Power Analysis for Population-Based Longitudinal Studies Investigating Gene-Environment Interactions in Chronic Diseases: A Simulation Study
Identifieur interne : 000151 ( Pmc/Curation ); précédent : 000150; suivant : 000152Power Analysis for Population-Based Longitudinal Studies Investigating Gene-Environment Interactions in Chronic Diseases: A Simulation Study
Auteurs : Jinhui Ma [Canada] ; Lehana Thabane [Canada] ; Joseph Beyene [Canada] ; Parminder Raina [Canada]Source :
- PLoS ONE [ 1932-6203 ] ; 2016.
Abstract
Conventional methods for sample size calculation for population-based longitudinal studies tend to overestimate the statistical power by overlooking important determinants of the required sample size, such as the measurement errors and unmeasured etiological determinants, etc. In contrast, a simulation-based sample size calculation, if designed properly, allows these determinants to be taken into account and offers flexibility in accommodating complex study design features. The Canadian Longitudinal Study on Aging (CLSA) is a Canada-wide, 20-year follow-up study of 30,000 people between the ages of 45 and 85 years, with in-depth information collected every 3 years. A simulation study, based on an illness-death model, was conducted to: (1) investigate the statistical power profile of the CLSA to detect the effect of environmental and genetic risk factors, and their interaction on age-related chronic diseases; and (2) explore the design alternatives and implementation strategies for increasing the statistical power of population-based longitudinal studies in general. The results showed that the statistical power to identify the effect of environmental and genetic risk exposures, and their interaction on a disease was boosted when: (1) the prevalence of the risk exposures increased; (2) the disease of interest is relatively common in the population; and (3) risk exposures were measured accurately. In addition, the frequency of data collection every three years in the CLSA led to a slightly lower statistical power compared to the design assuming that participants underwent health monitoring continuously. The CLSA had sufficient power to detect a small (1
Url:
DOI: 10.1371/journal.pone.0149940
PubMed: 26901422
PubMed Central: 4762766
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<front><div type="abstract" xml:lang="en"><p>Conventional methods for sample size calculation for population-based longitudinal studies tend to overestimate the statistical power by overlooking important determinants of the required sample size, such as the measurement errors and unmeasured etiological determinants, etc. In contrast, a simulation-based sample size calculation, if designed properly, allows these determinants to be taken into account and offers flexibility in accommodating complex study design features. The Canadian Longitudinal Study on Aging (CLSA) is a Canada-wide, 20-year follow-up study of 30,000 people between the ages of 45 and 85 years, with in-depth information collected every 3 years. A simulation study, based on an illness-death model, was conducted to: (1) investigate the statistical power profile of the CLSA to detect the effect of environmental and genetic risk factors, and their interaction on age-related chronic diseases; and (2) explore the design alternatives and implementation strategies for increasing the statistical power of population-based longitudinal studies in general. The results showed that the statistical power to identify the effect of environmental and genetic risk exposures, and their interaction on a disease was boosted when: (1) the prevalence of the risk exposures increased; (2) the disease of interest is relatively common in the population; and (3) risk exposures were measured accurately. In addition, the frequency of data collection every three years in the CLSA led to a slightly lower statistical power compared to the design assuming that participants underwent health monitoring continuously. The CLSA had sufficient power to detect a small (1</p>
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<title-group><article-title>Power Analysis for Population-Based Longitudinal Studies Investigating Gene-Environment Interactions in Chronic Diseases: A Simulation Study</article-title>
<alt-title alt-title-type="running-head">Statistical Power of Population-Based Cohort Studies</alt-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Ma</surname>
<given-names>Jinhui</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="currentaff001"><sup>¤a</sup>
</xref>
<xref ref-type="author-notes" rid="currentaff002"><sup>¤b</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Thabane</surname>
<given-names>Lehana</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff004"><sup>4</sup>
</xref>
<xref ref-type="aff" rid="aff005"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Beyene</surname>
<given-names>Joseph</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Raina</surname>
