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<!DOCTYPE html>
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  <title>Chapter 10 Reporting Guidelines | Understanding Propensity Score Matching</title>
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<li><a href="./">Understanding Propensity Score Matching</a></li>

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<li class="chapter" data-level="" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i>Preamble</a>
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<li class="chapter" data-level="1" data-path="terms.html"><a href="terms.html"><i class="fa fa-check"></i><b>1</b> Defining Parameter</a>
<ul>
<li class="chapter" data-level="1.1" data-path="terms.html"><a href="terms.html#epidemiological-research-goals"><i class="fa fa-check"></i><b>1.1</b> Epidemiological research goals</a></li>
<li class="chapter" data-level="1.2" data-path="terms.html"><a href="terms.html#potential-outcome"><i class="fa fa-check"></i><b>1.2</b> Potential outcome</a></li>
<li class="chapter" data-level="1.3" data-path="terms.html"><a href="terms.html#parameters-of-interest"><i class="fa fa-check"></i><b>1.3</b> Parameters of interest</a>
<ul>
<li class="chapter" data-level="1.3.1" data-path="terms.html"><a href="terms.html#te"><i class="fa fa-check"></i><b>1.3.1</b> TE</a></li>
<li class="chapter" data-level="1.3.2" data-path="terms.html"><a href="terms.html#ate"><i class="fa fa-check"></i><b>1.3.2</b> ATE</a></li>
<li class="chapter" data-level="1.3.3" data-path="terms.html"><a href="terms.html#interpretation-of-ate"><i class="fa fa-check"></i><b>1.3.3</b> Interpretation of ATE</a></li>
<li class="chapter" data-level="1.3.4" data-path="terms.html"><a href="terms.html#identifiability-assumptions"><i class="fa fa-check"></i><b>1.3.4</b> Identifiability Assumptions</a></li>
<li class="chapter" data-level="1.3.5" data-path="terms.html"><a href="terms.html#att"><i class="fa fa-check"></i><b>1.3.5</b> ATT</a></li>
<li class="chapter" data-level="1.3.6" data-path="terms.html"><a href="terms.html#interpretation-of-att"><i class="fa fa-check"></i><b>1.3.6</b> Interpretation of ATT</a></li>
<li class="chapter" data-level="1.3.7" data-path="terms.html"><a href="terms.html#att-vs.-ate"><i class="fa fa-check"></i><b>1.3.7</b> ATT vs. ATE</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="2" data-path="balance.html"><a href="balance.html"><i class="fa fa-check"></i><b>2</b> Balance and Overlap</a>
<ul>
<li class="chapter" data-level="2.1" data-path="balance.html"><a href="balance.html#balance-1"><i class="fa fa-check"></i><b>2.1</b> Balance</a>
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<li class="chapter" data-level="2.1.1" data-path="balance.html"><a href="balance.html#measures-of-balance"><i class="fa fa-check"></i><b>2.1.1</b> Measures of Balance</a></li>
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<li class="chapter" data-level="2.2" data-path="balance.html"><a href="balance.html#adjustment"><i class="fa fa-check"></i><b>2.2</b> Adjustment</a>
<ul>
<li class="chapter" data-level="2.2.1" data-path="balance.html"><a href="balance.html#why-adjust"><i class="fa fa-check"></i><b>2.2.1</b> Why adjust?</a></li>
<li class="chapter" data-level="2.2.2" data-path="balance.html"><a href="balance.html#adjustment-methods"><i class="fa fa-check"></i><b>2.2.2</b> Adjustment Methods</a></li>
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<li class="chapter" data-level="2.3" data-path="balance.html"><a href="balance.html#lack-of-overlap"><i class="fa fa-check"></i><b>2.3</b> Lack of overlap</a></li>
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<li class="chapter" data-level="3" data-path="ps.html"><a href="ps.html"><i class="fa fa-check"></i><b>3</b> Propensity score</a>
<ul>
<li class="chapter" data-level="3.1" data-path="ps.html"><a href="ps.html#motivating-problem"><i class="fa fa-check"></i><b>3.1</b> Motivating problem</a></li>
<li class="chapter" data-level="3.2" data-path="ps.html"><a href="ps.html#defining-propensity-score"><i class="fa fa-check"></i><b>3.2</b> Defining Propensity score</a>
