

objectivesWe present a review on study designs, statistical methods and their appropriateness in original articles reporting on predictors of all-cause mortality in HIV-infected people between January 2002 and December 2011. Statistical methods were compared between 2002-2006 and 2007-2011. Time-to-event analysis techniques were considered appropriate. DATA SOURCES: Pubmed/Medline. STUDY ELIGIBILITY CRITERIA: Original English-language articles were abstracted. Letters to the editor, editorials, reviews, systematic reviews, meta-analysis, case reports and any other ineligible articles were excluded.

resultsA total of 189 studies were identified (n = 91 in 2002-2006 and n = 98 in 2007-2011) out of which 130 (69%) were prospective and 56 (30%) were retrospective. One hundred and eighty-two (96%) studies described their sample using descriptive statistics while 32 (17%) made comparisons using t-tests. Kaplan-Meier methods for time-to-event analysis were commonly used in the earlier period (n = 69, 76% vs. n = 53, 54%, p = 0.002). Predictors of mortality in the two periods were commonly determined using Cox regression analysis (n = 67, 75% vs. n = 63, 64%, p = 0.12). Only 7 (4%) used advanced survival analysis methods of Cox regression analysis with frailty in which 6 (3%) were used in the later period. Thirty-two (17%) used logistic regression while 8 (4%) used other methods. There were significantly more articles from the first period using appropriate methods compared to the second (n = 80, 88% vs. n = 69, 70%, p-value = 0.003).

conclusionDescriptive statistics and survival analysis techniques remain the most common methods of analysis in publications on predictors of all-cause mortality in HIV-infected cohorts while prospective research designs are favoured. Sophisticated techniques of time-dependent Cox regression and Cox regression with frailty are scarce. This motivates for more training in the use of advanced time-to-event methods.

Indexed as

Cause of DeathCohort StudiesHIV InfectionsHumansKaplan-Meier EstimateLogistic ModelsOutcome Assessment, Health CarePrognosisProportional Hazards ModelsResearch Design

Identifiers

PMID24498313
PMCPMC3911971

What OpenQuestion holds

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Registered trials

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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.