array(41) {
  ["project_title"]=>
  string(81) "Optimizing Trial design to Achieve Personalized prevention of Alzheimer?s disease"
  ["project_narrative_summary"]=>
  string(553) "We aim to improve trial design with the ultimate objective to achieve a future of effective and efficient personalized prevention of AD. To achieve this goal, we will identify subgroups of patients responding to specific compounds in existing trial data sets of patients with early AD.
We will evaluate disease course over time in terms of (i) clinical progression to MCI or dementia, (ii) cognitive decline over time in different cognitive domains, (iii) functional decline (instrumental activities of daily living), and (iv) behavioral outcomes." ["project_learn_source"]=> string(11) "data_holder" ["project_learn_source_exp"]=> string(0) "" ["project_key_personnel"]=> array(4) { [0]=> array(6) { ["p_pers_f_name"]=> string(5) "W. M." ["p_pers_l_name"]=> string(13) "van der Flier" ["p_pers_degree"]=> string(9) "Prof. dr." ["p_pers_pr_affil"]=> string(88) "Full professor, department of Neurology and department of Epidemiology and Data Science." ["p_pers_scop_id"]=> string(0) "" ["requires_data_access"]=> string(0) "" } [1]=> array(6) { ["p_pers_f_name"]=> string(2) "S." ["p_pers_l_name"]=> string(6) "Sikkes" ["p_pers_degree"]=> string(2) "Dr" ["p_pers_pr_affil"]=> string(152) "Assistant professor, VU University Medical Center Amsterdam, VUmc Alzheimer Center, Department of Neurology / Department of Epidemiology & Biostatistics" ["p_pers_scop_id"]=> string(0) "" ["requires_data_access"]=> string(0) "" } [2]=> array(6) { ["p_pers_f_name"]=> string(6) "E.G.B." ["p_pers_l_name"]=> string(10) "Vijverberg" ["p_pers_degree"]=> string(16) "Neurologist, dr." ["p_pers_pr_affil"]=> string(53) "Neurologist/Principal Investigator, Senior researcher" ["p_pers_scop_id"]=> string(0) "" ["requires_data_access"]=> string(0) "" } [3]=> array(6) { ["p_pers_f_name"]=> string(2) "L." ["p_pers_l_name"]=> string(9) "Ottenhoff" ["p_pers_degree"]=> string(3) "Msc" ["p_pers_pr_affil"]=> string(33) "neuropsychologist and PhD student" ["p_pers_scop_id"]=> string(0) "" ["requires_data_access"]=> string(0) "" } } ["project_ext_grants"]=> array(2) { ["value"]=> string(65) "External grants or funds are being used to support this research." ["label"]=> string(65) "External grants or funds are being used to support this research." } ["project_funding_source"]=> string(83) "Health~Holland, Top Sector Life Sciences & Health/ Brain Research Center, Amsterdam" ["project_assoc_trials"]=> array(2) { [0]=> object(WP_Post)#4629 (24) { ["ID"]=> int(1847) ["post_author"]=> string(4) "1363" ["post_date"]=> string(19) "2019-12-12 12:58:00" ["post_date_gmt"]=> string(19) "2019-12-12 12:58:00" ["post_content"]=> string(0) "" ["post_title"]=> string(251) "NCT00575055 - A Phase 3, Multicenter, Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Efficacy and Safety Trial of Bapineuzumab (AAB-001, ELN115727) In Patients With Mild to Moderate Alzheimer's Disease Who Are Apolipoprotein E4 Carriers" ["post_excerpt"]=> string(0) "" ["post_status"]=> string(7) "publish" ["comment_status"]=> string(4) "open" ["ping_status"]=> string(4) "open" ["post_password"]=> string(0) "" ["post_name"]=> string(189) "nct00575055-a-phase-3-multicenter-randomized-double-blind-placebo-controlled-parallel-group-efficacy-and-safety-trial-of-bapineuzumab-aab-001-eln115727-in-patients-with-mild-to-moderate-alz" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2023-02-06 13:28:23" ["post_modified_gmt"]=> string(19) "2023-02-06 13:28:23" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(238) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00575055-a-phase-3-multicenter-randomized-double-blind-placebo-controlled-parallel-group-efficacy-and-safety-trial-of-bapineuzumab-aab-001-eln115727-in-patients-with-mild-to-moderate-alz/" ["menu_order"]=> int(0) ["post_type"]=> string(14) "clinical_trial" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } [1]=> object(WP_Post)#4628 (24) { ["ID"]=> int(1848) ["post_author"]=> string(4) "1363" ["post_date"]=> string(19) "2019-12-12 13:04:00" ["post_date_gmt"]=> string(19) "2019-12-12 13:04:00" ["post_content"]=> string(0) "" ["post_title"]=> string(256) "NCT00574132 - A Phase 3, Multicenter, Randomized, Double-Blind, Placebo-Controlled, Parallel-Group, Efficacy and Safety Trial of Bapineuzumab (AAB-001, ELN115727) In Patients With Mild to Moderate Alzheimer's Disease Who Are Apolipoprotein E4 Non- Carriers" ["post_excerpt"]=> string(0) "" ["post_status"]=> string(7) "publish" ["comment_status"]=> string(4) "open" ["ping_status"]=> string(4) "open" ["post_password"]=> string(0) "" ["post_name"]=> string(189) "nct00574132-a-phase-3-multicenter-randomized-double-blind-placebo-controlled-parallel-group-efficacy-and-safety-trial-of-bapineuzumab-aab-001-eln115727-in-patients-with-mild-to-moderate-alz" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2023-02-06 13:28:23" ["post_modified_gmt"]=> string(19) "2023-02-06 13:28:23" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(238) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00574132-a-phase-3-multicenter-randomized-double-blind-placebo-controlled-parallel-group-efficacy-and-safety-trial-of-bapineuzumab-aab-001-eln115727-in-patients-with-mild-to-moderate-alz/" ["menu_order"]=> int(0) ["post_type"]=> string(14) "clinical_trial" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } } ["project_date_type"]=> string(91) "Individual Participant-Level Data, which includes Full CSR and all supporting documentation" ["property_scientific_abstract"]=> string(1617) "Background: With more than 40 million worldwide, Alzheimer disease (AD) is among the largest health care challenges of our century. However, curative therapy is not yet available. This may be due to a number of factors. Trials should focus on pre-dementia stage, trials need to evaluate different mechanism-based approaches as well and inclusion criteria do not reflect the mode of action of specific drugs and outcome measures lack sensitivity.
