Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1 to 12 of 12 entries
Sorted by: Best Match Show Resources per page
Bias modelling in evidence synthesis.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)

Turner RM, Spiegelhalter DJ, Smith GC, Thompson SG.
PMID: 19381328
J R Stat Soc Ser A Stat Soc. 2009 Jan;172(1):21-47. doi: 10.1111/j.1467-985X.2008.00547.x.

Policy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal...

Cite
Turner RM, Spiegelhalter DJ, Smith GC, et al. Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc. 2009;172(1):21-47doi: 10.1111/j.1467-985X.2008.00547.x.
Turner, R. M., Spiegelhalter, D. J., Smith, G. C., & Thompson, S. G. (2009). Bias modelling in evidence synthesis. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 172(1), 21-47. https://doi.org/10.1111/j.1467-985X.2008.00547.x
Turner, Rebecca M, et al. "Bias modelling in evidence synthesis." Journal of the Royal Statistical Society. Series A, (Statistics in Society) vol. 172,1 (2009): 21-47. doi: https://doi.org/10.1111/j.1467-985X.2008.00547.x
Turner RM, Spiegelhalter DJ, Smith GC, Thompson SG. Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc. 2009 Jan;172(1):21-47. doi: 10.1111/j.1467-985X.2008.00547.x. PMID: 19381328; PMCID: PMC2667303.
Copy Download .nbib Format: NLM
Accounting for uncertainty in health economic decision models by using model averaging.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)

Jackson CH, Thompson SG, Sharples LD.
PMID: 19381329
J R Stat Soc Ser A Stat Soc. 2009 Apr;172(2):383-404. doi: 10.1111/j.1467-985X.2008.00573.x.

Health economic decision models are subject to considerable uncertainty, much of which arises from choices between several plausible model structures, e.g. choices of covariates in a regression model. Such structural uncertainty is rarely accounted for formally in decision models...

Cite
Jackson CH, Thompson SG, Sharples LD. Accounting for uncertainty in health economic decision models by using model averaging. J R Stat Soc Ser A Stat Soc. 2009;172(2):383-404doi: 10.1111/j.1467-985X.2008.00573.x.
Jackson, C. H., Thompson, S. G., & Sharples, L. D. (2009). Accounting for uncertainty in health economic decision models by using model averaging. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 172(2), 383-404. https://doi.org/10.1111/j.1467-985X.2008.00573.x
Jackson, Christopher H, et al. "Accounting for uncertainty in health economic decision models by using model averaging." Journal of the Royal Statistical Society. Series A, (Statistics in Society) vol. 172,2 (2009): 383-404. doi: https://doi.org/10.1111/j.1467-985X.2008.00573.x
Jackson CH, Thompson SG, Sharples LD. Accounting for uncertainty in health economic decision models by using model averaging. J R Stat Soc Ser A Stat Soc. 2009 Apr;172(2):383-404. doi: 10.1111/j.1467-985X.2008.00573.x. PMID: 19381329; PMCID: PMC2667305.
Copy Download .nbib Format: NLM
A re-evaluation of random-effects meta-analysis.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)

Higgins JP, Thompson SG, Spiegelhalter DJ.
PMID: 19381330
J R Stat Soc Ser A Stat Soc. 2009 Jan;172(1):137-159. doi: 10.1111/j.1467-985X.2008.00552.x.

Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of...

Cite
Higgins JP, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc. 2009;172(1):137-159doi: 10.1111/j.1467-985X.2008.00552.x.
Higgins, J. P., Thompson, S. G., & Spiegelhalter, D. J. (2009). A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 172(1), 137-159. https://doi.org/10.1111/j.1467-985X.2008.00552.x
Higgins, Julian P T, et al. "A re-evaluation of random-effects meta-analysis." Journal of the Royal Statistical Society. Series A, (Statistics in Society) vol. 172,1 (2009): 137-159. doi: https://doi.org/10.1111/j.1467-985X.2008.00552.x
Higgins JP, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A Stat Soc. 2009 Jan;172(1):137-159. doi: 10.1111/j.1467-985X.2008.00552.x. PMID: 19381330; PMCID: PMC2667312.
Copy Download .nbib Format: NLM
Assessment of patient-derived tumour xenografts (PDXs) as a discovery tool for cancer epigenomics.

Genome medicine

Guilhamon P, Butcher LM, Presneau N, Wilson GA, Feber A, Paul DS, Schütte M, Haybaeck J, Keilholz U, Hoffman J, Ross MT, Flanagan AM, Beck S.
PMID: 25587359
Genome Med. 2014 Dec 12;6(12):116. doi: 10.1186/s13073-014-0116-0. eCollection 2014.

