The clustering on categorical variables has received intensive attention. In dataset with categorical features, some features show the superior performance on clustering procedure. In this paper, we propose a simple m...The clustering on categorical variables has received intensive attention. In dataset with categorical features, some features show the superior performance on clustering procedure. In this paper, we propose a simple method to find such distinctive features by comparing pooled within-cluster mean relative difference and then partition the data upon such features and give subspace of the subgroups. The applications on zoo data and soybean data illustrate the performance of the proposed method.展开更多
Three long-term field trials in humid regions of Canada and the USA were used to evaluate the influence of soil depth and sample numbers on soil organic carbon (SOC) sequestration in no-tillage (NT) and moldboard plow...Three long-term field trials in humid regions of Canada and the USA were used to evaluate the influence of soil depth and sample numbers on soil organic carbon (SOC) sequestration in no-tillage (NT) and moldboard plow (MP) corn (Zea mays L.) and soybean (Glycine max L.) production systems. The first trial was conducted on a Maryhill silt loam (Typic Hapludalf) at Elora, Ontario, Canada, the second on a Brookston clay loam (Typic Argiaquoll) at Woodslee, Ontario, Canada, and the third on a Thorp silt loam (Argiaquic Argialboll) at Urbana, Illinois, USA. No-tillage led to significantly higher SOC concentrations in the top 5 cm compared to MP at all 3 sites. However, NT resulted in significantly lower SOC in sub-surface soils as compared to MP at Woodslee (10-20 cm, P = 0.01) and Urbana (20-30 cm, P < 0.10). No-tillage had significantly more SOC storage than MP at the Elora site (3.3 Mg C ha-1) and at the Woodslee site (6.2 Mg C ha-1) on an equivalent mass basis (1350 Mg ha-1 soil equivalent mass). Similarly, NT had greater SOC storage than MP at the Urbana site (2.7 Mg C ha-1) on an equivalent mass basis of 675 Mg ha-1 soil. However, these differences disappeared when the entire plow layer was evaluated for both the Woodslee and Urbana sites as a result of the higher SOC concentrations in MP than in NT at depth. Using the minimum detectable difference technique, we observed that up to 1500 soil sample per tillage treatment comparison will have to be collected and analyzed for the Elora and Woodslee sites and over 40 soil samples per tillage treatment comparison for the Urbana to statistically separate significant differences in the SOC contents of sub-plow depth soils. Therefore, it is impracticable, and at the least prohibitively expensive, to detect tillage-induced differences in soil C beyond the plow layer in various soils.展开更多
AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint condition...AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint conditions at an orthopaedic clinic took the Patient-Reported Outcomes Measurement Information System Physical Function(PROMIS~? PF) computerized adaptive test(CAT), hip disability and osteoarthritis outcome score for joint reconstruction(HOOS JR), and the knee injury and osteoarthritis outcome score for joint reconstruction(KOOS JR) from February 2014 to April 2017. MCIDs were calculated using anchorbased and distribution-based methods. Patient reports of meaningful change in function since their first clinic encounter were used as an anchor.RESULTS There were 2226 patients who participated with a mean age of 61.16(SD = 12.84) years, 41.6% male, and 89.7% Caucasian. Mean change ranged from 7.29 to 8.41 for the PROMIS~? PF CAT, from 14.81 to 19.68 for the HOOS JR, and from 14.51 to 18.85 for the KOOS JR. ROC cut-offs ranged from 1.97-8.18 for the PF CAT, 6.33-43.36 for the HOOS JR, and 2.21-8.16 for the KOOS JR. Distribution-based methods estimated MCID values ranging from 2.45 to 21.55 for the PROMIS~? PF CAT; from 3.90 to 43.61 for the HOOS JR, and from 3.98 to 40.67 for the KOOS JR. The median MCID value in the range was similar to the mean change score for each measure and was 7.9 for the PF CAT, 18.0 for the HOOS JR, and 15.1 for the KOOS JR.CONCLUSION This is the first comprehensive study providing a wide range of MCIDs for the PROMIS? PF, HOOS JR, and KOOS JR in orthopaedic patients with joint ailments.展开更多
The simple adjusted estimator of risk difference in each center is easy constructed by adding a value c on the number of successes and on the number of failures in each arm of the proportion estimator. Assessing a tre...The simple adjusted estimator of risk difference in each center is easy constructed by adding a value c on the number of successes and on the number of failures in each arm of the proportion estimator. Assessing a treatment effect in multi-center studies, we propose minimum MSE (mean square error) weights of an adjusted summary estimate of risk difference under the assumption of a constant of common risk difference over all centers. To evaluate the performance of the proposed weights, we compare not only in terms of estimation based on bias, variance, and MSE with two other conventional weights, such as the Cochran-Mantel-Haenszel weights and the inverse variance (weighted least square) weights, but also we compare the potential tests based on the type I error probability and the power of test in a variety of situations. The results illustrate that the proposed weights in terms of point estimation and hypothesis testing perform well and should be recommended to use as an alternative choice. Finally, two applications are illustrated for the practical use.展开更多
In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence ...In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.展开更多
文摘The clustering on categorical variables has received intensive attention. In dataset with categorical features, some features show the superior performance on clustering procedure. In this paper, we propose a simple method to find such distinctive features by comparing pooled within-cluster mean relative difference and then partition the data upon such features and give subspace of the subgroups. The applications on zoo data and soybean data illustrate the performance of the proposed method.