Objective: We aim to improve trial design with the ultimate objective to achieve a future of effective and efficient personalized prevention of AD.
Study Design: meta analyses
Participants: We will identify subgroups of patients responding to specific compounds in existing trial data sets of patients with early AD. We want to use trial data sets trials in early AD (prodromal/ early dementia)
Main Outcome Measure: We will evaluate disease course over time in terms of (i) clinical progression to MCI or dementia, (ii) cognitive decline over time in different cognitive domains, (iii) functional decline (instrumental activities of daily living), and (iv) behavioral outcomes.
Statistical Analysis. We will define ?positive response? based on the primary and secondary outcome measures in each of the trial data sets. In the first step, we will define responders using different approaches; early endpoints (biomarker, cognitive and functional improvement/stabilization (primary cognitive outcome measure). Second; change of cognitive status, i.e. clinical progression to MCI; change of cognitive status, i.e. clinical progression to AD." ["project_brief_bg"]=> string(2017) "With more than 250.000 patients in the Netherlands and more than 40 million worldwide, Alzheimer disease (AD) is among the largest health care challenges of our century. However, curative therapy is not yet available. This may be due to a number of factors, that are slowly becoming clear as our understanding of the disease grows.
First, AD develops gradually, in the course of decades. Studies using biomarkers (Amyloid or Tau) and imaging (MRI or PET) have shown that brain changes associated with AD are present until 20 years before clinical manifestation of the disease. The stage of dementia is too late to reverse the brain damage which has accumulated over the decades before. This novel knowledge implies that trials should focus on pre-dementia stage, and hence that future treatment strategies for AD will have the form of secondary prevention.
Second, AD is a complex, diverse disease. Most drugs tested have focused on the amyloid pathway. It could be that amyloid is simply the wrong target. While this notion cannot be excluded, literature strongly supports an important role for amyloid in onset and progression of the disease. Nonetheless, it is essential to select the right patients most likely to benefit from anti-amyloid therapy with the right mode of action. In addition, it is increasingly recognized that amyloid does not explain the disease in its entirety. Therefore, trials need to evaluate different mechanism-based approaches as well, e.g. anti-tau with active or passive immunization, anti-inflammatory drugs and neuroprotective compounds, and we should find out which patients benefit most from with strategy.
Finally, taking and into account, one realizes that trial designs have been too crude; inclusion criteria do not reflect the mode of action of specific drugs and outcome measures lack sensitivity. To bring closer a future of personalized prevention of AD, we need to focus on early, pre-dementia disease stages, taking into account de diverse patient group" ["project_specific_aims"]=> string(553) "We aim to improve trial design with the ultimate objective to achieve a future of effective and efficient personalized prevention of AD. To achieve this goal, we will identify subgroups of patients responding to specific compounds in existing trial data sets of patients with early AD.
We will evaluate disease course over time in terms of (i) clinical progression to MCI or dementia, (ii) cognitive decline over time in different cognitive domains, (iii) functional decline (instrumental activities of daily living), and (iv) behavioral outcomes." ["project_study_design"]=> string(0) "" ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(7) { [0]=> array(2) { ["value"]=> string(69) "Meta-analysis using data from the YODA Project and other data sources" ["label"]=> string(69) "Meta-analysis using data from the YODA Project and other data sources" } [1]=> array(2) { ["value"]=> string(36) "Participant-level data meta-analysis" ["label"]=> string(36) "Participant-level data meta-analysis" } [2]=> array(2) { ["value"]=> string(69) "Meta-analysis using data from the YODA Project and other data sources" ["label"]=> string(69) "Meta-analysis using data from the YODA Project and other data sources" } [3]=> array(2) { ["value"]=> string(37) "Develop or refine statistical methods" ["label"]=> string(37) "Develop or refine statistical methods" } [4]=> array(2) { ["value"]=> string(34) "Research on clinical trial methods" ["label"]=> string(34) "Research on clinical trial methods" } [5]=> array(2) { ["value"]=> string(28) "Research