BACKGROUND: The use of tumour xenografts is a well-established research tool in cancer genomics but has not yet been comprehensively evaluated for cancer epigenomics.METHODS: In this study, we assessed the suitability of patient-derived tumour xenografts (PDXs) for methylome analysis...

Cite
Guilhamon P, Butcher LM, Presneau N, et al. Assessment of patient-derived tumour xenografts (PDXs) as a discovery tool for cancer epigenomics. Genome Med. 2014;6(12):116doi: 10.1186/s13073-014-0116-0.
Guilhamon, P., Butcher, L. M., Presneau, N., Wilson, G. A., Feber, A., Paul, D. S., Schütte, M., Haybaeck, J., Keilholz, U., Hoffman, J., Ross, M. T., Flanagan, A. M., & Beck, S. (2014). Assessment of patient-derived tumour xenografts (PDXs) as a discovery tool for cancer epigenomics. Genome medicine, 6(12), 116. https://doi.org/10.1186/s13073-014-0116-0
Guilhamon, Paul, et al. "Assessment of patient-derived tumour xenografts (PDXs) as a discovery tool for cancer epigenomics." Genome medicine vol. 6,12 (2014): 116. doi: https://doi.org/10.1186/s13073-014-0116-0
Guilhamon P, Butcher LM, Presneau N, Wilson GA, Feber A, Paul DS, Schütte M, Haybaeck J, Keilholz U, Hoffman J, Ross MT, Flanagan AM, Beck S. Assessment of patient-derived tumour xenografts (PDXs) as a discovery tool for cancer epigenomics. Genome Med. 2014 Dec 12;6(12):116. doi: 10.1186/s13073-014-0116-0. eCollection 2014. PMID: 25587359; PMCID: PMC4292812.
Copy Download .nbib Format: NLM
Structural and parameter uncertainty in Bayesian cost-effectiveness models.

Journal of the Royal Statistical Society. Series C, Applied statistics

Jackson CH, Sharples LD, Thompson SG.
PMID: 20383261
J R Stat Soc Ser C Appl Stat. 2010 Mar;59(2):233-253. doi: 10.1111/j.1467-9876.2009.00684.x.

Health economic decision models are subject to various forms of uncertainty, including uncertainty about the parameters of the model and about the model structure. These uncertainties can be handled within a Bayesian framework, which also allows evidence from previous...

Cite
Jackson CH, Sharples LD, Thompson SG. Structural and parameter uncertainty in Bayesian cost-effectiveness models. J R Stat Soc Ser C Appl Stat. 2010;59(2):233-253doi: 10.1111/j.1467-9876.2009.00684.x.
Jackson, C. H., Sharples, L. D., & Thompson, S. G. (2010). Structural and parameter uncertainty in Bayesian cost-effectiveness models. Journal of the Royal Statistical Society. Series C, Applied statistics, 59(2), 233-253. https://doi.org/10.1111/j.1467-9876.2009.00684.x
Jackson, Christopher H, et al. "Structural and parameter uncertainty in Bayesian cost-effectiveness models." Journal of the Royal Statistical Society. Series C, Applied statistics vol. 59,2 (2010): 233-253. doi: https://doi.org/10.1111/j.1467-9876.2009.00684.x
Jackson CH, Sharples LD, Thompson SG. Structural and parameter uncertainty in Bayesian cost-effectiveness models. J R Stat Soc Ser C Appl Stat. 2010 Mar;59(2):233-253. doi: 10.1111/j.1467-9876.2009.00684.x. PMID: 20383261; PMCID: PMC2847203.
Copy Download .nbib Format: NLM
Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression.

Research synthesis methods

White IR, Barrett JK, Jackson D, Higgins JP.
PMID: 26062085
Res Synth Methods. 2012 Jun;3(2):111-25. doi: 10.1002/jrsm.1045.

Network meta-analysis (multiple treatments meta-analysis, mixed treatment comparisons) attempts to make the best use of a set of studies comparing more than two treatments. However, it is important to assess whether a body of evidence is consistent or inconsistent....

Cite
White IR, Barrett JK, Jackson D, et al. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Res Synth Methods. 2012;3(2):111-25doi: 10.1002/jrsm.1045.
White, I. R., Barrett, J. K., Jackson, D., & Higgins, J. P. (2012). Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Research synthesis methods, 3(2), 111-25. https://doi.org/10.1002/jrsm.1045
White, Ian R, et al. "Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression." Research synthesis methods vol. 3,2 (2012): 111-25. doi: https://doi.org/10.1002/jrsm.1045
White IR, Barrett JK, Jackson D, Higgins JP. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Res Synth Methods. 2012 Jun;3(2):111-25. doi: 10.1002/jrsm.1045. PMID: 26062085; PMCID: PMC4433771.
Copy Download .nbib Format: NLM
Trajectories of alcohol consumption prior to the diagnosis of type 2 diabetes: a longitudinal case-cohort study.