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<li class="chapter" data-level="3.2.1" data-path="ps.html"><a href="ps.html#theoretical-result"><i class="fa fa-check"></i><b>3.2.1</b> Theoretical result</a></li>
<li class="chapter" data-level="3.2.2" data-path="ps.html"><a href="ps.html#assumptions"><i class="fa fa-check"></i><b>3.2.2</b> Assumptions</a></li>
<li class="chapter" data-level="3.2.3" data-path="ps.html"><a href="ps.html#ways-to-use-ps"><i class="fa fa-check"></i><b>3.2.3</b> Ways to use PS</a></li>
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<li class="chapter" data-level="3.3" data-path="ps.html"><a href="ps.html#ps-matching-steps"><i class="fa fa-check"></i><b>3.3</b> PS Matching Steps</a></li>
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<li class="chapter" data-level="4" data-path="s1.html"><a href="s1.html"><i class="fa fa-check"></i><b>4</b> Step 1: Exposure modelling</a>
<ul>
<li class="chapter" data-level="4.1" data-path="s1.html"><a href="s1.html#model-specification"><i class="fa fa-check"></i><b>4.1</b> Model specification</a>
<ul>
<li class="chapter" data-level="4.1.1" data-path="s1.html"><a href="s1.html#updating-model-specification"><i class="fa fa-check"></i><b>4.1.1</b> Updating model specification</a></li>
<li class="chapter" data-level="4.1.2" data-path="s1.html"><a href="s1.html#stability-of-ps"><i class="fa fa-check"></i><b>4.1.2</b> Stability of PS</a></li>
</ul></li>
<li class="chapter" data-level="4.2" data-path="s1.html"><a href="s1.html#variables-to-adjust"><i class="fa fa-check"></i><b>4.2</b> Variables to adjust</a>
<ul>
<li class="chapter" data-level="4.2.1" data-path="s1.html"><a href="s1.html#best-approach"><i class="fa fa-check"></i><b>4.2.1</b> Best approach</a></li>
<li class="chapter" data-level="4.2.2" data-path="s1.html"><a href="s1.html#general-guideline-of-type-of-variables"><i class="fa fa-check"></i><b>4.2.2</b> General guideline of type of variables</a></li>
<li class="chapter" data-level="4.2.3" data-path="s1.html"><a href="s1.html#what-not-to-include"><i class="fa fa-check"></i><b>4.2.3</b> What NOT to include</a></li>
<li class="chapter" data-level="4.2.4" data-path="s1.html"><a href="s1.html#mediators"><i class="fa fa-check"></i><b>4.2.4</b> Mediators</a></li>
<li class="chapter" data-level="4.2.5" data-path="s1.html"><a href="s1.html#unmeasured-confounding"><i class="fa fa-check"></i><b>4.2.5</b> Unmeasured confounding</a></li>
</ul></li>
<li class="chapter" data-level="4.3" data-path="s1.html"><a href="s1.html#model-selection"><i class="fa fa-check"></i><b>4.3</b> Model selection</a>
<ul>
<li class="chapter" data-level="4.3.1" data-path="s1.html"><a href="s1.html#based-on-association-with-outcome"><i class="fa fa-check"></i><b>4.3.1</b> Based on association with outcome</a></li>
<li class="chapter" data-level="4.3.2" data-path="s1.html"><a href="s1.html#based-on-association-with-exposure"><i class="fa fa-check"></i><b>4.3.2</b> Based on association with exposure</a></li>
</ul></li>
<li class="chapter" data-level="4.4" data-path="s1.html"><a href="s1.html#alternative-modelling-strategies"><i class="fa fa-check"></i><b>4.4</b> Alternative modelling strategies</a></li>
<li class="chapter" data-level="4.5" data-path="s1.html"><a href="s1.html#ps-estimation"><i class="fa fa-check"></i><b>4.5</b> PS estimation</a></li>
</ul></li>
<li class="chapter" data-level="5" data-path="s2.html"><a href="s2.html"><i class="fa fa-check"></i><b>5</b> Step 2: Propensity score Matching</a>
<ul>
<li class="chapter" data-level="5.1" data-path="s2.html"><a href="s2.html#matching-method-nn"><i class="fa fa-check"></i><b>5.1</b> Matching method NN</a></li>
<li class="chapter" data-level="5.2" data-path="s2.html"><a href="s2.html#initial-fit"><i class="fa fa-check"></i><b>5.2</b> Initial fit</a></li>
<li class="chapter" data-level="5.3" data-path="s2.html"><a href="s2.html#fine-tuning-add-caliper"><i class="fa fa-check"></i><b>5.3</b> Fine tuning: add caliper</a></li>
<li class="chapter" data-level="5.4" data-path="s2.html"><a href="s2.html#things-to-keep-track-of"><i class="fa fa-check"></i><b>5.4</b> Things to keep track of</a></li>