on comparison group" ["label"]=> string(28) "Research on comparison group" } [6]=> array(2) { ["value"]=> string(50) "Research on clinical prediction or risk prediction" ["label"]=> string(50) "Research on clinical prediction or risk prediction" } } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> array(2) { ["value"]=> string(86) "not_analyzing_participant_level_data__plan_to_use_another_secure_data_sharing_platform" ["label"]=> string(92) "I am not analyzing participant-level data / plan to use another secure data sharing platform" } ["project_software_used_exp"]=> string(9) "Vivli.org" ["project_research_methods"]=> string(863) "For this project, we want to use trial data sets trials in early AD (prodromal/ early dementia) which have recently been finalized. We would like to include data sets of trials using compounds with different modes of action, in particular (i) anti-amyloid, (ii) anti-inflammation, and (iii) neuroprotective treatment strategies. We?d like to use trial data sets which include patients with MCI due to AD or mild AD from 50-90 years. Mini-Mental State Examination (MMSE) score range from 18 to 28. Inclusion criteria further comprise evidence of amyloid pathology by Amyloid PET determined by visual inspection or based on concentration of abeta 1-42 in cerebrospinal fluid derived by lumbar puncture (if available).
NCT01424436
NCT00459550
NCT02423200
NCT01739348
NCT01953601
NCT00762411
NCT00594568
NCT02337907" ["project_main_outcome_measure"]=> string(291) "We will define ?positive response? based on the primary and secondary outcome measures in each of the trial data sets. We will than make a codebook of existing variables, including demographic variables, clinical data, genetic markers and biomarker values available in each of the data sets." ["project_main_predictor_indep"]=> string(629) "In the first step, we will define responders using different approaches:
1) early endpoints:
a) biomarker improvement/stabilization;
b) cognitive improvement/stabilization (primary cognitive outcome measure),
c) cognitive improvement/stabilization and functional improvement/stabilization (primary cognitive & functional outcome measures),
2) late endpoints:
a) change of cognitive status, i.e. clinical progression to MCI;
b) change of cognitive status, i.e. clinical progression to AD dementia
we will define studies on their target: anti-amyloid, anti-tau and anti inflammation." ["project_other_variables_interest"]=> string(826) "The table provides an overview of data that may be available.
? Demographic variables (age, sex, education)
? Diagnostic markers (Abeta 1-42, Tau, P- tau).
? Safety parameters (vital signs, blood pressure, heart rate)
? Physical examination
? Neurological examination
? Brain MRI (atrophy, white matter hyperintensities, microbleeds)
? Neuropsychological outcomes (tests for memory, attention, executive functions)
? Instrumental activities of daily living
? Genetic markers (e.g. APOE)
? Exploratory biomarkers such as
o QC enzyme.
o Panel of Abeta peptide versions of various length (X-40/42).
o Panel for pGluAbeta and its substrates Abeta 3-40/42 and 11-40/42.
o Panel of Abeta-Oligomers of different a length.
o Neurofilament light" ["project_stat_analysis_plan"]=> string(1547) "In two steps, we aim to identify a combination of patient characteristics (demographic, clinical, biomarker) associated with a positive response to treatment.
In the first step, we will define responders using different approaches:
1) early endpoints:
a) biomarker improvement/stabilization;
b) cognitive improvement/stabilization (primary cognitive outcome measure),
c) cognitive improvement/stabilization and functional improvement/stabilization (primary cognitive & functional outcome measures),
2) late endpoints:
a) change of cognitive status, i.e. clinical progression to Mild Cognitive Impairment (MCI);
b) change of cognitive status, i.e. clinical progression to AD dementia
Different responder definitions (1abc and 2ab) will be used to dichotomize treatment response. In the second step, we will model treatment effect, by applying a Causal Forest machine learning model. This predictive technique is a flexible and powerful predictor, in particular when higher order interactions are expected [Wager and Athey, 2018]. Using this model, we can model treatment-specific effects of medication. The following characteristics will be included in the model: age, sex, disease status, ethnicity, presence or absence of co-morbidities, baseline cognitive status, biomarkers of tau and amyloid. Based on these analyses, we will develop new knowledge on which combination of patient characteristics (demographic, clinical, biomarker) predisposes for a treatment effect to which type of drugs." ["project_timeline"]=> string(403) "We planned one year in total for preparing data sets, and one year to do (responder) analyses.
Task 1: Trial data sets ready for analysis (M7)
Expected: Q3 2021
Task 2: Variables for patient stratification identified (M24)
Expected: Q3 2021
Task 3: date of data analysis completion
Expected Q1/2 2022
Task 3: date of manuscript completion
Expected Q2 2022" ["project_dissemination_plan"]=> string(182) "This report can guide pharmaceutical companies and CRO?s in the effective and efficient design of prevention trials.
Alzheimers Research & Therapy
Alzheimer's & Dementia" ["project_bibliography"]=> string(1560) "

