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1.
J Orthop Sports Phys Ther ; 51(10): 517-525, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34592832

RESUMO

SYNOPSIS: Participating in sport carries inherent risk of injury. Clinicians execute high-level clinical reasoning and decision making to support athletes to achieve the best outcomes. Accurately diagnosing a problem, estimating prognosis, or selecting the most suitable intervention for each athlete is challenging. Clinical prediction models are tools to assist clinicians in estimating the risk or probability of a health outcome for an individual by using data from multiple predictors. Although common in general medical literature, clinical prediction models are rare in sports medicine. The purpose of this article was to (1) describe the steps required to develop and validate (ie, evaluate) a clinical prediction model for clinical researchers, and (2) help sports medicine clinicians understand and interpret clinical prediction model studies. Using a case study to illustrate how to implement clinical prediction models in practice, we address the following issues in developing and validating a clinical prediction model: study design and data, sample size, missing data, selecting predictors, handling continuous predictors, model fitting, internal and external validation, performance measures, reporting, and model presentation. Our work builds on initiatives to improve diagnostic and prognostic clinical research, including the PROGnosis RESearch Strategy (PROGRESS) series of papers and textbook and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement. J Orthop Sports Phys Ther 2021;51(10):517-525. doi:10.2519/jospt.2021.10697.

2.
JAMA Netw Open ; 4(10): e2128199, 2021 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-34605914

RESUMO

Importance: There is limited research investigating injury and illness among professional basketball players during their rookie season. By improving the understanding of injury incidence and risk specific to rookie players, sports medicine clinicians may be able to further individualize injury mitigation programs that address the unique needs of rookie players. Objective: To compare incidence and rate ratio (RR) of injury and illness among professional National Basketball Association (NBA) players in their rookie season with veteran players and to explore the association of sustaining an injury rookie season with career longevity. Design, Setting, and Participants: This retrospective cohort study used an online data repository and extracted publicly available data about NBA players between the 2007 and 2008 season to the 2018 and 2019 season. Available data for initial injury and all subsequent injuries were extracted during this time frame. Exposures: Injury and illness based on injury status during the rookie season of professional NBA players. Main Outcomes and Measures: Injury and illness incidence and RR. Association of injury during the rookie season with career longevity was assessed via Poisson regressions. Results: Of the 12 basketball seasons analyzed, 904 NBA players were included (mean [SD] age, 24.6 [3.9] years; body mass index, 24.8 [1.8]). The injury and illness incidence for rookie players was 14.28 per 1000 athlete game exposures (AGEs). Among all body regions, ankle injuries had the greatest injury incidence among players injured during their rookie season (3.17 [95% CI, 3.15-3.19] per 1000 AGEs). Rookie athletes demonstrated higher RR compared with veterans across multiple regions of the body (ankle: 1.32; 95% CI, 1.12 to 1.52; foot/toe: 1.29; 95% CI, 0.97 to 1.61; shoulder/arm/elbow: 1.43; 95% CI, 1.10 to 1.77; head/neck: 1.21; 95% CI, 0.61 to 1.81; concussions: 2.39; 95% CI, 1.89 to 2.90; illness: 1.14; 95% CI, 0.87 to 1.40), and demonstrated a higher rate of initial injuries compared with veteran players (1.41; 95% CI, 1.29 to 1.53). Players who sustained an injury rookie season demonstrated an unadjusted decrease in total seasons played (-0.4 [95% CI, -0.5 to -0.3] log years; P < .001), but this decrease was not observed within adjusted analysis (0.1 [95% CI, -0.1 to 0.2] log years; P = .36). Conclusions and Relevance: In this study, rookie athletes demonstrated the highest injury incidence at the ankle and increased RR across multiple regions. These findings may reflect differences in preseason conditioning or load variables impacting rookie athletes and warrant further investigation. Future research is needed to determine the association of cumulative injury burden vs a singular injury event on career longevity.