文摘Three long-term field trials in humid regions of Canada and the USA were used to evaluate the influence of soil depth and sample numbers on soil organic carbon (SOC) sequestration in no-tillage (NT) and moldboard plow (MP) corn (Zea mays L.) and soybean (Glycine max L.) production systems. The first trial was conducted on a Maryhill silt loam (Typic Hapludalf) at Elora, Ontario, Canada, the second on a Brookston clay loam (Typic Argiaquoll) at Woodslee, Ontario, Canada, and the third on a Thorp silt loam (Argiaquic Argialboll) at Urbana, Illinois, USA. No-tillage led to significantly higher SOC concentrations in the top 5 cm compared to MP at all 3 sites. However, NT resulted in significantly lower SOC in sub-surface soils as compared to MP at Woodslee (10-20 cm, P = 0.01) and Urbana (20-30 cm, P < 0.10). No-tillage had significantly more SOC storage than MP at the Elora site (3.3 Mg C ha-1) and at the Woodslee site (6.2 Mg C ha-1) on an equivalent mass basis (1350 Mg ha-1 soil equivalent mass). Similarly, NT had greater SOC storage than MP at the Urbana site (2.7 Mg C ha-1) on an equivalent mass basis of 675 Mg ha-1 soil. However, these differences disappeared when the entire plow layer was evaluated for both the Woodslee and Urbana sites as a result of the higher SOC concentrations in MP than in NT at depth. Using the minimum detectable difference technique, we observed that up to 1500 soil sample per tillage treatment comparison will have to be collected and analyzed for the Elora and Woodslee sites and over 40 soil samples per tillage treatment comparison for the Urbana to statistically separate significant differences in the SOC contents of sub-plow depth soils. Therefore, it is impracticable, and at the least prohibitively expensive, to detect tillage-induced differences in soil C beyond the plow layer in various soils.
基金National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health,No.U01AR067138.
文摘AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint conditions at an orthopaedic clinic took the Patient-Reported Outcomes Measurement Information System Physical Function(PROMIS~? PF) computerized adaptive test(CAT), hip disability and osteoarthritis outcome score for joint reconstruction(HOOS JR), and the knee injury and osteoarthritis outcome score for joint reconstruction(KOOS JR) from February 2014 to April 2017. MCIDs were calculated using anchorbased and distribution-based methods. Patient reports of meaningful change in function since their first clinic encounter were used as an anchor.RESULTS There were 2226 patients who participated with a mean age of 61.16(SD = 12.84) years, 41.6% male, and 89.7% Caucasian. Mean change ranged from 7.29 to 8.41 for the PROMIS~? PF CAT, from 14.81 to 19.68 for the HOOS JR, and from 14.51 to 18.85 for the KOOS JR. ROC cut-offs ranged from 1.97-8.18 for the PF CAT, 6.33-43.36 for the HOOS JR, and 2.21-8.16 for the KOOS JR. Distribution-based methods estimated MCID values ranging from 2.45 to 21.55 for the PROMIS~? PF CAT; from 3.90 to 43.61 for the HOOS JR, and from 3.98 to 40.67 for the KOOS JR. The median MCID value in the range was similar to the mean change score for each measure and was 7.9 for the PF CAT, 18.0 for the HOOS JR, and 15.1 for the KOOS JR.CONCLUSION This is the first comprehensive study providing a wide range of MCIDs for the PROMIS? PF, HOOS JR, and KOOS JR in orthopaedic patients with joint ailments.
文摘The simple adjusted estimator of risk difference in each center is easy constructed by adding a value c on the number of successes and on the number of failures in each arm of the proportion estimator. Assessing a treatment effect in multi-center studies, we propose minimum MSE (mean square error) weights of an adjusted summary estimate of risk difference under the assumption of a constant of common risk difference over all centers. To evaluate the performance of the proposed weights, we compare not only in terms of estimation based on bias, variance, and MSE with two other conventional weights, such as the Cochran-Mantel-Haenszel weights and the inverse variance (weighted least square) weights, but also we compare the potential tests based on the type I error probability and the power of test in a variety of situations. The results illustrate that the proposed weights in terms of point estimation and hypothesis testing perform well and should be recommended to use as an alternative choice. Finally, two applications are illustrated for the practical use.
文摘In the practice of healthcare,patient-reported outcomes(PROs)and PRO measures(PROMs)are used as an attempt to observe the changes in complex clinical situations.They guide us in making decisions based on the evidence regarding patient care by recording the change in outcomes for a particular treatment to a given condition and finally to understand whether a patient will benefit from a particular treatment and to quantify the treatment effect.For any PROM to be usable in health care,we need it to be reliable,encapsulating the points of interest with the potential to detect any real change.Using structured outcome measures routinely in clinical practice helps the physician to understand the functional limitation of a patient that would otherwise not be clear in an office interview,and this allows the physician and patient to have a meaningful conver-sation as well as a customized plan for each patient.Having mentioned the rationale and the benefits of PROMs,understanding the quantification process is crucial before embarking on management decisions.A better interpretation of change needs to identify the treatment effect based on clinical relevance for a given condition.There are a multiple set of measurement indices to serve this effect and most of them are used interchangeably without clear demarcation on their differences.This article details the various quantification metrics used to evaluate the treatment effect using PROMs,their limitations and the scope of usage and implementation in clinical practice.
