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2. A Bayesian Selection Model for Correcting Outcome Reporting Bias With Application to a Meta-analysis on Heart Failure Interventions

Author

Bai, Ray, Liu, Xiaokang, Lin, Lifeng, Liu, Yulun, Kimmel, Stephen E., Chu, Haitao, and Chen, Yong

Subjects
Statistics - Applications
Abstract

Multivariate meta-analysis (MMA) is a powerful tool for jointly estimating multiple outcomes' treatment effects. However, the validity of results from MMA is potentially compromised by outcome reporting bias (ORB), or the tendency for studies to selectively report outcomes. Until recently, ORB has been understudied. Since ORB can lead to biased conclusions, it is crucial to correct the estimates of effect sizes and quantify their uncertainty in the presence of ORB. With this goal, we develop a Bayesian selection model to adjust for ORB in MMA. We further propose a measure for quantifying the impact of ORB on the results from MMA. We evaluate our approaches through a meta-evaluation of 748 bivariate meta-analyses from the Cochrane Database of Systematic Reviews. Our model is motivated by and applied to a meta-analysis of interventions on hospital readmission and quality of life for heart failure patients. In our analysis, the relative risk (RR) of hospital readmission for the intervention group changes from a significant decrease (RR: 0.931, 95% confidence interval [CI]: 0.862-0.993) to a statistically nonsignificant effect (RR: 0.955, 95% CI: 0.876-1.051) after adjusting for ORB. This study demonstrates that failing to account for ORB can lead to different conclusions in a meta-analysis., Comment: 26 pages, 5 tables, 8 figures

Published
2021

5. Testing small study effects in multivariate meta-analysis

Author

Hong, Chuan, Salanti, Georgia, Morton, Sally, Riley, Richard, Chu, Haitao, Kimmel, Stephen E., and Chen, Yong

Subjects
Statistics - Methodology
Abstract

Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects include publication bias, outcome reporting bias and clinical heterogeneity. Methods to account for small study effects in univariate meta-analysis have been extensively studied. However, detecting small study effects in a multivariate meta-analysis setting remains an untouched research area. One of the complications is that different types of selection processes can be involved in the reporting of multivariate outcomes. For example, some studies may be completely unpublished while others may selectively report multiple outcomes. In this paper, we propose a score test as an overall test of small study effects in multivariate meta-analysis. Two detailed case studies are given to demonstrate the advantage of the proposed test over various naive applications of univariate tests in practice. Through simulation studies, the proposed test is found to retain nominal Type I error with considerable power in moderate sample size settings. Finally, we also evaluate the concordance between the proposed test with the naive application of univariate tests by evaluating 44 systematic reviews with multiple outcomes from the Cochrane Database., Comment: 20 pages, 3 figures, 3 tables

Published
2018

6. Sodium-glucose cotransporter-2 inhibitors and incidence of atrial fibrillation in older adults with type 2 diabetes: a retrospective cohort analysis.

Author

Yujia Li, Huilin Tang, Yi Guo, Hui Shao, Kimmel, Stephen E., Jiang Bian, Schatz, Desmond A., and Jingchuan Guo

Subjects
ATRIAL fibrillation, TYPE 2 diabetes, SODIUM-glucose cotransporters, OLDER people, COHORT analysis, CHRONIC kidney failure, CD26 antigen
Abstract

