Evidence map›Paper›PMID 35562655›Full record

ArticleBMC medical research methodology2022

Raking of data from a large Australian cohort study improves generalisability of estimates of prevalence of health and behaviour characteristics and cancer incidence.

Sarsha Yap, Qingwei Luo, Stephen Wade, Marianne Weber, Emily Banks, Karen Canfell, Dianne L O'Connell, Julia Steinberg

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Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
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1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Sarsha Yap *The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 153 Dowling St, Woolloomooloo, NSW, 2011, Australia. sarshay@nswcc.org.au.
Qingwei Luo *The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 153 Dowling St, Woolloomooloo, NSW, 2011, Australia.
Stephen WadeThe Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 153 Dowling St, Woolloomooloo, NSW, 2011, Australia.
Marianne WeberThe Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 153 Dowling St, Woolloomooloo, NSW, 2011, Australia.
Emily BanksNational Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
Karen CanfellThe Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 153 Dowling St, Woolloomooloo, NSW, 2011, Australia.
Dianne L O'Connell *The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 153 Dowling St, Woolloomooloo, NSW, 2011, Australia.
Julia Steinberg *The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, 153 Dowling St, Woolloomooloo, NSW, 2011, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth surveys are commonly somewhat non-representative of their target population, potentially limiting the generalisability of prevalence estimates for health/behaviour characteristics and disease to the population. To reduce bias, weighting methods have been developed, though few studies have validated weighted survey estimates against generally accepted high-quality independent population benchmark estimates.

methodsWe applied post-stratification and raking methods to the Australian 45 and Up Study using Census data and compared the resulting prevalence of characteristics to accepted population benchmark estimates and separately, the incidence rates of lung, colorectal, breast and prostate cancer to whole-of-population estimates using Standardised Incidence Ratios (SIRs).

resultsThe differences between 45 and Up Study and population benchmark estimates narrowed following sufficiently-informed raking, e.g. 13.6% unweighted prevalence of self-reported fair/poor overall health, compared to 17.0% after raking and 17.9% from a population benchmark estimate. Raking also improved generalisability of cancer incidence estimates. For example, unweighted 45 and Up Study versus whole-of-population SIRs were 0.700 (95%CI:0.574-0.848) for male lung cancer and 1.098 (95%CI:1.002-1.204) for prostate cancer, while estimated SIRs after sufficiently-informed raking were 0.828 (95%CI:0.684-0.998) and 1.019 (95%CI:0.926-1.121), respectively.

conclusionRaking may be a useful tool for improving the generalisability of exposure prevalence and disease incidence from surveys to the population.

Indexed as

Prostatic NeoplasmsAustraliaCohort StudiesHealth BehaviorHumansIncidenceMalePrevalenceCancer incidenceCohort studiesHealth surveysNonresponse biasPoststratificationRakingRepresentativenessStatistical weighting

Identifiers

PMID35562655
PMCPMC9107206

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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.