1. Organization WH. Dementia: a public health priority, ISBN 978 92 4 156445 8 ed2012.
2. Bateman RJ, Xiong C, Benzinger TL, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 2012;367:795-804.
3. Scheltens P, Blennow K, Breteler MM, et al. Alzheimer’s disease. Lancet 2016;388:505-517.
4. Gilman S, Koller M, Black RS, et al. Clinical effects of Abeta immunization (AN1792) in patients with AD in an interrupted trial. Neurology 2005;64:1553-1562.
5. Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med 2014;370:322-333.
6. Jansen WJ, Ossenkoppele R, Knol DL, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA 2015;313:1924-1938.
7. Sevigny J, Chiao P, Bussiere T, et al. The antibody aducanumab reduces Abeta plaques in Alzheimer’s disease. Nature 2016;537:50-56.
8. Slot RER, Verfaillie SCJ, Overbeek JM, et al. Subjective Cognitive Impairment Cohort (SCIENCe): study design and first results. Alzheimers Res Ther 2018;10:76.
9. Jessen F, Amariglio RE, van BM, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement 2014;10:844-852.
10. Slot RER, Sikkes SAM, Berkhof J, et al. Subjective cognitive decline and rates of incident Alzheimer’s disease and non-Alzheimer’s disease dementia. Alzheimers Dement 2018.