Objectives: To investigate the risk of atrial fibrillation (AF) with sodium-glucose cotransporter-2 inhibitors (SGLT2is) compared to dipeptidyl peptidase-4 inhibitor (DPP4i) use in older US adults and across diverse subgroups. Methods: We conducted a retrospective cohort analysis using claims data from 15% random samples of Medicare fee-for-service beneficiaries. Patients were adults with type 2 diabetes (T2D), no preexisting AF, and were newly initiated on SGLT2i or DPP4i. The outcome was the first incident AF. Inverse probability treatment weighting (IPTW) was used to balance the baseline covariates between the treatment groups including sociodemographics, comorbidities, and comedications. Cox regression models were used to assess the effect of SGLT2i compared to DPP4i on incident AF. Results: Of the 97,436 eligible individuals (mean age 71.2 ± 9.8 years, 54.6% women), 1.01% (n = 983) had incident AF over a median follow-up of 361 days. The adjusted incidence rate was 8.39 (95% CI: 6.67-9.99) and 11.70 (95% CI: 10.9-12.55) per 1,000 person-years in the SGLT2i and DPP4i groups, respectively. SGLT2is were associated with a significantly lower risk of incident AF (HR 0.73; 95% CI, 0.57 to 0.91; p = 0.01) than DPP4is. The risk reduction of incident AF was significant in non-Hispanic White individuals and subgroups with existing atherosclerotic cardiovascular diseases and chronic kidney disease. Conclusion: Compared to the use of DPP4i, that of SGLT2i was associated with a lower risk of AF in patients with T2D. Our findings contribute to the real-world evidence regarding the effectiveness of SGLT2i in preventing AF and support a tailored therapeutic approach to optimize treatment selection based on individual characteristics. [ABSTRACT FROM AUTHOR]

Published
2024
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10. A Pharmacogenetic versus a Clinical Algorithm for Warfarin Dosing

Author

Kimmel, Stephen E, French, Benjamin, Kasner, Scott E, Johnson, Julie A, Anderson, Jeffrey L, Gage, Brian F, Rosenberg, Yves D, Eby, Charles S, Madigan, Rosemary A, McBane, Robert B, Abdel-Rahman, Sherif Z, Stevens, Scott M, Yale, Steven, Mohler, Emile R, Fang, Margaret C, Shah, Vinay, Horenstein, Richard B, Limdi, Nita A, Muldowney, James AS, Gujral, Jaspal, Delafontaine, Patrice, Desnick, Robert J, Ortel, Thomas L, Billett, Henny H, Pendleton, Robert C, Geller, Nancy L, Halperin, Jonathan L, Goldhaber, Samuel Z, Caldwell, Michael D, Califf, Robert M, and Ellenberg, Jonas H

Subjects
Hematology, Genetics, Clinical Research, Clinical Trials and Supportive Activities, Evaluation of treatments and therapeutic interventions, Development of treatments and therapeutic interventions, 6.1 Pharmaceuticals, 5.1 Pharmaceuticals, Adult, Aged, Algorithms, Anticoagulants, Aryl Hydrocarbon Hydroxylases, Cytochrome P-450 CYP2C9, Double-Blind Method, Female, Follow-Up Studies, Genotype, Hemorrhage, Humans, International Normalized Ratio, Male, Pharmacogenetics, Thromboembolism, Treatment Failure, Vitamin K Epoxide Reductases, Warfarin, COAG Investigators, Medical and Health Sciences, General & Internal Medicine
Abstract

BackgroundThe clinical utility of genotype-guided (pharmacogenetically based) dosing of warfarin has been tested only in small clinical trials or observational studies, with equivocal results.MethodsWe randomly assigned 1015 patients to receive doses of warfarin during the first 5 days of therapy that were determined according to a dosing algorithm that included both clinical variables and genotype data or to one that included clinical variables only. All patients and clinicians were unaware of the dose of warfarin during the first 4 weeks of therapy. The primary outcome was the percentage of time that the international normalized ratio (INR) was in the therapeutic range from day 4 or 5 through day 28 of therapy.ResultsAt 4 weeks, the mean percentage of time in the therapeutic range was 45.2% in the genotype-guided group and 45.4% in the clinically guided group (adjusted mean difference, [genotype-guided group minus clinically guided group], -0.2; 95% confidence interval, -3.4 to 3.1; P=0.91). There also was no significant between-group difference among patients with a predicted dose difference between the two algorithms of 1 mg per day or more. There was, however, a significant interaction between dosing strategy and race (P=0.003). Among black patients, the mean percentage of time in the therapeutic range was less in the genotype-guided group than in the clinically guided group. The rates of the combined outcome of any INR of 4 or more, major bleeding, or thromboembolism did not differ significantly according to dosing strategy.ConclusionsGenotype-guided dosing of warfarin did not improve anticoagulation control during the first 4 weeks of therapy. (Funded by the National Heart, Lung, and Blood Institute and others; COAG ClinicalTrials.gov number, NCT00839657.).

