Implications of nonresponse patterns in the analysis of smoking cessation trials

In the statistical analysis of smoking cessation trials, participants with missing outcome data are commonly assumed to be continued smokers. Using algebraic formulas, a numerical example, and a real-life example, we evaluated the implications of nonresponse patterns on results obtained with a "missing = smoking" (MS) analysis compared with results obtained with an "available case" (AC) analysis, which excludes participants with missing outcome data. The algebraic formulas showed that MS and AC analysis provide consistent estimates of relative quit rates (RQR) when response rates in the treatment and control group are equal, regardless of the validity of the underlying assumption of both approaches. However, as shown in our numerical example, RQR estimated with both approaches can differ substantially in case of differential response rates. In the real-life example the proportion abstinent decreased from 16% to 5% in later response waves but did not reach zero. The estimates of the intervention effect from MS analysis and AC analysis converged when high and comparable response rates were achieved in both the treatment and control groups after multiple reminders. We conclude that smoking cessation studies should aim for high and equal response rates in the compared groups to ensure identification of all successful quitters and to be less susceptible to potential bias related to violation of the assumptions underlying the analytic strategies.