Evidence map›Paper›PMID 30473787›Full record

ArticleStat (International Statistical Institute)2017

Bias and estimation under misspecification of the risk period in self-controlled case series studies.

Luis Fernando Campos, Damla Şentürk, Yanjun Chen, Danh V Nguyen

Abstract read
In one paragraph

Article in Stat (International Statistical Institute), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Luis Fernando CamposDepartment of Statistics, Harvard University, Cambridge, MA 02138, USA.
Damla ŞentürkDepartment of Biostatistics, University of California, Los Angeles, CA 90095, USA.
Yanjun ChenInstitute for Clinical and Translational Science, Irvine, CA 92617, USA.
Danh V NguyenDepartment of Medicine, University of California Irvine, Orange, CA 92868, USA.

Funding

Multilevel Time-Dynamic Modeling of Hospitalization and Survival in Patients on DialysisR01DK092232 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KURUM, ESRA, NGUYEN, DANH V · 2011 to 2025
$4.9M
NIDDK NIH HHS R01 DK092232
6 · The paper itself

Abstract

The self-controlled case series (SCCS) method is useful for estimating the relative incidence (RI) of acute events, such as adverse events (AEs) during a specified risk period following an exposure (e.g., 6-week period after vaccinations or 30-day period after infection-related hospitalizations). In practice, the "optimal" risk period is unknown and must be specified. To date, two approaches are available to guide the specification of the risk period. Both methods do not fully utilize the nature of the bias due to misspecification, which to date has not been characterized. Thus, we elucidate the bias of SCCS estimate of the RI when the risk period is misspecified. We then propose a novel method that more effectively estimates the optimal risk period and the associated RI of AEs. The new method incorporates information on the functional form of the bias. Efficacy of the proposed approach is illustrated with substantial reduction in bias and variance in simulation studies. The proposed method is illustrated with two SCCS studies to determine the (1) risk of idiopathic thrombocytopenic purpura after measles-mumps-rubella vaccination in children and (2) risk of cardiovascular events after infection-related hospitalizations in older patients on dialysis.

Indexed as

case series analysisdialysisidiopathic thrombocytopenic purpurainfectionmaximum likelihoodmeasles-mumps-rubellanon-homogeneous Poisson processrisk lengthvaccination

Identifiers

PMID30473787
PMCPMC6249026

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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.