Using the Barthel Index and modified Rankin Scale as Outcome Measures for Stroke Rehabilitation Trials; A Comparison of Minimum Sample Size Requirements

Author(s):  
Kris McGill ◽  
Catherine Sackley ◽  
Jon Godwin ◽  
David Gavaghan ◽  
Myzoon Ali ◽  
...  
Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3056-3056
Author(s):  
Kareem Midlig ◽  
Regina Draliuk ◽  
Meir Preis ◽  
Barbara Silverman ◽  
Mouna Ballan

Abstract Background : Multiple myeloma (MM) is one of the most common hematological malignancies. The disease is characterized by multiple symptoms resulting from the disease itself, from complications related to therapy, and as a result of the involvement of other organ systems. MM influences various aspects of patient's and family's lives. Therefore, there is a need to better understand the balance between disease control and symptoms management. Objectives : The main goal of this study is to emphasize the power and importance of Patient Reported Outcome Measures (PROMs) and Family Reported Outcome Measures (FROMs) as additional tools for patient assessment. This study evaluated the correlation between Patient Reported Outcome Measures (PROMs) and Family Reported Outcome Measures (FROMs) and disease evaluation according to the International Myeloma Working Group (IMW) response criteria in active myeloma patients. A comparison between patient and family reporting (PROMs & FROMs) and the staging of the disease according to the revised international staging system (R-ISS) was done. In addition, this study examined the confounders that may explain the relationship between PROMs and FROMs and disease evaluation. Methods : This is a quantitative, prospective, observational and longitudinal study of active patients with MM. After receiving Carmel Institutional Review Board approval to conduct the study, we enrolled fifty seven MM patients, the participants completed questionnaires of PROMs and FROMs at intervals of 3 months for one year. In addition, we monitored multiple clinical measures of patient response to treatment. A descriptive analysis of the research variables has been performed; differences between the PROMs/FROM and clinical variables analyzed by Pearson correlation, comparing PROMs/FROMs mean at the beginning of the study with the results at 3, 6, 9 and 12 months . A mixed regression model was used to examine the predictive ability of the study. In other words, the ability of Patient/Family Reported Outcome to predict the disease evaluation. Sample size was calculated using Win-Pepi software, using 5% significance and 80% power. For a coefficient of 0.4 between Patient and Family Reported Outcome and MM clinical evaluations, the minimum sample size required is 47 patients, for a coefficient of 0.35, the minimum sample size required is 62 patients. for a coefficient of 0.50, the minimum sample size required is 37 patients. This study recruited a sample size of 57 patients. Results - Fifty-seven patients participated in this study. After 3 months of treatment, a better disease evaluation was associated with improvement in disease symptoms or side effects reported by the patient. Furthermore, a better disease response was associated with a better body image scale and better future perspective. We observed a similar association after 6 and 9 months. In addition, the more the patient reported side effects or disease symptoms, the more it affects the family member (PROMs were positively correlated with FROMs). A better body image and future perspective reported by patient was associated with a lower effect on family member (PROMs were negatively correlated with FROMs)). A positive significant correlation was found between physician ranking of physical status ECOG (Eastern Cooperative Oncology Group) and the effect on family members. In other words, the worse the physical status of the patient, the more it affect the family member (in months 0,3,6 and 9). These finding were supported by the mixed model analysis, which showed a significant effect of disease symptoms, appetite loss, physical function, future perspective, and global satisfaction in prediction of clinical status. Conclusion- There is a significant relation between PROM/FROM and the typical assessment tools. This study highlights the power of PROM/FROM tools to evaluate patient from his point of view and to adjust the treatment accordingly. Finally, this study raises up the importance of continuing the research about the effect on the family member as a result of the patient disease and clinical status. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Author(s):  
Richard D. Riley ◽  
Thomas P. A. Debray ◽  
Gary S. Collins ◽  
Lucinda Archer ◽  
Joie Ensor ◽  
...  

Nephron ◽  
1983 ◽  
Vol 34 (3) ◽  
pp. 192-195 ◽  
Author(s):  
M. Oberholzer ◽  
J. Torhorst ◽  
E. Perret ◽  
M.J. Mihatsch

2020 ◽  
Vol 15 ◽  
pp. 102-107
Author(s):  
Hunuwala Malawarage Suranjan Priyanath ◽  
Ranatunga RVSPK ◽  
Megama RGN

Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limitations. Finally, the study identified two methods that are more suitable in methodologically and technically which have identified by non-robust scholars who deeply addressed all the aspects of the techniques in the determination of minimum sample size for SEM analysis and thus, the prepare recommends these two methods to rectify the issue of the determination of minimum sample size when using SEM in a research project.


Author(s):  
Meiping Yun ◽  
Wenwen Qin

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.


Assessment ◽  
2020 ◽  
pp. 107319112091360
Author(s):  
Zhengguo Gu ◽  
Wilco H. M. Emons ◽  
Klaas Sijtsma

To interpret a person’s change score, one typically transforms the change score into, for example, a percentile, so that one knows a person’s location in a distribution of change scores. Transformed scores are referred to as norms and the construction of norms is referred to as norming. Two often-used norming methods for change scores are the regression-based change approach and the T Scores for Change method. In this article, we discuss the similarities and differences between these norming methods, and use a simulation study to systematically examine the precision of the two methods and to establish the minimum sample size requirements for satisfactory precision.


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