Suicide research is vitally important, yet-like psychology research more broadly-faces methodological challenges. In recent years, researchers have raised concerns about standard practices in psychological research, concerns that apply to suicide research and raise questions about its robustness and validity. In the present paper, we review these concerns and the corresponding solutions put forth by the "open science" community. These include using open science platforms, pre-registering studies, ensuring reproducible analyses, using high-powered studies, ensuring open access to research materials and products, and conducting replication studies. We build upon existing guides, address specific obstacles faced by suicide researchers, and offer a clear set of recommended practices for suicide researchers. In particular, we consider challenges that suicide researchers may face in seeking to adopt "open science" practices (e.g., prioritizing large samples) and suggest possible strategies that the field may use in order to ensure robust and transparent research, despite these challenges. To provide researchers, clinicians and policy makers with a primer to study designs, statistical approaches and graphical reporting methods for suicide research in real world data (RWD). Study designs, statistical method and graphical reporting standards are detailed with examples from the recently published literature. Data sources and codes for identifying suicidal behavior are described. Study designs are described in detail for post-market surveillance, retrospective cohort studies, case control and nested case-control studies, and self-controlled (within-individual) studies including applications of marginal structural models. Graphical reporting of designs is described using an original research study. Compared to RCTs, RWE studies offer larger sample sizes, greater generalizability, and real-world validity. However, these non-experimental data risk uncontrolled confounding and potential introduction of bias unless data, design and statistical approaches are rigorously aligned.Compared to RCTs, RWE studies offer larger sample sizes, greater generalizability, and real-world validity. However, these non-experimental data risk uncontrolled confounding and potential introduction of bias unless data, design and statistical approaches are rigorously aligned. To present an approach for integrating recently developed methods in behavioral economics into suicidology research. At present, existing applications of delay discounting in suicidology have focused predominantly on hypothetical choices related to monetary value as a proxy to "risky" choices linked to unsafe or suicidal behavior. In this report, we outline a more targeted approach that directly indexes choices related to treatment in suicide prevention initiatives and incorporates the strengths afforded by multi-level modeling. This more targeted approach precludes the need for multi-step comparisons (improving power), avoids compressing choice variability across delays into individual values (improving precision), and better accommodates decision-making at the upper and lower extremes (improving reliability). We present this analytical approach within the context of a Hypothetical Firearm Decision-making Task with simulated participants. A simulated study is provided to illustrate how this approach can be used to evaluate how individuals make temporally delayed decisions related to treatment for suicidal behavior (i.e., temporarily limiting their access to firearms while undergoing treatment). The results of this simulated study are provided to illustrate how more advanced behavioral decision-making models can be used to supplement existing research methods in suicidology.The results of this simulated study are provided to illustrate how more advanced behavioral decision-making models can be used to supplement existing research methods in suicidology.Interpersonal risk and resilience factors are prominent in current conceptual models of suicide. A growing body of empirical evidence links suicidal thoughts and behaviors to a range of interpersonal phenomenon adding further support to the value of this line of inquiry. At present, research on interpersonal phenomenon focuses on assessing individuals' perceptions of interpersonal phenomenon, such as appraisals of burdensomeness, experienced loneliness, and thwarted belongingness. As this line of research continues to develop, we argue that it would be valuable to consider incorporating conceptual models of interpersonal phenomenon and corresponding methodological approaches from closely allied fields. After providing a brief overview of interpersonal models of suicide, we present an introduction to conceptual models of interpersonal phenomenon developed in relationship science, describe how these models can be applied to the study of interpersonal phenomenon in suicide research, and close with a guided tutorial on data collection and statistical analysis methods for testing hypotheses derived from these conceptual approaches. References for additional reading are provided, and the Appendix S1 provides simulated data sets and statistical code for the analyses in the tutorial section. To introduce the research methods of computerized text mining and its possible applications in suicide research and to demonstrate the procedures of applying a specific text mining area, document classification, to a suicide-related study. A systematic search of academic papers that applied text mining methods to suicide research was conducted. Relevant papers were reviewed focusing on their research objectives and sources of data. Furthermore, a case of using natural language processing and document classification methods to analyze a large amount of suicide news was elaborated to showcase the methods. Eighty-six papers using text mining methods for suicide research have been published since 2001. The most common research objective (72.1%) was to classify which documents exhibit suicide risk or were written by suicidal people. The most frequently used data source was online social media posts (45.3%), followed by e-healthcare records (25.6%). GF120918 purchase For the news classification case, the top three classifiers trained for classification tasks achieved 84% or higher accuracy.