3.
Stat Methods Med Res ; : 9622802211046388, 2021 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-34623193

RESUMO

Recent minimum sample size formula (Riley et al.) for developing clinical prediction models help ensure that development datasets are of sufficient size to minimise overfitting. While these criteria are known to avoid excessive overfitting on average, the extent of variability in overfitting at recommended sample sizes is unknown. We investigated this through a simulation study and empirical example to develop logistic regression clinical prediction models using unpenalised maximum likelihood estimation, and various post-estimation shrinkage or penalisation methods. While the mean calibration slope was close to the ideal value of one for all methods, penalisation further reduced the level of overfitting, on average, compared to unpenalised methods. This came at the cost of higher variability in predictive performance for penalisation methods in external data. We recommend that penalisation methods are used in data that meet, or surpass, minimum sample size requirements to further mitigate overfitting, and that the variability in predictive performance and any tuning parameters should always be examined as part of the model development process, since this provides additional information over average (optimism-adjusted) performance alone. Lower variability would give reassurance that the developed clinical prediction model will perform well in new individuals from the same population as was used for model development.

5.
JAMA ; 326(11): 1045-1056, 2021 09 21.
Artigo em Inglês | MEDLINE | ID: mdl-34546296

RESUMO

Importance: Mediation analyses of randomized trials and observational studies can generate evidence about the mechanisms by which interventions and exposures may influence health outcomes. Publications of mediation analyses are increasing, but the quality of their reporting is suboptimal. Objective: To develop international, consensus-based guidance for the reporting of mediation analyses of randomized trials and observational studies (A Guideline for Reporting Mediation Analyses; AGReMA). Design, Setting, and Participants: The AGReMA statement was developed using the Enhancing Quality and Transparency of Health Research (EQUATOR) methodological framework for developing reporting guidelines. The guideline development process included (1) an overview of systematic reviews to assess the need for a reporting guideline; (2) review of systematic reviews of relevant evidence on reporting mediation analyses; (3) conducting a Delphi survey with panel members that included methodologists, statisticians, clinical trialists, epidemiologists, psychologists, applied clinical researchers, clinicians, implementation scientists, evidence synthesis experts, representatives from the EQUATOR Network, and journal editors (n = 19; June-November 2019); (4) having a consensus meeting (n = 15; April 28-29, 2020); and (5) conducting a 4-week external review and pilot test that included methodologists and potential users of AGReMA (n = 21; November 2020). Results: A previously reported overview of 54 systematic reviews of mediation studies demonstrated the need for a reporting guideline. Thirty-three potential reporting items were identified from 3 systematic reviews of mediation studies. Over 3 rounds, the Delphi panelists ranked the importance of these items, provided 60 qualitative comments for item refinement and prioritization, and suggested new items for consideration. All items were reviewed during a 2-day consensus meeting and participants agreed on a 25-item AGReMA statement for studies in which mediation analyses are the primary focus and a 9-item short-form AGReMA statement for studies in which mediation analyses are a secondary focus. These checklists were externally reviewed and pilot tested by 21 expert methodologists and potential users, which led to minor adjustments and consolidation of the checklists. Conclusions and Relevance: The AGReMA statement provides recommendations for reporting primary and secondary mediation analyses of randomized trials and observational studies. Improved reporting of studies that use mediation analyses could facilitate peer review and help produce publications that are complete, accurate, transparent, and reproducible.


Assuntos
Guias como Assunto , Análise de Mediação , Estudos Observacionais como Assunto , Ensaios Clínicos Controlados Aleatórios como Assunto , Lista de Checagem , Técnica Delfos , Humanos , Revisão por Pares , Revisões Sistemáticas como Assunto
6.
BMJ Open ; 11(8): e052598, 2021 08 27.
Artigo em Inglês | MEDLINE | ID: mdl-34452970

