Additional file 1: of Progression of diabetes, ischemic heart disease, and chronic kidney disease in a three chronic conditions multistate model

State occupation probabilities and transition hazards: A supplementary description of statistical methods used for estimating state occupation probabilities and transition hazards. Table S1. A summary matrix showing state-to-state transition counts for the chronic disease network. NA indicates transitions that are not applicable for the system. Table S2. Estimated regression coefficients that represent effects of covariates: age, gender, race/ethnicity, CCI and dual eligibility, on the state occupation probability at selected time points along with p-values (in brackets). Table S3. Estimated regression coefficients that represent effects of covariates: age, gender, race/ethnicity, CCI and dual eligibility, on cumulative transition hazards at selected time points along with p-values (in brackets). Figure S1. Marginally estimated cumulative state-to-state transition hazards from state DM to subsequent states. Figure S2. Marginally estimated cumulative state-to-state transition hazards from state IHD to subsequent states along with bootstrap based 95% point-wise confidence bands. Figure S3. Marginally estimated cumulative state-to-state transition hazards from state CKD to subsequent states along with bootstrap based 95% point-wise confidence bands. Figure S4. Marginally estimated cumulative state-to-state transition hazards from state DM+HD to subsequent states along with bootstrap based 95% point-wise confidence bands. Figure S5. Marginally estimated cumulative state-to-state transition hazards from state DM+CKD to subsequent states along with bootstrap based 95% point-wise confidence bands. Figure S6. Marginally estimated cumulative state-to-state transition hazards from state IHD+CKD to subsequent states along with bootstrap based 95% point-wise confidence bands. Figure S7. Marginally estimated state cumulative state-to-state transition hazards form state DM+IHD+CKD to Death state along with bootstrap based 95% point-wise confidence bands. (PDF 406 kb

MOESM1 of Statistical prediction of immunity to placental malaria based on multi-assay antibody data for malarial antigens

Additional file 1. A heatmap that illustrates the observed antibody levels, along with clustering among antibodies (dendrogram in right) and 1377 patients (dendrogram on top). Below the top dendrogram, the panel with red and blue vertical bars represents malaria infected (in red) and none infected (in blue) subjects. Note that log-transformed antibody levels was used for the ease of visualization