The Missing=Smoking Assumption: A Fallacy in Internet-Based Smoking Cessation Trials?

Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco
Matthijs BlankersMargriet van Laar

Abstract

In this study, penalized imputation (PI), a common approach to handle missing smoking status data and sometimes referred to as "missing=smoking," is compared with other missing data approaches using data from internet-based smoking cessation trials. Two hypotheses were tested: (1) PI leads to more conservative effect estimates than complete observations analysis; and (2) PI and multiple imputation (MI) lead to similar effect estimates under balanced (equal missingness proportions among the trial arms) and unbalanced missingness. First, the outcomes of 22 trials included in a recent Cochrane review on internet-based smoking cessation interventions were reanalyzed using only the complete observations, and after applying PI. Second, in a simulation study outcomes under PI, complete observations analysis, and two types of MI were compared. For this purpose, individual patient data from one of the Cochrane review trials were used. Results of the missing data approaches were compared with reference data without missing observations, upon which balanced and unbalanced missingness scenarios were imposed. In the reanalysis of 22 trials, relative risks (RR = 1.15 [1.00; 1.33]) after PI were nearly identical to those under complete observ...Continue Reading

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Citations

Aug 27, 2016·Journal of Medical Internet Research·Kang ZhaoAmanda L Graham

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