" ["project_suppl_material"]=> bool(false) ["project_coi"]=> array(5) { [0]=> array(1) { ["file_coi"]=> array(21) { ["ID"]=> int(9964) ["id"]=> int(9964) ["title"]=> string(49) "yoda_project_coi_form_for_data_requestors_2019_jv" ["filename"]=> string(53) "yoda_project_coi_form_for_data_requestors_2019_jv.pdf" ["filesize"]=> int(77943) ["url"]=> string(102) "https://yoda.yale.edu/wp-content/uploads/2020/02/yoda_project_coi_form_for_data_requestors_2019_jv.pdf" ["link"]=> string(95) "https://yoda.yale.edu/data-request/2021-4567/yoda_project_coi_form_for_data_requestors_2019_jv/" ["alt"]=> string(0) "" ["author"]=> string(4) "1363" ["description"]=> string(0) "" ["caption"]=> string(0) "" ["name"]=> string(49) "yoda_project_coi_form_for_data_requestors_2019_jv" ["status"]=> string(7) "inherit" ["uploaded_to"]=> int(5396) ["date"]=> string(19) "2023-07-31 16:05:20" ["modified"]=> string(19) "2023-08-01 01:02:07" ["menu_order"]=> int(0) ["mime_type"]=> string(15) "application/pdf" ["type"]=> string(11) "application" ["subtype"]=> string(3) "pdf" ["icon"]=> string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png" } } [1]=> array(1) { ["file_coi"]=> array(21) { ["ID"]=> int(9970) ["id"]=> int(9970) ["title"]=> string(49) "yoda_project_coi_form_for_data_requestors_2019_lo" ["filename"]=> string(53) "yoda_project_coi_form_for_data_requestors_2019_lo.pdf" ["filesize"]=> int(233150) ["url"]=> string(102) "https://yoda.yale.edu/wp-content/uploads/2019/12/yoda_project_coi_form_for_data_requestors_2019_lo.pdf" ["link"]=> string(95) "https://yoda.yale.edu/data-request/2021-4567/yoda_project_coi_form_for_data_requestors_2019_lo/" ["alt"]=> string(0) "" ["author"]=> string(4) "1363" ["description"]=> string(0) "" ["caption"]=> string(0) "" ["name"]=> string(49) "yoda_project_coi_form_for_data_requestors_2019_lo" ["status"]=> string(7) "inherit" ["uploaded_to"]=> int(5396) ["date"]=> string(19) "2023-07-31 16:05:35" ["modified"]=> string(19) "2023-08-01 01:02:07" ["menu_order"]=> int(0) ["mime_type"]=> string(15) "application/pdf" ["type"]=> string(11) "application" ["subtype"]=> string(3) "pdf" ["icon"]=> string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png" } } [2]=> array(1) { ["file_coi"]=> array(21) { ["ID"]=> int(10011) ["id"]=> int(10011) ["title"]=> string(60) "yoda_project_coi_form_for_data_requestors_2019_van_der_flier" ["filename"]=> string(64) "yoda_project_coi_form_for_data_requestors_2019_van_der_flier.pdf" ["filesize"]=> int(329281) ["url"]=> string(113) "https://yoda.yale.edu/wp-content/uploads/2020/03/yoda_project_coi_form_for_data_requestors_2019_van_der_flier.pdf" ["link"]=> string(106) "https://yoda.yale.edu/data-request/2021-4567/yoda_project_coi_form_for_data_requestors_2019_van_der_flier/" ["alt"]=> string(0) "" ["author"]=> string(4) "1363" ["description"]=> string(0) "" ["caption"]=> string(0) "" ["name"]=> string(60) "yoda_project_coi_form_for_data_requestors_2019_van_der_flier" ["status"]=> string(7) "inherit" ["uploaded_to"]=> int(5396) ["date"]=> string(19) "2023-07-31 16:07:36" ["modified"]=> string(19) "2023-08-01 01:02:07" ["menu_order"]=> int(0) ["mime_type"]=> string(15) "application/pdf" ["type"]=> string(11) "application" ["subtype"]=> string(3) "pdf" ["icon"]=> string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png" } } [3]=> array(1) { ["file_coi"]=> array(21) { ["ID"]=> int(9996) ["id"]=> int(9996) ["title"]=> string(51) "yoda_project_coi_form_for_data_requestors_2019_sams" ["filename"]=> string(55) "yoda_project_coi_form_for_data_requestors_2019_sams.pdf" ["filesize"]=> int(349770) ["url"]=> string(104) "https://yoda.yale.edu/wp-content/uploads/2016/02/yoda_project_coi_form_for_data_requestors_2019_sams.pdf" ["link"]=> string(97) "https://yoda.yale.edu/data-request/2021-4567/yoda_project_coi_form_for_data_requestors_2019_sams/" ["alt"]=> string(0) "" ["author"]=> string(4) "1363" ["description"]=> string(0) "" ["caption"]=> string(0) "" ["name"]=> string(51) "yoda_project_coi_form_for_data_requestors_2019_sams" ["status"]=> string(7) "inherit" ["uploaded_to"]=> int(5396) ["date"]=> string(19) "2023-07-31 