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47 results on '"Rybin, Denis V."'

1. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

Author

Mahajan, Anubha, Wessel, Jennifer, Willems, Sara M, Zhao, Wei, Robertson, Neil R, Chu, Audrey Y, Gan, Wei, Kitajima, Hidetoshi, Taliun, Daniel, Rayner, N William, Guo, Xiuqing, Lu, Yingchang, Li, Man, Jensen, Richard A, Hu, Yao, Huo, Shaofeng, Lohman, Kurt K, Zhang, Weihua, Cook, James P, Prins, Bram Peter, Flannick, Jason, Grarup, Niels, Trubetskoy, Vassily Vladimirovich, Kravic, Jasmina, Kim, Young Jin, Rybin, Denis V, Yaghootkar, Hanieh, Müller-Nurasyid, Martina, Meidtner, Karina, Li-Gao, Ruifang, Varga, Tibor V, Marten, Jonathan, Li, Jin, Smith, Albert Vernon, An, Ping, Ligthart, Symen, Gustafsson, Stefan, Malerba, Giovanni, Demirkan, Ayse, Tajes, Juan Fernandez, Steinthorsdottir, Valgerdur, Wuttke, Matthias, Lecoeur, Cécile, Preuss, Michael, Bielak, Lawrence F, Graff, Marielisa, Highland, Heather M, Justice, Anne E, Liu, Dajiang J, Marouli, Eirini, Peloso, Gina Marie, Warren, Helen R, ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, Afaq, Saima, Afzal, Shoaib, Ahlqvist, Emma, Almgren, Peter, Amin, Najaf, Bang, Lia B, Bertoni, Alain G, Bombieri, Cristina, Bork-Jensen, Jette, Brandslund, Ivan, Brody, Jennifer A, Burtt, Noël P, Canouil, Mickaël, Chen, Yii-Der Ida, Cho, Yoon Shin, Christensen, Cramer, Eastwood, Sophie V, Eckardt, Kai-Uwe, Fischer, Krista, Gambaro, Giovanni, Giedraitis, Vilmantas, Grove, Megan L, de Haan, Hugoline G, Hackinger, Sophie, Hai, Yang, Han, Sohee, Tybjærg-Hansen, Anne, Hivert, Marie-France, Isomaa, Bo, Jäger, Susanne, Jørgensen, Marit E, Jørgensen, Torben, Käräjämäki, Annemari, Kim, Bong-Jo, Kim, Sung Soo, Koistinen, Heikki A, Kovacs, Peter, Kriebel, Jennifer, Kronenberg, Florian, Läll, Kristi, Lange, Leslie A, Lee, Jung-Jin, Lehne, Benjamin, Li, Huaixing, and Lin, Keng-Hung

Subjects
ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, Humans, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Chromosome Mapping, Alleles, European Continental Ancestry Group, Female, Male, Genetic Variation, Genome-Wide Association Study, Whole Exome Sequencing, Diabetes Mellitus, Type 2, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P

Published
2018

3. Association of the IGF1 gene with fasting insulin levels

Author

Willems, Sara M, Cornes, Belinda K, Brody, Jennifer A, Morrison, Alanna C, Lipovich, Leonard, Dauriz, Marco, Chen, Yuning, Liu, Ching-Ti, Rybin, Denis V, Gibbs, Richard A, Muzny, Donna, Pankow, James S, Psaty, Bruce M, Boerwinkle, Eric, Rotter, Jerome I, Siscovick, David S, Vasan, Ramachandran S, Kaplan, Robert C, Isaacs, Aaron, Dupuis, Josée, van Duijn, Cornelia M, and Meigs, James B

Subjects
Biological Sciences, Genetics, Human Genome, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Adult, Aged, Fasting, Female, Humans, Insulin, Insulin-Like Growth Factor I, Male, Middle Aged, Polymorphism, Single Nucleotide, Clinical Sciences, Genetics & Heredity, Clinical sciences
Abstract

