This editorial overview provides an introduction to the Suicide and Life-Threatening Behaviors Special Issue "Analytic and Methodological Innovations for Suicide-Focused Research." We outline several challenges faced by modern suicidologists, such as the need to integrate different analytical and methodological techniques from other fields with the unique data problems in suicide research. Therefore, the overall aim of this issue was to provide up-to-date methodological and analytical guidelines, recommendations, and considerations when conducting suicide-focused research. The articles herein present this information in an accessible way for researchers/clinicians and do not require a comprehensive background in quantitative methods. We introduce the topics covered in this special issue, which include how to conduct power analyses using simulations, work with large data sets, use experimental therapeutics, and choose covariates, as well as open science considerations, decision-making models, ordinal regression, machine learning, network analysis, and measurement considerations. Many of the topics covered in this issue provide step-by-step walkthroughs using worked examples with the accompanied code in free statistical programs (i.e., R). It is our hope that these articles provide suicidologists with valuable information and strategies that can help overcome some of the past limitations of suicide research, and improve the methodological rigor of our field. Power analysis is critical for both planning future research samples and evaluating the reasonability of answers produced by pre-existing and fixed samples. Unfortunately, the irregularity of suicide-related data and the need for increasingly complex models in suicide research can make traditional power formulas inaccurate or even unusable. Ignoring these common problems risks both over- and under-recruiting, as well as obscuring the true quality of the results (up and down) to future reviewers and readers. A better option is to use Monte Carlo power simulations. These techniques produce answers that are equivalent to traditional power formulas when traditional assumptions are met, but produce more accurate results in the common case when those assumptions are violated. What follows is a tutorial on how suicide researchers can conduct such simulations. It begins by building the reader's intuition for why simulations work, followed by two worked examples in R. Discussion also includes guidelines for conducting and reporting simulations, along with answers to frequently asked questions. Appendices provide code examples researchers can model and adapt to their own simulations as needed. What follows is a tutorial on how suicide researchers can conduct such simulations. It begins by building the reader's intuition for why simulations work, followed by two worked examples in R. Discussion also includes guidelines for conducting and reporting simulations, along with answers to frequently asked questions. Appendices provide code examples researchers can model and adapt to their own simulations as needed. As recent advances in suicide research have underscored the importance of studying distinct suicide outcomes (i.e., suicidal thinking vs. behavior), there is a need to consider the theoretical meaningfulness of our statistical approach(es). As an alternative to more popular statistical methods, we introduce ordinal regression, detailing specific forms that are well-aligned to examine outcomes specific to suicide research. Ordinal regression models allow for assessment of the influences of covariates on the experience of lower (i.e., suicidal ideation) to higher (i.e., suicidal planning) suicide risk outcomes. As an empirical application, we fit a sequential ordinal regression model with 17 theoretically selected covariates and modeled category specific effects for each covariate. Results detailed from depression and presence of nonsuicidal self-injury demonstrate the utility of ordinal regression in consideration of transitions across suicide outcomes. Ordinal regression models may be particularly informative in identifying risk factors unique to each suicide outcome, which has the potential to meaningfully inform theoretical models of suicide and suicide risk prediction. Results detailed from depression and presence of nonsuicidal self-injury demonstrate the utility of ordinal regression in consideration of transitions across suicide outcomes. Ordinal regression models may be particularly informative in identifying risk factors unique to each suicide outcome, which has the potential to meaningfully inform theoretical models of suicide and suicide risk prediction. Suicidal behavior is the result of complex interactions between many different factors that change over time. A network perspective may improve our understanding of these complex dynamics. Within the network perspective, psychopathology is considered to be a consequence of symptoms that directly interact with one another in a network structure. https://www.selleckchem.com/products/furimazine.html To view suicidal behavior as the result of such a complex system is a good starting point to facilitate moving away from traditional linear thinking. To review the existing paradigms and theories and their application to suicidal behavior. In the first part of this paper, we introduce the relevant concepts within network analysis such as network density and centrality. Where possible, we refer to studies that have applied these concepts within the field of suicide prevention. In the second part, we move one step further, by understanding the network perspective as an initial step toward complex system theory. The latter is a branch of science that models interactnd this complexity. The application of concepts from complexity science to the field of psychopathology and suicide research offers exciting and promising possibilities for our understanding and prevention of suicide. Clinicians and scientists are increasingly conceptualizing suicidal behavior as the result of the complex interaction between many different biological, social, and psychological risk and protective factors. Novel statistical techniques such as network analysis can help the field to better understand this complexity. The application of concepts from complexity science to the field of psychopathology and suicide research offers exciting and promising possibilities for our understanding and prevention of suicide.