A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s disease
Abstract Background Missing data can complicate the interpretability of a clinical trial, especially if the proportion is substantial and if there are different, potentially outcome-dependent causes. Methods We aimed to obtain unbiased estimates, in the presence of a high level of missing data, for...
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| Format: | Article |
| Language: | English |
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BMC
2021-03-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-021-00801-y |
| _version_ | 1827770721323974656 |
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| author | Floor M. van Oudenhoven Sophie H. N. Swinkels Hilkka Soininen Miia Kivipelto Tobias Hartmann Dimitris Rizopoulos on behalf of the LipiDiDiet clinical study group |
| author_facet | Floor M. van Oudenhoven Sophie H. N. Swinkels Hilkka Soininen Miia Kivipelto Tobias Hartmann Dimitris Rizopoulos on behalf of the LipiDiDiet clinical study group |
| author_sort | Floor M. van Oudenhoven |
| collection | DOAJ |
| description | Abstract Background Missing data can complicate the interpretability of a clinical trial, especially if the proportion is substantial and if there are different, potentially outcome-dependent causes. Methods We aimed to obtain unbiased estimates, in the presence of a high level of missing data, for the intervention effects in a prodromal Alzheimer’s disease trial: the LipiDiDiet study. We used a competing risk joint model that can simultaneously model each patient’s longitudinal outcome trajectory in combination with the timing and type of missingness. Results Using the competing risk joint model, we were able to provide unbiased estimates of the intervention effects in the presence of the different types of missingness. For the LipiDiDiet study, the intervention effects remained statistically significant after this correction for the timing and type of missingness. Conclusion Missing data is a common problem in (Alzheimer) clinical trials. It is important to realize that statistical techniques make specific assumptions about the missing data mechanisms. When there are different missing data sources, a competing risk joint model is a powerful method because it can explicitly model the association between the longitudinal data and each type of missingness. Trial registration Dutch Trial Register, NTR1705 . Registered on 9 March 2009 |
| first_indexed | 2024-03-11T12:43:34Z |
| format | Article |
| id | doaj.art-401f49181aa54d9cb31a7b3753c39b6d |
| institution | Directory Open Access Journal |
| issn | 1758-9193 |
| language | English |
| last_indexed | 2024-03-11T12:43:34Z |
| publishDate | 2021-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj.art-401f49181aa54d9cb31a7b3753c39b6d2023-11-05T12:09:43ZengBMCAlzheimer’s Research & Therapy1758-91932021-03-0113111210.1186/s13195-021-00801-yA competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s diseaseFloor M. van Oudenhoven0Sophie H. N. Swinkels1Hilkka Soininen2Miia Kivipelto3Tobias Hartmann4Dimitris Rizopoulos5on behalf of the LipiDiDiet clinical study groupDepartment of Biostatistics, Erasmus Medical CenterDanone Nutricia ResearchDepartment of Neurology, Institute of Clinical Medicine, University of Eastern FinlandDepartment of Neurology, Institute of Clinical Medicine, University of Eastern FinlandDeutsches Institut für Demenz Prävention (DIDP), Medical Faculty, Saarland UniversityDepartment of Biostatistics, Erasmus Medical CenterAbstract Background Missing data can complicate the interpretability of a clinical trial, especially if the proportion is substantial and if there are different, potentially outcome-dependent causes. Methods We aimed to obtain unbiased estimates, in the presence of a high level of missing data, for the intervention effects in a prodromal Alzheimer’s disease trial: the LipiDiDiet study. We used a competing risk joint model that can simultaneously model each patient’s longitudinal outcome trajectory in combination with the timing and type of missingness. Results Using the competing risk joint model, we were able to provide unbiased estimates of the intervention effects in the presence of the different types of missingness. For the LipiDiDiet study, the intervention effects remained statistically significant after this correction for the timing and type of missingness. Conclusion Missing data is a common problem in (Alzheimer) clinical trials. It is important to realize that statistical techniques make specific assumptions about the missing data mechanisms. When there are different missing data sources, a competing risk joint model is a powerful method because it can explicitly model the association between the longitudinal data and each type of missingness. Trial registration Dutch Trial Register, NTR1705 . Registered on 9 March 2009https://doi.org/10.1186/s13195-021-00801-yAlzheimer’s diseaseProdromalJoint modelFortasynRandomized controlled trialDropout |
| spellingShingle | Floor M. van Oudenhoven Sophie H. N. Swinkels Hilkka Soininen Miia Kivipelto Tobias Hartmann Dimitris Rizopoulos on behalf of the LipiDiDiet clinical study group A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s disease Alzheimer’s Research & Therapy Alzheimer’s disease Prodromal Joint model Fortasyn Randomized controlled trial Dropout |
| title | A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s disease |
| title_full | A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s disease |
| title_fullStr | A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s disease |
| title_full_unstemmed | A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s disease |
| title_short | A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s disease |
| title_sort | competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal alzheimer s disease |
| topic | Alzheimer’s disease Prodromal Joint model Fortasyn Randomized controlled trial Dropout |
| url | https://doi.org/10.1186/s13195-021-00801-y |
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