Skip to main content
Top
Published in: BMC Medical Research Methodology 1/2019

Open Access 01-12-2019 | Alzheimer's Disease | Research article

Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect

Authors: Floor M. van Oudenhoven, Sophie H.N. Swinkels, Tobias Hartmann, Hilkka Soininen, Anneke M.J. van Hees, Dimitris Rizopoulos

Published in: BMC Medical Research Methodology | Issue 1/2019

Login to get access

Abstract

Background

Many prodromal Alzheimer’s disease trials collect two types of data: the time until clinical diagnosis of dementia and longitudinal patient information. These data are often analysed separately, although they are strongly associated. By combining the longitudinal and survival data into a single statistical model, joint models can account for the dependencies between the two types of data.

Methods

We illustrate the major steps in a joint modelling approach, motivated by data from a prodromal Alzheimer’s disease study: the LipiDiDiet trial.

Results

By using joint models we are able to disentangle baseline confounding from the intervention effect and moreover, to investigate the association between longitudinal patient information and the time until clinical dementia diagnosis.

Conclusions

Joint models provide a valuable tool in the statistical analysis of clinical studies with longitudinal and survival data, such as in prodromal Alzheimer’s disease trials, and have several added values compared to separate analyses.
Appendix
Available only for authorised users
Literature
1.
go back to reference Aisen PS, Cummings J, Jack CR, Morris JC, Sperling R, Frölich L, Jones RW, Dowsett SA, Matthews BR, Raskin J, et al.On the path to 2025: understanding the Alzheimer’s disease continuum. Alzheimer’s Res Ther. 2017; 9(1):60.CrossRef Aisen PS, Cummings J, Jack CR, Morris JC, Sperling R, Frölich L, Jones RW, Dowsett SA, Matthews BR, Raskin J, et al.On the path to 2025: understanding the Alzheimer’s disease continuum. Alzheimer’s Res Ther. 2017; 9(1):60.CrossRef
2.
go back to reference Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004; 256(3):183–94.PubMedCrossRef Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004; 256(3):183–94.PubMedCrossRef
3.
go back to reference Vaughan RM, Coen RF, Kenny R, Lawlor BA. Semantic and phonemic verbal fluency discrepancy in mild cognitive impairment: Potential predictor of progression to alzheimer’s disease. J Am Geriatr Soc. 2018; 66(4):755–9.PubMedCrossRef Vaughan RM, Coen RF, Kenny R, Lawlor BA. Semantic and phonemic verbal fluency discrepancy in mild cognitive impairment: Potential predictor of progression to alzheimer’s disease. J Am Geriatr Soc. 2018; 66(4):755–9.PubMedCrossRef
4.
go back to reference Munro CE, Donovan NJ, Amariglio RE, Papp KV, Marshall GA, Rentz DM, Pascual-Leone A, Sperling RA, Locascio JJ, Vannini P. The impact of awareness of and concern about memory performance on the prediction of progression from mild cognitive impairment to alzheimer disease dementia. Am J Geriatr Psychiatr. 2018; 26(8):896–904.CrossRef Munro CE, Donovan NJ, Amariglio RE, Papp KV, Marshall GA, Rentz DM, Pascual-Leone A, Sperling RA, Locascio JJ, Vannini P. The impact of awareness of and concern about memory performance on the prediction of progression from mild cognitive impairment to alzheimer disease dementia. Am J Geriatr Psychiatr. 2018; 26(8):896–904.CrossRef
5.
go back to reference Baldeiras I, Santana I, Leitão MJ, Gens H, Pascoal R, Tábuas-Pereira M, Beato-Coelho J, Duro D, Almeida MR, Oliveira CR. Addition of the A β42/40 ratio to the cerebrospinal fluid biomarker profile increases the predictive value for underlying alzheimer’s disease dementia in mild cognitive impairment. Alzheimer’s Res Ther. 2018; 10(1):33.CrossRef Baldeiras I, Santana I, Leitão MJ, Gens H, Pascoal R, Tábuas-Pereira M, Beato-Coelho J, Duro D, Almeida MR, Oliveira CR. Addition of the A β42/40 ratio to the cerebrospinal fluid biomarker profile increases the predictive value for underlying alzheimer’s disease dementia in mild cognitive impairment. Alzheimer’s Res Ther. 2018; 10(1):33.CrossRef
6.
go back to reference Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1):330–9.PubMedCrossRef Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1):330–9.PubMedCrossRef
7.
