Biostatistics

19.

Data Management and Quality

19.1.

Data Collection Planning

19.1.1.

Data Collection Instruments

19.1.1.1.

Questionnaire Design

19.1.1.2.

Case Report Forms

19.1.1.3.

Electronic Data Capture

19.1.2.

Variable Definition

19.1.2.1.

Operational Definitions

19.1.2.2.

19.1.2.3.

Data Dictionaries

19.1.3.

Quality Control Procedures

19.1.3.1.

Training and Standardization

19.1.3.2.

Monitoring and Auditing

19.1.3.3.

Error Prevention Strategies

19.2.

Data Cleaning and Preparation

19.2.1.

Data Entry Verification

19.2.1.1.

Double Data Entry

19.2.1.2.

Range and Logic Checks

19.2.1.3.

Consistency Verification

19.2.2.

Outlier Detection

19.2.2.1.

Statistical Methods

19.2.2.2.

Clinical Review

19.2.2.3.

19.2.3.

Data Transformation

19.2.3.1.

Variable Recoding

19.2.3.2.

Scale Transformations

19.2.3.3.

Derived Variables

19.3.

19.3.1.

Missing Data Mechanisms

19.3.1.1.

Missing Completely at Random (MCAR)

19.3.1.2.

Missing at Random (MAR)

19.3.1.3.

Missing Not at Random (MNAR)

19.3.2.

Missing Data Patterns

19.3.2.1.

Monotone vs Non-Monotone

19.3.2.2.

Pattern Analysis

19.3.3.

Handling Strategies

19.3.3.1.

Complete Case Analysis

19.3.3.2.

Available Case Analysis

19.3.3.3.

Imputation Methods

19.3.3.3.1.

Single Imputation

19.3.3.3.2.

Multiple Imputation

19.3.3.3.3.

Hot Deck Imputation

19.3.3.3.4.

Regression Imputation

19.3.4.

Sensitivity Analysis

19.3.4.1.

Assessing Impact of Missing Data

19.3.4.2.

Alternative Assumptions

19.3.4.3.

Robustness of Results

19.4.

Data Security and Privacy

19.4.1.

Data De-identification

19.4.1.1.

Direct Identifiers

19.4.1.2.

Indirect Identifiers

19.4.1.3.

Safe Harbor Method

19.4.2.

Data Storage and Access

19.4.2.1.

Secure Storage Systems

19.4.2.2.

Access Controls

19.4.2.3.

19.4.3.

Data Sharing Considerations

19.4.3.1.

Privacy Protection

19.4.3.2.

Consent Requirements

19.4.3.3.

Regulatory Compliance

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