# Chenhaodev Med Stats Skills Clinical Data Management > Clinical Data Management - Skill: `tomevault-io/chenhaodev-med-stats-skills-clinical-data-management` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/chenhaodev-med-stats-skills-clinical-data-management` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/chenhaodev-med-stats-skills-clinical-data-management/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/chenhaodev-med-stats-skills-clinical-data-management --- # Clinical Data Management ## Quick Reference | Phase | SOP/Example | Key Activities | Reference | |-------|-------------|----------------|-----------| | **Interim Analysis** | SOP | Protocol, statistical stopping rules, reporting | [SOP](references/interim-analysis-report.md) | | **Cohort Splitting** | Data Source Utility Plan | Training/validation split, stratification | [SOP](references/data-source-utility-cohort-split.md) | | **Exploratory Analysis** | Study Data Explore/Develop | Data profiling, feature exploration, visualization | [SOP](references/study-data-explore-develop.md) | | **Trial Design Example** | Heart Failure | AIMPower trial - remote monitoring protocol | [Example](references/trial-protocol-aimpower-heart-failure.md) | | **Trial Design Example** | Cancer Care | CART trial - remote monitoring in oncology | [Example](references/trial-protocol-cart-cancer-remote-monitoring.md) | ## When to Use Use this skill when you are involved in the lifecycle of clinical trial data, specifically: * **Planning Data Strategy**: Defining how data sources will be utilized and cohorts defined (Data Source Utility). * **Exploratory Phase**: Conducting initial data profiling, quality checks, and hypothesis generation (Study Data Explore/Develop). * **Interim Monitoring**: Performing formal interim analyses to assess safety, efficacy, or futility during an ongoing trial. * **Trial Design**: Structuring data management plans for digital health interventions (e.g., remote monitoring). * **Predictive Modeling**: Preparing clinical datasets for machine learning, including rigorous training/validation splitting. ## How to Use 1. **Identify the Current Phase**: Determine if you are in the planning, exploration, or monitoring phase of the clinical trial. 2. **Select the Appropriate SOP**: Use the Quick Reference table to find the Standard Operating Procedure (SOP) that matches your current task. 3. **Review Real-World Examples**: If designing a new protocol, especially for digital health, consult the AIMPower (Heart Failure) and CART (Cancer) examples for structural guidance. 4. **Apply Statistical Rigor**: Use the linked Shared Resources to ensure appropriate statistical tests are selected for your analysis plan. ## Data Preparation SOPs This section details the Standard Operating Procedures for critical data management tasks. ### Interim Analysis Reporting **File**: [references/interim-analysis-report.md](references/interim-analysis-report.md) Guidelines for conducting and reporting interim analyses. This SOP covers: * Establishing the Data Safety Monitoring Board (DSMB) charter. * Defining statistical stopping rules (e.g., O'Brien-Fleming boundaries). * Structuring the interim analysis report to maintain blinding where necessary. ### Data Source Utility & Cohort Splitting **File**: [references/data-source-utility-cohort-split.md](references/data-source-utility-cohort-split.md) Methodology for defining data utility and splitting cohorts for analysis. This SOP covers: * **Data Source Utility Plan (DSUP)**: Documenting the provenance and intended use of each data source. * **Cohort Splitting**: Strategies for creating training, validation, and test sets, ensuring balanced stratification of key clinical variables. ### Study Data Exploration & Development **File**: [references/study-data-explore-develop.md](references/study-data-explore-develop.md) A framework for Exploratory Data Analysis (EDA) in clinical research. This SOP covers: * Data profiling and quality assessment (missingness, outliers). * Univariate and bivariate analysis to understand distributions and relationships. * Feature engineering and selection for downstream analysis. ## Digital Health Trial Examples Real-world examples of clinical trial protocols focusing on digital health and remote monitoring. ### Heart Failure Remote Monitoring (AIMPower) **File**: [references/trial-protocol-aimpower-heart-failure.md](references/trial-protocol-aimpower-heart-failure.md) An example protocol for the AIMPower trial, demonstrating: * Integration of remote monitoring devices in heart failure management. * Data collection schedules and patient compliance tracking. * Endpoints related to readmission reduction and quality of life. ### Cancer Care Remote Monitoring (CART) **File**: [references/trial-protocol-cart-cancer-remote-monitoring.md](references/trial-protocol-cart-cancer-remote-monitoring.md) An example protocol for the CART trial, illustrating: * Remote symptom monitoring for cancer patients undergoing treatment. * Alerting algorithms for clinical decision support. * Implementation of patient-reported outcomes (PROs) in a digital workflow. ## Shared Resources * **[Statistical Test Selection Guide](../shared/statistical-test-selection.md)**: Decision trees and criteria for choosing appropriate statistical tests for clinical trial data analysis. Essential for validating the statistical analysis plan (SAP) associated with these data management activities. ## References * **SOPs**: Derived from standard clinical data management practices and adapted for digital health contexts. * **Trial Examples**: Based on the AIMPower and CART study protocols. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/chenhaodev) — claim your Tome and manage your conversions.