British Journal of Addiction · 1989 · 1.3K citations · 31 references
Public HealthBiostatisticsPsychometricsPulmonary Function TestingTobacco ControlTobacco EpidemiologyTobacco UseCarbon Monoxide MeasurementCigarette Consumption AssessmentSmoking Status RatingTobacco Use Measurement
Self‑reported measures of smoking heaviness—number of cigarettes per day and time to first cigarette—are widely used, yet the optimal scoring method has received little attention. The study aims to determine the best data transformations for these self‑report measures by comparing them with biochemical indicators of smoking heaviness. Alveolar carbon monoxide and cotinine levels were used to evaluate regression and categorical transformations of the self‑report data. A four‑category scoring scheme for both time to first cigarette (5, 6–30, 31–60, 61+ min) and cigarettes per day (1–10, 11–20, 21–30, 31+ cigarettes) was found to be the most powerful, and logarithmic transformations are recommended for regression or correlation analyses to mitigate ceiling effects.
Summary Two simple self‐report measures have been used to assess the heaviness of smoking,‘number of cigarettes per day’(CPD) and‘time to the first cigarette of the day’(TTF). Little attention, however, has been given to the precise method of scoring this information. Using biochemical indicators of heaviness of smoking (alveolar carbon monoxide and cotinine), we explore the optimum data transformations for regression analysis and categorical analysis. We suggest a four category scoring scheme for both time to the first cigarette of the day (5, 6–30, 31–60 and 61 + mm) and average daily consumption of cigarettes (1–10, 11–20, 21–30, 31 + cigarettes) as the most powerful and practical categorical scoring of these variables. Due to possible ceiling effects on biochemical measures, we suggest using logarithmic transformations of CPD or TTF for regression or correlation analyses.
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Technometrics · 2005 · 18.1K citations
Business, Biostatistics, Mathematics +17
Applied Linear Statistical Models
Eric R. Ziegel, John Neter, William Wasserman et al. · Technometrics · 1992 · 10K citations
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Gerald J. Hahn, Norman R. Draper, Harry Smith · Technometrics · 1982 · 5.8K citations
Applied Linear Statistical Models.
Frederick O. Lorenz, John Neter, William Wasserman et al. · Journal of the American Statistical Association · 1986 · 5.7K citations