Filters








7 Hits in 0.47 sec

Lag Penalized Weighted Correlation for Time Series Clustering [article]

Thevaa Chandereng, Anthony Gitter
<span title="2018-03-31">2018</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure. We propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of
more &raquo; ... e series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns pairs of time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies. The LPWC R package is available at https://github.com/gitter-lab/LPWC and CRAN under a MIT license.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/292615">doi:10.1101/292615</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/25ljs4sghjb5hphlj6qaxplyee">fatcat:25ljs4sghjb5hphlj6qaxplyee</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190504085220/https://www.biorxiv.org/content/biorxiv/early/2018/06/20/292615.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ba/d8/bad85c66108176e6d6c2a641fed9b026c3755927.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/292615"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Robust Blocked Response-Adaptive Randomization Designs [article]

Thevaa Chandereng, Rick Chappell
<span title="2019-09-12">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In most clinical trials, patients are randomized with equal probability among treatments to obtain an unbiased estimate of the treatment effect. Response-adaptive randomization (RAR) has been proposed for ethical reasons, where the randomization ratio is tilted successively to favor the better performing treatment. However, the substantial disagreement regarding bias due to time-trends in adaptive randomization is not fully recognized. The type-I error is inflated in the traditional Bayesian
more &raquo; ... approaches when a time-trend is present. In our approach, patients are assigned in blocks and the randomization ratio is recomputed for blocks rather than traditional adaptive randomization where it is done per patient. We further investigate the design with a range of scenarios for both frequentist and Bayesian designs. We compare our method with equal randomization and with different numbers of blocks including the traditional RAR design where randomization ratio is altered patient by patient basis. The analysis is stratified if there are two or more patients in each block. Small blocks should be avoided due to the possibility of not acquiring any information from the μ_i. On the other hand, RAR with large blocks has a good balance between efficiency and treating more subjects to the better-performing treatment, while retaining blocked RAR's unique unbiasedness.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.07758v2">arXiv:1904.07758v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/balcqszhc5efthjf3c6qy53q2a">fatcat:balcqszhc5efthjf3c6qy53q2a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200905152223/https://arxiv.org/pdf/1904.07758v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/51/7b/517b41e728a55e4cbeda464b40332cb93f7a3707.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.07758v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Lag penalized weighted correlation for time series clustering

Thevaa Chandereng, Anthony Gitter
<span title="2020-01-16">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets generated with high-throughput biological assays, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure. We propose a clustering similarity measure called Lag Penalized
more &raquo; ... Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies clusters with distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies. LPWC achieves both of its time series clustering goals. It groups time series with correlated changes over time, even if those patterns occur earlier or later in some of the time series. In addition, it refrains from introducing large shifts in time when searching for temporal patterns by applying a lag penalty. The LPWC R package is available at https://github.com/gitter-lab/LPWCand CRANunder a MIT license.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-019-3324-1">doi:10.1186/s12859-019-3324-1</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31948388">pmid:31948388</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6966853/">pmcid:PMC6966853</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5fa55cnrpvcg7ghlp7ueqkbdcy">fatcat:5fa55cnrpvcg7ghlp7ueqkbdcy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200213064727/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-019-3324-1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/59/f8/59f85aea9aa87fba1c4c7692a8798c3446c4ca54.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-019-3324-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966853" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

MOESM1 of Lag penalized weighted correlation for time series clustering

Thevaa Chandereng, Anthony Gitter
<span title="2020-01-17">2020</span> <i title="figshare"> Figshare </i> &nbsp;
Additional file 1 Supplementary figures, tables, and methods.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.11636340">doi:10.6084/m9.figshare.11636340</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rcoiafvsp5f6xapokfk5daz5na">fatcat:rcoiafvsp5f6xapokfk5daz5na</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200214121200/https://s3-eu-west-1.amazonaws.com/pstorage-npg-968563215/21105729/12859_2019_3324_MOESM1_ESM.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a2/84/a284f2095074ec031d26fab52f6202ecb3319a61.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.11636340"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> figshare.com </button> </a>

A Markov Decision Process for Response-Adaptive Randomization in Clinical Trials [article]

David Merrell, Thevaa Chandereng, Yeonhee Park
<span title="2021-09-29">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This agrees with the blocked RAR procedure described by Chandereng and Chappell (2019) .  ...  However, Chandereng and Chappell (2019) designed trials with equal-sized blocks, which is not generally optimal.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.14642v1">arXiv:2109.14642v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wdhgekdpmzeuhpowieeacp6ffq">fatcat:wdhgekdpmzeuhpowieeacp6ffq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211003072358/https://arxiv.org/pdf/2109.14642v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/82/82/8282df796bf73a574f18e7c3945970c74ade4ff9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.14642v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Imbalanced Randomization in Clinical Trials [article]

Thevaa Chandereng, Xiaodan Wei, Rick Chappell
<span title="2018-09-18">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Randomization is a common technique used in clinical trials to eliminate potential bias and confounders in a patient population. Equal allocation to treatment groups is the standard due to its optimal efficiency in many cases. However, in certain scenarios, unequal allocation can improve efficiency. In superiority trials with more than two groups, the optimal randomization is not always a balanced randomization. In non-inferiority trials, additive margin with equal variance is the only instance
more &raquo; ... with balanced randomization. Optimal randomization for non-inferiority trials can be far from 1:1 and can greatly improve efficiency, a fact which is commonly overlooked. A tool for sample size calculation for non-inferiority trials with additive or multiplicative margin with normal, binomial or Poisson distribution is available at http://www.statlab.wisc.edu/shiny/SSNI/.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1806.06020v3">arXiv:1806.06020v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/juvmzln5ajdq5c2fdescpl2lie">fatcat:juvmzln5ajdq5c2fdescpl2lie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826070521/https://arxiv.org/pdf/1806.06020v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ac/a3/aca3fd69d74ab6e032b77e2f846265fa72eccffa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1806.06020v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Abstracts From the NCCN 2021 Virtual Annual Conference

<span title="">2021</span> <i title="Harborside Press, LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qahd5dg7kjhwlh7j4q7veptyve" style="color: black;">The Journal of the National Comprehensive Cancer Network</a> </i> &nbsp;
Tevaarwerk, MD 1,2 ; Thevaa Chandereng, PhD 3 ; Elena M. Smith, MS 4 ; Cibele Carroll, MD, MPH 2 ; Hamid Emamekhoo, MD 1,2 ; and Mary E.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6004/jnccn.2021.5025">doi:10.6004/jnccn.2021.5025</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yuucrknh4fbdhger763btk6gx4">fatcat:yuucrknh4fbdhger763btk6gx4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211130074021/https://jnccn.org/downloadpdf/journals/jnccn/19/5.5/article-p583.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8f/51/8f5139acdb08edaed1ccb8431d439188f3c91a79.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6004/jnccn.2021.5025"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>