RESUMO

OBJECTIVES: To evaluate whether a home-based rehabilitation programme for people assessed as being at risk of a poor outcome after knee arthroplasty offers superior outcomes to traditional outpatient physiotherapy. DESIGN: A prospective, single-blind, two-arm randomised controlled superiority trial. SETTING: 14 National Health Service physiotherapy departments in the UK. PARTICIPANTS: 621 participants identified at high risk of a poor outcome after knee arthroplasty using a bespoke screening tool. INTERVENTIONS: A multicomponent home-based rehabilitation programme delivered by rehabilitation assistants with supervision from qualified therapists versus usual care outpatient physiotherapy. MAIN OUTCOME MEASURES: The primary outcome was the Late-Life Function and Disability Instrument (LLFDI) at 12 months. Secondary outcomes were the Oxford Knee Score (a disease-specific measure of function), Knee injury and Osteoarthritis Outcome Score Quality of Life subscale, Physical Activity Scale for the Elderly, 5 dimension, 5 level version of Euroqol (EQ-5D-5L) and physical function assessed using the Figure of 8 Walk test, 30 s Chair Stand Test and Single Leg Stance. RESULTS: 621 participants were randomised between March 2015 and January 2018. 309 were assigned to CORKA (Community Rehabilitation after Knee Arthroplasty) home-based rehabilitation, receiving a median five treatment sessions (IQR 4-7). 312 were assigned to usual care, receiving a median 4 sessions (IQR 2-6). The primary outcome, LLFDI function total score at 12 months, was collected for 279 participants (89%) in the home-based CORKA group and 287 participants (92%) in the usual care group. No clinically or statistically significant difference was found between the groups (intention-to-treat adjusted difference=0.49 points; 95% CI -0.89 to 1.88; p=0.48). There were no statistically significant differences between the groups on any of the patient-reported or physical secondary outcome measures at 6 or 12 months.There were 18 participants in the intervention group reporting a serious adverse event (5.8%), only one directly related to the intervention, all other adverse events recorded throughout the trial related to underlying chronic medical conditions. CONCLUSIONS: The CORKA intervention was not superior to usual care. The trial detected no significant differences, clinical or statistical, between the two groups on either primary or secondary outcomes. CORKA offers an evaluation of an intervention utilising a different service delivery model for this patient group. TRIAL REGISTRATION NUMBER: ISRCTN13517704.


Assuntos
Artroplastia do Joelho , Idoso , Análise Custo-Benefício , Humanos , Modalidades de Fisioterapia , Estudos Prospectivos , Qualidade de Vida , Método Simples-Cego , Medicina Estatal
7.
J Clin Epidemiol ; 138: 60-72, 2021 Jun 29.
Artigo em Inglês | MEDLINE | ID: mdl-34214626

RESUMO

OBJECTIVE: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING: We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. RESULTS: Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). CONCLUSION: Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste.

8.
BMJ Open ; 11(7): e048008, 2021 07 09.
Artigo em Inglês | MEDLINE | ID: mdl-34244270

RESUMO

INTRODUCTION: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques. METHODS AND ANALYSIS: TRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications. PROSPERO REGISTRATION NUMBER: CRD42019140361 and CRD42019161764.


Assuntos
Inteligência Artificial , Lista de Checagem , Viés , Humanos , Prognóstico , Projetos de Pesquisa , Medição de Risco
10.
J Athl Train ; 2021 Jun 15.
Artigo em Inglês | MEDLINE | ID: mdl-34129678

RESUMO

CONTEXT: It is currently unclear how different pitching roles affect arm injury risk in professional pitchers. OBJECTIVE: 1) Investigate the differences in arm injury hazard between professional baseball starting and relief pitchers; 2) Separately investigate elbow and shoulder injury hazard between professional baseball starting and relief pitchers. STUDY DESIGN: Prospective cohort Setting: Minor League Baseball (MiLB) from 2013-2019 Patients or Other Participants: Pitchers Main Outcome Measures: Pitchers were followed for the entire MiLB season and athletic exposures (AE's) and injuries were recorded. Risk ratios and risk difference were calculated between starting and relieving MiLB pitchers. A cox survival analysis was then performed in relation to time to arm injury between starting and relieving MiLB pitchers. Subgroup analyses were performed for elbow and shoulder. RESULTS: 297 pitchers were included with a total of 85,270 player days recorded. Arm injury incidence was 11.4 arm injuries per 10,000 AE's. Starting pitchers demonstrated greater risk ratio (1.2 (95% CI: 1.1-1.3)) and risk difference (13.6 (95% CI: 5.6-21.6)) and hazard of arm injury (2.4 (95% CI: 1.5-4.0)) compared to relief pitchers. No differences were observed for hazard of elbow injury between starting and relief pitchers (1.9 (95% CI: 0.8-4.2)). Starting pitchers demonstrated greater hazard of shoulder injury compared to relief pitchers (3.8 (95% CI: 2.0-7.1)). CONCLUSIONS: Starting pitchers demonstrated almost two and a half times greater hazard of arm injury compared to relief pitchers. Subgroup analyses demonstrated that starters exhibited greater hazard of shoulder injury compared to relievers; but, no differences were observed for hazard of elbow injury. However, due to the wide confidence intervals, these subgroup analyses should be interpreted with caution. Clinicians may need to consider cumulative exposure and fatigue and how these factors relate to different pitching roles when assessing pitching arm injury risk.

