Evidence map›Paper›PMID 28530002›Full record

ArticleObservational studies2016

Cohort restriction based on prior enrollment: Examining potential biases in estimating cancer and mortality risk.

Susan M Shortreed, Eric Johnson, Carolyn M Rutter, Aruna Kamineni, Karen J Wernli, Jessica Chubak

Abstract read
In one paragraph

Article in Observational studies, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

6 authors.

Susan M ShortreedBiostatistics Unit, Group Health Research Institute, Seattle, WA, U.S.A.
Eric JohnsonBiostatistics Unit, Group Health Research Institute, Seattle, WA, U.S.A.
Carolyn M RutterRAND Corporation, Santa Monica, CA, U.S.A.
Aruna KamineniGroup Health Research Institute, Seattle, WA, U.S.A.
Karen J WernliGroup Health Research Institute, Seattle, WA, U.S.A.
Jessica ChubakGroup Health Research Institute, Seattle, WA, U.S.A.

Funding

Studying Colorectal Cancer Effectiveness of Screening Strategies (SuCCESS)U54CA163261 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI CHUBAK, JESSICA · 2011 to 2017
$7.3M
SURVEILLANCE EPIDEMIOLOGY &END RESULTS PROGRAMN01CN067009 · CN · FRED HUTCHINSON CANCER RESEARCH CENTER · PI THOMAS, DAVID B · 1996 to 1997
–
NCI NIH HHS N01 CN067009NCI NIH HHS N01 PC035142NCI NIH HHS U54 CA163261
6 · The paper itself

Abstract

Electronic health records and administrative databases provide rich, longitudinal data for health-related research. These data cover large, diverse populations creating excellent research opportunities, but have limitations. In particular, information is available only for individuals who are enrolled in a particular health system; thus, studies often exclude individual's with short enrollment history. Such cohort restriction may cause selection bias in absolute risk estimates for the full enrollee population. We use hazard ratios (HRs) to estimate the association between length of prior enrollment and cancer and all-cause mortality risk. HRs different from one indicate restricted cohorts would produce biased risk estimates for the full enrollee population. Our study sample included 170,708 enrollees of a Western Washington healthcare delivery system. Unadjusted models found individuals with 10 or more years of prior enrollment had higher risk of cancer and death compared to those with less than 5 years prior enrollment (HRs ranged from 1.29 - 3.01). Age- and sex-adjusted models accounted for much of this difference (HRs: 0.93 - 1.24). Models adjusting for additional covariates had similar results (HRs: 0.91 - 1.14). After evaluating potential selection bias, we conclude that, in this setting, age- and sex-standardizing risk estimates can remove most of the bias due to lengthy, prior-enrollment cohort restrictions. Before generalizing estimates based on a selected sample of patients meeting prior enrollment criteria, researchers should assess the potential for selection bias.

Indexed as

administrative databasescancer mortality risk estimationelectronic health recordsenrollment historySelection bias

Identifiers

PMID28530002
PMCPMC5435370

What OpenQuestion holds

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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.