A tutorial on principal stratification-based sensitivity analysis: application to smoking cessation studies

Abstract

Background One problem with assessing effects of smoking cessation interventions on withdrawal symptoms is that symptoms are affected by whether participants abstain from smoking during trials. Those who enter a randomized trial but do not change smoking behavior might not experience withdrawal related symptoms. Purpose We present a tutorial of how one can use a principal stratification sensitivity analysis to account for abstinence in the estimation of smoking cessation intervention effects. The paper is intended to introduce researchers to principal stratification and describe how they might implement the methods. Methods We provide a hypothetical example that demonstrates why estimating effects within observed abstention groups is problematic. We demonstrate how estimation of effects within groups defined by potential abstention that an individual would have in either arm of a study can provide meaningful inferences. We describe a sensitivity analysis method to estimate such effects, and use it to investigate effects of a combined behavioral and nicotine replacement therapy intervention on withdrawal symptoms in a female prisoner population. Results Overall, the intervention was found to reduce withdrawal symptoms but the effect was not statistically significant in the group that was observed to abstain. More importantly, the intervention was found to be highly effective in the group that would abstain regardless of intervention assignment. The effectiveness of the intervention in other potential abstinence strata depends on the sensitivity analysis assumptions. Limitations We make assumptions to narrow the range of our sensitivity parameter estimates. While appropriate in this situation, such assumptions might not be plausible in all situations. Conclusions A principal stratification sensitivity analysis provides a meaningful method of accounting for abstinence effects in the evaluation of smoking cessation interventions on withdrawal symptoms. Smoking researchers have previously recommended analyses in subgroups defined by observed abstention status in the evaluation of smoking cessation interventions. We believe that principal stratification analyses should replace such analyses as the preferred means of accounting for post-randomization abstinence effects in the evaluation of smoking cessation programs.

The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models

“…11 In contrast, the structure-less approach of Rubin (1974) has spawned other definitions of direct effects, normally referred to as "principal-strata direct effect (PSDE)" (Frangakis and Rubin, 2002;Mealli and Rubin, 2003;Rubin, 2004Rubin, , 2005Egleston et al, 2010). Whereas the natural direct effect measures the average effect that would be transmitted in the population with all mediating paths (hypothetically) deactivated, the PSDE is defined as the effect transmitted in those units only for whom mediating paths happened to be deactivated in the study.…”

Kaplan–Meier curves for survivor causal effects with time-to-event outcomes

“…A future research is to develop a simple method of sensitivity analysis for Assumptions 1 and 2. For the outcome with no censoring, simple methods have been proposed [17][18][19]. For the outcome with censoring, Shepherd et al [1] proposed a method, but the formulas shown in Appendix D are complex, and thus, it is difficult to implement this method.…”

Response to Pearl's Comments on Principal Stratification

“…For example, I recently used principal stratification in an investigation of the effect of a smoking cessation intervention on smoking withdrawal symptoms (Egleston et al, 2010). The intervention arm in the study received nicotine replacement therapy with a behavioral intervention.…”

“…Subject matter experts have noted this problem and issued guidance recommending that smoking withdrawal symptoms only be investigated among those observed to abstain in a study (Shiffman et al, 2004). Since abstinence is a post-randomization variable, we can show that examining intervention effects within those stratified by observed abstinence status, as recommended (Shiffman et al, 2004), is flawed (Egleston et al, 2010;Pearl, 2009;Cole and Hernán, 2002;Imai et al, 2010).…”