16:06:49" ["modified"]=> string(19) "2023-08-01 01:02:07" ["menu_order"]=> int(0) ["mime_type"]=> string(15) "application/pdf" ["type"]=> string(11) "application" ["subtype"]=> string(3) "pdf" ["icon"]=> string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png" } } [4]=> array(1) { ["file_coi"]=> array(21) { ["ID"]=> int(9997) ["id"]=> int(9997) ["title"]=> string(54) "yoda_project_coi_form_for_data_requestors_2019_sams2.0" ["filename"]=> string(58) "yoda_project_coi_form_for_data_requestors_2019_sams2.0.pdf" ["filesize"]=> int(355102) ["url"]=> string(107) "https://yoda.yale.edu/wp-content/uploads/2016/01/yoda_project_coi_form_for_data_requestors_2019_sams2.0.pdf" ["link"]=> string(100) "https://yoda.yale.edu/data-request/2021-4567/yoda_project_coi_form_for_data_requestors_2019_sams2-0/" ["alt"]=> string(0) "" ["author"]=> string(4) "1363" ["description"]=> string(0) "" ["caption"]=> string(0) "" ["name"]=> string(54) "yoda_project_coi_form_for_data_requestors_2019_sams2-0" ["status"]=> string(7) "inherit" ["uploaded_to"]=> int(5396) ["date"]=> string(19) "2023-07-31 16:06:52" ["modified"]=> string(19) "2023-08-01 01:02:07" ["menu_order"]=> int(0) ["mime_type"]=> string(15) "application/pdf" ["type"]=> string(11) "application" ["subtype"]=> string(3) "pdf" ["icon"]=> string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png" } } } ["data_use_agreement_training"]=> bool(true) ["certification"]=> bool(true) ["project_send_email_updates"]=> bool(true) ["project_status"]=> string(7) "ongoing" ["project_publ_available"]=> bool(true) ["project_year_access"]=> string(4) "2021" ["project_rep_publ"]=> array(1) { [0]=> array(1) { ["publication_link"]=> string(0) "" } } ["project_assoc_data"]=> array(0) { } ["project_due_dil_assessment"]=> bool(false) ["project_title_link"]=> array(21) { ["ID"]=> int(10920) ["id"]=> int(10920) ["title"]=> string(41) "yoda_project_protocol_2021-4567_-21-05-19" ["filename"]=> string(45) "yoda_project_protocol_2021-4567_-21-05-19.pdf" ["filesize"]=> int(27279) ["url"]=> string(94) "https://yoda.yale.edu/wp-content/uploads/2023/08/yoda_project_protocol_2021-4567_-21-05-19.pdf" ["link"]=> string(87) "https://yoda.yale.edu/data-request/2021-4567/yoda_project_protocol_2021-4567_-21-05-19/" ["alt"]=> string(0) "" ["author"]=> string(4) "1363" ["description"]=> string(0) "" ["caption"]=> string(0) "" ["name"]=> string(41) "yoda_project_protocol_2021-4567_-21-05-19" ["status"]=> string(7) "inherit" ["uploaded_to"]=> int(5396) ["date"]=> string(19) "2023-08-09 17:17:25" ["modified"]=> string(19) "2023-08-09 19:15:46" ["menu_order"]=> int(0) ["mime_type"]=> string(15) "application/pdf" ["type"]=> string(11) "application" ["subtype"]=> string(3) "pdf" ["icon"]=> string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png" } ["project_review_link"]=> array(21) { ["ID"]=> int(10632) ["id"]=> int(10632) ["title"]=> string(36) "yoda_project_review_-_2021-4567_site" ["filename"]=> string(40) "yoda_project_review_-_2021-4567_site.pdf" ["filesize"]=> int(1313219) ["url"]=> string(89) "https://yoda.yale.edu/wp-content/uploads/2023/08/yoda_project_review_-_2021-4567_site.pdf" ["link"]=> string(82) "https://yoda.yale.edu/data-request/2021-4567/yoda_project_review_-_2021-4567_site/" ["alt"]=> string(0) "" ["author"]=> string(4) "1363" ["description"]=> string(0) "" ["caption"]=> string(0) "" ["name"]=> string(36) "yoda_project_review_-_2021-4567_site" ["status"]=> string(7) "inherit" ["uploaded_to"]=> int(5396) ["date"]=> string(19) "2023-08-09 17:05:21" ["modified"]=> string(19) "2023-08-09 19:15:46" ["menu_order"]=> int(0) ["mime_type"]=> string(15) "application/pdf" ["type"]=> string(11) "application" ["subtype"]=> string(3) "pdf" ["icon"]=> string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png" } ["project_highlight_button"]=> string(0) "" ["search_order"]=> string(5) "-7540" } data partner
array(1) { [0]=> string(15) "johnson-johnson" }