go back to reference Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000; 1(4):465–80.PubMedCrossRef Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000; 1(4):465–80.PubMedCrossRef
8.
go back to reference Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Stat Sin. 2004; 14(3):809–34. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Stat Sin. 2004; 14(3):809–34.
9.
go back to reference Rizopoulos D. Joint Models for Longitudinal and Time-to-event Data: With Applications in R. Boca Raton: Chapman and Hall/CRC; 2012.CrossRef Rizopoulos D. Joint Models for Longitudinal and Time-to-event Data: With Applications in R. Boca Raton: Chapman and Hall/CRC; 2012.CrossRef
10.
go back to reference Sweeting MJ, Thompson SG. Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture. Biom J. 2011; 53(5):750–63.PubMedPubMedCentralCrossRef Sweeting MJ, Thompson SG. Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture. Biom J. 2011; 53(5):750–63.PubMedPubMedCentralCrossRef
11.
go back to reference Prentice R. Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika. 1982; 69(2):331–42.CrossRef Prentice R. Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika. 1982; 69(2):331–42.CrossRef
12.
go back to reference Dafni UG, Tsiatis AA. Evaluating surrogate markers of clinical outcome when measured with error. Biometrics. 1998; 54(4):1445–62.PubMedCrossRef Dafni UG, Tsiatis AA. Evaluating surrogate markers of clinical outcome when measured with error. Biometrics. 1998; 54(4):1445–62.PubMedCrossRef
13.
go back to reference Tsiatis AA, Davidian M. A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error. Biometrika. 2001; 88(2):447–58.CrossRef Tsiatis AA, Davidian M. A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error. Biometrika. 2001; 88(2):447–58.CrossRef
14.
go back to reference Li K, Luo S. Functional joint model for longitudinal and time-to-event data: an application to Alzheimer’s disease. Stat Med. 2017; 36(22):3560–72.PubMedPubMedCentralCrossRef Li K, Luo S. Functional joint model for longitudinal and time-to-event data: an application to Alzheimer’s disease. Stat Med. 2017; 36(22):3560–72.PubMedPubMedCentralCrossRef
15.
go back to reference Yu B, Ghosh P. Joint modeling for cognitive trajectory and risk of dementia in the presence of death. Biometrics. 2010; 66(1):294–300.PubMedCrossRef Yu B, Ghosh P. Joint modeling for cognitive trajectory and risk of dementia in the presence of death. Biometrics. 2010; 66(1):294–300.PubMedCrossRef
16.
go back to reference Dantan E, Joly P, Dartigues J-F, Jacqmin-Gadda H. Joint model with latent state for longitudinal and multistate data. Biostatistics. 2011; 12(4):723–36.PubMedCrossRef Dantan E, Joly P, Dartigues J-F, Jacqmin-Gadda H. Joint model with latent state for longitudinal and multistate data. Biostatistics. 2011; 12(4):723–36.PubMedCrossRef
17.
go back to reference Proust-Lima C, Dartigues J-F, Jacqmin-Gadda H. Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach. Stat Med. 2016; 35(3):382–98.PubMedCrossRef Proust-Lima C, Dartigues J-F, Jacqmin-Gadda H. Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach. Stat Med. 2016; 35(3):382–98.PubMedCrossRef
18.
19.
go back to reference Ibrahim JG, Chen M-H, Sinha D. Bayesian methods for joint modeling of longitudinal and survival data with applications to cancer vaccine trials. Stat Sin. 2004; 14(3):863–83. Ibrahim JG, Chen M-H, Sinha D. Bayesian methods for joint modeling of longitudinal and survival data with applications to cancer vaccine trials. Stat Sin. 2004; 14(3):863–83.
20.
go back to reference Crowther MJ, Lambert PC, Abrams KR. Adjusting for measurement error in baseline prognostic biomarkers included in a time-to-event analysis: a joint modelling approach. BMC Med Res Methodol. 2013; 13(1):146.PubMedPubMedCentralCrossRef Crowther MJ, Lambert PC, Abrams KR. Adjusting for measurement error in baseline prognostic biomarkers included in a time-to-event analysis: a joint modelling approach. BMC Med Res Methodol. 2013; 13(1):146.PubMedPubMedCentralCrossRef
21.
go back to reference Andrinopoulou E-R, Rizopoulos D, Jin R, Bogers AJ, Lesaffre E, Takkenberg JJ. An introduction to mixed models and joint modeling: analysis of valve function over time. Ann Thorac Surg. 2012; 93(6):1765–72.PubMedCrossRef Andrinopoulou E-R, Rizopoulos D, Jin R, Bogers AJ, Lesaffre E, Takkenberg JJ. An introduction to mixed models and joint modeling: analysis of valve function over time. Ann Thorac Surg. 2012; 93(6):1765–72.PubMedCrossRef