Published
2013

11. Multisite Investigation of Outcomes With Implementation of CYP2C19 Genotype-Guided Antiplatelet Therapy After Percutaneous Coronary Intervention

Author

Cavallari, Larisa H., Lee, Craig R., Beitelshees, Amber L., Cooper-DeHoff, Rhonda M., Duarte, Julio D., Voora, Deepak, Kimmel, Stephen E., McDonough, Caitrin W., Gong, Yan, Dave, Chintan V., Pratt, Victoria M., Alestock, Tameka D., Anderson, R. David, Alsip, Jorge, Ardati, Amer K., Brott, Brigitta C., Brown, Lawrence, Chumnumwat, Supatat, Clare-Salzler, Michael J., Coons, James C., Denny, Joshua C., Dillon, Chrisly, Elsey, Amanda R., Hamadeh, Issam S., Harada, Shuko, Hillegass, William B., Hines, Lindsay, Horenstein, Richard B., Howell, Lucius A., Jeng, Linda J.B., Kelemen, Mark D., Lee, Yee Ming, Magvanjav, Oyunbileg, Montasser, May, Nelson, David R., Nutescu, Edith A., Nwaba, Devon C., Pakyz, Ruth E., Palmer, Kathleen, Peterson, Josh F., Pollin, Toni I., Quinn, Alison H., Robinson, Shawn W., Schub, Jamie, Skaar, Todd C., Smith, D. Max, Sriramoju, Vindhya B., Starostik, Petr, Stys, Tomasz P., Stevenson, James M., Varunok, Nicholas, Vesely, Mark R., Wake, Dyson T., Weck, Karen E., Weitzel, Kristin W., Wilke, Russell A., Willig, James, Zhao, Richard Y., Kreutz, Rolf P., Stouffer, George A., Empey, Philip E., Limdi, Nita A., Shuldiner, Alan R., Winterstein, Almut G., and Johnson, Julie A.

Published
2018
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18. Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network’s Common Measures Working Group

Author

Orlando, Lori A, Sperber, Nina R, Voils, Corrine, Nichols, Marshall, Myers, Rachel A, Wu, R Ryanne, Rakhra-Burris, Tejinder, Levy, Kenneth D, Levy, Mia, Pollin, Toni I, Guan, Yue, Horowitz, Carol R, Ramos, Michelle, Kimmel, Stephen E, McDonough, Caitrin W, Madden, Ebony B, and Damschroder, Laura J

Published
2018
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21. The development and initial validation of the PROMIS®+HF‐27 and PROMIS+HF‐10 profiles

Author

Ahmad, Faraz S., primary, Jackson, Kathryn L., additional, Yount, Susan E., additional, Rothrock, Nan E., additional, Kallen, Michael A., additional, Lacson, Leilani, additional, Bilimoria, Karl Y., additional, Kho, Abel N., additional, Mutharasan, Raja Kannan, additional, McCullough, Peter A., additional, Bruckel, Jeffrey, additional, Fedson, Savitri, additional, Kimmel, Stephen E., additional, Eton, David T., additional, Grady, Kathleen L., additional, Yancy, Clyde W., additional, and Cella, David, additional

Published
2022
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27. Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups

Author

Limdi, Nita A., Wadelius, Mia, Cavallari, Larisa, Eriksson, Niclas, Crawford, Dana C., Lee, Ming-Ta M., Chen, Chien-Hsiun, Motsinger-Reif, Alison, Sagreiya, Hersh, Liu, Nianjun, Wu, Alan H.B., Gage, Brian F., Jorgensen, Andrea, Pirmohamed, Munir, Shin, Jae-Gook, Suarez-Kurtz, Guilherme, Kimmel, Stephen E., Johnson, Julie A., Klein, Teri E., and Wagner, Michael J.

