Methodology Standard Categories	Strategy to address standard in the project
Standards for Formulating Research Questions (RQ)
RQ-1 Identify gaps in evidence.	Identified the lack of any patient decision aids about lung cancer screening at the time of the study.
RQ-2 Develop a formal study protocol.	A study protocol was prepared and submitted to ClinicalTrials.gov.
RQ-3 Identify specific populations and health decision(s) affected by the research.	The population includes high risk current and former smokers meeting eligibility criteria from the Centers for Medicare & Medicaid for lung cancer screening. The health decision is whether or not to be screened for lung cancer.
RQ-4 Identify and assess participant subgroups.	High risk smokers are the target population. Subgroups include African American and non-Hispanic white smokers.
RQ-5 Select appropriate interventions and comparators.	The intervention is a video-based patient decision aid. The comparator is an information sheet describing lung cancer screening without presenting it as a decision involving tradeoffs.
RQ-6 Measure outcomes that people representing the population of interest notice and care about.	Smokers need information about lung cancer, screening, their options, and the harms and benefits of screening. Outcomes include feeling prepared to make a decision and being clear about personal values related to the choice.
Standards Associated with Patient-Centeredness (PC)
PC-1 Engage people representing the population of interest and other relevant stakeholders in ways that are appropriate and necessary in a given research context.	Patient Advisory Group: includes five current/former smokers, one patient advocate. Stakeholder Advisory Group: primary care and cancer prevention clinicians, leadership from quitline organizations/service providers, expert in health equity.
PC-2 Identify, select, recruit, and retain study participants representative of the spectrum of the population of interest and ensure that data are collected thoroughly and systematically from all study participants.	Subjects are recruited through state quitlines. Includes multiple states and quitline service providers. Research staff use a systematic approach to retain participants, including a detailed tracking program. Flexibility in mode of data collection is used (mail, telephone).
PC-3 Use patient-reported outcomes when patients or people at risk for a condition are the best source of information.	All data collected in this study is patient-reported.
PC-4 Support dissemination and implementation of study results.	A dissemination involves ongoing input for quitlines through the Stakeholder Advisory Group.
Standards for Data Integrity and Rigorous Analyses (IR)
IR-1 Assess data source adequacy.	Not applicable to this study.
IR-2 Describe data linkage plans, if applicable.	Not applicable to this study.
IR-3 A priori, specify plans for data analysis that correspond to major aims.	Primary endpoints will be assessed using two-group t-tests. Linear mixed effect models for longitudinal measures will be used to assess change in outcomes over time. Analyses will be performed based on intension-to-treat.
IR-4 Document validated scales and tests.	Outcome measures include the Decisional Conflict Scale (18), Preparation for Decision Making Scale (17), Lung Cancer Screening Knowledge (LCS-12) (20).
IR-5 Use sensitivity analyses to determine the impact of key assumptions.	Four primary assumptions are being made: 1) the outcome data are normally distributed; 2) self-report will not differ by mode of administration (mailed, telephone); 3) outcomes will not differ by quitline service provider; and 4) we do not know the impact of exposure to the intervention on the outcomes (patients will be asked how much of the materials they reviewed; it is not feasible to collect objective data on exposure). Sensitivity analyses will be performed to test these assumptions.
IR-6 Provide sufficient information in reports to allow for assessments of the study’s internal and external validity.	We will follow the CONSORT reporting guidelines for reporting on the study’s internal and external validity.
Standards for Preventing and Handling Missing Data (MD)
MD-1 Describe methods to prevent and monitor missing data.	We will systematically track subjects. Subjects will be contacted for follow-up assessments will use multiple methods (mail, telephone). The overall length of the assessments will be addressed to minimize subject burden. Loss to attrition will be monitored following CONSORT guidelines.
MD-2 Describe statistical methods to handle missing data.	We will check validity of accuracy of imputation by comparing with a data set which omits subjects with missingness.
MD-3 Use validated methods to deal with missing data that properly account for statistical uncertainty due to missingness.	We will first record reasons for missing data and check missing-data mechanism. If the missing data are missing at random (MAR), we will use multiple imputation techniques implemented in SAS PROC MI procedure to handle missing data according to type of missing data pattern assuming that the model parameters of the data model and the parameters of the missing data indicators are distinct. If the missing data are missing not at random (MNAR), we will apply methods that add an explicit model for the missing mechanism to the data model.
MD-4 Record and report all reasons for dropout and missing data, and account for all patients in report.	We will maintain a tracking log for all patients assessed for eligibility, enrolled in the study, and lost to follow-up including reasons for dropout.
MD-5 Examine sensitivity of inferences to missing data methods and assumptions, and incorporate into interpretation.	We will conduct sensitivity analysis fo different data-missing mechanisms, data-missing patterns as well as different imputation methods and provide appropriate interpretation of the results.r
Standards for Heterogeneity of Treatment Effects (HTE)
HT-1 State goals of HTE analyses.	The goals of HTE analyses in this study are to examine whether or not the decision aid has differential effects by age, gender or race.
HT-2 For all HTE analyses, pre-specify the analysis plan; for hypothesis-driven HTE analysis, pre-specify hypotheses and supporting evidence base.	We will assess the HTE between different subject groups by testing the interaction between the decision aid and covariate of interest such as race with the main effects of the decision aid and covariate in the models.
HT-3 All HTE claims must be based on appropriate statistical contrasts among groups being compared, such as interaction tests or estimates of differences in treatment effect.	We will use CONTRAST statement in SAS PROC GENMOD procedure to estimate treatment differences within each subgroup if the interaction between the decision aid and covariate of interest is statistically significant.
HT-4 For any HTE analyses, report all pre-specified analyses and, at minimum, the number of post-hoc analysis, including all subgroups and outcomes analyzed.	We will explore the differential effects of the decision aid by age, gender or race by testing the interaction first and then perform subgroup analyses using appropriate statistical contrasts if the interaction is statistically significant.

Note. The relevant PCORI methodology standard is denoted by the abbreviation for each standard and its number. For example, RQ-1 is the first standard for reporting research questions. The full methodology standards can be found here www.pcori.org.