R package to estimate intracluster correlation coefficient with confidence interval for binary data

Chakraborty, Hrishikesh; Hossain, Al Amin

doi:10.1016/j.cmpb.2017.10.023

Cited by 65 publications

(54 citation statements)

References 18 publications

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“…The stromal and immune scores were calculated using the ESTIMATE packages in R [90,91]. The expression profiles of 141 stroma-related genes and 141 immune-related genes (provided in Table S2) were analyzed to obtain stromal score and immune score, respectively.…”

Section: Estimation Of Tumor Cellular Components and Crc Subtypesmentioning

confidence: 99%

“…The expression profiles of 141 stroma-related genes and 141 immune-related genes (provided in Table S2) were analyzed to obtain stromal score and immune score, respectively. By running ESTIMATE on TCGA RNA-seq data, the stromal and immune score of each sample can be estimated as previously described [90][91][92]. We quantified the proportion of cells that belonged to each of 10 immune cell types (B cells, M1 macrophages, M2 macrophages, monocytes, neutrophils, NK cells, CD4+ T cells, CD8+ T cells, regulatory T cells, and dendritic cells) using the quanTIseq package in R [93].…”

Section: Estimation Of Tumor Cellular Components and Crc Subtypesmentioning

confidence: 99%

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Cancer Stem Cell Marker DCLK1 Correlates with Tumorigenic Immune Infiltrates in the Colon and Gastric Adenocarcinoma Microenvironments

Weygant

et al. 2020

Cancers

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Immunotherapy that has proven efficacy in several solid cancers plays a partial role in improving clinical outcomes of advanced gastrointestinal (GI) cancers. There is an unmet need to find new immune-related therapeutic targets. Doublecortin-like kinase 1 (DCLK1) marks tuft cells which are recognized as cancer-initiating cells and regulators of the type II immune response, and has been studied for its role in many cancers including colon and gastric cancers, but its role in tumor immunity remains unexplored. In the current study, we analyzed colon and gastric cancer RNA sequencing data from 283 and 415 patients, respectively, from The Cancer Genome Atlas (TCGA). High DCLK1 expression predicted the worse clinical outcomes in colon and gastric cancer patients and correlated with increased immune and stromal components. Further analysis indicated that DCLK1 was strongly linked to infiltration of multiple immune cell types, especially TAMs and Treg, and strongly correlated with increased CD8+ T cell inhibitors TGFB1 and CXCL12 and their receptors, suggesting it may contribute to TAM-mediated inhibition of CD8+ T cells. Interestingly, we found that DCLK1 was a prognostic biomarker in left-sided colon cancer, which has worse outcomes and demonstrates a reduced response to existing immunotherapies. In conclusion, our results demonstrate that DCLK1 is linked with functional regulation of the tumor microenvironment and may have potential as a prognostic biomarker and adjuvant target to promote immunotherapy sensitivity in colon and gastric cancer patients.

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Section: Estimation Of Tumor Cellular Components and Crc Subtypesmentioning

confidence: 99%

Section: Estimation Of Tumor Cellular Components and Crc Subtypesmentioning

confidence: 99%

Cancer Stem Cell Marker DCLK1 Correlates with Tumorigenic Immune Infiltrates in the Colon and Gastric Adenocarcinoma Microenvironments

Weygant

et al. 2020

Cancers

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“…where MS indicates the mean square values from ANOVA and m is the average cluster size. The 95% confidence interval (CI) was estimated using Smith's large sample approximation [20,21], which caters for large and normally distributed data. For binary variables, we used the ANOVA method with Smith's large sample approximation [20,21] in the ICCbin package [20] to generate the ICC and 95% CI.…”

Section: Resultsmentioning

confidence: 99%

“…The 95% confidence interval (CI) was estimated using Smith's large sample approximation [20,21], which caters for large and normally distributed data. For binary variables, we used the ANOVA method with Smith's large sample approximation [20,21] in the ICCbin package [20] to generate the ICC and 95% CI. Examples of binary variables were whether a process of care such as foot examination was conducted for a diabetes patient and the proportion of patients achieving target values for intermediate clinical outcomes.…”

Section: Resultsmentioning

confidence: 99%

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Intra-cluster correlation coefficients in primary care patients with type 2 diabetes and hypertension

