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1.
Res Synth Methods ; 2022 Oct 05.
Artículo en Inglés | MEDLINE | ID: mdl-36200133

RESUMEN

A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure-outcome associations and between-study heterogeneity in IPD-MA, where the extent and nature of exposure misclassification may vary across studies. We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure-outcome association, and was more accurate than analyses restricted to gold standard measurements. Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent.

2.
Clin Kidney J ; 15(10): 1924-1931, 2022 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-36158156

RESUMEN

Background: Previous studies suggest that haemodiafiltration reduces mortality compared with haemodialysis in patients with end-stage kidney disease (ESKD), but the controversy surrounding its benefits remains and it is unclear to what extent individual patients benefit from haemodiafiltration. This study is aimed to develop and validate a treatment effect prediction model to determine which patients would benefit most from haemodiafiltration compared with haemodialysis in terms of all-cause mortality. Methods: Individual participant data from four randomized controlled trials comparing haemodiafiltration with haemodialysis on mortality were used to derive a Royston-Parmar model for the prediction of absolute treatment effect of haemodiafiltration based on pre-specified patient and disease characteristics. Validation of the model was performed using internal-external cross validation. Results: The median predicted survival benefit was 44 (Q1-Q3: 44-46) days for every year of treatment with haemodiafiltration compared with haemodialysis. The median survival benefit with haemodiafiltration ranged from 2 to 48 months. Patients who benefitted most from haemodiafiltration were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine and albumin levels. Internal-external cross validation showed adequate discrimination and calibration. Conclusion: Although overall mortality is reduced by haemodiafiltration compared with haemodialysis in ESKD patients, the absolute survival benefit can vary greatly between individuals. Our results indicate that the effects of haemodiafiltration on survival can be predicted using a combination of readily available patient and disease characteristics, which could guide shared decision-making.

3.
BMJ ; 378: e069881, 2022 07 12.
Artículo en Inglés | MEDLINE | ID: mdl-35820692

RESUMEN

OBJECTIVE: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. DESIGN: Two stage individual participant data meta-analysis. SETTING: Secondary and tertiary care. PARTICIPANTS: 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. DATA SOURCES: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge. MODEL SELECTION AND ELIGIBILITY CRITERIA: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. METHODS: Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. MAIN OUTCOME MEASURES: 30 day mortality or in-hospital mortality. RESULTS: Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28). CONCLUSION: The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.


Asunto(s)
COVID-19 , Modelos Estadísticos , Análisis de Datos , Mortalidad Hospitalaria , Humanos , Pronóstico
4.
Sci Total Environ ; 847: 157584, 2022 Nov 15.
Artículo en Inglés | MEDLINE | ID: mdl-35882339

RESUMEN

Freshwater ecosystems are an important source of the greenhouse gas methane (CH4), and their emissions are expected to increase due to eutrophication. Two commonly applied management techniques to reduce eutrophication are the addition of phosphate-binding lanthanum modified bentonite (LMB, trademark Phoslock©) and dredging, but their effect on CH4 emissions is still poorly understood. Here, this study researched how LMB and dredging affected CH4 emissions using a full-factorial mesocosm design monitored for 18 months. The effect was tested by measuring diffusive and ebullitive CH4 fluxes, plant community composition, methanogen and methanotroph activity and community composition, and a range of physicochemical water and sediment variables. LMB addition decreased total CH4 emissions, while dredging showed a trend towards decreasing CH4 emissions. Total CH4 emissions in all mesocosms were much higher in the summer of the second year, likely because of higher algal decomposition and organic matter availability. First, LMB addition lowered CH4 emissions by decreasing P-availability, which reduced coverage of the floating fern Azolla filiculoides, and thereby prevented anoxia and decreased surface water NH4+ concentrations, lowering CH4 production rates. Second, dredging decreased CH4 emissions in the first summer, possibly it removed the methanogenic community, and in the second year by preventing autumn and winter die-off of the rooted macrophyte Potamogeton cripsus. Finally, methanogen community composition was related to surface water NH4+ and O2, and porewater total phosphorus, while methanotroph community composition was related to organic matter content. To conclude, LMB addition and dredging not only improve water quality, but also decrease CH4 emissions, mitigating climate change.