<li class="chapter" data-level="5.5" data-path="s2.html"><a href="s2.html#matches"><i class="fa fa-check"></i><b>5.5</b> Matches</a></li>
<li class="chapter" data-level="5.6" data-path="s2.html"><a href="s2.html#other-matching-algorithms"><i class="fa fa-check"></i><b>5.6</b> Other matching algorithms</a></li>
</ul></li>
<li class="chapter" data-level="6" data-path="s3.html"><a href="s3.html"><i class="fa fa-check"></i><b>6</b> Step 3: Balance and overlap</a>
<ul>
<li class="chapter" data-level="6.1" data-path="s3.html"><a href="s3.html#assessment-of-balance-by-smd"><i class="fa fa-check"></i><b>6.1</b> Assessment of Balance by SMD</a></li>
<li class="chapter" data-level="6.2" data-path="s3.html"><a href="s3.html#smd-vs.-p-values"><i class="fa fa-check"></i><b>6.2</b> SMD vs. P-values</a></li>
<li class="chapter" data-level="6.3" data-path="s3.html"><a href="s3.html#vizualization-for-overlap"><i class="fa fa-check"></i><b>6.3</b> Vizualization for Overlap</a></li>
<li class="chapter" data-level="6.4" data-path="s3.html"><a href="s3.html#variance-ratio-1"><i class="fa fa-check"></i><b>6.4</b> Variance ratio</a></li>
<li class="chapter" data-level="6.5" data-path="s3.html"><a href="s3.html#close-inspection-of-boundaries"><i class="fa fa-check"></i><b>6.5</b> Close inspection of boundaries</a></li>
<li class="chapter" data-level="6.6" data-path="s3.html"><a href="s3.html#unsatirfactory-balance"><i class="fa fa-check"></i><b>6.6</b> Unsatirfactory balance</a></li>
</ul></li>
<li class="chapter" data-level="7" data-path="s4.html"><a href="s4.html"><i class="fa fa-check"></i><b>7</b> Step 4: Outcome modelling</a>
<ul>
<li class="chapter" data-level="7.1" data-path="s4.html"><a href="s4.html#crude-outcome-model"><i class="fa fa-check"></i><b>7.1</b> Crude outcome model</a></li>
<li class="chapter" data-level="7.2" data-path="s4.html"><a href="s4.html#double-adjustment"><i class="fa fa-check"></i><b>7.2</b> Double-adjustment</a></li>
<li class="chapter" data-level="7.3" data-path="s4.html"><a href="s4.html#adjusted-outcome-model"><i class="fa fa-check"></i><b>7.3</b> Adjusted outcome model</a></li>
<li class="chapter" data-level="7.4" data-path="s4.html"><a href="s4.html#variance-considerations"><i class="fa fa-check"></i><b>7.4</b> Variance considerations</a>
<ul>
<li class="chapter" data-level="7.4.1" data-path="s4.html"><a href="s4.html#cluster-option"><i class="fa fa-check"></i><b>7.4.1</b> Cluster option</a></li>
<li class="chapter" data-level="7.4.2" data-path="s4.html"><a href="s4.html#bootstrap"><i class="fa fa-check"></i><b>7.4.2</b> Bootstrap</a></li>
</ul></li>
<li class="chapter" data-level="7.5" data-path="s4.html"><a href="s4.html#estimate-obtained"><i class="fa fa-check"></i><b>7.5</b> Estimate obtained</a></li>
</ul></li>
<li class="chapter" data-level="8" data-path="compare.html"><a href="compare.html"><i class="fa fa-check"></i><b>8</b> PS vs. Regression</a>
<ul>
<li class="chapter" data-level="8.1" data-path="compare.html"><a href="compare.html#data-simulation"><i class="fa fa-check"></i><b>8.1</b> Data Simulation</a></li>
<li class="chapter" data-level="8.2" data-path="compare.html"><a href="compare.html#treatment-effect-from-counterfactuals"><i class="fa fa-check"></i><b>8.2</b> Treatment effect from counterfactuals</a></li>
<li class="chapter" data-level="8.3" data-path="compare.html"><a href="compare.html#treatment-effect-from-regression"><i class="fa fa-check"></i><b>8.3</b> Treatment effect from Regression</a></li>
<li class="chapter" data-level="8.4" data-path="compare.html"><a href="compare.html#treatment-effect-from-ps"><i class="fa fa-check"></i><b>8.4</b> Treatment effect from PS</a></li>
<li class="chapter" data-level="8.5" data-path="compare.html"><a href="compare.html#non-linear-model"><i class="fa fa-check"></i><b>8.5</b> Non-linear Model</a>
<ul>
<li class="chapter" data-level="8.5.1" data-path="compare.html"><a href="compare.html#data-generation"><i class="fa fa-check"></i><b>8.5.1</b> Data generation</a></li>
<li class="chapter" data-level="8.5.2" data-path="compare.html"><a href="compare.html#regression"><i class="fa fa-check"></i><b>8.5.2</b> Regression</a></li>
