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  ["project_title"]=>
  string(112) "Mediators of Mortality Risk in Older Adults with Dementia: Analysis of Multiple Pathways Using Risperidone Trial"
  ["project_narrative_summary"]=>
  string(672) "This study examines how risperidone contributes to mortality risk in older adults with dementia. By analyzing clinical trial data, we will investigate specific health events (pneumonia, fractures, stroke, heart failure, kidney injury, infections) that may mediate this risk and assess how timing of risperidone use affects outcomes. Using advanced mediation analysis, we aim to quantify each event’s contribution to mortality risk and understand combined effects. Findings will help identify high-risk complications, guide monitoring, and inform safer prescribing and preventive strategies to mitigate risks associated with risperidone use in this vulnerable population."
  ["project_learn_source"]=>
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  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(7) "Ismaeel"
    ["last_name"]=>
    string(6) "Yunusa"
    ["degree"]=>
    string(16) "PharmD, PhD, MPH"
    ["primary_affiliation"]=>
    string(48) "University of South Carolina College of Pharmacy"
    ["email"]=>
    string(22) "Iyunusa@mailbox.sc.edu"
    ["state_or_province"]=>
    string(14) "South Carolina"
    ["country"]=>
    string(13) "United States"
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    ["label"]=>
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  ["property_scientific_abstract"]=>
  string(1639) "Background:

Risperidone is commonly prescribed to manage behavioral symptoms in older adults with dementia, but its use is associated with increased mortality risk. While prior studies indicate higher mortality with antipsychotic use, the specific health complications driving this risk are not well understood, nor is the impact of timing in these risks.



Objective:

To investigate the mediating pathways through which risperidone contributes to mortality in older adults, quantifying the role of specific adverse events and assessing how timing of risperidone use influences mortality risk.



Study Design:

A secondary analysis of randomized controlled trial data to examine associations between risperidone use and mortality mediated by various adverse events.



Participants:

Older adults with dementia who participated in randomized trials comparing risperidone to placebo.



Primary and Secondary Outcome Measures:

The primary outcome is mortality within the follow-up period. Secondary outcomes include incidence of pneumonia, fractures, stroke, heart failure, acute kidney injury, venous thromboembolism, and bacterial infections.



Statistical Analysis:

Causal mediation analysis will be conducted to quantify the contribution of each adverse event to mortality risk, as well as joint and sequential mediation effects. Temporal analysis will assess event timing across current, recent, and past use periods. Sensitivity analyses will be performed to address potential misclassification of mediating events.



"
  ["project_brief_bg"]=>
  string(807) "Antipsychotic medications like risperidone are frequently prescribed to manage behavioral symptoms in older adults with dementia, despite known risks. Studies have shown increased mortality linked with antipsychotic use in this population, yet the specific health complications and pathways contributing to mortality has not been fully elucidated. This study aims to analyze how risperidone affects mortality risk by examining specific adverse events, including pneumonia, fractures, stroke, heart failure, and kidney injury. Understanding these pathways is crucial for improving clinical guidelines and helping healthcare providers minimize risk. Insights from this analysis could lead to safer prescribing practices and enhanced monitoring strategies to reduce adverse outcomes in vulnerable older adults."
  ["project_specific_aims"]=>
  string(411) "1.	Quantify the mediating effects of various health complications (such as infections, cardiovascular events, and fractures) on the overall mortality risk associated with risperidone.

2.	Estimate the proportion of mortality risk explained by each specific pathway.

3.	Assess the combined effects of multiple adverse events to understand how they interact in contributing to mortality risk.

"
  ["project_study_design"]=>
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    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
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  ["project_purposes"]=>
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      ["label"]=>
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    }
    [1]=>
    array(2) {
      ["value"]=>
      string(69) "confirm_or_validate previously_conducted_research_on_treatment_safety"
      ["label"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(59) "preliminary_research_to_be_used_as_part_of_a_grant_proposal"
      ["label"]=>
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    }
    [3]=>
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      ["value"]=>
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      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [4]=>
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      ["value"]=>
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      ["label"]=>
      string(51) "Meta-analysis using only data from the YODA Project"
    }
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  ["project_research_methods"]=>
  string(986) "Data Source:

The study will use individual patient data (IPD) from clinical trials of risperidone for dementia-related behavioral symptoms, accessed through the YODA Project.



Inclusion Criteria:

Patients aged 65 years or older

Diagnosis of dementia or dementia-related behavioral symptoms

Initiated treatment with risperidone or received a placebo as part of the trial

Complete data on mortality outcomes and adverse events of interest (e.g., pneumonia, fractures, stroke, cardiovascular events)



Exclusion Criteria:

Patients with pre-existing conditions that contraindicate antipsychotic use (e.g., severe cardiac conditions)

Patients with insufficient follow-up data or missing key outcome measures

This selection aims to provide a well-defined sample of older adults with dementia, ensuring the relevance and reliability of results for assessing mortality risks associated with risperidone.

