The aim of Topic Group5 is to provide accessible and accurate guidance in the design of observational studies, and our efforts have focused on exposition of established designs. Collaboration with other Topic Groups in STRATOS offers potential opportunities for investigation of new designs or design adaptations needed for emerging areas of research. Some examples follow. 1. Many observational study designs, such as case-cohort and nested case-control designs, can be regarded as subsampling a cohort with missingness by design. There may be opportunities for strengthening such designs by augmenting the samples or by obtaining ancillary data on all cohort members to strengthen the analyses of the subsampled data(1) (TG1, TG8). 2. Dose-response modeling is an important goal of some observational studies. What aspects of study design can improve information for dose-response modeling (TG2)? 3. Validation sub-samples can improve inference from error-prone observational data from electronic data bases(2). Can one advance this approach by judiciously selecting the validation sub-samples (TG4)? 4. The development and validation of risk models for predicting incident disease, for diagnosis and for prognosis require data for model-building and for validation. Often, the data for independent validation do not include complete covariate information on some or all members of the validation sample. Can the design of the validation sample be improved by including supplemental data (TG6)? 5. There has been an explosion of interest in causal analysis and related topics such as Mendelian randomization and mediation analysis. What work on design has been done in these areas, and what design considerations might improve studies in these areas (TG7)? 6. The design and analysis of observational studies with high-dimensional outcomes or exposure data pose special challenges in data quality and preprocessing, control of false positive discoveries, and replicability of results. Is it possible to improve the design of a sequence of such studies for the discovery and replicability of associations with phenotype (TG9)? 7. Study design can promote data quality and completeness, limit biases in estimates of exposure effects from errors in laboratory measurements, improve control for confounding, and limit or help define selection biases. Such issues are of importance to all TGs. These 7 topics are meant to start a conversation that will lead to more and better suggestions for collaboration on design issues by members of the various TGs. 1. Breslow NE, Lumley T, Ballantyne CM, Chambless LE, Kulich M. Using the Whole Cohort in the Analysis of Case-Cohort Data. Am J Epidemiol. 2009;169(11):1398-405. 2. Oh EJ, Shepherd BE, Lumley T, Shaw PA. Considerations for analysis of time-to-event outcomes measured with error: Bias and correction with SIMEX. Stat Med. 2018;37(8):1276-89. (Presentation 40 min. + Discussion 20 min.)