A practical guide to selecting models for exploration, inference, and prediction in ecology.
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ABSTRACT:
SUBMITTER: Tredennick AT
PROVIDER: S-EPMC8187274 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This poses a difficult modeling scenario that entails large models with a comparably limited number of observations given the number of parameters. We here describe a particular strategy to overcoming this challenge, called regularization. Regularization, a method to penalize model complexity during estimation, has proven a viable option for estimating parameters in this small n, large p setting, but has so far mostly been used in linear regression models. Here we show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts, and how it can be extended to regularized structural equation modeling (Jacobucci, Grimm, & McArdle, 2016). Our approach is evaluated through the use of a simulation study, showing that regularized SEM outperforms traditional SEM estimation methods in situations with a large number of predictors and small sample size. We illustrate the power of this approach in two empirical examples: modeling the neural determinants of visual short term memory, as well as identifying demographic correlates of stress, anxiety and depression. We illustrate the performance of the method and discuss practical aspects of modeling empirical data, and provide a step-by-step online tutorial.
| S-EPMC6713564 | biostudies-literature
Project description:BackgroundActive surveillance (AS) is a management option for men diagnosed with lower risk prostate cancer. There is wide variation in all aspects of AS internationally, from patient selection to investigations and follow-up intervals, and a lack of clear evidence on the optimal approach to AS. This study aimed to provide guidance for clinicians from an international panel of prostate cancer experts.MethodsA modified Delphi approach was undertaken, utilising two rounds of online questionnaires followed by a face-to-face workshop. Participants indicated their level of agreement with statements relating to patient selection for AS via online questionnaires on a 7-point Likert scale. Factors not achieving agreement were iteratively developed between the two rounds of questionnaires. Draft statements were presented at the face-to-face workshop for discussion and consensus building.Results12 prostate cancer experts (9 urologists, 2 academics, 1 radiation oncologist) participated in this study from a range of geographical regions (4 USA, 4 Europe, 4 Australia). Complete agreement on statements presented to the participants was 29.4% after Round One and 69.0% after Round Two. Following robust discussions at the face-to-face workshop, agreement was reached on the remaining statements. PSA, PSA density, Multiparametric MRI, and systematic biopsy (with or without targeted biopsy) were identified as minimum diagnostic tests required upon which to select patients to recommend AS as a treatment option for prostate cancer. Patient factors and clinical parameters that identified patients appropriate to potentially receive AS were agreed. Genetic and genomic testing was not recommended for use in clinical decision-making regarding AS.ConclusionsThe lack of consistency in the practice of AS for men with lower risk prostate cancer between and within countries was reflected in this modified Delphi study. There are, however, areas of common practice and agreement from which clinicians practicing in the current environment can use to inform their clinical practice to achieve the best outcomes for patients.
| S-EPMC7863517 | biostudies-literature
Project description:BackgroundAs in other fields of medicine, development of new medications for management of neuropathic pain has been difficult since preclinical rodent models do not necessarily translate to the clinics. Aside from ongoing pain with burning or shock-like qualities, neuropathic pain is often characterized by pain hypersensitivity (hyperalgesia and allodynia), most often towards mechanical stimuli, reflecting sensitization of neural transmission.Data treatmentWe therefore performed a systematic literature review (PubMed-Medline, Cochrane, WoS, ClinicalTrials) and semi-quantitative meta-analysis of human pain models that aim to induce central sensitization, and generate hyperalgesia surrounding a real or simulated injury.ResultsFrom an initial set of 1569 reports, we identified and analysed 269 studies using more than a dozen human models of sensitization. Five of these models (intradermal or topical capsaicin, low- or high-frequency electrical stimulation, thermode-induced heat-injury) were found to reliably induce secondary hyperalgesia to pinprick and have been implemented in multiple laboratories. The ability of these models to induce dynamic mechanical allodynia was however substantially lower. The proportion of subjects who developed hypersensitivity was rarely provided, giving rise to significant reporting bias. In four of these models pharmacological profiles allowed to verify similarity to some clinical conditions, and therefore may inform basic research for new drug development.ConclusionsWhile there is no single "optimal" model of central sensitization, the range of validated and easy-to-use procedures in humans should be able to inform preclinical researchers on helpful potential biomarkers, thereby narrowing the translation gap between basic and clinical data.SignificanceBeing able to mimic aspects of pathological pain directly in humans has a huge potential to understand pathophysiology and provide animal research with translatable biomarkers for drug development. One group of human surrogate models has proven to have excellent predictive validity: they respond to clinically active medications and do not respond to clinically inactive medications, including some that worked in animals but failed in the clinics. They should therefore inform basic research for new drug development.
| S-EPMC8360051 | biostudies-literature
Project description:BackgroundA multitude of theories, models and frameworks relating to implementing evidence-based practice in health care exist, which can be overwhelming for clinicians and clinical researchers new to the field of implementation science. Clinicians often bear responsibility for implementation, but may be unfamiliar with theoretical approaches designed to inform or understand implementation.Main textIn this article, a multidisciplinary group of clinicians and health service researchers present a pragmatic guide to help clinicians and clinical researchers understand what implementation theories, models and frameworks are; how a theoretical approach to implementation might be used; and some prompts to consider when selecting a theoretical approach for an implementation project. Ten commonly used and highly cited theoretical approaches are presented, none of which have been utilised to their full potential in the literature to date. Specifically, theoretical approaches tend to be applied retrospectively to evaluate or interpret findings from a completed implementation project, rather than being used to plan and design theory-informed implementation strategies which would intuitively have a greater likelihood of success. We emphasise that there is no right or wrong way of selecting a theoretical approach, but encourage clinicians to carefully consider the project's purpose, scope and available data and resources to allow them to select an approach that is most likely to "value-add" to the implementation project.ConclusionBy assisting clinicians and clinical researchers to become confident in selecting and applying theoretical approaches to implementation, we anticipate an increase in theory-informed implementation projects. This then will contribute to more nuanced advice on how to address evidence-practice gaps and ultimately to contribute to better health outcomes.