"
  ["project_main_outcome_measure"]=>
  string(675) "Primary Outcome:

All-cause mortality (Binary): Defined as death from any cause within the study follow-up period, used to assess overall mortality risk.



Secondary Outcomes: 

Pneumonia (Binary): Diagnosis recorded during follow-up.

Fractures (Binary): Any reported fractures, with a focus on hip fractures.

Stroke (Binary): Ischemic or hemorrhagic stroke as verified in records.

Cardiovascular Events (Binary): Includes heart failure and venous thromboembolism.

Acute Kidney Injury (AKI) (Binary): Documented AKI instances.

Bacterial Infections (Binary): Other infections documented aside from pneumonia.

"
  ["project_main_predictor_indep"]=>
  string(214) "Independent Variable: Risperidone Use

Category: Binary

Definition: Indicates whether the patient received risperidone (1) or placebo (0) during the trial. This variable serves as the primary exposure."
  ["project_other_variables_interest"]=>
  string(1113) "Age (Continuous): Patient age in years at trial start, used to adjust for age-related risks.

Sex (Binary): Patient gender, coded as male (1) or female (0).

Race (Categorical): Self-identified race, categorized into groups such as White, Black, Asian, Hispanic, and Other, to control for racial differences in health outcomes.

Comorbidities (Binary for each condition): Presence of pre-existing conditions such as cardiovascular disease, diabetes, respiratory disorders, and kidney disease, coded as present (1) or absent (0).

Concomitant Medications (Binary for each medication type): Use of additional medications that could influence outcomes, including antihypertensives, anticoagulants, and antidiabetic drugs, coded as used (1) or not used (0).

Duration of Exposure to Risperidone (Continuous): Total days of risperidone exposure, allowing dose-response analysis.

Timing of Adverse Events (Categorical): Timing relative to risperidone initiation: current use (0–90 days), recent use (90–180 days), and past use (after 180 days), to assess timing effects on risk."
  ["project_stat_analysis_plan"]=>
  string(3061) "Our analysis will proceed through several structured phases to address the study objectives systematically. We will begin with propensity score analysis to account for potential confounding in treatment assignment. Using logistic regression with all baseline covariates, we will calculate propensity scores and implement inverse probability of treatment weighting (IPTW). Treatment groups will be balanced when standardized differences are less than 0.1. To ensure robustness, we will conduct sensitivity analyses using alternative propensity score methods including matching  weights and overlap weights.



For the first objective of quantifying mediating effects, we will implement a formal causal mediation analysis for each potential pathway. This involves fitting sequential models: a logistic regression model for the exposure-mediator relationship and a Cox proportional hazards model for the mediator-outcome relationship, both incorporating IPTW adjustment. We will calculate natural direct and indirect effects using the product method, and estimate controlled direct effects under hypothetical interventions. For each pathway, we will compute the proportion mediated on the risk difference scale with bootstrapped 95% confidence intervals.



To address the second objective regarding the proportion of risk explained by each pathway, we will employ sequential mediation analysis for ordered multiple mediators. This approach will use inverse odds ratio weighting to address mediator-outcome confounding. We will calculate path-specific effects through different combinations of mediators and apply structural equation modeling approaches for parallel multiple mediators.



For the third objective examining combined effects, we will estimate joint mediation effects using VanderWeele's approach and calculate interaction measures between mediators on both additive and multiplicative scales. We will apply g-computation for complex pathways involving multiple mediators and use random forest models to identify important mediator combinations. Time-varying analyses will employ marginal structural models with time-varying IPTW and analyze mediator-outcome relationships using time-dependent Cox models. We will assess for time-varying effect modification and evaluate the temporal ordering of multiple adverse events.



All analyses will incorporate robust standard errors and bootstrap resampling (n=1000) for confidence intervals. Results will be presented as risk ratios, risk differences, and proportions mediated with 95% confidence intervals. To account for multiple testing, we will apply the Benjamini-Hochberg procedure for adjustment of p-values. Missing data exceeding 5% will be handled using multiple imputation with chained equations, creating 20 imputed datasets and combining results using Rubin's rules. This comprehensive analytical approach will provide robust estimates of both direct and indirect effects while accounting for potential sources of bias and confounding.

"
  ["project_software_used"]=>
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    ["label"]=>
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  }
  ["project_timeline"]=>
  string(803) "Following data access approval, we anticipate completing this project within 12 months. The key milestone dates are:

Data Access and Initial Setup: January 2025. We will spend the first month organizing data and establishing analysis protocols.

Preliminary Analysis Completion: March 2025. Initial descriptive statistics and propensity score analyses will be completed.

Primary Analysis Completion: June 2025. Mediation analyses and sensitivity analyses will be finalized.

Manuscript Preparation: July-August 2025. First draft of the manuscript will be completed and internally reviewed.

Manuscript Submission: September 2025. Initial submission to target journal.

Results Reported to YODA Project: October 2025. Complete findings will be reported back to YODA."
  ["project_dissemination_plan"]=>
  string(90) "Our primary manuscript will target JAMA Psychiatry or the American Journal of Psychiatry. "
  ["project_bibliography"]=>
  string(3819) "

Mok P L H, Carr M J, Guthrie B, Morales D R, Sheikh A, Elliott R A et al. Multiple adverse outcomes associated with antipsychotic use in people with dementia: population based matched cohort study BMJ 2024; 385 :e076268 doi:10.1136/bmj-2023-076268
John W. Jackson, Tyler J. VanderWeele, Anand Viswanathan, Deborah Blacker, Sebastian Schneeweiss, The Explanatory Role of Stroke as a Mediator of the Mortality Risk Difference Between Older Adults Who Initiate First- Versus Second-Generation Antipsychotic Drugs, American Journal of Epidemiology, Volume 180, Issue 8, 15 October 2014, Pages 847–852, https://doi.org/10.1093/aje/kwu210
Jackson JW, Schneeweiss S, VanderWeele TJ, Blacker D (2014) Quantifying the Role of Adverse Events in the Mortality Difference between First and Second-Generation Antipsychotics in Older Adults: Systematic Review and Meta-Synthesis. PLoS ONE 9(8): e105376. https://doi.org/10.1371/journal.pone.0105376
Jackson, John W; VanderWeele, Tyler J; Blacker, Deborah; Schneeweiss, Sebastiana. Mediators of First- Versus Second-generation Antipsychotic-related Mortality in Older Adults. Epidemiology 26(5):p 700-709, September 2015. doi: 10.1097/EDE.0000000000000321
Aguiar JP, Bernardo C, Gama Marques J, Leufkens H and Alves da Costa F (2020) Identification of a Set of Patient-Related Features to Foster Safe Prescribing of Specific Antipsychotics in the Elderly With Dementia. Front. Psychiatry 11:604201. doi: 10.3389/fpsyt.2020.604201
Desai R J, Franklin J M. Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners BMJ 2019; 367 :l5657 doi:10.1136/bmj.l5657
Lee H, Herbert RD, McAuley JH. Mediation Analysis. JAMA. 2019;321(7):697–698. doi:10.1001/jama.2018.21973
VanderWeele TJ. Mediation Analysis: A Practitioner’s Guide. Annu Rev Public Health. 2016;37:17-32. doi: 10.1146/annurev-publhealth-032315-021402. Epub 2015 Nov 30. PMID: 26653405.
Lee H, Cashin AG, Lamb SE, et al. A Guideline for Reporting Mediation Analyses of Randomized Trials and Observational Studies: The AGReMA Statement. JAMA. 2021;326(11):1045–1056. doi:10.1001/jama.2021.14075
Rodriguez-Valadez JM, Tahsin M, Masharani U, Park M, Hunink MGM, Yeboah J, Li L, Weber E, Berkalieva A, Avezaat L, Max W, Fleischmann KE, Ferket BS. Potential Mediators for Treatment Effects of Novel Diabetes Medications on Cardiovascular and Renal Outcomes: A Meta-Regression Analysis. J Am Heart Assoc. 2024 Feb 20;13(4):e032463. doi: 10.1161/JAHA.123.032463.
MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593-614. doi: 10.1146/annurev.psych.58.110405.085542.

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  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(16150)
    ["id"]=>
    int(16150)
    ["title"]=>
    string(28) "Data Request Approved Notice"
    ["filename"]=>
    string(32) "Data-Request-Approved-Notice.pdf"
    ["filesize"]=>
    int(195663)
    ["url"]=>
    string(81) "https://yoda.yale.edu/wp-content/uploads/2024/11/Data-Request-Approved-Notice.pdf"
    ["link"]=>
    string(77) "https://yoda.yale.edu/data-request/2024-0944/data-request-approved-notice-22/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(31) "data-request-approved-notice-22"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(15956)
    ["date"]=>
    string(19) "2024-12-04 16:08:18"
    ["modified"]=>
    string(19) "2024-12-04 16:08:18"
    ["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"]=>
  bool(false)
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["request_overridden_res"]=>
  string(1) "3"
  ["human_research_protection_training"]=>
  bool(false)
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(1) {
  [0]=>
  string(9) "risperdal"
}


num of trials
array(1) {
  [0]=>
  string(1) "5"
}


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