You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
<li>Expanding on item 2.3 “Sample sizes”, when presenting a descriptive statistics table (a “Table 1”), the recommended best practice is to report two pieces of information for each variable :</li>
1680
+
<li><strong>Reporting of Tables</strong>:</li>
1681
1681
</ol>
1682
+
<p>Expanding on item 2.3 “Sample sizes”, when presenting a descriptive statistics table (a “Table 1”), the recommended best practice is to report two pieces of information for each variable :</p>
1682
1683
<oltype="1">
1683
1684
<li>The <strong>unweighted sample size (n)</strong> for each category.</li>
1684
1685
<li>The <strong>weighted percentage (%) or mean</strong> for each category.</li>
<li>The “lonely PSU” problem, also known as the “singleton PSU” problem is a technical issue that can arise when analyzing complex survey data. The Taylor Series Linearization (TSL) method for variance estimation works by measuring the variability between Primary Sampling Units (PSUs) within each stratum. To calculate variance (which is a measure of spread), you need at least two points to compare. This problem most often occurs during subpopulation analysis. While the full NHANES sample is designed to have at least two PSUs in every stratum, when you analyze a very specific subgroup (e.g., non-Hispanic Black participants who started smoking before age 10), it’s possible that your subgroup of interest exists in only one PSU within a particular stratum. Solutions are to [i] Centering at the grand mean (conservative approach), [ii] The stratum with the lonely PSU is merged with another, similar stratum, or [iii] use Replication methods.</li>
1702
+
<li><strong>Reporting of lonely/singleton PSU</strong>:</li>
1702
1703
</ol>
1704
+
<p>The “lonely PSU” problem, also known as the “singleton PSU” problem is a technical issue that can arise when analyzing complex survey data. The Taylor Series Linearization (TSL) method for variance estimation works by measuring the variability between Primary Sampling Units (PSUs) within each stratum. To calculate variance (which is a measure of spread), you need at least two points to compare. This problem most often occurs during subpopulation analysis. While the full NHANES sample is designed to have at least two PSUs in every stratum, when you analyze a very specific subgroup (e.g., non-Hispanic Black participants who started smoking before age 10), it’s possible that your subgroup of interest exists in only one PSU within a particular stratum. Solutions are to [i] Centering at the grand mean (conservative approach), [ii] The stratum with the lonely PSU is merged with another, similar stratum, or [iii] use Replication methods.</p>
1705
+
<olstart="3" type="a">
1706
+
<li><strong>Reporting of reliability of estimates</strong>:</li>
1707
+
</ol>
1708
+
<ul>
1709
+
<li>design effect (deff): A value >1 indicates the complex design increases variance (e.g., 1.234 means ~23% inflation vs. SRS). Report it in footnotes or Methods for transparency.</li>
1710
+
<li>Relative standard error (RSE) or %RSE = (Standard error of estimate / Estimate) * 100: Should be <30% for “stable” estimates per <ahref="https://wwwn.cdc.gov/nchs/nhanes/tutorials/reliabilityofestimates.aspx">CDC guidelines</a>; suppress or flag unstable ones (e.g., wide CIs).</li>
<p>For <strong>subpopulation analysis</strong>, always define the survey design on the full dataset first, then use a subset command. When using NHANES, select the correct <strong>weight</strong> based on the “least common denominator” of your variables (e.g., interview vs. exam weight). When <strong>combining survey cycles</strong>, divide the 2-year weights by the number of cycles, but use special formulas for the pandemic-era data. For descriptive tables, always report both <strong>unweighted counts (n)</strong> and <strong>weighted percentages (%)</strong>.</p>