Insulin-like growth factor 1 (IGF-I) has been associated with insulin resistance. Genome-wide association studies (GWASs) of fasting insulin (FI) identified single-nucleotide variants (SNVs) near the IGF1 gene, raising two hypotheses: (1) these associations are mediated by IGF-I levels and (2) these noncoding variants either tag other functional variants in the region or are directly functional. In our study, analyses including 5141 individuals from population-based cohorts suggest that FI associations near IGF1 are not mediated by IGF-I. Analyses of targeted sequencing data in 3539 individuals reveal a large number of novel rare variants at the IGF1 locus and show a FI association with a subset of rare nonsynonymous variants (PSKAT=5.7 × 10(-4)). Conditional analyses suggest that this association is partly explained by the GWAS signal and the presence of a residual independent rare variant effect (Pconditional=0.019). Annotation using ENCODE data suggests that the GWAS variants may have a direct functional role in insulin biology. In conclusion, our study provides insight into variation present at the IGF1 locus and into the genetic architecture underlying FI levels, suggesting that FI associations of SNVs near IGF1 are not mediated by IGF-I and suggesting a role for both rare nonsynonymous and common functional variants in insulin biology.

Published
2016

4. Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin

Author

Liu, Ching-Ti, Raghavan, Sridharan, Maruthur, Nisa, Kabagambe, Edmond Kato, Hong, Jaeyoung, Ng, Maggie CY, Hivert, Marie-France, Lu, Yingchang, An, Ping, Bentley, Amy R, Drolet, Anne M, Gaulton, Kyle J, Guo, Xiuqing, Armstrong, Loren L, Irvin, Marguerite R, Li, Man, Lipovich, Leonard, Rybin, Denis V, Taylor, Kent D, Agyemang, Charles, Palmer, Nicholette D, Cade, Brian E, Chen, Wei-Min, Dauriz, Marco, Delaney, Joseph AC, Edwards, Todd L, Evans, Daniel S, Evans, Michele K, Lange, Leslie A, Leong, Aaron, Liu, Jingmin, Liu, Yongmei, Nayak, Uma, Patel, Sanjay R, Porneala, Bianca C, Rasmussen-Torvik, Laura J, Snijder, Marieke B, Stallings, Sarah C, Tanaka, Toshiko, Yanek, Lisa R, Zhao, Wei, Becker, Diane M, Bielak, Lawrence F, Biggs, Mary L, Bottinger, Erwin P, Bowden, Donald W, Chen, Guanjie, Correa, Adolfo, Couper, David J, Crawford, Dana C, Cushman, Mary, Eicher, John D, Fornage, Myriam, Franceschini, Nora, Fu, Yi-Ping, Goodarzi, Mark O, Gottesman, Omri, Hara, Kazuo, Harris, Tamara B, Jensen, Richard A, Johnson, Andrew D, Jhun, Min A, Karter, Andrew J, Keller, Margaux F, Kho, Abel N, Kizer, Jorge R, Krauss, Ronald M, Langefeld, Carl D, Li, Xiaohui, Liang, Jingling, Liu, Simin, Lowe, William L, Mosley, Thomas H, North, Kari E, Pacheco, Jennifer A, Peyser, Patricia A, Patrick, Alan L, Rice, Kenneth M, Selvin, Elizabeth, Sims, Mario, Smith, Jennifer A, Tajuddin, Salman M, Vaidya, Dhananjay, Wren, Mary P, Yao, Jie, Zhu, Xiaofeng, Ziegler, Julie T, Zmuda, Joseph M, Zonderman, Alan B, Zwinderman, Aeilko H, Consortium, AAAG, Consortium, CARe, Consortium, COGENT-BP, Consortium, eMERGE, Consortium, MEDIA, Adeyemo, Adebowale, Boerwinkle, Eric, Ferrucci, Luigi, Hayes, M Geoffrey, and Kardia, Sharon LR

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Health Disparities, Human Genome, Diabetes, Minority Health, Metabolic and endocrine, Asian People, Black People, Blood Glucose, Diabetes Mellitus, Type 2, Enhancer Elements, Genetic, Ethnicity, Fasting, Female, Gene Frequency, Genome-Wide Association Study, Humans, Insulin, Insulin Resistance, Introns, Islets of Langerhans, Male, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Racial Groups, Transcription Factors, White People, AAAG Consortium, CARe Consortium, COGENT-BP Consortium, eMERGE Consortium, MEDIA Consortium, MAGIC Consortium, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci.

Published
2016

15. Trust in Sources of Advice about Infant Care Practices: The SAFE Study

Author

Hwang, Sunah S., Rybin, Denis V., Heeren, Timothy C., Colson, Eve R., and Corwin, Michael J.