Asunto(s)
Gases de Efecto Invernadero , Lagos , Bentonita , Ecosistema , Lagos/química , Lantano , Metano/análisis , Fosfatos , Fósforo/análisis
5.
Clin Microbiol Infect ; 28(12): 1558-1566, 2022 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-35788049

RESUMEN

BACKGROUND: Randomised controlled trials (RCTs) investigated analgesics, herbal formulations, delayed prescription of antibiotics, and placebo to prevent overprescription of antibiotics in women with uncomplicated urinary tract infections (uUTI). OBJECTIVES: To estimate the effect of these strategies and to identify symptoms, signs, or other factors that indicate a benefit from these strategies. DATA SOURCES: MEDLINE, EMBASE, Web of Science, LILACS, Cochrane Database of Systematic Reviews and of Controlled Trials, and ClinicalTrials. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND INTERVENTIONS: RCTs investigating any strategies to reduce antibiotics vs. immediate antibiotics in adult women with uUTI in primary care. METHODS: We extracted individual participant data (IPD) if available, otherwise aggregate data (AD). Bayesian random-effects meta-analysis of the AD was used for pairwise comparisons. Candidate moderators and prognostic indicators of treatment effects were investigated using generalised linear mixed models based on IPD. RESULTS: We analysed IPD of 3524 patients from eight RCTs and AD of 78 patients. Non-antibiotic strategies increased the rates of incomplete recovery (OR 3.0; 95% credible interval (CrI), 1.7-5.5; Bayesian p-value (pB) = 0.0017; τ = 0.6), subsequent antibiotic treatment (OR 3.5; 95% CrI, 2.1-5.8; pB = 0.0003) and pyelonephritis (OR 5.6; 95% CrI, 2.3-13.9; pB = 0.0003). Conversely, they decreased overall antibiotic use by 63%. Patients positive for urinary erythrocytes and urine culture were at increased risk for incomplete recovery (OR 4.7; 95% CrI, 2.1-10.8; pB = 0.0010), but no difference was apparent where both were negative (OR 0.8; 95% CrI, 0.3-2.0; pB = 0.667). In patients treated using non-antibiotic strategies, urinary erythrocytes and positive urine culture were independent prognostic indicators for subsequent antibiotic treatment and pyelonephritis. CONCLUSIONS: Compared to immediate antibiotics, non-antibiotic strategies reduce overall antibiotic use but result in poorer clinical outcomes. The presence of erythrocytes and tests to confirm bacteria in urine could be used to target antibiotic prescribing.


Asunto(s)
Pielonefritis , Infecciones Urinarias , Femenino , Adulto , Humanos , Antibacterianos , Revisiones Sistemáticas como Asunto , Infecciones Urinarias/tratamiento farmacológico , Infecciones Urinarias/prevención & control , Pielonefritis/tratamiento farmacológico
6.
Colorectal Dis ; 24(11): 1285-1294, 2022 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-35712806

RESUMEN

AIM: The aim of this systematic review was to analyse recurrence rates after different surgical techniques for perineal hernia repair. METHOD: All original studies (n ≥ 2 patients) reporting recurrence rates after perineal hernia repair after abdominoperineal resection (APR) were included. The electronic database PubMed was last searched in December 2021. The primary outcome was recurrent perineal hernia. A weighted average of the logit proportions was determined by the use of the generic inverse variance method and random effects model. RESULTS: A total of 19 studies involving 172 patients were included. The mean age of patients was 64 ± 5.6 years and the indication for APR was predominantly cancer (99%, 170/172). The pooled percentage of recurrent perineal hernia was 39% (95% CI: 27%-52%) after biological mesh closure, 29% (95% CI: 21%-39%) after synthetic mesh closure, 37% (95% CI: 14%-67%) after tissue flap reconstruction only and 9% (95% CI: 1%-45%) after tissue flap reconstruction combined with mesh. CONCLUSION: Recurrence rates after mesh repair of perineal hernia are high, without a clear difference between biological and synthetic meshes. The addition of a tissue flap to mesh repair seemed to have a favourable outcome, which warrants further investigation.