<li class="chapter" data-level="8.5.3" data-path="compare.html"><a href="compare.html#ps-1"><i class="fa fa-check"></i><b>8.5.3</b> PS</a></li>
<li class="chapter" data-level="8.5.4" data-path="compare.html"><a href="compare.html#machine-learning"><i class="fa fa-check"></i><b>8.5.4</b> Machine learning</a></li>
<li class="chapter" data-level="8.5.5" data-path="compare.html"><a href="compare.html#regression-is-doomed"><i class="fa fa-check"></i><b>8.5.5</b> Regression is doomed?</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="9" data-path="misspecify.html"><a href="misspecify.html"><i class="fa fa-check"></i><b>9</b> PS vs. Double robust methods</a>
<ul>
<li class="chapter" data-level="9.1" data-path="misspecify.html"><a href="misspecify.html#complex-data-simulation"><i class="fa fa-check"></i><b>9.1</b> Complex Data Simulation</a>
<ul>
<li class="chapter" data-level="" data-path="misspecify.html"><a href="misspecify.html#true-exposure-model"><i class="fa fa-check"></i>True Exposure Model</a></li>
<li class="chapter" data-level="" data-path="misspecify.html"><a href="misspecify.html#true-outcome-model"><i class="fa fa-check"></i>True Outcome Model</a></li>
<li class="chapter" data-level="" data-path="misspecify.html"><a href="misspecify.html#outcomes-and-exposures-are-complex-functions-of-measured-covariates"><i class="fa fa-check"></i>Outcomes and exposures are complex functions of measured covariates</a></li>
</ul></li>
<li class="chapter" data-level="9.2" data-path="misspecify.html"><a href="misspecify.html#understanding-finite-sample-bias"><i class="fa fa-check"></i><b>9.2</b> Understanding finite sample bias</a></li>
<li class="chapter" data-level="9.3" data-path="misspecify.html"><a href="misspecify.html#estimation-using-different-methods"><i class="fa fa-check"></i><b>9.3</b> Estimation using different methods</a>
<ul>
<li class="chapter" data-level="9.3.1" data-path="misspecify.html"><a href="misspecify.html#regression-1"><i class="fa fa-check"></i><b>9.3.1</b> Regression</a></li>
<li class="chapter" data-level="9.3.2" data-path="misspecify.html"><a href="misspecify.html#propensity-score"><i class="fa fa-check"></i><b>9.3.2</b> Propensity score</a></li>
<li class="chapter" data-level="9.3.3" data-path="misspecify.html"><a href="misspecify.html#double-machine-learning-method"><i class="fa fa-check"></i><b>9.3.3</b> Double machine learning method</a></li>
<li class="chapter" data-level="9.3.4" data-path="misspecify.html"><a href="misspecify.html#augmented-inverse-probability-weighting"><i class="fa fa-check"></i><b>9.3.4</b> Augmented Inverse probability weighting</a></li>
<li class="chapter" data-level="9.3.5" data-path="misspecify.html"><a href="misspecify.html#double-robust-method-tmle"><i class="fa fa-check"></i><b>9.3.5</b> Double robust method (TMLE)</a></li>
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<li class="chapter" data-level="10" data-path="guide.html"><a href="guide.html"><i class="fa fa-check"></i><b>10</b> Reporting Guidelines</a>
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<li class="chapter" data-level="10.1" data-path="guide.html"><a href="guide.html#discipline-specific-reviews"><i class="fa fa-check"></i><b>10.1</b> Discipline-specific Reviews</a></li>
<li class="chapter" data-level="10.2" data-path="guide.html"><a href="guide.html#suggested-guidelines"><i class="fa fa-check"></i><b>10.2</b> Suggested Guidelines</a></li>
<li class="chapter" data-level="10.3" data-path="guide.html"><a href="guide.html#additional-topics"><i class="fa fa-check"></i><b>10.3</b> Additional topics</a></li>
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<li class="chapter" data-level="11" data-path="final.html"><a href="final.html"><i class="fa fa-check"></i><b>11</b> Final Words</a>
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<li class="chapter" data-level="11.1" data-path="final.html"><a href="final.html#common-misconception"><i class="fa fa-check"></i><b>11.1</b> Common misconception</a></li>
<li class="chapter" data-level="11.2" data-path="final.html"><a href="final.html#benifits-of-ps"><i class="fa fa-check"></i><b>11.2</b> Benifits of PS</a></li>