| S-EPMC6236961 | biostudies-literature
Project description:There are numerous statistical models used to identify individuals at high risk of cancer due to inherited mutations. Mendelian models predict future risk of cancer by using family history with estimated cancer penetrances (age- and sex-specific risk of cancer given the genotype of the mutations) and mutation prevalences. However, there is often residual risk heterogeneity across families even after accounting for the mutations in the model, due to environmental or unobserved genetic risk factors. We aim to improve Mendelian risk prediction by incorporating a frailty model that contains a family-specific frailty vector, impacting the cancer hazard function, to account for this heterogeneity. We use a discrete uniform population frailty distribution and implement a marginalized approach that averages each family's risk predictions over the family's frailty distribution. We apply the proposed approach to improve breast cancer prediction in BRCAPRO, a Mendelian model that accounts for inherited mutations in the BRCA1 and BRCA2 genes to predict breast and ovarian cancer. We evaluate the proposed model's performance in simulations and real data from the Cancer Genetics Network and show improvements in model calibration and discrimination. We also discuss alternative approaches for incorporating frailties and their strengths and limitations.
| S-EPMC7895423 | biostudies-literature
Project description:In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a 'predictimand' framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference.
| S-EPMC7387325 | biostudies-literature
Project description:Frailty is a common syndrome that is associated with vulnerability to poor health outcomes. Frail older people have increased risk of morbidity, institutionalization and death, resulting in burden to individuals, their families, health care services and society. Assessment and treatment of the frail individual provide many challenges to clinicians working with older people. Despite frailty being increasingly recognized in the literature, there is a paucity of direct evidence to guide interventions to reduce frailty. In this paper we review methods for identification of frailty in the clinical setting, propose a model for assessment of the frail older person and summarize the current best evidence for treating the frail older person. We provide an evidence-based framework that can be used to guide the diagnosis, assessment and treatment of frail older people.
| S-EPMC3146844 | biostudies-other
Project description:Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting breeding values for agronomically important traits. In this study, the best prediction strategies were determined for yield, 1000 grain weight, number of grains per panicle, and number of tillers per plant of hybrid rice (derived from recombinant inbred lines) by comprehensively evaluating all possible combinations of omic datasets with different prediction methods. It was demonstrated that, in rice, the predictions using a combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including least absolute shrinkage and selection operator (LASSO), stochastic search variable selection (SSVS), support vector machines with radial basis function and epsilon regression (SVM-R(EPS)), support vector machines with radial basis function and nu regression (SVM-R(NU)), support vector machines with polynomial kernel and epsilon regression (SVM-P(EPS)), support vector machines with polynomial kernel and nu regression (SVM-P(NU)) and partial least squares regression (PLS). This study has provided guidelines for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability. The answer to these questions will benefit academic research and will also greatly reduce the operative cost for the industry which specializes in breeding and selection.
| S-EPMC6781126 | biostudies-literature
Project description:The National Lung Screening Trial (NLST) demonstrated that low dose computed tomography (LDCT) screening could reduce lung cancer mortality by 20% in high-risk individuals. The United States Preventive Services Task Force (USPSTF) and Centers for Medicare and Medicaid Services (CMS) approved lung cancer screening. The NLST, USPSTF and CMS define high risk as smoking ?30 pack-years, smoking within the past 15 years, and being ages 55-74, 55-80 or 55-77. Retrospective studies demonstrated selection using model-estimated risk is superior to NLST-like criteria: higher sensitivity and positive predictive value (PPV), more deaths averted and higher cost-effectiveness. Projects are underway that may additionally support use of risk to determine eligibility. Firstly, the International Lung Screen Trial (ILST) is prospectively enrolling 4,000 individuals for screening if individuals have PLCOm2012 model risk ?1.5% or are USPSTF+ve. Six-year follow-up will allow comparisons. Interim results support the risk approach. Secondly, Cancer Care Ontario started the Lung Cancer Screening Pilot for People at High Risk in order to find optimal design for province-wide programmatic screening. They are enrolling 3,000 individuals to screening based on PLCOm2012 risk ?2%. Some hesitation to recommend screening selection based on model risk comes from the observation that selected individuals are older, have more comorbidities, are expected to have fewer life years and quality-adjusted life years (QALY) and are more likely to die from competing causes. We show that 25.6% of NLST eligible smokers are at low risk (6-year lung cancer incidence proportion =0.008). This group will not benefit from screening but has lower age, fewer comorbidities and fewer competing causes of death. When they are excluded from the NLST+ve group, age, comorbidity count and competing causes of death are similar to those in the PLCOm2012+ve group. In some jurisdictions, model-based lung cancer screening selection needs to take into consideration the elevated risk in blacks and indigenous peoples.
| S-EPMC6037970 | biostudies-other
Project description:Single-molecule fluorescence resonance energy transfer (smFRET) is one of the most general and adaptable single-molecule techniques. Despite the explosive growth in the application of smFRET to answer biological questions in the last decade, the technique has been practiced mostly by biophysicists. We provide a practical guide to using smFRET, focusing on the study of immobilized molecules that allow measurements of single-molecule reaction trajectories from 1 ms to many minutes. We discuss issues a biologist must consider to conduct successful smFRET experiments, including experimental design, sample preparation, single-molecule detection and data analysis. We also describe how a smFRET-capable instrument can be built at a reasonable cost with off-the-shelf components and operated reliably using well-established protocols and freely available software.
| S-EPMC3769523 | biostudies-literature