Standard error of the difference between means

Burns, Robert B.; Dobson, Clifford B.

doi:10.1007/978-94-011-7241-7_15

Cited by 8 publications

(7 citation statements)

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“…where q is a constant specifying the width of the CFL (1.96, 2.58, and 3.29, for 95, 99, and 99.9%, respectively) and SE 1 and SE 2 are the predicted standard errors for the points on the two surface 66,67 . In cases where outcomes were transformed, or modeled using a link function, CFLs were constricted on the latent scale, before overall surfaces were back transformed for interpretation.…”

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confidence: 99%

Defining the impact of dietary macronutrient balance on PCOS traits

et al. 2020

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Lifestyle, mainly dietary, interventions are first-line treatment for women with polycystic ovary syndrome (PCOS), but the optimal diet remains undefined. We combined a hyperandrogenized PCOS mouse model with a systematic macronutrient approach, to elucidate the impact of dietary macronutrients on the development of PCOS. We identify that an optimum dietary macronutrient balance of a low protein, medium carbohydrate and fat diet can ameliorate key PCOS reproductive traits. However, PCOS mice display a hindered ability for their metabolic system to respond to diet variations, and varying macronutrient balance did not have a beneficial effect on the development of metabolic PCOS traits. We reveal that PCOS traits in a hyperandrogenic PCOS mouse model are ameliorated selectively by diet, with reproductive traits displaying greater sensitivity than metabolic traits to dietary macronutrient balance. Hence, providing evidence to support the development of evidence-based dietary interventions as a promising strategy for the treatment of PCOS, especially reproductive traits.

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confidence: 99%

Defining the impact of dietary macronutrient balance on PCOS traits

et al. 2020

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“…The analyses were performed using Stata ® software (StataCorp, version 14) through the "metan" command, which requires two input parameters, effect estimate and standard error. In case the standard deviation was reported for the effect estimate, it was transformed into standard error according to formulas presented in Burns et al [24].…”

Section: Methodsmentioning

confidence: 99%

Second-Generation Antipsychotic Drugs for Patients with Schizophrenia: Systematic Literature Review and Meta-analysis of Metabolic and Cardiovascular Side Effects

2021

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Background and Objectives Second-generation antipsychotics (SGAs) for schizophrenia show different risk profiles, whose evidence has been evaluated through comparative reviews on randomized controlled trials (RCTs) and observational studies. Methods We performed a systematic review and meta-analysis of weight gains, metabolic and cardiovascular side effects of SGAs, relying on both RCTs and observational studies, by comparing variations between the start of treatment and the end of follow-up. The systematic review refers to papers published from June 2009 to November 2020. PRISMA criteria were followed. No restrictions on heterogeneity level have been considered for meta-analysis. A test for the summary effect measure and heterogeneity (I 2 metric) was used. Results Seventy-nine papers were selected from 3076 studies (61% RCTs, 39% observational studies). Olanzapine and risperidone reported the greatest weight gain and olanzapine the largest BMI increase. Paliperidone showed the highest increase in total cholesterol, but is the only drug reporting an increase in the HDL cholesterol. Quetiapine XR showed the highest decrease in fasting glucose. Lurasidone showed the lowest increase in body weight and a reduction in BMI and was also the only treatment reporting a decrease in total cholesterol and triglycerides. The highest increase in systolic and diastolic blood pressure was reported by quetiapine XR. Conclusions Despite some limitations (differences in the mean dosages per patient and other side effects not included) this paper provides the first complete meta-analysis on SGAs in variations on metabolic risk profile between start of treatment and end of follow-up, with useful results for clinical practice and possibly for future economic evaluation studies.

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“…An additional analysis of subgroups was performed in patients with AS and impaired LV function. Mean differences of hemodynamic parameters between rest and stress and respective standard errors of the difference between means were calculated (40). Differences between baseline characteristics of study arms grouped according to stress type were checked by Kruskal-Wallis test, followed by Dunn's test with Bonferroni correction where appropriate.…”

Section: Methodsmentioning

confidence: 99%

Hemodynamic Changes During Physiological and Pharmacological Stress Testing in Healthy Subjects, Aortic Stenosis and Aortic Coarctation Patients–A Systematic Review and Meta-Analysis

Runte

Brosien

Salcher‐Konrad

et al. 2019

Front. Cardiovasc. Med.

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Introduction: Exercise testing has become a diagnostic standard in the evaluation and management of heart disease. While different methods of exercise and pharmacological stress testing exist, only little is known about their comparability. We aimed to assess hemodynamic changes during dynamic exercise, isometric exercise, and dobutamine stress testing at different stress intensities in healthy subjects and patients with aortic stenosis (AS) and aortic coarctation (CoA). Methods: A systematic literature search (PROSPERO 2017:CRD42017078608) in MEDLINE of interventional trials was conducted to identify eligible studies providing evidence of changes in hemodynamic parameters under different stress conditions acquired by MRI or echocardiography. A random effects model was used to estimate pooled mean changes in hemodynamics. Results: One hundred and twenty-eight study arms with a total of 3,139 stress-examinations were included. In healthy subjects/(where available) in AS, pooled mean changes (95% CIs) during light dynamic stress were 31.78 (27.82–35.74) bpm in heart rate (HR) and 6.59 (2.58–10.61) ml in stroke volume (SV). Changes during light pharmacological stress were 13.71 (7.87–19.56)/14.0 (9.82–18.18) bpm in HR, and 5.47 (0.3–10.63)/8.0 (3.82–12.18) ml in SV. Changes during light isometric stress were 18.44 (10.74–26.14)/5.0 (−1.17–11.17) bpm in HR and −4.17 (−14.37–6.03)/−4.0 (−16.43–8.43) ml in SV. Changes during moderate dynamic stress were 49.57 (40.03–59.1)/46.45 (42.63–50.27) bpm in HR and 11.64 (5.87–17.42) ml in SV. During moderate pharmacological stress, changes in HR were 42.83 (36.94–48.72)/18.66 (2.38–34.93) bpm and in SV 6.29 (−2.0–14.58)/13.11 (7.99–18.23) ml. During high intensity dynamic stress changes in HR were 89.31 (81.46–97.17)/55.32 (47.31–63.33) bpm and in SV 21.31 (13.42–29.21)/−0.96 (−5.27–3.35) ml. During high pharmacological stress, changes in HR were 53.58 (36.53–70.64)/42.52 (32.77–52.28) bpm, and in SV 0.98 (−9.32–11.27)/14.06 (−1.62–29.74) ml. HR increase and age were inversely correlated at high stress intensities. In CoA, evidence was limited to single studies. Conclusion: This systematic review and meta-analysis presents pooled hemodynamic changes under light, moderate and high intensity exercise and pharmacological stress, while considering the potential influence of age. Despite limited availability of comparative studies, the reference values presented in this review allow estimation of the expected individual range of a circulatory response in healthy individuals and patients with AS and may contribute to future study planning and patient-specific models even when stress testing is contraindicated.

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Standard error of the difference between means

Cited by 8 publications

References 0 publications

Defining the impact of dietary macronutrient balance on PCOS traits

Defining the impact of dietary macronutrient balance on PCOS traits

Second-Generation Antipsychotic Drugs for Patients with Schizophrenia: Systematic Literature Review and Meta-analysis of Metabolic and Cardiovascular Side Effects

Hemodynamic Changes During Physiological and Pharmacological Stress Testing in Healthy Subjects, Aortic Stenosis and Aortic Coarctation Patients–A Systematic Review and Meta-Analysis

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