Subjects
Medical professions -- Practice -- Psychological aspects, Trust (Psychology) -- Analysis, Infant care -- Analysis, Health promotion -- Analysis, Physician-patient relations -- Analysis, Health care industry
Abstract

Objectives (1) Determine the prevalence of maternal trust in advice sources on infant care practices; (2) Investigate the association of maternal and infant characteristics with trust in advice sources on infant care practices. Methods Using probability sampling methods, we recruited mothers from 32 U.S. maternity hospitals with oversampling of Black and Hispanic women resulting in a nationally representative sample of mothers of infants aged 2-6 months. Survey questions assessed maternal trust in advice sources (physicians, nurses, family, friends, and media) regarding infant care practices including infant sleep practices (sleep position, bed sharing, and pacifier use), feeding, and vaccination. Weighted frequencies of maternal trust in advice sources were calculated to obtain prevalence estimates. Multivariable logistic regression was used to assess the association of maternal and infant characteristics with maternal trust in advice sources. Results Mothers had the greatest trust in doctors for advice on all infant care practices (56-89 %), while trust was lowest for friends (13-22 %) and the media (10-14 %). In the adjusted analyses, there were significant associations of maternal race/ethnicity, education, and age with trust in advice sources. Conclusions for Practice Maternal trust in advice about infant care practices varied significantly by source. A better understanding of which advice sources are most trusted by mothers, as well as the factors associated with maternal trust, may guide the development of more effective strategies to improve adherence to health promoting infant care practices., Author(s): Sunah S. Hwang[sup.1] [sup.2] , Denis V. Rybin[sup.3] , Timothy C. Heeren[sup.4] , Eve R. Colson[sup.5] , Michael J. Corwin[sup.6] Author Affiliations: (1) Section of Neonatology, Department of Pediatrics, [...]

Published
2016
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18. Repurposing Diflunisal for Familial Amyloid Polyneuropathy: A Randomized Clinical Trial

Author

Berk, John L., Suhr, Ole B., Obici, Laura, Sekijima, Yoshiki, Zeldenrust, Steven R., Yamashita, Taro, Heneghan, Michael A., Gorevic, Peter D., Litchy, William J., Wiesman, Janice F., Nordh, Erik, Corato, Manuel, Lozza, Alessandro, Cortese, Andrea, Robinson-Papp, Jessica, Colton, Theodore, Rybin, Denis V., Bisbee, Alice B., Ando, Yukio, Ikeda, Shu-ichi, Seldin, David C., Merlini, Giampaolo, Skinner, Martha, Kelly, Jeffery W., and Dyck, Peter J.

Published
2013
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19. Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis

Author

Wheeler, Eleanor, Leong, Aaron, Liu, Ching-Ti, Hivert, Marie-France, Strawbridge, Rona J., Podmore, Clara, Li, Man, Yao, Jie, Sim, Xueling, Hong, Jaeyoung, Chu, Audrey Y., Zhang, Weihua, Wang, Xu, Chen, Peng, Maruthur, Nisa M., Porneala, Bianca C., Sharp, Stephen J., Jia, Yucheng, Kabagambe, Edmond K., Chang, Li-Ching, Chen, Wei-Min, Elks, Cathy E., Evans, Daniel S., Fan, Qiao, Giulianini, Franco, Go, Min Jin, Hottenga, Jouke-Jan, Hu, Yao, Jackson, Anne U., Kanoni, Stavroula, Kim, Young Jin, Kleber, Marcus E., Ladenvall, Claes, Lecoeur, Cecile, Lim, Sing-Hui, Lu, Yingchang, Mahajan, Anubha, Marzi, Carola, Nalls, Mike A., Navarro, Pau, Nolte, Ilja M., Rose, Lynda M., Rybin, Denis V., Sanna, Serena, Shi, Yuan, Stram, Daniel O., Takeuchi, Fumihiko, Tan, Shu Pei, van der Most, Peter J., Van Vliet-Ostaptchouk, Jana V., Wong, Andrew, Yengo, Loic, Zhao, Wanting, Goel, Anuj, Martinez Larrad, Maria Teresa, Radke, Dörte, Salo, Perttu, Tanaka, Toshiko, van Iperen, Erik P. A., Abecasis, Goncalo, Afaq, Saima, Alizadeh, Behrooz Z., Bertoni, Alain G., Bonnefond, Amelie, Böttcher, Yvonne, Bottinger, Erwin P., Campbell, Harry, Carlson, Olga D., Chen, Chien-Hsiun, Cho, Yoon Shin, Garvey, W. Timothy, Gieger, Christian, Goodarzi, Mark O., Grallert, Harald, Hamsten, Anders, Hartman, Catharina A., Herder, Christian, Hsiung, Chao Agnes, Huang, Jie, Igase, Michiya, Isono, Masato, Katsuya, Tomohiro, Khor, Chiea-Chuen, Kiess, Wieland, Kohara, Katsuhiko, Kovacs, Peter, Lee, Juyoung, Lee, Wen-Jane, Lehne, Benjamin, Li, Huaixing, Liu, Jianjun, Lobbens, Stephane, Luan, Jian'an, Lyssenko, Valeriya, Meitinger, Thomas, Miki, Tetsuro, Miljkovic, Iva, Moon, Sanghoon, Mulas, Antonella, Müller, Gabriele, Müller-Nurasyid, Martina, Nagaraja, Ramaiah, Nauck, Matthias, Pankow, James S., Polasek, Ozren, Prokopenko, Inga, Ramos, Paula S., Rasmussen-Torvik, Laura, Rathmann, Wolfgang, Rich, Stephen S., Robertson, Neil R., Roden, Michael, Roussel, Ronan, Rudan, Igor, Scott, Robert A., Scott, William R., Sennblad, Bengt, Siscovick, David S., Strauch, Konstantin, Sun, Liang, Swertz, Morris, Tajuddin, Salman M., Taylor, Kent D., Teo, Yik-Ying, Tham, Yih Chung, Tönjes, Anke, Wareham, Nicholas J., Willemsen, Gonneke, Wilsgaard, Tom, Hingorani, Aroon D., Egan, Josephine, Ferrucci, Luigi, Hovingh, G. Kees, Jula, Antti, Kivimaki, Mika, Kumari, Meena, Njølstad, Inger, Palmer, Colin N. A., Serrano Ríos, Manuel, Stumvoll, Michael, Watkins, Hugh, Aung, Tin, Blüher, Matthias, Boehnke, Michael, Boomsma, Dorret I., Bornstein, Stefan R., Chambers, John C., Chasman, Daniel I., Chen, Yii-Der Ida, Chen, Yduan-Tsong, Cheng, Ching-Yu, Cucca, Francesco, de Geus, Eco J. C., Deloukas, Panos, Evans, Michele K., Fornage, Myriam, Friedlander, Yechiel, Froguel, Philippe, Groop, Leif, Gross, Myron D., Harris, Tamara B., Hayward, Caroline, Heng, Chew-Kiat, Ingelsson, Erik, Kato, Norihiro, Kim, Bong-Jo, Koh, Woon-Puay, Kooner, Jaspal S., Körner, Antje, Kuh, Diana, Kuusisto, Johanna, Laakso, Markku, Lin, Xu, Liu, Yongmei, Loos, Ruth J. F., Magnusson, Patrik K. E., März, Winfried, McCarthy, Mark I., Oldehinkel, Albertine J., Ong, Ken K., Pedersen, Nancy L., Pereira, Mark A., Peters, Annette, Ridker, Paul M., Sabanayagam, Charumathi, Sale, Michele, Saleheen, Danish, Saltevo, Juha, Schwarz, Peter EH., Sheu, Wayne H. H., Snieder, Harold, Spector, Timothy D., Tabara, Yasuharu, Tuomilehto, Jaakko, van Dam, Rob M., Wilson, James G., Wilson, James F., Wolffenbuttel, Bruce H. R., Wong, Tien Yin, Wu, Jer-Yuarn, Yuan, Jian-Min, Zonderman, Alan B., Soranzo, Nicole, Guo, Xiuqing, Roberts, David J., Florez, Jose C., Sladek, Robert, Dupuis, Josée, Morris, Andrew P., Tai, E-Shyong, Selvin, Elizabeth, Rotter, Jerome I., Langenberg, Claudia, Barroso, Inês, and Meigs, James B.