International journal of epidemiology

Knott CS, Britton A, Bell S.
PMID: 29342257
Int J Epidemiol. 2018 Jun 01;47(3):953-965. doi: 10.1093/ije/dyx274.

BACKGROUND: Non-linear associations have been reported between baseline measures of alcohol consumption and type 2 diabetes (T2DM). However, given that drinking varies over the adult life course, we investigated whether differences existed in the longitudinal trajectory of alcohol consumption...

Cite
Knott CS, Britton A, Bell S. Trajectories of alcohol consumption prior to the diagnosis of type 2 diabetes: a longitudinal case-cohort study. Int J Epidemiol. 2018;47(3):953-965doi: 10.1093/ije/dyx274.
Knott, C. S., Britton, A., & Bell, S. (2018). Trajectories of alcohol consumption prior to the diagnosis of type 2 diabetes: a longitudinal case-cohort study. International journal of epidemiology, 47(3), 953-965. https://doi.org/10.1093/ije/dyx274
Knott, Craig S, et al. "Trajectories of alcohol consumption prior to the diagnosis of type 2 diabetes: a longitudinal case-cohort study." International journal of epidemiology vol. 47,3 (2018): 953-965. doi: https://doi.org/10.1093/ije/dyx274
Knott CS, Britton A, Bell S. Trajectories of alcohol consumption prior to the diagnosis of type 2 diabetes: a longitudinal case-cohort study. Int J Epidemiol. 2018 Jun 01;47(3):953-965. doi: 10.1093/ije/dyx274. PMID: 29342257; PMCID: PMC6005149.
Copy Download .nbib Format: NLM
Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.

Nature communications

Pattaro C, Teumer A, Gorski M, Chu AY, Li M, Mijatovic V, Garnaas M, Tin A, Sorice R, Li Y, Taliun D, Olden M, Foster M, Yang Q, Chen MH, Pers TH, Johnson AD, Ko YA, Fuchsberger C, Tayo B, Nalls M, Feitosa MF, Isaacs A, Dehghan A, d'Adamo P, Adeyemo A, Dieffenbach AK, Zonderman AB, Nolte IM, van der Most PJ, Wright AF, Shuldiner AR, Morrison AC, Hofman A, Smith AV, Dreisbach AW, Franke A, Uitterlinden AG, Metspalu A, Tonjes A, Lupo A, Robino A, Johansson Å, Demirkan A, Kollerits B, Freedman BI, Ponte B, Oostra BA, Paulweber B, Krämer BK, Mitchell BD, Buckley BM, Peralta CA, Hayward C, Helmer C, Rotimi CN, Shaffer CM, Müller C, Sala C, van Duijn CM, Saint-Pierre A, Ackermann D, Shriner D, Ruggiero D, Toniolo D, Lu Y, Cusi D, Czamara D, Ellinghaus D, Siscovick DS, Ruderfer D, Gieger C, Grallert H, Rochtchina E, Atkinson EJ, Holliday EG, Boerwinkle E, Salvi E, Bottinger EP, Murgia F, Rivadeneira F, Ernst F, Kronenberg F, Hu FB, Navis GJ, Curhan GC, Ehret GB, Homuth G, Coassin S, Thun GA, Pistis G, Gambaro G, Malerba G, Montgomery GW, Eiriksdottir G, Jacobs G, Li G, Wichmann HE, Campbell H, Schmidt H, Wallaschofski H, Völzke H, Brenner H, Kroemer HK, Kramer H, Lin H, Leach IM, Ford I, Guessous I, Rudan I, Prokopenko I, Borecki I, Heid IM, Kolcic I, Persico I, Jukema JW, Wilson JF, Felix JF, Divers J, Lambert JC, Stafford JM, Gaspoz JM, Smith JA, Faul JD, Wang JJ, Ding J, Hirschhorn JN, Attia J, Whitfield JB, Chalmers J, Viikari J, Coresh J, Denny JC, Karjalainen J, Fernandes JK, Endlich K, Butterbach K, Keene KL, Lohman K, Portas L, Launer LJ, Lyytikäinen LP, Yengo L, Franke L, Ferrucci L, Rose LM, Kedenko L, Rao M, Struchalin M, Kleber ME, Cavalieri M, Haun M, Cornelis MC, Ciullo M, Pirastu M, de Andrade M, McEvoy MA, Woodward M, Adam M, Cocca M, Nauck M, Imboden M, Waldenberger M, Pruijm M, Metzger M, Stumvoll M, Evans MK, Sale MM, Kähönen M, Boban M, Bochud M, Rheinberger M, Verweij N, Bouatia-Naji N, Martin NG, Hastie N, Probst-Hensch N, Soranzo N, Devuyst O, Raitakari O, Gottesman O, Franco OH, Polasek O, Gasparini P, Munroe PB, Ridker PM, Mitchell P, Muntner P, Meisinger C, Smit JH, Kovacs P, Wild PS, Froguel P, Rettig R, Mägi R, Biffar R, Schmidt R, Middelberg RP, Carroll RJ, Penninx BW, Scott RJ, Katz R, Sedaghat S, Wild SH, Kardia SL, Ulivi S, Hwang SJ, Enroth S, Kloiber S, Trompet S, Stengel B, Hancock SJ, Turner ST, Rosas SE, Stracke S, Harris TB, Zeller T, Zemunik T, Lehtimäki T, Illig T, Aspelund T, Nikopensius T, Esko T, Tanaka T, Gyllensten U, Völker U, Emilsson V, Vitart V, Aalto V, Gudnason V, Chouraki V, Chen WM, Igl W, März W, Koenig W, Lieb W, Loos RJ, Liu Y, Snieder H, Pramstaller PP, Parsa A, O'Connell JR, Susztak K, Hamet P, Tremblay J, de Boer IH, Böger CA, Goessling W, Chasman DI, Köttgen A, Kao WH, Fox CS.
PMID: 26831199
Nat Commun. 2016 Jan 21;7:10023. doi: 10.1038/ncomms10023.

Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up...

Cite
Pattaro C, Teumer A, Gorski M, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023doi: 10.1038/ncomms10023.
Pattaro, C., Teumer, A., Gorski, M., Chu, A. Y., Li, M., Mijatovic, V., Garnaas, M., Tin, A., Sorice, R., Li, Y., Taliun, D., Olden, M., Foster, M., Yang, Q., Chen, M. H., Pers, T. H., Johnson, A. D., Ko, Y. A., Fuchsberger, C., Tayo, B., Nalls, M., Feitosa, M. F., Isaacs, A., Dehghan, A., d'Adamo, P., Adeyemo, A., Dieffenbach, A. K., Zonderman, A. B., Nolte, I. M., van der Most, P. J., Wright, A. F., Shuldiner, A. R., Morrison, A. C., Hofman, A., Smith, A. V., Dreisbach, A. W., Franke, A., Uitterlinden, A. G., Metspalu, A., Tonjes, A., Lupo, A., Robino, A., Johansson, �. �., Demirkan, A., Kollerits, B., Freedman, B. I., Ponte, B., Oostra, B. A., Paulweber, B., Krämer, B. K., Mitchell, B. D., Buckley, B. M., Peralta, C. A., Hayward, C., Helmer, C., Rotimi, C. N., Shaffer, C. M., Müller, C., Sala, C., van Duijn, C. M., Saint-Pierre, A., Ackermann, D., Shriner, D., Ruggiero, D., Toniolo, D., Lu, Y., Cusi, D., Czamara, D., Ellinghaus, D., Siscovick, D. S., Ruderfer, D., Gieger, C., Grallert, H., Rochtchina, E., Atkinson, E. J., Holliday, E. G., Boerwinkle, E., Salvi, E., Bottinger, E. P., Murgia, F., Rivadeneira, F., Ernst, F., Kronenberg, F., Hu, F. B., Navis, G. J., Curhan, G. C., Ehret, G. B., Homuth, G., Coassin, S., Thun, G. A., Pistis, G., Gambaro, G., Malerba, G., Montgomery, G. W., Eiriksdottir, G., Jacobs, G., Li, G., Wichmann, H. E., Campbell, H., Schmidt, H., Wallaschofski, H., Völzke, H., Brenner, H., Kroemer, H. K., Kramer, H., Lin, H., Leach, I. M., Ford, I., Guessous, I., Rudan, I., Prokopenko, I., Borecki, I., Heid, I. M., Kolcic, I., Persico, I., Jukema, J. W., Wilson, J. F., Felix, J. F., Divers, J., Lambert, J. C., Stafford, J. M., Gaspoz, J. M., Smith, J. A., Faul, J. D., Wang, J. J., Ding, J., Hirschhorn, J. N., Attia, J., Whitfield, J. B., Chalmers, J., Viikari, J., Coresh, J., Denny, J. C., Karjalainen, J., Fernandes, J. K., Endlich, K., Butterbach, K., Keene, K. L., Lohman, K., Portas, L., Launer, L. J., Lyytikäinen, L. P., Yengo, L., Franke, L., Ferrucci, L., Rose, L. M., Kedenko, L., Rao, M., Struchalin, M., Kleber, M. E., Cavalieri, M., Haun, M., Cornelis, M. C., Ciullo, M., Pirastu, M., de Andrade, M., McEvoy, M. A., Woodward, M., Adam, M., Cocca, M., Nauck, M., Imboden, M., Waldenberger, M., Pruijm, M., Metzger, M., Stumvoll, M., Evans, M. K., Sale, M. M., Kähönen, M., Boban, M., Bochud, M., Rheinberger, M., Verweij, N., Bouatia-Naji, N., Martin, N. G., Hastie, N., Probst-Hensch, N., Soranzo, N., Devuyst, O., Raitakari, O., Gottesman, O., Franco, O. H., Polasek, O., Gasparini, P., Munroe, P. B., Ridker, P. M., Mitchell, P., Muntner, P., Meisinger, C., Smit, J. H., Kovacs, P., Wild, P. S., Froguel, P., Rettig, R., Mägi, R., Biffar, R., Schmidt, R., Middelberg, R. P., Carroll, R. J., Penninx, B. W., Scott, R. J., Katz, R., Sedaghat, S., Wild, S. H., Kardia, S. L., Ulivi, S., Hwang, S. J., Enroth, S., Kloiber, S., Trompet, S., Stengel, B., Hancock, S. J., Turner, S. T., Rosas, S. E., Stracke, S., Harris, T. B., Zeller, T., Zemunik, T., Lehtimäki, T., Illig, T., Aspelund, T., Nikopensius, T., Esko, T., Tanaka, T., Gyllensten, U., Völker, U., Emilsson, V., Vitart, V., Aalto, V., Gudnason, V., Chouraki, V., Chen, W. M., Igl, W., März, W., Koenig, W., Lieb, W., Loos, R. J., Liu, Y., Snieder, H., Pramstaller, P. P., Parsa, A., O'Connell, J. R., Susztak, K., Hamet, P., Tremblay, J., de Boer, I. H., Böger, C. A., Goessling, W., Chasman, D. I., Köttgen, A., Kao, W. H., & Fox, C. S. (2016). Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nature communications, 710023. https://doi.org/10.1038/ncomms10023
