Site Co-Is will oversee site activities and provide weekly, face-to-face supervision of the RAs. They are responsible for recruitment, retention, safety, fidelity to protocol, and supervision and support of RAs. The RAs will assist with recruitment, screening, enrollment and baseline screening measure collection, as well as the day-to-day functioning. RAs will be blind to random assignment. RA-Interventionists will be trained to implement the protocol. Only the RA-Assessor will be permitted to administer post-randomization assessments. Children's National will serve as the data coordinating center and will be responsible for database design and maintenance and the statistical analyses. Sites will be overseen by a Safety Monitoring Committee (SMC). A 2-day Investigator Meeting will be held in Washington, District of Columbia (DC) and will include all site Co-Is, RA-Assessors and consultants. The protocol, its scientific rationale, underlying ethics issues, implementation including recruitment and retention, will be reviewed with the entire team. In month 9 of Year 1 there will be a 3-day training meeting of the RA-Interventionists and RA-Assessors on the intervention, which site-Co-Is will also attend. RA-Assessors will attend only one day of this training in order to maintain blindness. Site Initiation. To begin screening/enrollment (1) the protocol must be approved at each site's Institutional Review Board (IRB) and all personnel must be certified in Human Subjects Research training, (2) personnel are recruited and trained for RA roles, and (3) each RA-Interventionist must complete certification as a Next Steps-ACP Facilitator. Dr. Lyon and the Research Coordinator will verify that the site has all components in place for the logistics of screening, enrolling, scheduling, performing assessments, administering interventions, and collecting the data. To assure continuous quality for the intervention and its evaluation, monitoring will be ongoing. Dr. Lyon and Ms. Briggs will review the first 5 DVD/audio recordings of intervention sessions from each site to ensure fidelity with the protocol. Thereafter, they will randomly review 1 DVD per week, rotating sites. Dr. Lyon and Ms. Briggs will use a competency checklist. Dr. Lyon and Research Coordinator will monitor ongoing site IRB approval documentation and assist sites in annual continuing reviews. The Research Coordinator will keep copies of all regulatory forms, including consent-stamped templates from each site and staff members' Human Subjects Research Training approval certificates. Dr. Lyon and the Research Coordinator will perform twice yearly site monitoring visits while the intervention is being implemented to assure standardization of procedures and resolve any problems that are identified. They will review and confirm that all consents have occurred properly at the sites and that the sites are maintaining all participant and regulatory data. The REDCap database from the FACE-TC pilot will be updated and expanded for this study, and the systems for data entry will be revised to address multi-site implementation. A data dictionary will be created. An external Safety Monitoring Committee (SMC) will be assembled by Dr. Lyon with the responsibility of reviewing safety information, study progress, and other relevant data. The SMC will meet a minimum of once a year. Prior to parametric testing, scale reliabilities for multi-item measures (e.g., pain/fatigue, child and parent psychological, spiritual/religious measures) will be assessed using Cronbach's alpha and their composite scores will be used for data analyses. Analytical Plan for AIM 1. To evaluate the efficacy of FACE-TC on adolescent-family congruence in treatment preferences. Congruence in decision-making for medical treatment will be tested based on agreement (i.e., both patient and his/her family choose the same option) on the Statement of Treatment Preferences in four different cancer-related situations. Kappa coefficients will be applied to assess chance-adjusted agreement between patient and family responses. Change in Kappa coefficient (congruence improvement) from baseline to each follow-up time point during the study period will be tested using bootstrapping technique. The latent growth model (LGM) with categorical outcome will be used to test Hypotheses H1a, i.e., FACE-TC participants will have a higher congruence rate over time. In the LGM, the investigators will set time scores, except those for identification purpose, as free parameters to let the shape of growth trajectory be determined by data. As such, the congruence development trajectory would have an empirically based nonlinear shape, instead of assuming a linear or nonlinear polynomial function. The investigators will apply the growth mixture model (GMM) to test heterogeneity of congruence development trajectories and identify possible patterns of congruence in development trajectories in the full sample. The latent class variable estimated from the GMM captures the pattern of congruence development trajectories. Time-invariant covariates will be used to predict the memberships of the latent trajectory groups; and time-varying covariates will be included to predict the level of congruence at different time points. To test Hypotheses H1b, the investigators will regress the latent growth slope factor and the latent class variable on FACE-TC intervention, controlling for covariates in the GMM to assess 1) how FACE-TC would affect the membership of the latent classes of congruence development; and 2) how the effect of FACE-TC on congruence change over time varies across the latent trajectory classes. Analytical Plan for AIM 2. To evaluate efficacy of FACE-TC on AYA quality of life and family QOL. The LGM and GMM models proposed for evaluating Aim 1 can be readily applied to evaluate Aim 2 and test Hypotheses H2 where the outcome measures are continuous variables. When examining the effects of FACE-TC on QOL for AYAs with cancer and their families, socio-demographics will be controlled as time-invariant covariates, while time-varying covariates will be included in the model to predict measures of QOL at different time points. In addition, family caregiver appraisal/depression measured at the end of the study period will be included as a distal outcome in the GMM models, and how this distal outcome is associated with the patterns of the developmental trajectories of AYA QOL will be assessed. As the same model will be used to evaluate multiple outcomes, Bonferroni correction will be applied to exert a stringent control over false discovery. As attrition is inevitable in longitudinal studies, robust model estimator (e.g., MLR) using the full information maximum likelihood will be used for model estimation. Importantly, missing at random (MAR), instead of missing completely at random, can be assumed in MLR. MAR is a plausible assumption that allows missingness to be dependent on observed measures like intervention assignment. Analytical Plan for AIM 3. To evaluate the efficacy of FACE-TC on early completion of pACP goals of care and advance directives. First, the investigators will use the two-proportion z-test to test the differences in proportions of completion of pACP goals of care and advance directives between FACE-TC and control groups. Then logistic regressions will be used to test the Hypothesis H3, controlling for socio-demographics. Interaction between intervention and ethnicity will be included in the models to test ethnic disparity in regard to intervention efficacy. The investigators will also explore if FACE-TC improves the match between patients' goals of care and the medical care received at the EOL among the adolescents who may die. Descriptive statistics will be used to estimate the frequencies of the study variables. Chi-square statistics with Fisher Exact tests will be used to assess the difference in the match between FACE-TC and controls; and exact logistic regression will be applied to examine the effect of FACE-TC on such a match, controlling for covariates. For continuous outcomes with a modest observation autocorrelation (p=0.20) and moderate effect size (delta=0.35), the estimated sample size to achieve a power of 0.80 at =0.05 level and detect a moderate effect size (delta=0.35) is about N=76 individuals at each of the 5 observation time points. For binary outcomes, a sample size of N=70 can achieve a power of 0.80 to detect a moderate response probability difference of d=0.17 given p=0.20. Our proposed sample of N=130 dyads will ensure a large enough statistical power for our proposed longitudinal analyses on patient data and parent data, respectively. For the cross-sectional logistic regression model proposed to evaluate Aim 3, a sample size of N=100 would achieve a power of 0.83 at alpha=0.05 level to detect an odds ratio of 4.