Asunto(s)
Hernia Abdominal , Proctectomía , Humanos , Persona de Mediana Edad , Anciano , Mallas Quirúrgicas , Hernia Abdominal/etiología , Hernia Abdominal/cirugía , Proctectomía/efectos adversos , Perineo/cirugía , Herniorrafia/efectos adversos , Herniorrafia/métodos
7.
Knee Surg Sports Traumatol Arthrosc ; 30(11): 3871-3880, 2022 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-35508553

RESUMEN

PURPOSE: To determine the diagnostic value of injury history, physical examination, six syndesmosis tests and overall clinical suspicion for syndesmosis injury. METHODS: All athletes (> 18 yrs) with an acute ankle injury presenting within 7 days post-injury were assessed for eligibility. Acute ankle injuries were excluded if imaging studies demonstrated a frank fracture or 3 T MRI could not be acquired within 10 days post-injury. Standardized injury history was recorded, and physical examination was performed by an Orthopaedic Surgeon or Sports Medicine Physician. Overall clinical suspicion was documented prior to MRI. Multivariate logistic regression was used to determine the association between independent predictors and syndesmosis injury. RESULTS: Between September 2016 and July 2019, a total of 150 acute ankle injuries were included. The median time from injury to acute clinical evaluation was 2 days (IQR 2). Prior to clinical evaluation, the median patient reported Visual Analog Scale for pain was 8/10 (IQR 2). Syndesmosis injury was present in 26 acute ankle injuries. An eversion mechanism of injury had a positive LR 3.47 (CI 95% 1.55-7.77). The squeeze tests had a positive LR of 2.20 (CI 95% 1.29-3.77) and a negative LR of 0.68 (CI 95% 0.48-0.98). Overall clinical suspicion had a sensitivity of 73% (CI 95% 52-88) and negative predictive value of 89% (CI 95% 78-95). Multivariate regression analyses demonstrated significant association for eversion mechanism of injury (OR 4.99; CI 95% 1.56-16.01) and a positive squeeze test (OR 3.25; CI 95% 1.24-8.51). CONCLUSIONS: In an acute clinical setting with patients reporting high levels of ankle pain, a negative overall clinical suspicion reduces the probability of syndesmosis injury. Eversion mechanism of injury and a positive squeeze test are associated with higher odds of syndesmosis injury. LEVEL OF EVIDENCE: Level III.


Asunto(s)
Traumatismos del Tobillo , Fracturas Óseas , Medicina Deportiva , Traumatismos del Tobillo/diagnóstico , Articulación del Tobillo , Humanos , Dolor , Examen Físico/métodos
8.
Nature ; 602(7896): 240-244, 2022 02.
Artículo en Inglés | MEDLINE | ID: mdl-35140385

RESUMEN

Ferroics, especially ferromagnets, can form complex topological spin structures such as vortices1 and skyrmions2,3 when subjected to particular electrical and mechanical boundary conditions. Simple vortex-like, electric-dipole-based topological structures have been observed in dedicated ferroelectric systems, especially ferroelectric-insulator superlattices such as PbTiO3/SrTiO3, which was later shown to be a model system owing to its high depolarizing field4-8. To date, the electric dipole equivalent of ordered magnetic spin lattices driven by the Dzyaloshinskii-Moriya interaction (DMi)9,10 has not been experimentally observed. Here we examine a domain structure in a single PbTiO3 epitaxial layer sandwiched between SrRuO3 electrodes. We observe periodic clockwise and anticlockwise ferroelectric vortices that are modulated by a second ordering along their toroidal core. The resulting topology, supported by calculations, is a labyrinth-like pattern with two orthogonal periodic modulations that form an incommensurate polar crystal that provides a ferroelectric analogue to the recently discovered incommensurate spin crystals in ferromagnetic materials11-13. These findings further blur the border between emergent ferromagnetic and ferroelectric topologies, clearing the way for experimental realization of further electric counterparts of magnetic DMi-driven phases.

10.
J Clin Epidemiol ; 145: 29-38, 2022 05.
Artículo en Inglés | MEDLINE | ID: mdl-35045316

RESUMEN

OBJECTIVES: Among ID studies seeking to make causal inferences and pooling individual-level longitudinal data from multiple infectious disease cohorts, we sought to assess what methods are being used, how those methods are being reported, and whether these factors have changed over time. STUDY DESIGN AND SETTING: Systematic review of longitudinal observational infectious disease studies pooling individual-level patient data from 2+ studies published in English in 2009, 2014, or 2019. This systematic review protocol is registered with PROSPERO (CRD42020204104). RESULTS: Our search yielded 1,462 unique articles. Of these, 16 were included in the final review. Our analysis showed a lack of causal inference methods and of clear reporting on methods and the required assumptions. CONCLUSION: There are many approaches to causal inference which may help facilitate accurate inference in the presence of unmeasured and time-varying confounding. In observational ID studies leveraging pooled, longitudinal IPD, the absence of these causal inference methods and gaps in the reporting of key methodological considerations suggests there is ample opportunity to enhance the rigor and reporting of research in this field. Interdisciplinary collaborations between substantive and methodological experts would strengthen future work.