<li class="chapter" data-level="11.3" data-path="final.html"><a href="final.html#limitations-of-ps"><i class="fa fa-check"></i><b>11.3</b> Limitations of PS</a></li>
<li class="chapter" data-level="11.4" data-path="final.html"><a href="final.html#when-ps-may-not-be-useful"><i class="fa fa-check"></i><b>11.4</b> When PS may not be useful?</a></li>
<li class="chapter" data-level="11.5" data-path="final.html"><a href="final.html#software"><i class="fa fa-check"></i><b>11.5</b> Software</a></li>
<li class="chapter" data-level="11.6" data-path="final.html"><a href="final.html#further-resources"><i class="fa fa-check"></i><b>11.6</b> Further Resources</a></li>
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<li class="chapter" data-level="" data-path="references.html"><a href="references.html"><i class="fa fa-check"></i>References</a></li>
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<li><a href="https://ehsank.com/" target="blank">Ehsan Karim</a></li>

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            <i class="fa fa-circle-o-notch fa-spin"></i><a href="./">Understanding Propensity Score Matching</a>
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<div id="guide" class="section level1 hasAnchor" number="10">
<h1><span class="header-section-number">Chapter 10</span> Reporting Guidelines<a href="guide.html#guide" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p>While writing journal articles or reports, what are the components we should report?</p>
<div id="discipline-specific-reviews" class="section level2 hasAnchor" number="10.1">
<h2><span class="header-section-number">10.1</span> Discipline-specific Reviews<a href="guide.html#discipline-specific-reviews" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<ul>
<li>Propensity score matching most popular</li>
<li>Guidelines available for some discipline-specific areas:
<ul>
<li>Cardiovascular <span class="citation">(<a href="#ref-austin2007propensity" role="doc-biblioref">Austin 2007</a>)</span>,</li>
<li>Infective endocarditis,</li>
<li>Intensive care</li>
<li>Critical care,</li>
<li>anesthesiology,</li>
<li>Sepsis,</li>
<li>Psychology</li>
<li>Cancer <span class="citation">(<a href="#ref-yao2017reporting" role="doc-biblioref">Yao et al. 2017</a>)</span>,</li>
<li>Multiple sclerosis <span class="citation">(<a href="#ref-karim2020use" role="doc-biblioref">Karim et al. 2020</a>)</span></li>
</ul></li>
</ul>
</div>
<div id="suggested-guidelines" class="section level2 hasAnchor" number="10.2">
<h2><span class="header-section-number">10.2</span> Suggested Guidelines<a href="guide.html#suggested-guidelines" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<table>
<tbody>
<tr class="odd">
<td align="left"><strong>Population</strong></td>
<td align="left">Be specific about population of interest</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- ATT vs. ATE</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">- exclusion criteria</td>
</tr>
<tr class="even">
<td align="left"><strong>Intervention</strong></td>
<td align="left">Be specific about exposure</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">- no multiple version of treatment</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- no interference</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">- comparator</td>
</tr>
<tr class="even">
<td align="left"><strong>Covariates</strong></td>
<td align="left">How variables are selected</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">- Any important variables not measured? Proxy?</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- Large list of covariates? See <span class="citation">King and Nielsen (<a href="#ref-king2019propensity" role="doc-biblioref">2019</a>)</span></td>
</tr>
<tr class="odd">
<td align="left"><strong>PS Model</strong></td>
<td align="left">Model selection</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- interaction or polynomials</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">- logistic vs. machine learning</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- Residual imbalance and refit PS model</td>
</tr>
<tr class="odd">
