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Testing plausible biopsychosocial models in diverse community samples: Common pitfalls and strategies.

ABSTRACT: It is imperative that research interrogating the biological pathways linking stress processes to health continue to translate the results of basic, preclinical experimental research to diverse and under-represented populations, particularly those at elevated risk for morbidity and mortality. Conducting research within these populations and in community settings involves a number of challenges that ultimately contribute to their rarity and uneven quality in the scientific literature. In this review, we summarize the experiences and insights of members of an expert panel on this topic held at the 2018 meeting of the International Society of Psychoneuroendocrinology in Newport Beach, CA. The goals of the session were to identify challenges and share strategies for testing plausible biopsychosocial models within diverse community samples in order to encourage others and improve future research. The present paper is organized into three themes: 1) Recruitment and retention, 2) Collecting biological samples outside of the laboratory, 3) Data analysis, interpretation, and dissemination. Our goal in composing this overview of the conference session was to share within the field of psychoneuroendocrinology the challenges inherent in translating basic research to community populations.

SUBMITTER: Kuhlman KR

PROVIDER: S-EPMC6635037 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Testing plausible biopsychosocial models in diverse community samples: Common pitfalls and strategies.

Kuhlman Kate Ryan KR Urizar Guido G GG Robles Theodore F TF Yim Ilona S IS Dunkel Schetter Christine C

Psychoneuroendocrinology 20190520

It is imperative that research interrogating the biological pathways linking stress processes to health continue to translate the results of basic, preclinical experimental research to diverse and under-represented populations, particularly those at elevated risk for morbidity and mortality. Conducting research within these populations and in community settings involves a number of challenges that ultimately contribute to their rarity and uneven quality in the scientific literature. In this revi ...[more]

PMID: 31150964

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Project description:1. The prevalence of phylogenetic niche conservatism (PNC) in nature is still a conflicting issue. Disagreement arises from confusion over its precise definition and the variety of approaches to measure its prevalence. Recent work highlighted that common measures of PNC strongly depend on the assumptions of the underlying model of niche evolution. However, this warning has not been well recognized in the applied literature and questionable approaches are still frequently applied. 2. The aim of this paper is to draw attention to the assumptions underlying commonly applied simple measures of PNC. We used a series of simulations to illustrate how misleading results can be if assumptions of niche evolution are violated, that the violation of assumptions is a common phenomenon and that testing assumptions requires in-depth pre-test. 3. We conclude that the seemingly simple measures of PNC, such as phylogenetic sign6al and evolutionary rate, are not so easy to apply if one accounts for the necessity to test model assumptions. In addition, these measures can be difficult to interpret. The common assumption that strong phylogenetic signal indicates PNC will be often invalid. In addition, the interpretation of some measures, e.g. the conclusion that evolutionary rate is slow enough to indicate PNC, requires a comparison with another clade, another trait or well-developed null model assumptions and thus additional data. 4. We suggest that studies investigating PNC should always compare alternative evolutionary models, and that model comparisons should in particular include flexible niche evolution models such as multiple-optima OU models, although these are computational intensive. These models are directly inherited from the concept of macro-evolutionary adaptive landscape, and can indicate PNC either by relative few peak shifts or by narrow peaks in the adaptive landscape. A test of PNC thus requires comparing these parameters of the macroevolutionary landscape between clades or time periods. 5. The general prevalence of PNC in nature should be evaluated only based on studies keeping up to the high standards of communicating the used definition of PNC, testing the assumptions made in the modelling approaches and including newly developed models in a model comparison approach.

Project description:BackgroundPneumonitis is one of the most common adverse events induced by the use of immune checkpoint inhibitors (ICI), accounting for a 20% of all ICI-associated deaths. Despite numerous efforts to identify risk factors and develop predictive models, there is no clinically deployed risk prediction model for patient risk stratification or for guiding subsequent monitoring. We believe this is due to systemic suboptimal approaches in study designs and methodologies in the literature. The nature and prevalence of different methodological approaches has not been thoroughly examined in prior systematic reviews.MethodsThe PubMed, medRxiv and bioRxiv databases were used to identify studies that aimed at risk factor discovery and/or risk prediction model development for ICI-induced pneumonitis (ICI pneumonitis). Studies were then analysed to identify common methodological pitfalls and their contribution to the risk of bias, assessed using the QUIPS and PROBAST tools.ResultsThere were 51 manuscripts eligible for the review, with Japan-based studies over-represented, being nearly half (24/51) of all papers considered. Only 2/51 studies had a low risk of bias overall. Common bias-inducing practices included unclear diagnostic method or potential misdiagnosis, lack of multiple testing correction, the use of univariate analysis for selecting features for multivariable analysis, discretization of continuous variables, and inappropriate handling of missing values. Results from the risk model development studies were also likely to have been overoptimistic due to lack of holdout sets.ConclusionsStudies with low risk of bias in their methodology are lacking in the existing literature. High-quality risk factor identification and risk model development studies are urgently required by the community to give the best chance of them progressing into a clinically deployable risk prediction model. Recommendations and alternative approaches for reducing the risk of bias were also discussed to guide future studies.

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Testing plausible biopsychosocial models in diverse community samples: Common pitfalls and strategies.

Publications

Testing plausible biopsychosocial models in diverse community samples: Common pitfalls and strategies.

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