MOESM3 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

Additional file 3. Sensitivity and PPV of individual Shewhart’s control rules for predicting manic episodes within the next four weeks using individual-moving range charts

MOESM4 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

Additional file 4. Sensitivity and PPV of individual Shewhart’s control rules for predicting depressive episodes within the next four weeks using individual-moving range charts

MOESM6 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

Additional file 6. Sensitivity and PPV of run sum procedure for predicting depressive episodes within the next four weeks

MOESM8 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

Additional file 8. Sensitivity and PPV of any Shewhart’s control rule’ for predicting manic and depressive episodes within the next four weeks, when a depressive episode is defined as a QIDS score ≥ 11 and a manic episode is defined as an ASRM score ≥ 6

MOESM7 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

Additional file 7. Shewhart’s control rules sensitivity and PPV for predicting manic episodes within the next eight weeks

MOESM5 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

Additional file 5. Sensitivity and PPV of run sum procedure for predicting manic episodes within the next four weeks

MOESM2 of Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

Additional file 2. Sensitivity and PPV of individual Shewhart’s control rules for predicting depressive episodes within the next four weeks using universal and personalized X-bar charts

Guidelines for studying diverse types of compound weather and climate events

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (1) preconditioned, (2) multivariate, (3) temporally compounding, and (4) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (1) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (2) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (3) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (4) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors