• Study methods consultation
• Study design consultation
• Proposal development, writing, and refinement
• Full critical proposal review

We provide study design support for a variety of study types, including: longitudinal, cross-sectional, nested cohort study designs, randomized controlled trials, cluster randomized controlled trials, secondary data analysis plans, nested cohort studies, and bio-behavioral approaches (e.g., two-stage studies, case cohort, case control studies).

Our study design support includes:

• Formulating aims and hypotheses
• Writing and editing analysis plans
• Sample selection methods
• Power and sample size calculations
• Protocol development
• Randomization strategies

• Electronic informed consent capture
• In-person data collection (via Blaise, REDCap) of assessments, interviews, biomarkers data, etc. with built-in scripts, validation, adaptive sequencing, and data integrity monitoring tools
• Web-survey (via custom system, Qualtrics, REDCap) including automated mailings and incentive distribution
• Cleaning, double entry, and reconciliation of paper-based data collection

• Participant and study activity tracking from recruitment through closure
• Basic: support setting up tracking in Excel or Access database
• Complex: custom interactive web-based systems that integrate participant information, study activity tracking, data files received, reporting, and alerts

• Database development
• Data cleaning, error checking and reconciliation, and field staff
• Harmonization of variables across time points, versions, and variable changes
• Scoring standardized assessment instruments
• Validation
• Data conversion/formatting (e.g., NDAR/ECHO)
• Cohort building
• Data linkage (i.e., building secondary datasets from multiple/varied sources)
• Auditing data/variables (i.e., quality assurance)

• Descriptive data
• Simple GIS analysis
• SEM
• Cluster randomized controlled analysis
• Hierarchical linear regression
• Time-to-event analysis (survival analysis)
• Marginal structural models
• Longitudinal data analysis
• Missing data assessment and methods
• Population/sampling weights
• Inverse probability weights
• Intent to treat analyses (ITT)
• Treatment on the treated (ToT)

• Secure data sharing and receiving, coordinating across sites, de-identification of data, and uploading to registries
• Preparation of figures, tables, and graphic representations for presentations and publications
• Writing methods, results, and discussion sections of reports and manuscripts
• Interactive website for data visualization (e.g., Tableau, R/Shiny)