A Sexual Partnership Duration: Characterizing Sampling Conditions That Permit unbiased Estimation of Survivorship and Effect on It of Covariates.

Yared Gurmu, Jing Qian, Victor De Gruttola

Abstract read

In one paragraph

Article in Research & reviews. Journal of statistics and mathematical sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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5 · Who and what money

Authors and funding

3 authors.

Yared GurmuDivision of Cardiovascular Medicine, Harvard Medical School, USA.

Jing QianDepartment of Biostatistics and Epidemiology, University of Massachusetts, USA.

Victor De GruttolaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, USA.

Funding

BIOSTATISTICS/EPIDEMIOLOGY TRAINING GRANTS IN AIDST32AI007358 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Michael David Hughes · 1989 to 2026

$12.4M

Methods to Advance the HIV Prevention Research AgendaR37AI051164 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI DEGRUTTOLA, VICTOR GERARD · 2012 to 2019

$3.3M

NIAID NIH HHS R37 AI051164NIAID NIH HHS T32 AI007358

6 · The paper itself

Abstract

Partnership duration data are commonly obtained through surveys that collect information on relationships that are ongoing during a fixed time window. This sampling mechanism leads to duration data that are left truncated and right censored; such data have been analysed using the standard truncation product limit estimator (TPLE). In this paper, we describe a common sampling scheme for collecting sexual partnership data, discuss a key assumption required for the TPLE to be unbiased, and provide the conditions under which the nonparametric maximum likelihood estimator of the relationship duration distribution is unique and consistent. We also investigate the conditions required for the consistency of the regression coefcient from a Cox proportional hazards model that apply even when the distribution of duration is not completely identifiable due to restrictions on the support of the truncation distribution. Lastly, we will provide some illustrative examples on estimating distribution of most recent partnerships and present spline regression results based on partnership data collected from sexual behavior survey in Mochudi, Botswana.

Indexed as

Consistent estimationLeft truncationPartnership duration samplingRight censoringTruncation product limit estimator

Identifiers

PMID30264038

PMCPMC6155996

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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.