pi country
array(1) { [0]=> string(15) "The Netherlands" }

pi affil
array(1) { [0]=> string(8) "Academia" }

products
array(0) { }

num of trials
array(1) { [0]=> string(1) "2" }

res
array(1) { [0]=> string(1) "3" }

2021-4567

Research Proposal

Project Title: Optimizing Trial design to Achieve Personalized prevention of Alzheimer?s disease

Scientific Abstract: Background: With more than 40 million worldwide, Alzheimer disease (AD) is among the largest health care challenges of our century. However, curative therapy is not yet available. This may be due to a number of factors. Trials should focus on pre-dementia stage, trials need to evaluate different mechanism-based approaches as well and inclusion criteria do not reflect the mode of action of specific drugs and outcome measures lack sensitivity.
Objective: We aim to improve trial design with the ultimate objective to achieve a future of effective and efficient personalized prevention of AD.
Study Design: meta analyses
Participants: We will identify subgroups of patients responding to specific compounds in existing trial data sets of patients with early AD. We want to use trial data sets trials in early AD (prodromal/ early dementia)
Main Outcome Measure: We will evaluate disease course over time in terms of (i) clinical progression to MCI or dementia, (ii) cognitive decline over time in different cognitive domains, (iii) functional decline (instrumental activities of daily living), and (iv) behavioral outcomes.
Statistical Analysis. We will define ?positive response? based on the primary and secondary outcome measures in each of the trial data sets. In the first step, we will define responders using different approaches; early endpoints (biomarker, cognitive and functional improvement/stabilization (primary cognitive outcome measure). Second; change of cognitive status, i.e. clinical progression to MCI; change of cognitive status, i.e. clinical progression to AD.

Brief Project Background and Statement of Project Significance: With more than 250.000 patients in the Netherlands and more than 40 million worldwide, Alzheimer disease (AD) is among the largest health care challenges of our century. However, curative therapy is not yet available. This may be due to a number of factors, that are slowly becoming clear as our understanding of the disease grows.
First, AD develops gradually, in the course of decades. Studies using biomarkers (Amyloid or Tau) and imaging (MRI or PET) have shown that brain changes associated with AD are present until 20 years before clinical manifestation of the disease. The stage of dementia is too late to reverse the brain damage which has accumulated over the decades before. This novel knowledge implies that trials should focus on pre-dementia stage, and hence that future treatment strategies for AD will have the form of secondary prevention.
Second, AD is a complex, diverse disease. Most drugs tested have focused on the amyloid pathway. It could be that amyloid is simply the wrong target. While this notion cannot be excluded, literature strongly supports an important role for amyloid in onset and progression of the disease. Nonetheless, it is essential to select the right patients most likely to benefit from anti-amyloid therapy with the right mode of action. In addition, it is increasingly recognized that amyloid does not explain the disease in its entirety. Therefore, trials need to evaluate different mechanism-based approaches as well, e.g. anti-tau with active or passive immunization, anti-inflammatory drugs and neuroprotective compounds, and we should find out which patients benefit most from with strategy.
Finally, taking and into account, one realizes that trial designs have been too crude; inclusion criteria do not reflect the mode of action of specific drugs and outcome measures lack sensitivity. To bring closer a future of personalized prevention of AD, we need to focus on early, pre-dementia disease stages, taking into account de diverse patient group

Specific Aims of the Project: We aim to improve trial design with the ultimate objective to achieve a future of effective and efficient personalized prevention of AD. To achieve this goal, we will identify subgroups of patients responding to specific compounds in existing trial data sets of patients with early AD.
We will evaluate disease course over time in terms of (i) clinical progression to MCI or dementia, (ii) cognitive decline over time in different cognitive domains, (iii) functional decline (instrumental activities of daily living), and (iv) behavioral outcomes.

Study Design:

What is the purpose of the analysis being proposed? Please select all that apply.: Meta-analysis using data from the YODA Project and other data sources Participant-level data meta-analysis Meta-analysis using data from the YODA Project and other data sources Develop or refine statistical methods Research on clinical trial methods Research on comparison group Research on clinical prediction or risk prediction

Software Used: I am not analyzing participant-level data / plan to use another secure data sharing platform

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: For this project, we want to use trial data sets trials in early AD (prodromal/ early dementia) which have recently been finalized. We would like to include data sets of trials using compounds with different modes of action, in particular (i) anti-amyloid, (ii) anti-inflammation, and (iii) neuroprotective treatment strategies. We?d like to use trial data sets which include patients with MCI due to AD or mild AD from 50-90 years. Mini-Mental State Examination (MMSE) score range from 18 to 28. Inclusion criteria further comprise evidence of amyloid pathology by Amyloid PET determined by visual inspection or based on concentration of abeta 1-42 in cerebrospinal fluid derived by lumbar puncture (if available).
NCT01424436
NCT00459550
NCT02423200
NCT01739348
NCT01953601
NCT00762411
NCT00594568
NCT02337907