Pattaro, Cristian, et al. "Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function." Nature communications vol. 7 (2016): 10023. doi: https://doi.org/10.1038/ncomms10023
Pattaro C, Teumer A, Gorski M, Chu AY, Li M, Mijatovic V, Garnaas M, Tin A, Sorice R, Li Y, Taliun D, Olden M, Foster M, Yang Q, Chen MH, Pers TH, Johnson AD, Ko YA, Fuchsberger C, Tayo B, Nalls M, Feitosa MF, Isaacs A, Dehghan A, d'Adamo P, Adeyemo A, Dieffenbach AK, Zonderman AB, Nolte IM, van der Most PJ, Wright AF, Shuldiner AR, Morrison AC, Hofman A, Smith AV, Dreisbach AW, Franke A, Uitterlinden AG, Metspalu A, Tonjes A, Lupo A, Robino A, Johansson Å, Demirkan A, Kollerits B, Freedman BI, Ponte B, Oostra BA, Paulweber B, Krämer BK, Mitchell BD, Buckley BM, Peralta CA, Hayward C, Helmer C, Rotimi CN, Shaffer CM, Müller C, Sala C, van Duijn CM, Saint-Pierre A, Ackermann D, Shriner D, Ruggiero D, Toniolo D, Lu Y, Cusi D, Czamara D, Ellinghaus D, Siscovick DS, Ruderfer D, Gieger C, Grallert H, Rochtchina E, Atkinson EJ, Holliday EG, Boerwinkle E, Salvi E, Bottinger EP, Murgia F, Rivadeneira F, Ernst F, Kronenberg F, Hu FB, Navis GJ, Curhan GC, Ehret GB, Homuth G, Coassin S, Thun GA, Pistis G, Gambaro G, Malerba G, Montgomery GW, Eiriksdottir G, Jacobs G, Li G, Wichmann HE, Campbell H, Schmidt H, Wallaschofski H, Völzke H, Brenner H, Kroemer HK, Kramer H, Lin H, Leach IM, Ford I, Guessous I, Rudan I, Prokopenko I, Borecki I, Heid IM, Kolcic I, Persico I, Jukema JW, Wilson JF, Felix JF, Divers J, Lambert JC, Stafford JM, Gaspoz JM, Smith JA, Faul JD, Wang JJ, Ding J, Hirschhorn JN, Attia J, Whitfield JB, Chalmers J, Viikari J, Coresh J, Denny JC, Karjalainen J, Fernandes JK, Endlich K, Butterbach K, Keene KL, Lohman K, Portas L, Launer LJ, Lyytikäinen LP, Yengo L, Franke L, Ferrucci L, Rose LM, Kedenko L, Rao M, Struchalin M, Kleber ME, Cavalieri M, Haun M, Cornelis MC, Ciullo M, Pirastu M, de Andrade M, McEvoy MA, Woodward M, Adam M, Cocca M, Nauck M, Imboden M, Waldenberger M, Pruijm M, Metzger M, Stumvoll M, Evans MK, Sale MM, Kähönen M, Boban M, Bochud M, Rheinberger M, Verweij N, Bouatia-Naji N, Martin NG, Hastie N, Probst-Hensch N, Soranzo N, Devuyst O, Raitakari O, Gottesman O, Franco OH, Polasek O, Gasparini P, Munroe PB, Ridker PM, Mitchell P, Muntner P, Meisinger C, Smit JH, Kovacs P, Wild PS, Froguel P, Rettig R, Mägi R, Biffar R, Schmidt R, Middelberg RP, Carroll RJ, Penninx BW, Scott RJ, Katz R, Sedaghat S, Wild SH, Kardia SL, Ulivi S, Hwang SJ, Enroth S, Kloiber S, Trompet S, Stengel B, Hancock SJ, Turner ST, Rosas SE, Stracke S, Harris TB, Zeller T, Zemunik T, Lehtimäki T, Illig T, Aspelund T, Nikopensius T, Esko T, Tanaka T, Gyllensten U, Völker U, Emilsson V, Vitart V, Aalto V, Gudnason V, Chouraki V, Chen WM, Igl W, März W, Koenig W, Lieb W, Loos RJ, Liu Y, Snieder H, Pramstaller PP, Parsa A, O'Connell JR, Susztak K, Hamet P, Tremblay J, de Boer IH, Böger CA, Goessling W, Chasman DI, Köttgen A, Kao WH, Fox CS. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016 Jan 21;7:10023. doi: 10.1038/ncomms10023. PMID: 26831199; PMCID: PMC4735748.
Copy Download .nbib Format: NLM
Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies.