Published
2010
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30. Development and Validation of a Seizure Prediction Model in Neonates After Cardiac Surgery

Author

Naim, Maryam Y., primary, Putt, Mary, additional, Abend, Nicholas S., additional, Mastropietro, Christopher W., additional, Frank, Deborah U., additional, Chen, Jonathan M., additional, Fuller, Stephanie, additional, Gangemi, James J., additional, Gaynor, J. William, additional, Heinan, Kristin, additional, Licht, Daniel J., additional, Mascio, Christopher E., additional, Massey, Shavonne, additional, Roeser, Mark E., additional, Smith, Clyde J., additional, and Kimmel, Stephen E., additional

Published
2021
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32. Additional file 1 of Comparison of dynamic updating strategies for clinical prediction models

Author

Schnellinger, Erin M., Yang, Wei, and Kimmel, Stephen E.

Abstract

Additional file 1: Table 1. Sample size, number of deaths, and mortality risk per quarter in the post-baseline period. Figure 1. Proportion of deaths versus time in the post-baseline period. Figure 2. Overall calibration for each update strategy when update interval equals 1 quarter and 100% new data are used. Triangles represent deciles of predicted probabilities; vertical lines at bottom of plot represent the distribution of subjects stratified by outcome status (below x-axis = alive, above x-axis = dead). Figure 3. Brier Score versus time when update interval equals 1 quarter and 100% new data are used. Figure 4. AUC vs. time when update interval equals 1 quarter and 100% new data are used. Figure 5. Hosmer-Lemeshow statistic vs. time when update interval equals 1 quarter and 100% new data are used. Figure 6. Calibration vs. time when update interval equals 1 quarter and 100% new data are used. A) calibration intercept (calibration-in-the-large); B) calibration slope. Table 2. Pairwise comparisons (Wilcoxon Signed Rank Test) of Brier Score (BS) at each quarter when the update interval equals 1, 2, 4, or 8 quarters and 100% new data are used for each update. Values represent the median difference (p-value) in BS between the strategy listed in the row header and that listed in the column header. Negative differences indicate that the row header has lower BS (performs better) than the column header; positive differences indicate that the row header has higher BS (performs worse) than the column header. Table 3. Pairwise comparisons (Wilcoxon Signed Rank Test) of Brier Score (BS) at each quarter when the sliding window equals 1, 2, 4, or 8 quarters and the update interval equals 1 quarter. Values represent the median difference (p-value) in BS between the strategy listed in the row header and that listed in the column header. Negative differences indicate that the row header has lower BS (performs better) than the column header; positive differences indicate that the row header has higher BS (performs worse) than the column header. Table 4. Brier Score (BS), AUC, Hosmer-Lemeshow statistic, calibration intercept and slope (from logistic calibration), and number of updates performed under each update strategy when the sliding window equals 4 or 8 quarters and the update interval equals 4 quarters. Table 5. Number of times each candidate update model was selected by the Closed Testing Procedure when the sliding window equals 4 or 8 quarters and the update interval equals 4 quarters. Table 6. Pairwise comparisons (Wilcoxon Signed Rank Test) of Brier Score (BS) at each quarter when the sliding window equals 4 or 8 quarters and the update interval equals 4 quarters. Values represent the median difference (p-value) in BS between the strategy listed in the row header and that listed in the column header. Negative differences indicate that the row header has lower BS (performs better) than the column header; positive differences indicate that the row header has higher BS (performs worse) than the column header. Figure 7. Boxplot of difference in Brier Score between each update strategy and never update calculated at each quarter versus sliding window length, when the update interval equals 4 quarters. Horizontal bars represent medians and interquartile ranges (IQR). Negative values reflect improvement compared never update. Figure 8. Overall calibration for each update strategy when update interval equals 4 quarters and 100% new data are used. Triangles represent deciles of predicted probabilities; vertical lines at bottom of plot represent the distribution of subjects stratified by outcome status (below x-axis = alive, above x-axis = dead). Figure 9. Brier Score versus time when update interval equals 4 quarters and 100% new data are used. Figure 10. AUC vs. time when update interval equals 4 quarters and 100% new data are used. Figure 11. Hosmer-Lemeshow statistic vs. time when update interval equals 4 quarters and 100% new data are used. Figure 12. Calibration vs. time when update interval equals 4 quarters and 100% new data are used. A) calibration intercept (calibration-in-the-large); B) calibration slope.