22.
go back to reference Soininen H, Solomon A, Visser PJ, Hendrix SB, Blennow K, Kivipelto M, Hartmann T, Hallikainen I, Hallikainen M, Helisalmi S, et al.24-month intervention with a specific multinutrient in people with prodromal alzheimer’s disease (lipiDiDiet): a randomised, double-blind, controlled trial. Lancet Neurol. 2017; 16(12):965–75.PubMedPubMedCentralCrossRef Soininen H, Solomon A, Visser PJ, Hendrix SB, Blennow K, Kivipelto M, Hartmann T, Hallikainen I, Hallikainen M, Helisalmi S, et al.24-month intervention with a specific multinutrient in people with prodromal alzheimer’s disease (lipiDiDiet): a randomised, double-blind, controlled trial. Lancet Neurol. 2017; 16(12):965–75.PubMedPubMedCentralCrossRef
23.
go back to reference de Wilde MC, Vellas B, Girault E, Yavuz AC, Sijben JW. Lower brain and blood nutrient status in alzheimer’s disease: results from meta-analyses. Alzheimer’s Dement: Transl Res Clin Interv. 2017; 3(3):416–31. de Wilde MC, Vellas B, Girault E, Yavuz AC, Sijben JW. Lower brain and blood nutrient status in alzheimer’s disease: results from meta-analyses. Alzheimer’s Dement: Transl Res Clin Interv. 2017; 3(3):416–31.
24.
go back to reference McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack Jr CR, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, et al.The diagnosis of dementia due to alzheimer’s disease: Recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011; 7(3):263–9.CrossRef McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack Jr CR, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, et al.The diagnosis of dementia due to alzheimer’s disease: Recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011; 7(3):263–9.CrossRef
25.
go back to reference Verbeke G, Molenberghs G. Linear Mixed Models for Longitudinal Data. New York: Springer; 2000. Verbeke G, Molenberghs G. Linear Mixed Models for Longitudinal Data. New York: Springer; 2000.
26.
go back to reference Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G. Longitudinal Data Analysis. Boca Raton: CRC press; 2008.CrossRef Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G. Longitudinal Data Analysis. Boca Raton: CRC press; 2008.CrossRef
27.
go back to reference Brown ER, Ibrahim JG, DeGruttola V. A flexible B-spline model for multiple longitudinal biomarkers and survival. Biometrics. 2005; 61(1):64–73.PubMedCrossRef Brown ER, Ibrahim JG, DeGruttola V. A flexible B-spline model for multiple longitudinal biomarkers and survival. Biometrics. 2005; 61(1):64–73.PubMedCrossRef
28.
go back to reference Cox DR. Regression models and life-tables. J R Stat Soc Ser B Methodol. 1972; 34(2):187–202. Cox DR. Regression models and life-tables. J R Stat Soc Ser B Methodol. 1972; 34(2):187–202.
29.
go back to reference Klein JP, Moeschberger ML. Survival Analysis: Techniques for Censored and Truncated Data. New York: Springer; 2006. Klein JP, Moeschberger ML. Survival Analysis: Techniques for Censored and Truncated Data. New York: Springer; 2006.
30.
go back to reference Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York: Springer; 2013. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York: Springer; 2013.
31.
go back to reference Tseng Y-K, Hsieh F, Wang J-L. Joint modelling of accelerated failure time and longitudinal data. Biometrika. 2005; 92(3):587–603.CrossRef Tseng Y-K, Hsieh F, Wang J-L. Joint modelling of accelerated failure time and longitudinal data. Biometrika. 2005; 92(3):587–603.CrossRef
32.
go back to reference Rizopoulos D. JM: An R package for the joint modelling of longitudinal and time-to-event data. J Stat Softw Online. 2010; 35(9):1–33. Rizopoulos D. JM: An R package for the joint modelling of longitudinal and time-to-event data. J Stat Softw Online. 2010; 35(9):1–33.
Metadata
Title
Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect
Authors
Floor M. van Oudenhoven
Sophie H.N. Swinkels
Tobias Hartmann
Hilkka Soininen
Anneke M.J. van Hees
Dimitris Rizopoulos
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2019
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-019-0791-z

Other articles of this Issue 1/2019

BMC Medical Research Methodology 1/2019 Go to the issue