<td align="left"><strong>PS approach</strong></td>
<td align="left">Why PS matching (or other approach) was selected?</td>
</tr>
<tr class="even">
<td align="left"><strong>Sample size</strong></td>
<td align="left">Reduction % of the matched data: major issue!</td>
</tr>
<tr class="odd">
<td align="left"><strong>Diagnostics</strong></td>
<td align="left">Overlap vs. balance assessments</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- numeric and visual</td>
</tr>
<tr class="odd">
<td align="left"><strong>Software</strong></td>
<td align="left">Report software, packages</td>
</tr>
</tbody>
</table>
</div>
<div id="additional-topics" class="section level2 hasAnchor" number="10.3">
<h2><span class="header-section-number">10.3</span> Additional topics<a href="guide.html#additional-topics" class="anchor-section" aria-label="Anchor link to header"></a></h2>
<p>Some of the advanced topics not covered here.</p>
<table>
<colgroup>
<col width="50%" />
<col width="50%" />
</colgroup>
<tbody>
<tr class="odd">
<td align="left"><strong>Sensitivity analysis</strong></td>
<td align="left">- unmeasured confounding: proxy, or how much of an effect of unmeasured confounder necessary to nullify the results (e-value)</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- any positivity issue? Deleting extremes has consequences!</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">- ad-hoc methods: truncation / trimming: bias-variance trade-off</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- different matching methods and allowing different thresholds: caliper, ratio, WR/WOR</td>
</tr>
<tr class="odd">
<td align="left"><strong>Subgroup analysis</strong></td>
<td align="left">Refit within each group for matching</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- See <span class="citation">Ali et al. (<a href="#ref-ali2019propensity" role="doc-biblioref">2019</a>)</span>, <span class="citation">Rassen et al. (<a href="#ref-rassen2012applying" role="doc-biblioref">2012</a>)</span>, <span class="citation">Radice et al. (<a href="#ref-radice2012evaluating" role="doc-biblioref">2012</a>)</span>, <span class="citation">Kreif et al. (<a href="#ref-kreif2012methods" role="doc-biblioref">2012</a>)</span>, <span class="citation">Green and Stuart (<a href="#ref-green2014examining" role="doc-biblioref">2014</a>)</span>, <span class="citation">Girman et al. (<a href="#ref-girman2014assessing" role="doc-biblioref">2014</a>)</span>, <span class="citation">Eeren et al. (<a href="#ref-eeren2015estimating" role="doc-biblioref">2015</a>)</span>, <span class="citation">Wang et al. (<a href="#ref-wang2018relative" role="doc-biblioref">2018</a>)</span>, <span class="citation">Liu et al. (<a href="#ref-liu2020propensity" role="doc-biblioref">2020</a>)</span>, <span class="citation">Dong et al. (<a href="#ref-dong2020subgroup" role="doc-biblioref">2020</a>)</span> for a more complete list</td>
</tr>
<tr class="odd">
<td align="left"><strong>Missing data</strong></td>
<td align="left">Report clearly about missing data</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">- how missing data handled</td>
</tr>
</tbody>
</table>

</div>
</div>
<h3>References<a href="references.html#references" class="anchor-section" aria-label="Anchor link to header"></a></h3>
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-ali2019propensity" class="csl-entry">
Ali, M Sanni, Daniel Prieto-Alhambra, Luciane Cruz Lopes, Dandara Ramos, Nivea Bispo, Maria Y Ichihara, Julia M Pescarini, et al. 2019. <span>“Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances.”</span> <em>Frontiers in Pharmacology</em> 10: 973.
</div>
<div id="ref-austin2007propensity" class="csl-entry">
Austin, Peter C. 2007. <span>“Propensity-Score Matching in the Cardiovascular Surgery Literature from 2004 to 2006: A Systematic Review and Suggestions for Improvement.”</span> <em>The Journal of Thoracic and Cardiovascular Surgery</em> 134 (5): 1128–35.
</div>
<div id="ref-dong2020subgroup" class="csl-entry">
Dong, Jing, Junni L Zhang, Shuxi Zeng, and Fan Li. 2020. <span>“Subgroup Balancing Propensity Score.”</span> <em>Statistical Methods in Medical Research</em> 29 (3): 659–76.
