Spearman Correlation Coefficients, Differences between

Myers, Leann; Sirois, Maria J.

doi:10.1002/9781118445112.stat02802

Cited by 112 publications

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“…Spearman's correlation coefficients were calculated to evaluate the associations between variables. 21 The number of PHR logins was added to those variables to predict medication Table 3 reports on the sociodemographics of the study participants. The groups were similar in terms of baseline characteristics.…”

Section: Discussionmentioning

confidence: 99%

The Use of Electronic Personal Health Records to Improve Medication Adherence and Patient Engagement: A Randomized Study of Non-valvular Atrial Fibrillation Patients

Chen¹,

Roebuck²,

Sami³

et al. 2017

J Innov Cardiac Rhythm Manage

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ABSTRACT. Embolic stroke is a major complication of atrial fibrillation (AF) that frequently results in disability or death. The administration of oral anticoagulation can reduce stroke risk in AF patients; however, medication non-adherence can eliminate this benefit. To date, reported patient adherence rates to oral anticoagulation regimens vary. The objective of the current study was to examine the impact of medication-specific education delivered via a personal health record (PHR) system on medication adherence. A randomized, prospective study was conducted from February 2014 to June 2014 at Parkview Health, a not-for-profit, community-based health care clinic that serves a northeastern Indiana population of more than 820,000. AF patients receiving dabigatran (Pradaxa s ; Boehringer Ingelheim GmbH, Ingelheim am Rhein, Germany) to prevent stroke participated in this study. The study participants were predominantly Caucasian males over 65 years of age who were educated, insured, and living above the poverty level. Patients were allowed to view online, download, and transmit health information via a PHR. The intervention group received PHR training and dabigatran education via the PHR. The control group received standard care and PHR access without training. A longitudinal survey pertaining to medication knowledge, medication adherence, and patient engagement was administered at baseline and at the end of the study. Medication-dispensing data collected from pharmacy refill prescriptions were used for calculating the medication possession ratio (MPR). Ninety patients were included in this study, and were randomly assigned to either the intervention group (n ¼ 46) or the control group (n ¼ 44). All participants completed the baseline survey, and 95.6% of patients finished the follow-up survey. The mean score for knowledge increased significantly in the intervention group (from 3.77 to 4.23, p ¼ 0.005), but not in the control group (from 3.70 to 3.95, p ¼ 0.72). The MPR was significantly higher in the intervention group (97.47% vs. 87.67%, p ¼ 0.001). Both groups had similar levels of improvement in Patient Activation Measure scores (from 63.0 to 65.8, p ¼ 0.078 vs. from 63.1 to 63.6, p ¼ 0.814). Patients who used the PHR achieved greater medication knowledge, resulting in improved medication adherence. To our knowledge, no published randomized trial has reported on the use of PHRs to improve medication adherence and knowledge. This study is the first to demonstrate a positive impact on anticoagulation adherence with PHR use.

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Section: Discussionmentioning

confidence: 99%

The Use of Electronic Personal Health Records to Improve Medication Adherence and Patient Engagement: A Randomized Study of Non-valvular Atrial Fibrillation Patients

Chen¹,

Roebuck²,

Sami³

et al. 2017

J Innov Cardiac Rhythm Manage

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“…A non-parametric test for correlation was used, since the variables except nucleus accumbens volumes, were not normally distributed in at least one group. Statistical differences between the within-group correlation coefficients obtained in the PCP and HC groups, respectively, were tested with Fisher's r-to-z transformation (Myers and Sirois, 2006). Exact p-values are reported, and p ≤ 0.05 was considered statistical significant.…”