Published
2021
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35. Comparable Cardiorenal Benefits of SGLT2 Inhibitors and GLP-1RAs in Asian and White Populations: An Updated Meta-analysis of Results From Randomized Outcome Trials.

Author

Huilin Tang, Kimmel, Stephen E., Smith, Steven M., Cusi, Kenneth, Weilong Shi, Gurka, Matthew, Winterstein, Almut G., and Guo, Jingchuan

Abstract

Background: Whether the cardiorenal benefits of sodium-glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide 1 receptor agonists (GLP-1RAs) are comparable between White and Asian populations remains unclear.Purpose: To compare the cardiorenal benefits of SGLT2 inhibitors and GLP-1RAs between White and Asian populations and to compare the cardiorenal benefits between the two agents in Asian patients.Data Sources: Electronic databases were searched up to 28 March 2021.Study Selection: We included the cardiovascular (CV) and renal outcome trials of SGLT2 inhibitors and GLP-1RAs where investigators reported major adverse CV events (MACE), CV death/hospitalization for heart failure (HHF), or composite renal outcomes with stratification by race.Data Extraction: We extracted the hazard ratio of each outcome stratified by race (Asian vs. White populations).Data Synthesis: In 10 SGLT2 inhibitor trials, there was no significant difference between Asian and White populations for MACE (P = 0.55), CV death/HHF (P = 0.87), or composite renal outcomes (P = 0.97). In seven GLP-1RA trials, we observed a similar MACE benefit between Asian and White populations (P = 0.10). In our networkmeta-analysis we found a comparable benefit for MACE between SGLT2 inhibitors and GLP-1RAs in Asian patients.Limitations: The data were from stratified analyses.Conclusions: There appear to be comparable cardiorenal benefits of SGLT2 inhibitors and GLP-1RAs between Asian and White participants enrolled in CV and renal outcome trials; the two therapies seem to have similar CV benefits for Asian participants. [ABSTRACT FROM AUTHOR]

Published
2022
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37. sj-pdf-2-pul-10.1177_2045894020966889 - Supplemental material for Risk factors for 30-day readmission in adults hospitalized for pulmonary hypertension

Author

Bhattacharya, Priyanka T., Hameed, Asif M. Abdul, Shubhadeep T. Bhattacharya, Chirinos, Julio A., Hwang, Wei-Ting, Birati, Edo Y., Menachem, Jonathan N., Saurav Chatterjee, Giri, Jay S., Kawut, Steven M., Kimmel, Stephen E., and Mazurek, Jeremy A.