</div>
<div id="ref-eeren2015estimating" class="csl-entry">
Eeren, Hester V, Marieke D Spreeuwenberg, Anna Bartak, Mark de Rooij, and Jan JV Busschbach. 2015. <span>“Estimating Subgroup Effects Using the Propensity Score Method: A Practical Application in Outcomes Research.”</span> <em>Medical Care</em> 53 (4): 366–73.
</div>
<div id="ref-girman2014assessing" class="csl-entry">
Girman, Cynthia J, Mugdha Gokhale, Tzuyung Doug Kou, Kimberly G Brodovicz, Richard Wyss, and Til Stürmer. 2014. <span>“Assessing the Impact of Propensity Score Estimation and Implementation on Covariate Balance and Confounding Control Within and Across Important Subgroups in Comparative Effectiveness Research.”</span> <em>Medical Care</em> 52 (3): 280.
</div>
<div id="ref-green2014examining" class="csl-entry">
Green, Kerry M, and Elizabeth A Stuart. 2014. <span>“Examining Moderation Analyses in Propensity Score Methods: Application to Depression and Substance Use.”</span> <em>Journal of Consulting and Clinical Psychology</em> 82 (5): 773.
</div>
<div id="ref-karim2020use" class="csl-entry">
Karim, Mohammad Ehsanul, Fabio Pellegrini, Robert W Platt, Gabrielle Simoneau, Julie Rouette, and Carl de Moor. 2020. <span>“The Use and Quality of Reporting of Propensity Score Methods in Multiple Sclerosis Literature: A Review.”</span> <em>Multiple Sclerosis Journal</em>, 1352458520972557.
</div>
<div id="ref-king2019propensity" class="csl-entry">
King, Gary, and Richard Alexander Nielsen. 2019. <span>“Why Propensity Scores Should Not Be Used for Matching.”</span>
</div>
<div id="ref-kreif2012methods" class="csl-entry">
Kreif, Noemi, Richard Grieve, Rosalba Radice, Zia Sadique, Roland Ramsahai, and Jasjeet S Sekhon. 2012. <span>“Methods for Estimating Subgroup Effects in Cost-Effectiveness Analyses That Use Observational Data.”</span> <em>Medical Decision Making</em> 32 (6): 750–63.
</div>
<div id="ref-liu2020propensity" class="csl-entry">
Liu, Shan-Yu, Chunyan Liu, Eddie Nehus, Maurizio Macaluso, Bo Lu, and Mi-Ok Kim. 2020. <span>“Propensity Score Analysis for Correlated Subgroup Effects.”</span> <em>Statistical Methods in Medical Research</em> 29 (4): 1067–80.
</div>
<div id="ref-radice2012evaluating" class="csl-entry">
Radice, Rosalba, Roland Ramsahai, Richard Grieve, Noemi Kreif, Zia Sadique, and Jasjeet S Sekhon. 2012. <span>“Evaluating Treatment Effectiveness in Patient Subgroups: A Comparison of Propensity Score Methods with an Automated Matching Approach.”</span> <em>The International Journal of Biostatistics</em> 8 (1).
</div>
<div id="ref-rassen2012applying" class="csl-entry">
Rassen, Jeremy A, Robert J Glynn, Kenneth J Rothman, Soko Setoguchi, and Sebastian Schneeweiss. 2012. <span>“Applying Propensity Scores Estimated in a Full Cohort to Adjust for Confounding in Subgroup Analyses.”</span> <em>Pharmacoepidemiology and Drug Safety</em> 21 (7): 697–709.
</div>
<div id="ref-wang2018relative" class="csl-entry">
Wang, Shirley V, Yinzhu Jin, Bruce Fireman, Susan Gruber, Mengdong He, Richard Wyss, HoJin Shin, et al. 2018. <span>“Relative Performance of Propensity Score Matching Strategies for Subgroup Analyses.”</span> <em>American Journal of Epidemiology</em> 187 (8): 1799–1807.
</div>
<div id="ref-yao2017reporting" class="csl-entry">
Yao, Xiaoxin I, Xiaofei Wang, Paul J Speicher, E Shelley Hwang, Perry Cheng, David H Harpole, Mark F Berry, Deborah Schrag, and Herbert H Pang. 2017. <span>“Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies.”</span> <em>JNCI: Journal of the National Cancer Institute</em> 109 (8): djw323.
</div>
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