Thwarting science by protecting the received wisdom on tobacco addiction from the scientific method

Abstract

In their commentary, Dar and Frenk call into question the validity of all published data that describe the onset of nicotine addiction. They argue that the data that describe the early onset of nicotine addiction is so different from the conventional wisdom that it is irrelevant. In this rebuttal, the author argues that the conventional wisdom cannot withstand an application of the scientific method that requires that theories be tested and discarded when they are contradicted by data. The author examines the origins of the threshold theory that has represented the conventional wisdom concerning the onset of nicotine addiction for 4 decades. The major tenets of the threshold theory are presented as hypotheses followed by an examination of the relevant literature. Every tenet of the threshold theory is contradicted by all available relevant data and yet it remains the conventional wisdom. The author provides an evidence-based account of the natural history of nicotine addiction, including its onset and development as revealed by case histories, focus groups, and surveys involving tens of thousands of smokers. These peer-reviewed and replicated studies are the work of independent researchers from around the world using a variety of measures, and they provide a consistent and coherent clinical picture. The author argues that the scientific method demands that the fanciful conventional wisdom be discarded and replaced with the evidence-based description of nicotine addiction that is backed by data. The author charges that in their attempt to defend the conventional wisdom in the face of overwhelming data to the contrary, Dar and Frenk attempt to destroy the credibility of all who have produced these data. Dar and Frenk accuse other researchers of committing methodological errors and showing bias in the analysis of data when in fact Dar and Frenk commit several errors and reveal their bias by using a few outlying data points to misrepresent an entire body of research, and by grossly and consistently mischaracterizing the claims of those whose research they attack.