<h2class="anchored" data-anchor-id="the-sex-and-gender-equity-in-research-sager-guidelines">5.4 The Sex and Gender Equity in Research (SAGER) guidelines</h2>
1721
+
<p>For reporting sex- and gender-based analyses (not necessarily part of survey data analysis), we recommend <ahref="https://doi.org/10.1186/s41073-016-0007-6">SAGER guidelines</a><spanclass="citation" data-cites="heidari2016sex">(<ahref="#ref-heidari2016sex" role="doc-biblioref">Heidari et al. 2016</a>)</span>. We apply SAGER checklist <spanclass="citation" data-cites="van2022sex">(<ahref="#ref-van2022sex" role="doc-biblioref">Van Epps et al. 2022</a>)</span> [<ahref="https://doi.org/10.3897/ese.2022.e86910">link</a>] on an example article <spanclass="citation" data-cites="karim2025examining">(<ahref="#ref-karim2025examining" role="doc-biblioref">Karim, Hossain, and Zheng 2025</a>)</span> [<ahref="https://doi.org/10.1016/j.focus.2024.100282">link</a>].</p>
1722
+
<tableclass="caption-top table">
1723
+
<colgroup>
1724
+
<colstyle="width: 11%">
1725
+
<colstyle="width: 41%">
1726
+
<colstyle="width: 46%">
1727
+
</colgroup>
1728
+
<thead>
1729
+
<trclass="header">
1730
+
<thstyle="text-align: left;">SAGER Item</th>
1731
+
<thstyle="text-align: left;">What the Manuscript Said</th>
1732
+
<thstyle="text-align: left;">What Should Have Been Said (to fully comply)</th>
1733
+
</tr>
1734
+
</thead>
1735
+
<tbody>
1736
+
<trclass="odd">
1737
+
<tdstyle="text-align: left;"><strong>1. General:</strong> Use terms sex/gender appropriately.</td>
1738
+
<tdstyle="text-align: left;">The article consistently uses “sex” when referring to the demographic variable from NHANES and discussing biological differences.</td>
1739
+
<tdstyle="text-align: left;"><em>This is correct.</em> The article appropriately uses “sex” when referring to the demographic variable from NHANES, which is consistent with the data source.</td>
1740
+
</tr>
1741
+
<trclass="even">
1742
+
<tdstyle="text-align: left;"><strong>3b. Abstract:</strong> Describe study population with sex/gender breakdown.</td>
1743
+
<tdstyle="text-align: left;">The abstract states the analysis explored “effect modification by race/ethnicity and sex” but does not provide the numerical breakdown of participants.</td>
1744
+
<tdstyle="text-align: left;">The abstract should have included the numbers: “Results: The analysis included <strong>50,549 participants (48.5% male)</strong>. The authors found that early smoking initiation…”</td>
1745
+
</tr>
1746
+
<trclass="odd">
1747
+
<tdstyle="text-align: left;"><strong>4a. Introduction:</strong> Cite previous studies on sex/gender differences.</td>
1748
+
<tdstyle="text-align: left;">The introduction cites literature on how “biological differences in nicotine metabolism… vary across sexes” and discusses disparities related to sex.</td>
1749
+
<tdstyle="text-align: left;"><em>This is correct.</em> The introduction properly cites existing literature to establish the rationale for investigating sex as a variable.</td>
1750
+
</tr>
1751
+
<trclass="even">
1752
+
<tdstyle="text-align: left;"><strong>5a. Methods:</strong> State the method used to define sex/gender.</td>
1753
+
<tdstyle="text-align: left;">The Methods section lists “sex (male, female)” as a variable but does not specify how it was collected or defined by the survey.</td>
1754
+
<tdstyle="text-align: left;">The Methods section should have specified the data collection method: “<strong>Sex (male, female) was based on participant self-report</strong> as recorded in the NHANES demographic files.”</td>
1755
+
</tr>
1756
+
<trclass="odd">
1757
+
<tdstyle="text-align: left;"><strong>6a. Results:</strong> Provide a complete sex/gender breakdown.</td>