Section: Statistical Analysesmentioning

confidence: 99%

Reward responsiveness in patients with chronic pain

Elvemo

Landrø

Borchgrevink

et al. 2015

European Journal of Pain

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Background: It is proposed that changes in reward processing in the brain are involved in the pathophysiology of pain based on experimental studies. The first aim of the present study was to investigate if reward drive and/or reward responsiveness was altered in patients with chronic pain (PCP) compared to controls matched for education, age and sex. The second aim was to investigate the relationship between reward processing and nucleus accumbens volume in PCP and controls. Nucleus accumbens is central in reward processing and its structure has been shown to be affected by chronic pain conditions in previous studies. Methods: Reward drive and responsiveness were assessed with the Behavioral Inhibition Scale/Behavioral Activation Scale, and nucleus accumbens volumes obtained from T1-weighted brain MRIs obtained at 3T in 19 PCP of heterogeneous aetiologies and 20 age-, sex-and education-matched healthy controls. Anhedonia was assessed with Beck's Depression Inventory II. Results: The PCP group had significantly reduced scores on the reward responsiveness, but not reward drive. There was a trend towards smaller nucleus accumbens volume in the PCP compared to control group. There was a significant positive partial correlation between reward responsiveness and nucleus accumbens volume in the PCP group adjusted for anhedonia, which was significantly different from the same relationship in the control group. Conclusions: Reward responsiveness is reduced in chronic pain patients of heterogeneous aetiology, and this reduction was associated with nucleus accumbens volume. Reduced reward responsiveness could be a marker of chronic pain vulnerability, and may indicate reduced opioid function.

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“…In this way, interactions between signaling pathways were constructed. In order to describe the strengths of signaling pathway interactions, the SCC method (14) was utilized to rank pairwise interactions between the CSVD samples and normal controls. The SCC absolute difference value of a pathway interaction between CSVD and normal samples was defined as the weight value of this pathway interaction.…”

Section: Genementioning

confidence: 99%

Identification of key signaling pathways in cerebral small vessel disease using differential pathway network analysis

Zheng¹,

Zheng²,

Xu³

2017

Exp Ther Med

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Abstract. Cerebral small vessel disease (CSVD) primarily affects the perforating cerebral arterioles and capillaries, and results in injury to subcortical grey and white matter. Despite advances in determining the genetic basis of CSVD, the molecular mechanisms underlying the development and progression of CSVD remain unclear. The present study aimed to identify significant signaling pathways associated with CSVD based on differential pathway network analysis. Combining CSVD microarray data with human protein-protein interaction data and data from the Reactome pathway database, pathway interactions were constructed using the Spearman's correlation coefficient strategy. Pathway interactions with weight values of >0.95 were selected to construct the differential pathway network, which contained 715 differential pathway interactions covering 312 nodes and was visualized using Cytoscape software. A total of 15 hub pathways with a top 5% degree distribution in the differential pathway network were identified. The top 5 hub pathways were associated with the synthesis and metabolism of fatty acids. The results of the present study indicate that the synthesis and metabolism of fatty acids is associated with the occurrence and development of CSVD, and may thus provide insights to improve the early diagnosis and treatment of CSVD. IntroductionCerebral small vessel disease (CSVD) predominantly affects the perforating cerebral arterioles and capillaries, and results in injury to subcortical grey and white matter (1). CSVD is associated with focal motor deficits, stroke and cognitive decline, which typically progresses to dementia (2). Genetic studies of CSVD indicate that the development and progression of the disease can be attributed to the accumulation of genomic changes (3). Gene expression profiling has been widely used to research the pathogenesis of diseases, including CSVD. However, despite advances in our knowledge of the genetic basis of CSVD, the underlying molecular mechanisms of the development and progression of CSVD remain unclear.Pathway analysis is a useful tool for gaining insight into the biological functions of genes and proteins (4). Given the complex nature of biological systems, signaling pathways are typically required in order for systems to function in a coordinated fashion to produce the appropriate physiological responses to internal and external stimuli (5). However, previous studies have focused on identifying altered signaling pathways between normal and diseased groups, and common genes between different signaling pathways. For example, a previous study identified differential interactions between two signaling pathways across diseased and normal samples (6). Network-based methods have been used to analyze this interaction data and gain insights into the underlying molecular mechanisms by which biological systems operate (7). Sun et al (8) introduced a network-based approach, differential expression network analysis, which reflects phenotypic differences at a network level. Similarly, in the ...

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Spearman Correlation Coefficients, Differences between

Cited by 112 publications

References 0 publications

The Use of Electronic Personal Health Records to Improve Medication Adherence and Patient Engagement: A Randomized Study of Non-valvular Atrial Fibrillation Patients

The Use of Electronic Personal Health Records to Improve Medication Adherence and Patient Engagement: A Randomized Study of Non-valvular Atrial Fibrillation Patients

Reward responsiveness in patients with chronic pain

Identification of key signaling pathways in cerebral small vessel disease using differential pathway network analysis

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