Research synthesis methods

Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR.
PMID: 26062084
Res Synth Methods. 2012 Jun;3(2):98-110. doi: 10.1002/jrsm.1044.

Meta-analyses that simultaneously compare multiple treatments (usually referred to as network meta-analyses or mixed treatment comparisons) are becoming increasingly common. An important component of a network meta-analysis is an assessment of the extent to which different sources of evidence...

Cite
Higgins JP, Jackson D, Barrett JK, et al. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012;3(2):98-110doi: 10.1002/jrsm.1044.
Higgins, J. P., Jackson, D., Barrett, J. K., Lu, G., Ades, A. E., & White, I. R. (2012). Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Research synthesis methods, 3(2), 98-110. https://doi.org/10.1002/jrsm.1044
Higgins, J P T, et al. "Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies." Research synthesis methods vol. 3,2 (2012): 98-110. doi: https://doi.org/10.1002/jrsm.1044
Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012 Jun;3(2):98-110. doi: 10.1002/jrsm.1044. PMID: 26062084; PMCID: PMC4433772.
Copy Download .nbib Format: NLM
Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference.

Journal of the Royal Statistical Society. Series B, Statistical methodology

Barrett J, Diggle P, Henderson R, Taylor-Robinson D.
PMID: 25866468
J R Stat Soc Series B Stat Methodol. 2015 Jan;77(1):131-148. doi: 10.1111/rssb.12060. Epub 2014 Apr 08.

Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and event history data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity,...

Cite
Barrett J, Diggle P, Henderson R, et al. Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference. J R Stat Soc Series B Stat Methodol. 2014;77(1):131-148doi: 10.1111/rssb.12060.
Barrett, J., Diggle, P., Henderson, R., & Taylor-Robinson, D. (2015). Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference. Journal of the Royal Statistical Society. Series B, Statistical methodology, 77(1), 131-148. https://doi.org/10.1111/rssb.12060
Barrett, Jessica, et al. "Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference." Journal of the Royal Statistical Society. Series B, Statistical methodology vol. 77,1 (2015): 131-148. doi: https://doi.org/10.1111/rssb.12060
Barrett J, Diggle P, Henderson R, Taylor-Robinson D. Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference. J R Stat Soc Series B Stat Methodol. 2015 Jan;77(1):131-148. doi: 10.1111/rssb.12060. Epub 2014 Apr 08. PMID: 25866468; PMCID: PMC4384944.
Copy Download .nbib Format: NLM
Comments on 'Empirical vs natural weighting in random effects meta-analysis'.