1758
+
<tdstyle="text-align: left;">Appendix Table 1 provides the complete unweighted and weighted breakdown: “Male 24,391 (48.54%)” and “Female 26,158 (51.46%)”.</td>
1759
+
<tdstyle="text-align: left;"><em>This is correct.</em> The article provides a full and appropriate breakdown of the study population by sex in an appendix table.</td>
1760
+
</tr>
1761
+
<trclass="even">
1762
+
<tdstyle="text-align: left;"><strong>6b. Results:</strong> Present data disaggregated by sex/gender.</td>
1763
+
<tdstyle="text-align: left;">Figure 2 presents hazard ratios stratified by “Male” and “Female”. Appendix Figure 2 shows smoking duration disaggregated by sex.</td>
1764
+
<tdstyle="text-align: left;"><em>This is correct.</em> The results are clearly and appropriately disaggregated by sex throughout the article and appendix.</td>
1765
+
</tr>
1766
+
<trclass="odd">
1767
+
<tdstyle="text-align: left;"><strong>7a. Discussion:</strong> Discuss the implications of sex/gender on the results.</td>
1768
+
<tdstyle="text-align: left;">The Discussion section analyzes the findings: “Effect modification by sex resulted in slightly higher HR estimates for the female subpopulation…”.</td>
1769
+
<tdstyle="text-align: left;"><em>This is correct.</em> The manuscript properly discusses and interprets the sex-specific findings.</td>
1770
+
</tr>
1771
+
</tbody>
1772
+
</table>
1773
+
<hr>
1774
+
</section>
1711
1775
<sectionid="glossary-of-terms" class="level2">
1712
1776
<h2class="anchored" data-anchor-id="glossary-of-terms">Glossary of Terms</h2>
Heidari, Shirin, Thomas F Babor, Paola De Castro, Sera Tort, and Mirjam Curno. 2016. <span>“Sex and Gender Equity in Research: Rationale for the SAGER Guidelines and Recommended Use.”</span><em>Research Integrity and Peer Review</em> 1 (1): 2.
Karim, Mohammad Ehsanul, Md Belal Hossain, and Chuyi Zheng. 2025. <span>“Examining the Role of Race/Ethnicity and Sex in Modifying the Association Between Early Smoking Initiation and Mortality: A 20-Year NHANES Analysis.”</span><em>AJPM Focus</em> 4 (2): 100282.
Thomas, D. R., and J. N. K. Rao. 1987. <span>“Small-Sample Comparisons of Level and Power for Simple Goodness-of-Fit Statistics Under Cluster Sampling.”</span><em>Journal of the American Statistical Association</em> 82 (398): 630–36.
Van Epps, Heather, Olaya Astudillo, Yaiza Del Pozo Martin, and Joan Marsh. 2022. <span>“The Sex and Gender Equity in Research (SAGER) Guidelines: Implementation and Checklist Development.”</span><em>European Science Editing</em> 48: e86910.
Copy file name to clipboardExpand all lines: ref.bib
+21Lines changed: 21 additions & 0 deletions
Original file line number
Diff line number
Diff line change
@@ -1,3 +1,24 @@
1
+
@article{heidari2016sex,
2
+
title={Sex and gender equity in research: rationale for the SAGER guidelines and recommended use},
3
+
author={Heidari, Shirin and Babor, Thomas F and De Castro, Paola and Tort, Sera and Curno, Mirjam},
4
+
journal={Research integrity and peer review},
5
+
volume={1},
6
+
number={1},
7
+
pages={2},
8
+
year={2016},
9
+
publisher={Springer}
10
+
}
11
+
12
+
@article{van2022sex,
13
+
title={The Sex and Gender Equity in Research (SAGER) guidelines: Implementation and checklist development},
14
+
author={Van Epps, Heather and Astudillo, Olaya and Martin, Yaiza Del Pozo and Marsh, Joan},
15
+
journal={European Science Editing},
16
+
volume={48},
17
+
pages={e86910},
18
+
year={2022},
19
+
publisher={European Association of Science Editors (EASE)}
20
+
}
21
+
1
22
@article{karim2025examining,
2
23
title={Examining the Role of Race/Ethnicity and Sex in Modifying the Association Between Early Smoking Initiation and Mortality: A 20-Year NHANES Analysis},
3
24
author={Karim, Mohammad Ehsanul and Hossain, Md Belal and Zheng, Chuyi},
0 commit comments