11.
Artigo em Inglês | MEDLINE | ID: mdl-34182149

RESUMO

BACKGROUND: Humeral torsion (HT) has been linked to pitching arm injury risk after controlling for shoulder range of motion. Currently measuring HT uses expensive equipment, which inhibits clinical assessment. Developing an HT predictive model can aid clinical baseball arm injury risk examination. Therefore, the purpose of this study was to develop and internally validate an HT prediction model using standard clinical tests and measures in professional baseball pitchers. METHODS: An 11-year (2009-2019) prospective professional baseball cohort was used for this study. Participants were included if they were able to participate in all practices and competitions and were under a Minor League Baseball contract. Preseason shoulder range of motion (external rotation [ER], internal rotation [IR], horizontal adduction [HA]) and HT were collected each season. Player age, arm dominance, arm injury history, and continent of origin were also collected. Examiners were blinded to arm dominance. An a priori power analysis determined that 244 players were needed for accurate prediction models. Missing data was low (<3%); thus, a complete case analysis was performed. Model development followed the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) recommendations. Regression models with restricted cubic splines were performed. Following primary model development, bootstrapping with 2000 iterations were performed to reduce overfitting and assess optimism shrinkage. Prediction model performance was assessed through root mean square error (RMSE), R2, and calibration slope with 95% confidence intervals (CIs). Sensitivity analyses included dominant and nondominant HT. RESULTS: A total of 407 professional pitchers (age: 23.2 [standard deviation 2.4] years, left-handed: 17%; arm history prevalence: 21%) participated. Predictors with the highest influence within the model include IR (0.4, 95% CI 0.3, 0.5; P < .001), ER (-0.3, 95% CI -0.4, -0.2; P < .001), HA (0.3, 95% CI 0.2, 0.4; P < .001), and arm dominance (right-handed: -1.9, 95% CI -3.6, -0.1; P = .034). Final model RMSE was 12, R2 was 0.41, and calibration was 1.00 (95% CI 0.94, 1.06). Sensitivity analyses demonstrated similar model performance. CONCLUSIONS: Every 3° of IR explained 1° of HT. Every 3° of ER explained 1° less of HT, and every 7° of HA explained 1° of HT. Right-handers had 2° less HT. Models demonstrated good predictive performance. This predictive model can be used by clinicians to infer HT using standard clinical test and measures. These data can be used to enhance professional baseball arm injury examination.

12.
J Clin Epidemiol ; 2021 May 30.
Artigo em Inglês | MEDLINE | ID: mdl-34077797

RESUMO

Covid-19 research made it painfully clear that the scandal of poor medical research, as denounced by Altman in 1994, persists today. The overall quality of medical research remains poor, despite longstanding criticisms. The problems are well known, but the research community fails to properly address them. We suggest that most problems stem from an underlying paradox: although methodology is undeniably the backbone of high-quality and responsible research, science consistently undervalues methodology. The focus remains more on the destination (research claims and metrics) than on the journey. Notwithstanding, research should serve society more than the reputation of those involved. While we notice that many initiatives are being established to improve components of the research cycle, these initiatives are too disjointed. The overall system is monolithic and slow to adapt. We assert that top-down action is needed from journals, universities, funders and governments to break the cycle and put methodology first. These actions should involve the widespread adoption of registered reports, balanced research funding between innovative, incremental and methodological research projects, full recognition and demystification of peer review, improved methodological review of reports, adherence to reporting guidelines, and investment in methodological education and research. Currently, the scientific enterprise is doing a major disservice to patients and society.