Asunto(s)
Enfermedades Transmisibles , Causalidad , Enfermedades Transmisibles/epidemiología , Humanos , Estudios Longitudinales
11.
NPJ Digit Med ; 5(1): 2, 2022 Jan 10.
Artículo en Inglés | MEDLINE | ID: mdl-35013569

RESUMEN

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy. PubMed, Web of Science, and the ACM Digital Library were searched, and AI experts were consulted. Topics were extracted from the identified literature and summarized across the six phases at the core of this review: (1) data preparation, (2) AIPM development, (3) AIPM validation, (4) software development, (5) AIPM impact assessment, and (6) AIPM implementation into daily healthcare practice. From 2683 unique hits, 72 relevant guidance documents were identified. Substantial guidance was found for data preparation, AIPM development and AIPM validation (phases 1-3), while later phases clearly have received less attention (software development, impact assessment and implementation) in the scientific literature. The six phases of the AIPM development, evaluation and implementation cycle provide a framework for responsible introduction of AI-based prediction models in healthcare. Additional domain and technology specific research may be necessary and more practical experience with implementing AIPMs is needed to support further guidance.

13.
BMJ Evid Based Med ; 27(2): 109-119, 2022 04.
Artículo en Inglés | MEDLINE | ID: mdl-33298465

RESUMEN

INTRODUCTION: High-quality randomised controlled trials (RCTs) provide the most reliable evidence on the comparative efficacy of new medicines. However, non-randomised studies (NRS) are increasingly recognised as a source of insights into the real-world performance of novel therapeutic products, particularly when traditional RCTs are impractical or lack generalisability. This means there is a growing need for synthesising evidence from RCTs and NRS in healthcare decision making, particularly given recent developments such as innovative study designs, digital technologies and linked databases across countries. Crucially, however, no formal framework exists to guide the integration of these data types. OBJECTIVES AND METHODS: To address this gap, we used a mixed methods approach (review of existing guidance, methodological papers, Delphi survey) to develop guidance for researchers and healthcare decision-makers on when and how to best combine evidence from NRS and RCTs to improve transparency and build confidence in the resulting summary effect estimates. RESULTS: Our framework comprises seven steps on guiding the integration and interpretation of evidence from NRS and RCTs and we offer recommendations on the most appropriate statistical approaches based on three main analytical scenarios in healthcare decision making (specifically, 'high-bar evidence' when RCTs are the preferred source of evidence, 'medium,' and 'low' when NRS is the main source of inference). CONCLUSION: Our framework augments existing guidance on assessing the quality of NRS and their compatibility with RCTs for evidence synthesis, while also highlighting potential challenges in implementing it. This manuscript received endorsement from the International Society for Pharmacoepidemiology.


Asunto(s)
Atención a la Salud , Proyectos de Investigación , Toma de Decisiones , Humanos , Ensayos Clínicos Controlados Aleatorios como Asunto
14.
Sci Rep ; 11(1): 22845, 2021 11 24.
Artículo en Inglés | MEDLINE | ID: mdl-34819535

RESUMEN

Scots pine is one of the most widely occurring pines, but future projections suggest a large reduction in its range, mostly at the southern European limits. A significant part of its range is located in the Caucasus, a global hot-spot of diversity. Pine forests are an important reservoir of biodiversity and endemism in this region. We explored demographic and biogeographical processes that shaped the genetic diversity of Scots pine in the Caucasus ecoregion and its probable future distribution under different climate scenarios. We found that the high genetic variability of the Caucasian populations mirrors a complex glacial and postglacial history that had a unique evolutionary trajectory compared to the main range in Europe. Scots pine currently grows under a broad spectrum of climatic conditions in the Caucasus, which implies high adaptive potential in the past. However, the current genetic resources of Scots pine are under high pressure from climate change. From our predictions, over 90% of the current distribution of Scots pine may be lost in this century. By threatening the stability of the forest ecosystems, this would dramatically affect the biodiversity of the Caucasus hot-spot.