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: We will define ?positive response? based on the primary and secondary outcome measures in each of the trial data sets. We will than make a codebook of existing variables, including demographic variables, clinical data, genetic markers and biomarker values available in each of the data sets.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: In the first step, we will define responders using different approaches:
1) early endpoints:
a) biomarker improvement/stabilization;
b) cognitive improvement/stabilization (primary cognitive outcome measure),
c) cognitive improvement/stabilization and functional improvement/stabilization (primary cognitive & functional outcome measures),
2) late endpoints:
a) change of cognitive status, i.e. clinical progression to MCI;
b) change of cognitive status, i.e. clinical progression to AD dementia
we will define studies on their target: anti-amyloid, anti-tau and anti inflammation.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: The table provides an overview of data that may be available.
? Demographic variables (age, sex, education)
? Diagnostic markers (Abeta 1-42, Tau, P- tau).
? Safety parameters (vital signs, blood pressure, heart rate)
? Physical examination
? Neurological examination
? Brain MRI (atrophy, white matter hyperintensities, microbleeds)
? Neuropsychological outcomes (tests for memory, attention, executive functions)
? Instrumental activities of daily living
? Genetic markers (e.g. APOE)
? Exploratory biomarkers such as
o QC enzyme.
o Panel of Abeta peptide versions of various length (X-40/42).
o Panel for pGluAbeta and its substrates Abeta 3-40/42 and 11-40/42.
o Panel of Abeta-Oligomers of different a length.
o Neurofilament light

Statistical Analysis Plan: In two steps, we aim to identify a combination of patient characteristics (demographic, clinical, biomarker) associated with a positive response to treatment.
In the first step, we will define responders using different approaches:
1) early endpoints:
a) biomarker improvement/stabilization;
b) cognitive improvement/stabilization (primary cognitive outcome measure),
c) cognitive improvement/stabilization and functional improvement/stabilization (primary cognitive & functional outcome measures),
2) late endpoints:
a) change of cognitive status, i.e. clinical progression to Mild Cognitive Impairment (MCI);
b) change of cognitive status, i.e. clinical progression to AD dementia
Different responder definitions (1abc and 2ab) will be used to dichotomize treatment response. In the second step, we will model treatment effect, by applying a Causal Forest machine learning model. This predictive technique is a flexible and powerful predictor, in particular when higher order interactions are expected [Wager and Athey, 2018]. Using this model, we can model treatment-specific effects of medication. The following characteristics will be included in the model: age, sex, disease status, ethnicity, presence or absence of co-morbidities, baseline cognitive status, biomarkers of tau and amyloid. Based on these analyses, we will develop new knowledge on which combination of patient characteristics (demographic, clinical, biomarker) predisposes for a treatment effect to which type of drugs.

Narrative Summary: We aim to improve trial design with the ultimate objective to achieve a future of effective and efficient personalized prevention of AD. To achieve this goal, we will identify subgroups of patients responding to specific compounds in existing trial data sets of patients with early AD.
We will evaluate disease course over time in terms of (i) clinical progression to MCI or dementia, (ii) cognitive decline over time in different cognitive domains, (iii) functional decline (instrumental activities of daily living), and (iv) behavioral outcomes.

Project Timeline: We planned one year in total for preparing data sets, and one year to do (responder) analyses.
Task 1: Trial data sets ready for analysis (M7)
Expected: Q3 2021
Task 2: Variables for patient stratification identified (M24)
Expected: Q3 2021
Task 3: date of data analysis completion
Expected Q1/2 2022
Task 3: date of manuscript completion
Expected Q2 2022

Dissemination Plan: This report can guide pharmaceutical companies and CRO?s in the effective and efficient design of prevention trials.
Alzheimers Research & Therapy
Alzheimer's & Dementia

Bibliography:

1. Organization WH. Dementia: a public health priority, ISBN 978 92 4 156445 8 ed2012.
2. Bateman RJ, Xiong C, Benzinger TL, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 2012;367:795-804.
3. Scheltens P, Blennow K, Breteler MM, et al. Alzheimer’s disease. Lancet 2016;388:505-517.
4. Gilman S, Koller M, Black RS, et al. Clinical effects of Abeta immunization (AN1792) in patients with AD in an interrupted trial. Neurology 2005;64:1553-1562.
5. Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med 2014;370:322-333.
6. Jansen WJ, Ossenkoppele R, Knol DL, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA 2015;313:1924-1938.
7. Sevigny J, Chiao P, Bussiere T, et al. The antibody aducanumab reduces Abeta plaques in Alzheimer’s disease. Nature 2016;537:50-56.
8. Slot RER, Verfaillie SCJ, Overbeek JM, et al. Subjective Cognitive Impairment Cohort (SCIENCe): study design and first results. Alzheimers Res Ther 2018;10:76.
9. Jessen F, Amariglio RE, van BM, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement 2014;10:844-852.
10. Slot RER, Sikkes SAM, Berkhof J, et al. Subjective cognitive decline and rates of incident Alzheimer’s disease and non-Alzheimer’s disease dementia. Alzheimers Dement 2018.