Holgersenchristie2732

We hope that "lessons learned" from the ketamine literature will provide a blueprint for all researchers evaluating rapid-acting treatments for suicidal thoughts, whether pharmacologic or psychotherapeutic.

Although causal inference is often straightforward in experimental contexts, few research questions in suicide are amenable to experimental manipulation and randomized control. Instead, suicide prevention specialists must rely on observational data and statistical control of confounding variables to make effective causal inferences. We provide a brief summary of recent covariate practice and a tutorial on casual inference tools for covariate selection in suicide research.

We provide an introduction to modern causal inference tools, suggestions for statistical control selection, and demonstrations using simulated data.

Statistical controls are often mistakenly selected due to their significant correlation with other study variables, their consistency with previous research, or no explicit reason at all. We clarify what it means to control for a variable and when controlling for the wrong covariates systematically distorts results. We describe directed acyclic graphs (DAGs) and tools for identifying the right choice of covariates. Finally, we provide four best practices for integrating causal inference tools in future studies.

The use of causal model tools, such as DAGs, allows researchers to carefully and thoughtfully select statistical controls and avoid presenting distorted findings; however, limitations of this approach are discussed.

The use of causal model tools, such as DAGs, allows researchers to carefully and thoughtfully select statistical controls and avoid presenting distorted findings; however, limitations of this approach are discussed.

Categorical data analysis is relevant to suicide risk and prevention research that focuses on discrete outcomes (e.g., suicide attempt status). Unfortunately, results from these analyses are often misinterpreted and not presented in a clinically tangible manner. We aimed to address these issues and highlight the relevance and utility of categorical methods in suicide research and clinical assessment. Additionally, we introduce relevant basic machine learning methods concepts and address the distinct utility of the current methods.

We review relevant background concepts and pertinent issues with references to helpful resources. We also provide non-technical descriptions and tutorials of how to convey categorical statistical results (logistic regression, receiver operating characteristic [ROC] curves, area under the curve [AUC] statistics, clinical cutoff scores) for clinical context and more intuitive use.

We provide comprehensive examples, using simulated data, and interpret results. We also note important considerations for conducting and interpreting these analyses. We provide a walk-through demonstrating how to convert logistic regression estimates into predicted probability values, which is accompanied by Appendices demonstrating how to produce publication-ready figures in R and Microsoft Excel.

Improving the translation of statistical estimates to practical, clinically tangible information may narrow the divide between research and clinical practice.

Improving the translation of statistical estimates to practical, clinically tangible information may narrow the divide between research and clinical practice.

Text-based responses may provide significant contributions to suicide risk prediction, yet research including text data is limited. This may be due to a lack of exposure and familiarity with statistical analyses for this data structure.

The current study provides an overview of data processing and statistical algorithms for text data, guided by an empirical example of 947 online participants who completed both open-ended items and traditional self-report measures. We give an introduction to a number of text-based statistical approaches, including dictionary-based methods, topic modeling, word embeddings, and deep learning.

We analyze responses from the open-ended question "How do you feel today?", detailing characteristics of the responses, as well as predicting past-year suicidal ideation.

We see the analysis of text from social media, open-ended questions, and other text sources (i.e., medical records) as an important form of complementary assessment to traditional scales, shedding insight on what we are missing in our current set of questionnaires, which may ultimately serve to improve both our understanding and prediction of suicide.

We see the analysis of text from social media, open-ended questions, and other text sources (i.e., medical records) as an important form of complementary assessment to traditional scales, shedding insight on what we are missing in our current set of questionnaires, which may ultimately serve to improve both our understanding and prediction of suicide.

Suicide risk is a nonlinear temporal process, but the ways in which suicide-focused interventions have statistically examined risk effects have ignored these nonlinearities. find more This paper highlights the potential benefits of using data analytic methods that account for nonlinear change patterns.

Using a dynamical systems perspective, interventions are framed in terms of attractor dynamics. An attractor has three primary qualities where an intervention can have an effect. These correspond to contextual differences, shifts in the underlying temporal patterns, and changes in the stability of the temporal pattern.

It is argued that the ideal effect is one in which there is both an observed change in stability and a shift in the underlying temporal pattern toward less risk. Other types of intervention effects can have alternate explanations that are less desirable. Mean, variance, and growth differences are discussed within a systems context, and an example model is provided using Latent Change Score Modeling (McArdle, Annual Review of Psychology, 60, 2009, 577-605).

It is argued that the ideal effect is one in which there is both an observed change in stability and a shift in the underlying temporal pattern toward less risk. Other types of intervention effects can have alternate explanations that are less desirable. Mean, variance, and growth differences are discussed within a systems context, and an example model is provided using Latent Change Score Modeling (McArdle, Annual Review of Psychology, 60, 2009, 577-605).