Asunto(s)
Aclimatación/genética , Cambio Climático , Ecosistema , Evolución Molecular , Bosques , Genes de Plantas , Pinus sylvestris/genética , Árboles/genética , Biodiversidad , Conservación de los Recursos Naturales , ADN Mitocondrial/genética , ADN de Plantas/genética , Regulación de la Expresión Génica de las Plantas , Georgia (República) , Repeticiones de Microsatélite , Pinus sylvestris/crecimiento & desarrollo , Árboles/crecimiento & desarrollo , Turquia
15.
Stat Med ; 40(26): 5961-5981, 2021 11 20.
Artículo en Inglés | MEDLINE | ID: mdl-34402094

RESUMEN

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.


Asunto(s)
Ensayos Clínicos Controlados Aleatorios como Asunto , Causalidad , Humanos , Estudios Longitudinales
16.
Saudi J Biol Sci ; 28(7): 3783-3788, 2021 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-34220232

RESUMEN

Sponges accommodate a diverse group of microorganisms with varied metabolic capabilities. The bacterial associates of sponges are widely studied while our understanding of archaeal counterparts is scanty. In the present study, we report the archaeal associates of two sponges, Pseudoceratina purpurea (NCBI barcode: KX454492) and Cinachyra sp. (NCBI barcode: KX454495), found in the coral reef ecosystems of Gulf of Mannar, India. Archaea in the water column was predominated by members of class Halobacteria of Phylum Euryarchaeota (97%) followed by a minor fraction (3%) of Nitrosopumilus sp. of phylum Thaumarchaeota. Interestingly, Thaumarchaeota was identified as the sole archaeal population associated with the two sponges studied, among which Nitrosopumilus sp. occuppied 80 and 100% of the sequences in the clone library of P. purpurea and Cinachyra sp. respectively. Other archaea found in the P. purpurea were Nitrososphaera (10%) and unclassified ones (10%). The study identified Nitrosopumilus sp. as a unique symbiotic archaeon of sponges, P. purpurea and Cinachyra sp. The existence of host driven factors in selecting specific associates from a diverse group of archaea in the environment may need further investigations.

17.
Res Synth Methods ; 12(6): 796-815, 2021 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-34312994

RESUMEN

Ideally, a meta-analysis will summarize data from several unbiased studies. Here we look into the less than ideal situation in which contributing studies may be compromised by non-differential measurement error in the exposure variable. Specifically, we consider a meta-analysis for the association between a continuous outcome variable and one or more continuous exposure variables, where the associations may be quantified as regression coefficients of a linear regression model. A flexible Bayesian framework is developed which allows one to obtain appropriate point and interval estimates with varying degrees of prior knowledge about the magnitude of the measurement error. We also demonstrate how, if individual-participant data (IPD) are available, the Bayesian meta-analysis model can adjust for multiple participant-level covariates, these being measured with or without measurement error.


Asunto(s)
Teorema de Bayes , Humanos , Modelos Lineales
18.
Stat Med ; 40(15): 3533-3559, 2021 07 10.
Artículo en Inglés | MEDLINE | ID: mdl-33948970

RESUMEN

Prediction models often yield inaccurate predictions for new individuals. Large data sets from pooled studies or electronic healthcare records may alleviate this with an increased sample size and variability in sample characteristics. However, existing strategies for prediction model development generally do not account for heterogeneity in predictor-outcome associations between different settings and populations. This limits the generalizability of developed models (even from large, combined, clustered data sets) and necessitates local revisions. We aim to develop methodology for producing prediction models that require less tailoring to different settings and populations. We adopt internal-external cross-validation to assess and reduce heterogeneity in models' predictive performance during the development. We propose a predictor selection algorithm that optimizes the (weighted) average performance while minimizing its variability across the hold-out clusters (or studies). Predictors are added iteratively until the estimated generalizability is optimized. We illustrate this by developing a model for predicting the risk of atrial fibrillation and updating an existing one for diagnosing deep vein thrombosis, using individual participant data from 20 cohorts (N = 10 873) and 11 diagnostic studies (N = 10 014), respectively. Meta-analysis of calibration and discrimination performance in each hold-out cluster shows that trade-offs between average and heterogeneity of performance occurred. Our methodology enables the assessment of heterogeneity of prediction model performance during model development in multiple or clustered data sets, thereby informing researchers on predictor selection to improve the generalizability to different settings and populations, and reduce the need for model tailoring. Our methodology has been implemented in the R package metamisc.