Statistics in medicine

Thompson SG, Higgins JP.
PMID: 20499331
Stat Med. 2010 May 30;29(12):1270-1; discussion 1272-81. doi: 10.1002/sim.3718.

No abstract available.

Cite
Thompson SG, Higgins JP. Comments on 'Empirical vs natural weighting in random effects meta-analysis'. Stat Med. 2010;29(12):1270-1; discussion 1272-81doi: 10.1002/sim.3718.
Thompson, S. G., & Higgins, J. P. (2010). Comments on 'Empirical vs natural weighting in random effects meta-analysis'. Statistics in medicine, 29(12), 1270-1; discussion 1272-81. https://doi.org/10.1002/sim.3718
Thompson, Simon G, and Higgins, Julian P T. "Comments on 'Empirical vs natural weighting in random effects meta-analysis'." Statistics in medicine vol. 29,12 (2010): 1270-1; discussion 1272-81. doi: https://doi.org/10.1002/sim.3718
Thompson SG, Higgins JP. Comments on 'Empirical vs natural weighting in random effects meta-analysis'. Stat Med. 2010 May 30;29(12):1270-1; discussion 1272-81. doi: 10.1002/sim.3718. PMID: 20499331.
Copy Download .nbib Format: NLM
Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Scientific data

Flannick J, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA, Thameem F, Wilson G, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JCN, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Mohlke KL, Meitinger T, Groop L, Abecasis G, Scott LJ, Morris AP, Kang HM, Altshuler D, Burtt NP, Florez JC, Boehnke M, McCarthy MI.
PMID: 29360107
Sci Data. 2018 Jan 23;5:180002. doi: 10.1038/sdata.2018.2.

This corrects the article DOI: 10.1038/sdata.2017.179.

Cite
Flannick J, Fuchsberger C, Mahajan A, et al. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data. 2018;5:180002doi: 10.1038/sdata.2018.2.
Flannick, J., Fuchsberger, C., Mahajan, A., Teslovich, T. M., Agarwala, V., Gaulton, K. J., Caulkins, L., Koesterer, R., Ma, C., Moutsianas, L., McCarthy, D. J., Rivas, M. A., Perry, J. R. B., Sim, X., Blackwell, T. W., Robertson, N. R., Rayner, N. W., Cingolani, P., Locke, A. E., Tajes, J. F., Highland, H. M., Dupuis, J., Chines, P. S., Lindgren, C. M., Hartl, C., Jackson, A. U., Chen, H., Huyghe, J. R., van de Bunt, M., Pearson, R. D., Kumar, A., Müller-Nurasyid, M., Grarup, N., Stringham, H. M., Gamazon, E. R., Lee, J., Chen, Y., Scott, R. A., Below, J. E., Chen, P., Huang, J., Go, M. J., Stitzel, M. L., Pasko, D., Parker, S. C. J., Varga, T. V., Green, T., Beer, N. L., Day-Williams, A. G., Ferreira, T., Fingerlin, T., Horikoshi, M., Hu, C., Huh, I., Ikram, M. K., Kim, B. J., Kim, Y., Kim, Y. J., Kwon, M. S., Lee, J., Lee, S., Lin, K. H., Maxwell, T. J., Nagai, Y., Wang, X., Welch, R. P., Yoon, J., Zhang, W., Barzilai, N., Voight, B. F., Han, B. G., Jenkinson, C. P., Kuulasmaa, T., Kuusisto, J., Manning, A., Ng, M. C. Y., Palmer, N. D., Balkau, B., Stančáková, A., Abboud, H. E., Boeing, H., Giedraitis, V., Prabhakaran, D., Gottesman, O., Scott, J., Carey, J., Kwan, P., Grant, G., Smith, J. D., Neale, B. M., Purcell, S., Butterworth, A. S., Howson, J. M. M., Lee, H. M., Lu, Y., Kwak, S. H., Zhao, W., Danesh, J., Lam, V. K. L., Park, K. S., Saleheen, D., So, W. Y., Tam, C. H. T., Afzal, U., Aguilar, D., Arya, R., Aung, T., Chan, E., Navarro, C., Cheng, C. Y., Palli, D., Correa, A., Curran, J. E., Rybin, D., Farook, V. S., Fowler, S. P., Freedman, B. I., Griswold, M., Hale, D. E., Hicks, P. J., Khor, C. C., Kumar, S., Lehne, B., Thuillier, D., Lim, W. Y., Liu, J., Loh, M., Musani, S. K., Puppala, S., Scott, W. R., Yengo, L., Tan, S. T., Taylor, H. A., Thameem, F., Wilson, G., Wong, T. Y., Njølstad, P. R., Levy, J. C., Mangino, M., Bonnycastle, L. L., Schwarzmayr, T., Fadista, J., Surdulescu, G. L., Herder, C., Groves, C. J., Wieland, T., Bork-Jensen, J., Brandslund, I., Christensen, C., Koistinen, H. A., Doney, A. S. F., Kinnunen, L., Esko, T., Farmer, A. J., Hakaste, L., Hodgkiss, D., Kravic, J., Lyssenko, V., Hollensted, M., Jørgensen, M. E., Jørgensen, T., Ladenvall, C., Justesen, J. M., Käräjämäki, A., Kriebel, J., Rathmann, W., Lannfelt, L., Lauritzen, T., Narisu, N., Linneberg, A., Melander, O., Milani, L., Neville, M., Orho-Melander, M., Qi, L., Qi, Q., Roden, M., Rolandsson, O., Swift, A., Rosengren, A. H., Stirrups, K., Wood, A. R., Mihailov, E., Blancher, C., Carneiro, M. O., Maguire, J., Poplin, R., Shakir, K., Fennell, T., DePristo, M., de Angelis, M. H., Deloukas, P., Gjesing, A. P., Jun, G., Nilsson, P., Murphy, J., Onofrio, R., Thorand, B., Hansen, T., Meisinger, C., Hu, F. B., Isomaa, B., Karpe, F., Liang, L., Peters, A., Huth, C., O'Rahilly, S. P., Palmer, C. N. A., Pedersen, O., Rauramaa, R., Tuomilehto, J., Salomaa, V., Watanabe, R. M., Syvänen, A. C., Bergman, R. N., Bharadwaj, D., Bottinger, E. P., Cho, Y. S., Chandak, G. R., Chan, J. C. N., Chia, K. S., Daly, M. J., Ebrahim, S. B., Langenberg, C., Elliott, P., Jablonski, K. A., Lehman, D. M., Jia, W., Ma, R. C. W., Pollin, T. I., Sandhu, M., Tandon, N., Froguel, P., Barroso, I., Teo, Y. Y., Zeggini, E., Loos, R. J. F., Small, K. S., Ried, J. S., DeFronzo, R. A., Grallert, H., Glaser, B., Metspalu, A., Wareham, N. J., Walker, M., Banks, E., Gieger, C., Ingelsson, E., Im, H. K., Illig, T., Franks, P. W., Buck, G., Trakalo, J., Buck, D., Prokopenko, I., Mägi, R., Lind, L., Farjoun, Y., Owen, K. R., Gloyn, A. L., Strauch, K., Tuomi, T., Kooner, J. S., Lee, J. Y., Park, T., Donnelly, P., Morris, A. D., Hattersley, A. T., Bowden, D. W., Collins, F. S., Atzmon, G., Chambers, J. C., Spector, T. D., Laakso, M., Strom, T. M., Bell, G. I., Blangero, J., Duggirala, R., Tai, E. S., McVean, G., Hanis, C. L., Wilson, J. G., Seielstad, M., Frayling, T. M., Meigs, J. B., Cox, N. J., Sladek, R., Lander, E. S., Gabriel, S., Mohlke, K. L., Meitinger, T., Groop, L., Abecasis, G., Scott, L. J., Morris, A. P., Kang, H. M., Altshuler, D., Burtt, N. P., Florez, J. C., Boehnke, M., & McCarthy, M. I. (2018). Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Scientific data, 5180002. https://doi.org/10.1038/sdata.2018.2
Flannick, Jason, et al. "Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls." Scientific data vol. 5 (2018): 180002. doi: https://doi.org/10.1038/sdata.2018.2
Flannick J, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA, Thameem F, Wilson G, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JCN, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Mohlke KL, Meitinger T, Groop L, Abecasis G, Scott LJ, Morris AP, Kang HM, Altshuler D, Burtt NP, Florez JC, Boehnke M, McCarthy MI. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data. 2018 Jan 23;5:180002. doi: 10.1038/sdata.2018.2. PMID: 29360107; PMCID: PMC5779067.
Copy Download .nbib Format: NLM
Showing 1 to 12 of 12 entries