Lee

Lim

Law

et al. 2020

Trials

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Introduction There are few sources of published data on intra-cluster correlation coefficients (ICCs) amongst patients with type 2 diabetes (T2D) and/or hypertension in primary care, particularly in low- and middle-income countries. ICC values are necessary for determining the sample sizes of cluster randomized trials. Hence, we aim to report the ICC values for a range of measures from a cluster-based interventional study conducted in Malaysia. Method Baseline data from a large study entitled Evaluation of Enhanced Primary Health Care interventions in public health clinics (EnPHC-EVA: Facility) were used in this analysis. Data from 40 public primary care clinics were collected through retrospective chart reviews and a patient exit survey. We calculated the ICCs for processes of care, clinical outcomes and patient experiences in patients with T2D and/or hypertension using the analysis of variance approach. Results Patient experience had the highest ICC values compared to processes of care and clinical outcomes. The ICC values ranged from 0.01 to 0.48 for processes of care. Generally, the ICC values for processes of care for patients with hypertension only are higher than those for T2D patients, with or without hypertension. However, both groups of patients have similar ICCs for antihypertensive medications use. In addition, similar ICC values were observed for clinical outcomes, ranging from 0.01 to 0.09. For patient experience, the ICCs were between 0.03 (proportion of patients who are willing to recommend the clinic to their friends and family) and 0.25 (for Patient Assessment of Chronic Illness Care item 9, Given a copy of my treatment plan). Conclusion The reported ICCs and their respective 95% confidence intervals for T2D and hypertension will be useful for estimating sample sizes and improving efficiency of cluster trials conducted in the primary care setting, particularly for low- and middle-income countries.

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Integrated Hepatitis C–Opioid Use Disorder Care Through Facilitated Telemedicine

Talal,

Markatou,

Liu

et al. 2024

JAMA

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ImportanceFacilitated telemedicine may promote hepatitis C virus elimination by mitigating geographic and temporal barriers.ObjectiveTo compare sustained virologic responses for hepatitis C virus among persons with opioid use disorder treated through facilitated telemedicine integrated into opioid treatment programs compared with off-site hepatitis specialist referral.Design, Setting, and ParticipantsProspective, cluster randomized clinical trial using a stepped wedge design. Twelve programs throughout New York State included hepatitis C–infected participants (n = 602) enrolled between March 1, 2017, and February 29, 2020. Data were analyzed from December 1, 2022, through September 1, 2023.InterventionHepatitis C treatment with direct-acting antivirals through comanagement with a hepatitis specialist either through facilitated telemedicine integrated into opioid treatment programs (n = 290) or standard-of-care off-site referral (n = 312).Main Outcomes and MeasuresThe primary outcome was hepatitis C virus cure. Twelve programs began with off-site referral, and every 9 months, 4 randomly selected sites transitioned to facilitated telemedicine during 3 steps without participant crossover. Participants completed 2-year follow-up for reinfection assessment. Inclusion criteria required 6-month enrollment in opioid treatment and insurance coverage of hepatitis C medications. Generalized linear mixed-effects models were used to test for the intervention effect, adjusted for time, clustering, and effect modification in individual-based intention-to-treat analysis.ResultsAmong 602 participants, 369 were male (61.3%); 296 (49.2%) were American Indian or Alaska Native, Asian, Black or African American, multiracial, or other (ie, no race category was selected, with race data collected according to the 5 standard National Institutes of Health categories); and 306 (50.8%) were White. The mean (SD) age of the enrolled participants in the telemedicine group was 47.1 (13.1) years; that of the referral group was 48.9 (12.8) years. In telemedicine, 268 of 290 participants (92.4%) initiated treatment compared with 126 of 312 participants (40.4%) in referral. Intention-to-treat cure percentages were 90.3% (262 of 290) in telemedicine and 39.4% (123 of 312) in referral, with an estimated logarithmic odds ratio of the study group effect of 2.9 (95% CI, 2.0-3.5; P &lt; .001) with no effect modification. Observed cure percentages were 246 of 290 participants (84.8%) in telemedicine vs 106 of 312 participants (34.0%) in referral. Subgroup effects were not significant, including fibrosis stage, urban or rural participant residence location, or mental health (anxiety or depression) comorbid conditions. Illicit drug use decreased significantly (referral: 95% CI, 1.2-4.8; P = .001; telemedicine: 95% CI, 0.3-1.0; P &lt; .001) among cured participants. Minimal reinfections (n = 13) occurred, with hepatitis C virus reinfection incidence of 2.5 per 100 person-years. Participants in both groups rated health care delivery satisfaction as high or very high.Conclusions and RelevanceOpioid treatment program–integrated facilitated telemedicine resulted in significantly higher hepatitis C virus cure rates compared with off-site referral, with high participant satisfaction. Illicit drug use declined significantly among cured participants with minimal reinfections.Trial RegistrationClinicalTrials.gov Identifier: NCT02933970

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R package to estimate intracluster correlation coefficient with confidence interval for binary data

Cited by 65 publications

References 18 publications

Cancer Stem Cell Marker DCLK1 Correlates with Tumorigenic Immune Infiltrates in the Colon and Gastric Adenocarcinoma Microenvironments

Cancer Stem Cell Marker DCLK1 Correlates with Tumorigenic Immune Infiltrates in the Colon and Gastric Adenocarcinoma Microenvironments

Intra-cluster correlation coefficients in primary care patients with type 2 diabetes and hypertension

Integrated Hepatitis C–Opioid Use Disorder Care Through Facilitated Telemedicine

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