Asunto(s)
Proyectos de Investigación , Calibración , Humanos
19.
Eur Urol Focus ; 2021 May 06.
Artículo en Inglés | MEDLINE | ID: mdl-33967010

RESUMEN

CONTEXT: While urinary incontinence (UI) commonly occurs after radical prostatectomy (RP), it is unclear what factors increase the risk of UI development. OBJECTIVE: To perform a systematic review of patient- and tumour-related prognostic factors for post-RP UI. The primary outcome was UI within 3 mo after RP. Secondary outcomes included UI at 3-12 mo and ≥12 mo after RP. EVIDENCE ACQUISITION: Databases including Medline, EMBASE, and CENTRAL were searched between January 1990 and May 2020. All studies reporting patient- and tumour-related prognostic factors in univariable or multivariable analyses were included. Surgical factors were excluded. Risk of bias (RoB) and confounding assessments were performed using the Quality In Prognosis Studies (QUIPS) tool. Random-effects meta-analyses were performed for all prognostic factor, where possible. EVIDENCE SYNTHESIS: A total of 119 studies (5 randomised controlled trials, 24 prospective, 88 retrospective, and 2 case-control studies) with 131 379 patients were included. RoB was high for study participation and confounding; moderate to high for statistical analysis, study attrition, and prognostic factor measurement; and low for outcome measurements. Significant prognostic factors for postoperative UI within 3 mo after RP were age (odds ratio [OR] per yearly increase 1.04, 95% confidence interval [CI] 1.03-1.05), membranous urethral length (MUL; OR per 1-mm increase 0.81, 95% CI 0.74-0.88), prostate volume (PV; OR per 1-ml increase 1.005, 95% CI 1.000-1.011), and Charlson comorbidity index (CCI; OR 1.28, 95% CI 1.09-1.50). CONCLUSIONS: Increasing age, shorter MUL, greater PV, and higher CCI are independent prognostic factors for UI within 3 mo after RP, with all except CCI remaining prognostic at 3-12 mo. PATIENT SUMMARY: We reviewed the literature to identify patient and disease factors associated with urinary incontinence after surgery for prostate cancer. We found increasing age, larger prostate volume, shorter length of a section of the urethra (membranous urethra), and lower fitness were associated with worse urinary incontinence for the first 3 mo after surgery, with all except lower fitness remaining predictive at 3-12 mo.

20.
Stat Med ; 40(19): 4230-4251, 2021 08 30.
Artículo en Inglés | MEDLINE | ID: mdl-34031906

RESUMEN

In prediction model research, external validation is needed to examine an existing model's performance using data independent to that for model development. Current external validation studies often suffer from small sample sizes and consequently imprecise predictive performance estimates. To address this, we propose how to determine the minimum sample size needed for a new external validation study of a prediction model for a binary outcome. Our calculations aim to precisely estimate calibration (Observed/Expected and calibration slope), discrimination (C-statistic), and clinical utility (net benefit). For each measure, we propose closed-form and iterative solutions for calculating the minimum sample size required. These require specifying: (i) target SEs (confidence interval widths) for each estimate of interest, (ii) the anticipated outcome event proportion in the validation population, (iii) the prediction model's anticipated (mis)calibration and variance of linear predictor values in the validation population, and (iv) potential risk thresholds for clinical decision-making. The calculations can also be used to inform whether the sample size of an existing (already collected) dataset is adequate for external validation. We illustrate our proposal for external validation of a prediction model for mechanical heart valve failure with an expected outcome event proportion of 0.018. Calculations suggest at least 9835 participants (177 events) are required to precisely estimate the calibration and discrimination measures, with this number driven by the calibration slope criterion, which we anticipate will often be the case. Also, 6443 participants (116 events) are required to precisely estimate net benefit at a risk threshold of 8%. Software code is provided.


Asunto(s)
Modelos Estadísticos , Modelos Teóricos , Calibración , Humanos , Pronóstico , Tamaño de la Muestra
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