abstcal

abstcal calculates abstinence metrics from timeline followback (TLFB) interview data to standardize abstinence definitions and support outcome assessment in smoking cessation research.

Key Features:

• Data verification: Performs verification of input TLFB data to ensure data integrity before analysis.
• Duplicate and outlier detection: Identifies duplicate entries and outliers within TLFB datasets.
• Missing-data imputation: Provides missing-data imputation techniques for incomplete TLFB records.
• Integration with biochemical verification data: Incorporates biochemical verification data into abstinence calculations.
• Calculation of multiple abstinence definitions: Calculates continuous abstinence, point-prevalence abstinence, and prolonged abstinence metrics from TLFB data.

Scientific Applications:

• Standardization of outcome measurement: Reduces variability in abstinence calculation methods across smoking cessation studies.
• Improving reproducibility: Enhances rigor and reproducibility of analyses in smoking and addiction research.
• Support for evidence synthesis: Facilitates comparison of results across studies for meta-analyses and systematic reviews.

Methodology:

Systematic processing of TLFB data including input verification, duplicate and outlier detection, missing-data imputation, optional integration of biochemical verification data, and calculation of continuous, point-prevalence, and prolonged abstinence metrics.