Additional file 1 of Bayesian adaptive design for pediatric clinical trials incorporating a community of prior beliefs

Additional file 1: Appendix I. FACTS screen-cuts. Appendix II. R code

Additional file 2 of Bayesian adaptive design for pediatric clinical trials incorporating a community of prior beliefs

Additional file 2: Appendix III: Table 1. Operating characteristics for Bayesian adaptive design 1. Table 2. Operating characteristics for Bayesian adaptive design 2. Table 3. Operating characteristics for Bayesian adaptive design 3 (proposed). Table 4. Operating characteristics for Bayesian adaptive design 4. Table 5. Operating characteristics for Frequentist group sequential design

Optimizing Sample Size Allocation and Power in a Bayesian Two-Stage Drop-the-Losers Design

When a researcher desires to test several treatment arms against a control arm, a two-stage adaptive design can be more efficient than a single-stage design where patients are equally allocated to all treatment arms and the control. We see this type of approach in clinical trials as a seamless Phase II–Phase III design. These designs require more statistical support and are less straightforward to plan and analyze than a standard single-stage design. To diminish the barriers associated with a Bayesian two-stage drop-the-losers design, we built a user-friendly point-and-click graphical user interface with R Shiny to aid researchers in planning such designs by allowing them to easily obtain trial operating characteristics, estimate statistical power and sample size, and optimize patient allocation in each stage to maximize power. We assume that endpoints are distributed normally with unknown but common variance between treatments. We recommend this software as an easy way to engage statisticians and researchers in two-stage designs as well as to actively investigate the power of two-stage designs relative to more traditional approaches. The software is freely available at https://github.com/stefangraw/Allocation-Power-Optimizer.</p

Additional file 1: of A novel method for expediting the development of patient-reported outcome measures and an evaluation of its performance via simulation

Additional Simulation and Application Results. Additional simulation and application results referenced in Sections 3, 4 and 5. (PDF 901 kb

Additional file 1: of Using automated electronic medical record data extraction to model ALS survival and progression

Linearity of 16 randomly selected patients who had > 3 visits. For 16 randomly selected patients with more than three recorded visits, we show their ALSFRS-R score versus time in months, along with the fit regression line. This gives the reader a general idea of the linear decline of the ALSFRS-R seen in patients. (PDF 8 kb

Additional file 1 of The need to study rural cancer outcome disparities at the local level: a retrospective cohort study in Kansas and Missouri

Additional file 1. Supplementary Material

Data_Sheet_1_Preliminary Investigation of a Mobile Nutrition Literacy Website for Parents and Young Children.docx

Parental nutrition literacy (PNL) correlates positively with child diet quality, but interventions for improving PNL are lacking. “Nutricity” is a novel bilingual (English/Spanish) mobile tool designed by the research team to engage parents and young children to interact with nutrition information to make nutrition decisions. The purpose of this study was to inform a future intervention through (1) assessing parental likability of Nutricity, and (2) collecting perceptions of pediatric clinic personnel on the feasibility of introducing Nutricity in pediatric clinics. PNL scores and feedback about Nutricity were collected using mixed methods from 15 English-speaking and 15 Spanish-speaking parents of 1–5 year-old children. Three parents from each language group provided additional feedback via semi-structured interviews. Interviews with 11 pediatric clinic personnel were also conducted to anticipate barriers and formulate strategies for implementing Nutricity as a clinic-based intervention. Nutricity was liked by both language groups and across all PNL levels, with a mean rating of 4.6 on a 5-point scale. Clinic personnel interviews affirmed need for and feasibility of offering Nutricity in clinics.</p

Supplement_File_01_-_Survey_Instrument_3 – Supplemental material for Utilization of Technology to Improve Efficiency in Investigational Drug Management Processes

Supplemental material, Supplement_File_01_-_Survey_Instrument_3 for Utilization of Technology to Improve Efficiency in Investigational Drug Management Processes by Dinesh Pal Mudaranthakam, Colin Cernik, Leslie Curtis, Blake Griffith, Jinxiang Hu, Jo Wick, Jeffrey Thompson, Byron Gajewski, Devin Koestler, Roy A. Jensen and Matthew S. Mayo in Journal of Pharmacy Technology</p

Supplement_File_02_-_Survey_Response_2 – Supplemental material for Utilization of Technology to Improve Efficiency in Investigational Drug Management Processes

Supplemental material, Supplement_File_02_-_Survey_Response_2 for Utilization of Technology to Improve Efficiency in Investigational Drug Management Processes by Dinesh Pal Mudaranthakam, Colin Cernik, Leslie Curtis, Blake Griffith, Jinxiang Hu, Jo Wick, Jeffrey Thompson, Byron Gajewski, Devin Koestler, Roy A. Jensen and Matthew S. Mayo in Journal of Pharmacy Technology</p

Supplemental Material - Statistical assessment of the prognostic and the predictive value of biomarkers- A biomarker assessment framework with applications to traumatic brain injury biomarker studies

Supplemental Material for Statistical assessment of the prognostic and the predictive value of biomarkers- A biomarker assessment fframework with applications to traumatic brain injury biomarker studies by Leonidas E Bantis, Kate J Young, John V Tsimikas, Brian R Mosier, Byron Gajewski, Sharon Yeatts, Renee L Martin, William Barsan, Robert Silbergleit, Gaylan Rockswold and Frederick K Korley in Research Methods in Medicine & Health Sciences</p