Subjects
Genotypes -- Research, African Americans -- Analysis -- Health aspects, Type 2 diabetes -- Risk factors -- Diagnosis, Biological sciences
Abstract

Background Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. Methods & findings Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 x 10.sup.-29 ); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. Conclusions As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses., Author(s): Eleanor Wheeler 1, Aaron Leong 2,3, Ching-Ti Liu 4, Marie-France Hivert 5,6, Rona J. Strawbridge 7,8, Clara Podmore 9,10, Man Li 11,12,13, Jie Yao 14, Xueling Sim 15, Jaeyoung [...]

Published
2017
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30. Reply

Author

Eslami, Mohammad H., primary, Doros, Gheorghe, additional, and Rybin, Denis V., additional

Published
2017
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45. Peripheral Blood Transcriptomic Signatures of Fasting Glucose and Insulin Concentrations.

Author

Chen, Brian H, Hivert, Marie-France, Peters, Marjolein J, Pilling, Luke C, Hogan, John D, Pham, Lisa M, Harries, Lorna W, Fox, Caroline S, Bandinelli, Stefania, Dehghan, Abbas, Hernandez, Dena G, Hofman, Albert, Hong, Jaeyoung, Joehanes, Roby, Johnson, Andrew D, Munson, Peter J, Rybin, Denis V, Singleton, Andrew B, Uitterlinden, André G, and Ying, Saixia

Subjects
BLOOD sugar, DISEASE susceptibility, FASTING, GENES, INSULIN, PROTEINS, RESEARCH funding, RNA, GENE expression profiling, SEQUENCE analysis, GENOTYPES
Abstract

Genome-wide association studies (GWAS) have successfully identified genetic loci associated with glycemic traits. However, characterizing the functional significance of these loci has proven challenging. We sought to gain insights into the regulation of fasting insulin and fasting glucose through the use of gene expression microarray data from peripheral blood samples of participants without diabetes in the Framingham Heart Study (FHS) (n = 5,056), the Rotterdam Study (RS) (n = 723), and the InCHIANTI Study (Invecchiare in Chianti) (n = 595). Using a false discovery rate q <0.05, we identified three transcripts associated with fasting glucose and 433 transcripts associated with fasting insulin levels after adjusting for age, sex, technical covariates, and complete blood cell counts. Among the findings, circulating IGF2BP2 transcript levels were positively associated with fasting insulin in both the FHS and RS. Using 1000 Genomes-imputed genotype data, we identified 47,587 cis-expression quantitative trait loci (eQTL) and 6,695 trans-eQTL associated with the 433 significant insulin-associated transcripts. Of note, we identified a trans-eQTL (rs592423), where the A allele was associated with higher IGF2BP2 levels and with fasting insulin in an independent genetic meta-analysis comprised of 50,823 individuals. We conclude that integration of genomic and transcriptomic data implicate circulating IGF2BP2 mRNA levels associated with glucose and insulin homeostasis. [ABSTRACT FROM AUTHOR]

Published
2016

47. Placebo response characteristic in sequential parallel comparison design studies

Author

Rybin, Denis V.

Subjects
Biostatistics, EM, Clinical trial, Latent variable, Missing data, Mixture, Placebo response
Abstract

The placebo response can affect inference in analysis of data from clinical trials. It can bias the estimate of the treatment effect, jeopardize the effort of all involved in a clinical trial and ultimately deprive patients of potentially efficacious treatment. The Sequential Parallel Comparison Design (SPCD) is one of the novel approaches addressing placebo response in clinical trials. The analysis of SPCD clinical trial data typically involves classification of subjects as ‘placebo responders’ or ‘placebo non-responders’. This classification is done using a specific criterion and placebo response is treated as a measurable characteristic. However, the use of criterion may lead to subject misclassification due to measurement error or incorrect criterion selection. Subsequently, misclassification can directly affect SPCD treatment effect estimate. We propose to view placebo response as an unknown random characteristic that can be estimated based on information collected during the trial. Two strategies are presented here. First strategy is to model placebo response using criterion classification as a starting point or the observed data, and to include the placebo response estimate into the treatment effect estimation. Second strategy is to jointly model latent placebo response and the observed data, and estimate treatment effect from the joint model. We evaluate both strategies on a wide range of simulated data scenarios in terms of type I error control, mean squared error and power. We then evaluate the strategies in presence of missing data and propose a method for missing data imputation under the non-informative missingness assumption. The data from a recent SPCD clinical trial is used to compare results of the proposed methods with reported results of the trial.

Published
2016

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