<given-names>Parminder</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="corresp" rid="cor001">*</xref>
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<aff id="aff001"><label>1</label>
<addr-line>Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada</addr-line>
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<aff id="aff002"><label>2</label>
<addr-line>McMaster University Evidence-based Practice Center, Hamilton, Ontario, Canada</addr-line>
</aff>
<aff id="aff003"><label>3</label>
<addr-line>Biostatistics Unit, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada</addr-line>
</aff>
<aff id="aff004"><label>4</label>
<addr-line>Centre for Evaluation of Medicines, St Joseph’s Healthcare Hamilton, Ontario, Canada</addr-line>
</aff>
<aff id="aff005"><label>5</label>
<addr-line>Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Olivier</surname>
<given-names>Jake</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>University of New South Wales, AUSTRALIA</addr-line>
</aff>
<author-notes><fn fn-type="conflict" id="coi001"><p><bold>Competing Interests: </bold>
The authors have declared that no competing interests exist.</p>
</fn>
<fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: PR JM. Performed the experiments: JM. Analyzed the data: JM LT JB. Contributed reagents/materials/analysis tools: JM LT. Wrote the paper: JM PR LT JB.</p>
</fn>
<fn fn-type="current-aff" id="currentaff001"><label>¤a</label>
<p>Current address: School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada</p>
</fn>
<fn fn-type="current-aff" id="currentaff002"><label>¤b</label>
<p>Current address: Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada</p>
</fn>
<corresp id="cor001">* E-mail: <email>praina@mcmaster.ca</email>
</corresp>
</author-notes>
<pub-date pub-type="epub"><day>22</day>
<month>2</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection"><year>2016</year>
</pub-date>
<volume>11</volume>
<issue>2</issue>
<elocation-id>e0149940</elocation-id>
<history><date date-type="received"><day>12</day>
<month>1</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>8</day>
<month>2</month>
<year>2016</year>
</date>
</history>
<permissions><copyright-statement>© 2016 Ma et al</copyright-statement>
<copyright-year>2016</copyright-year>
<copyright-holder>Ma et al</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>
, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="pone.0149940.pdf"></self-uri>
<abstract><p>Conventional methods for sample size calculation for population-based longitudinal studies tend to overestimate the statistical power by overlooking important determinants of the required sample size, such as the measurement errors and unmeasured etiological determinants, etc. In contrast, a simulation-based sample size calculation, if designed properly, allows these determinants to be taken into account and offers flexibility in accommodating complex study design features. The Canadian Longitudinal Study on Aging (CLSA) is a Canada-wide, 20-year follow-up study of 30,000 people between the ages of 45 and 85 years, with in-depth information collected every 3 years. A simulation study, based on an illness-death model, was conducted to: (1) investigate the statistical power profile of the CLSA to detect the effect of environmental and genetic risk factors, and their interaction on age-related chronic diseases; and (2) explore the design alternatives and implementation strategies for increasing the statistical power of population-based longitudinal studies in general. The results showed that the statistical power to identify the effect of environmental and genetic risk exposures, and their interaction on a disease was boosted when: (1) the prevalence of the risk exposures increased; (2) the disease of interest is relatively common in the population; and (3) risk exposures were measured accurately. In addition, the frequency of data collection every three years in the CLSA led to a slightly lower statistical power compared to the design assuming that participants underwent health monitoring continuously. The CLSA had sufficient power to detect a small (1</p>
</abstract>
<funding-group><funding-statement>This study was supported in part by funds from the Canadian Longitudinal Study on Aging (which is funded in part by the Canadian Institutes of Health Research (CIHR)), Canadian Network and Centre for Trials Internationally (CANNeCTIN) program, and the Drug Safety and Effectiveness Cross-Disciplinary Training (DSECT) Program in the form of studentship and training awards for the first author.</funding-statement>
</funding-group>
<counts><fig-count count="6"></fig-count>
<table-count count="3"></table-count>
<page-count count="20"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>All relevant data are within the paper and its Supporting Information files.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>All relevant data are within the paper and its Supporting Information files.</p>
</notes>
</front>
</pmc>
</record>
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