Subjects
110203 Respiratory Diseases, FOS: Clinical medicine, Cardiology
Abstract

Supplemental material, sj-pdf-2-pul-10.1177_2045894020966889 for Risk factors for 30-day readmission in adults hospitalized for pulmonary hypertension by Priyanka T. Bhattacharya, Asif M. Abdul Hameed, Shubhadeep T. Bhattacharya, Julio A. Chirinos, Wei-Ting Hwang, Edo Y. Birati, Jonathan N. Menachem, Saurav Chatterjee, Jay S. Giri, Steven M. Kawut, Stephen E. Kimmel and Jeremy A. Mazurek in Pulmonary Circulation

Published
2020
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38. sj-pdf-1-pul-10.1177_2045894020966889 - Supplemental material for Risk factors for 30-day readmission in adults hospitalized for pulmonary hypertension

Author

Bhattacharya, Priyanka T., Hameed, Asif M. Abdul, Shubhadeep T. Bhattacharya, Chirinos, Julio A., Hwang, Wei-Ting, Birati, Edo Y., Menachem, Jonathan N., Saurav Chatterjee, Giri, Jay S., Kawut, Steven M., Kimmel, Stephen E., and Mazurek, Jeremy A.

Subjects
110203 Respiratory Diseases, FOS: Clinical medicine, Cardiology
Abstract

Supplemental material, sj-pdf-1-pul-10.1177_2045894020966889 for Risk factors for 30-day readmission in adults hospitalized for pulmonary hypertension by Priyanka T. Bhattacharya, Asif M. Abdul Hameed, Shubhadeep T. Bhattacharya, Julio A. Chirinos, Wei-Ting Hwang, Edo Y. Birati, Jonathan N. Menachem, Saurav Chatterjee, Jay S. Giri, Steven M. Kawut, Stephen E. Kimmel and Jeremy A. Mazurek in Pulmonary Circulation

Published
2020
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39. ACCF/AHA/SCAI 2007 Update of the Clinical Competence Statement on Cardiac Interventional Procedures: A Report of the American College of Cardiology Foundation/American Heart Association/American College of Physicians Task Force on Clinical Competence and Training (Writing Committee to Update the 1998 Clinical Competence Statement on Recommendations for the Assessment and Maintenance of Proficiency in Coronary Interventional Procedures)

Author

King, Spencer B., III, Aversano, Thomas, Ballard, William L., Beekman, Robert H., III, Cowley, Michael J., Ellis, Stephen G., Faxon, David P., Hannan, Edward L., Hirshfeld, John W., Jr, Jacobs, Alice K., Kellett, Mirle A., Jr, Kimmel, Stephen E., Landzberg, Joel S., McKeever, Louis S., Moscucci, Mauro, Pomerantz, Richard M., Smith, Karen M., Vetrovec, George W., Creager, Mark A., Holmes, David R., Jr, Newby, L. Kristin, Weitz, Howard H., Merli, Geno, Piña, Ileana, Rodgers, George P., and Tracy, Cynthia M.

Published
2007
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41. Correction: Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network’s Common Measures Working Group

Author

Orlando, Lori A., primary, Sperber, Nina R., additional, Voils, Corrine, additional, Nichols, Marshall, additional, Myers, Rachel A., additional, Wu, R. Ryanne, additional, Rakhra-Burris, Tejinder, additional, Levy, Kenneth D., additional, Levy, Mia, additional, Pollin, Toni I., additional, Guan, Yue, additional, Horowitz, Carol R., additional, Ramos, Michelle, additional, Kimmel, Stephen E., additional, McDonough, Caitrin W., additional, Madden, Ebony B., additional, and Damschroder, Laura J., additional

Published
2020
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42. Risk factors for 30‐day readmission in adults hospitalized for pulmonary hypertension

Author

Bhattacharya, Priyanka T., primary, Hameed, Asif M. Abdul, additional, Bhattacharya, Shubhadeep T., additional, Chirinos, Julio A., additional, Hwang, Wei‐Ting, additional, Birati, Edo Y., additional, Menachem, Jonathan N., additional, Chatterjee, Saurav, additional, Giri, Jay S., additional, Kawut, Steven M., additional, Kimmel, Stephen E., additional, and Mazurek, Jeremy A., additional

Published
2020
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