13.
BMJ Open ; 11(6): e047709, 2021 06 28.
Artigo em Inglês | MEDLINE | ID: mdl-34183345

RESUMO

INTRODUCTION: Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI. METHODS AND ANALYSIS: The development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group's efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.


Assuntos
Inteligência Artificial , Testes Diagnósticos de Rotina , Humanos , Londres , Projetos de Pesquisa , Relatório de Pesquisa
14.
Pain ; 2021 Mar 26.
Artigo em Inglês | MEDLINE | ID: mdl-34001771

RESUMO

ABSTRACT: Data from the Global Burden of Disease Study 2019 were used to report the burden of migraine in 204 countries and territories during the period 1990 to 2019, through a systematic analysis of point prevalence, annual incidence, and years lived with disability (YLD). In 2019, the global age-standardised point prevalence and annual incidence rate of migraine were 14,107.3 (95% Uncertainty Interval [UI] 12,270.3-16,239) and 1142.5 (95% UI 995.9-1289.4) per 100,000, an increase of 1.7% (95% UI 0.7%-2.8%) and 2.1% (95% UI 1.1%-2.8%) since 1990, respectively. Moreover, the global age-standardised YLD rate in 2019 was 525.5 (95% UI 78.8-1194), an increase of 1.5% (95% UI -4.4% to 3.3%) since 1990. The global point prevalence of migraine in 2019 was higher in females and increased by age up to the 40 to 44 age group, then decreased with increased age. Belgium (22,400.6 [95% UI: 19,305.2-26,215.8]), Italy (20,337.7 [95% UI: 17,724.7-23,405.8]), and Germany (19,436.4 [95% UI: 16,806.2-22,810.3]) had the 3 highest age-standardised point prevalence rates for migraine in 2019. In conclusion, there were large intercountry differences in the burden of migraine, and this burden increased significantly across the measurement period. These findings suggest that migraine care needs to be included within the health system to increase population awareness regarding the probable risk factors and treatment strategies especially among young adults and middle-aged women, as well as to increase the data on migraines.

15.
Stat Med ; 40(19): 4230-4251, 2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34031906

RESUMO

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.

19.
Am Heart J ; 237: 62-67, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33722586

RESUMO

Covariate adjustment is integral to the validity of observational studies assessing causal effects. It is common practice to adjust for as many variables as possible in observational studies in the hopes of reducing confounding by other variables. However, indiscriminate adjustment for variables using standard regression models may actually lead to biased estimates. In this paper, we differentiate between confounders, mediators, colliders, and effect modifiers. We will discuss that while confounders should be adjusted for in the analysis, one should be wary of adjusting for colliders. Mediators should not be adjusted for when examining the total effect of an exposure on an outcome. Automated statistical programs should not be used to decide which variables to include in causal models. Using a case scenario in cardiology, we will demonstrate how to identify confounders, colliders, mediators and effect modifiers and the implications of adjustment or non-adjustment for each of them.


Assuntos
Doenças Cardiovasculares/epidemiologia , Modelos Estatísticos , Estudos Observacionais como Assunto , Saúde Global , Humanos , Morbidade/tendências
20.
J Clin Epidemiol ; 132: 142-145, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33775387

RESUMO

Clinical prediction models play an increasingly important role in contemporary clinical care, by informing healthcare professionals, patients and their relatives about outcome risks, with the aim to facilitate (shared) medical decision making and improve health outcomes. Diagnostic prediction models aim to calculate an individual's risk that a disease is already present, whilst prognostic prediction models aim to calculate the risk of particular heath states occurring in the future. This article serves as a primer for diagnostic and prognostic clinical prediction models, by discussing the basic terminology, some of the inherent challenges, and the need for validation of predictive performance and the evaluation of impact of these models in clinical care.


Assuntos
Técnicas de Apoio para a Decisão , Modelos Estatísticos , Humanos , Prognóstico
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