Analytic Plan
Creators: name: Kumar, Esha Rani
name: Mumby, Patricia
name: Schneider, Kristin
Publisher: name: OSF Registries
Year: 2021

description: Hypothesis 1a: Will examine the linear effect of time on social support. A linear mixed models using SPSS, Version 25.0 will determine the trajectory of social support across the four timepoints: Time 0 (pre-HSCT), Time 1 (three months post-HSCT), Time 2 (six months post-HSCT), and Time 3 (twelve months post-HSCT). The outcome variable will be self-reported social support based on both CIRS scores. The analysis will be performed twice: once with CIRS scores that do not include importance ratings, and once with CIRS scores with subscales weighted based on importance ratings. Results will be examined for consistency based off the two CIRS scores. The fixed effect will be time (the four timepoints, Time 0, Time 1, Time 2, and Time 3). The random effect will be the participant identification number. 

Hypothesis 1b: Will examine the linear effect of time on treatment adherence. A linear mixed model will determine the treatment adherence trajectory across the four timepoints: Time 0 (pre-HSCT), Time 1 (three months post-HSCT), Time 2 (six months post-HSCT), and Time 3 (twelve months post-HSCT). The outcome variable will be self-reported treatment adherence based on MOS scores. The fixed effect will include time (the four timepoints, Time 0, Time 1, Time 2, and Time 3). The random effect will be the participant identification number. 

Hypothesis 1c: Will examine the correlation between social support and treatment adherence. The predicted values of perceived social support, based on both scoring approaches for the CIRS, and of treatment adherence for each participant at each of the four timepoints from hypotheses 1a and 1b will be saved using the SAVE PRED function in SPSS. The slope of each participant will be computed by hand of each person’s trajectory for social support, for a total of two slopes per participant based on both scoring approaches for the CIRS, and treatment adherence and entered into SPSS to test hypothesis 1c using the following formulas: [(social support at Time 0 – social support at time 3 / (time 0 – time 3)] and [(treatment adherence at Time 0 – treatment adherence at time 3) / (time 0 – time 3)]. A Pearson r correlation analysis will be used to test whether the social support slopes are associated with the treatment adherence slopes. 

Hypothesis 2a: Will test the interaction effect of time and type of transplant on social support using linear mixed models in SPSS. The analysis will be performed twice, using CIRS scores without importance ratings and CIRS scores with weighted subscales based on importance ratings. Results of the analyses will be examined for consistency. The outcome variable will be self-reported social support based on CIRS scores. The fixed effects will include time (the four timepoints, Time 0, Time 1, Time 2, and Time 3), transplant type (allogeneic or autologous), and the time by transplant type interaction (time*transplant type). The random effect will be the participant identification number. 

Hypothesis 2b: Will determine the interaction effect of time and transplant type on treatment adherence using linear mixed models. The outcome variable will be self-reported treatment adherence based on MOS scores. The fixed effects will include time (the four timepoints, Time 0, Time 1, Time 2, and Time 3), transplant type (allogeneic or autologous), and the time by transplant type interaction (time*transplant type). The random effect will be the participant identification number. 

Hypothesis 3a: Will use a Cox proportional hazard model, or survival analysis, in SPSS to determine longevity as a function of the social support trajectory up to 40 months post-transplant or until time of death, whichever comes first, where the social support slopes from hypothesis 1a of each participant will be used to predict longevity. The survival analysis will first use social support slopes from CIRS scores that do not include importance ratings to predict longevity and will then use slopes of CIRS scores with subscales weighted based on importance ratings. Results of the analyses will be examined for consistency. Social support will be entered as a covariate and survival time in months since transplant will be entered as time. 

Hypothesis 3b: Will use a Cox proportional hazard model, or survival analysis, to determine longevity as a function of the treatment adherence trajectory up to 40 months post-transplant, or until time of death, where the treatment adherence slopes from hypothesis 1b of each participant will be used as predictors of longevity. Treatment adherence will be entered as a covariate and survival time in months since transplant will be entered as time.