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Dose Evaluation of Treatment Plans
Published in W. P. M. Mayles, A. E. Nahum, J.-C. Rosenwald, Handbook of Radiotherapy Physics, 2021
Margaret Bidmead, J.-C. Rosenwald
When analysing DVHs for a population of patients, it is useful to quantify inter-patient variations. A convenient solution is to divide the dataset into quartiles. For any given metric (e.g. DV), the data can be ranked and divided into four groups containing the same number of data elements. The limit between the first and second group is the first (or lower) quartile Q1. The limit between the second and third group is the second (or median) quartile Q2. Similarly, the upper quartile Q3 splits the lower 75% of data from the upper 25%. These can be presented in the form of a box-and-whiskers plot as illustrated in Figure 43.8. Similar concepts can be used to display statistical DVHs incorporating shaded areas representing quartile-based ranges, against which any given DVH can be compared (Mayo et al. 2016).
Basic stats
Published in O. Ajetunmobi, Making Sense of Critical Appraisal, 2021
Lower quartile = 1, that is, same value present on either side of the lower quartile. For the upper quartile, a weighted mean is taken between 5 and 6:
The Practice of Prior Elicitation
Published in Mani Lakshminarayanan, Fanni Natanegara, Bayesian Applications in Pharmaceutical Development, 2019
Timothy H. Montague, Karen L. Price, John W. Seaman
To determine the lower quartile, the expert is asked to assume that θ is below the median elicited above (a) and is then asked to choose a value, bL, such that θ < bL with the same probability that bL< θ < a. The upper quartile is chosen in a similar fashion. The “tertile” method is very similar with R being split into three intervals of equal probability. A prior can be selected from a family of distributions to approximately match these percentiles. These methods can be used to elicit both individual and consensus priors. (They are implemented in SHELF and MATCH.)
Multi-modal access method (eye-tracking + switch-scanning) for individuals with severe motor impairment: A preliminary investigation
Published in Assistive Technology, 2023
S. K. Fager, T. Sorenson, E. Jakobs, H. Koester, T. Jakobs, D. R. Beukelman
A multi-level method of data analysis proposed by Rapport et al. (1988) and Light (1999) was employed. This method includes a group statistical analysis as well as an intermediate/individual level of analysis. For the group, we used a two-way repeated measures ANOVA to analyze the main effects of access method (multi-modal versus eye-tracking alone) and trials (1 to 5) on typing performance (at the p < .05 level). Intermediate/individual level of analysis used descriptive statistics to examine how each individual performed relative to the overall group. Individual data points near the group median were considered “typical”, while those in the lower or upper quartile of the group were considered “low” or “high”, respectively. Visual examination of results grouped individuals with similar patterns of performance into subgroups.
Changes in serum pigment epithelium-derived factor levels after kidney transplantation in patients with end-stage renal disease
Published in Renal Failure, 2022
Réka Szentimrei, Hajnalka Lőrincz, Anita Szentpéteri, Viktória E. Varga, Mariann Harangi, Ildikó Seres, Réka P. Szabó, Balázs Nemes, György Paragh
The statistical analysis was performed by STATISTICA ver.14. (TIBCO Software Inc., Palo Alto, CA). The data were presented by a descriptive analysis (means ± standard deviation or medians [lower quartile – upper quartile]). A Kolmogorov–Smirnov test was used for testing the normality of the data distribution. The comparison of data between controls and transplanted patients was performed by an unpaired t test or a Mann–Whitney U test; while analyzing patient data during the follow-up was performed by repeated measures analysis of variance (ANOVA). The bivariate Pearson’s correlation was assessed to measure the strength and direction of linear relationships between pairs of continuous unrelated variables. A multiple regression analysis was performed in backward manner to determine the variables’ best predicting PEDF level. The results were considered to be significant at the level of p < 0.05.
PD-L2 based immune signature confers poor prognosis in HNSCC
Published in OncoImmunology, 2021
Yu Qiao, Chao Liu, Xiaoyue Zhang, Qianqian Zhou, Yatian Li, Yini Xu, Zhenyue Gao, Yiqi Xu, Lingping Kong, Aifeng Yang, Mei Mei, Yu Ren, Xudong Wang, Xuan Zhou
Formalin-fixed paraffin-embedded specimens were used for immunohistochemistry (IHC) analysis. Tissue slides were incubated with PNGase F overnight at 37°C to remove glycosylation. The tissue slides were incubated with primary antibodies against FOXP3 (Abcam 236A/E7, 1:100 dilution), CD3 (Abcam SP162, 1:150 dilution), CD8 (Invitrogen SP16, 1:100 dilution), PD1 (Abcam NAT105, 1:50 dilution), PD-L1 (Invitrogen MIH1, 1:50 dilution) and PD-L2 (R&D Systems 176611, 1:200 dilution). The sections were PD-L1-positive if ≥1% of tumor cells were stained in the cell membrane.31 The sections were PD-L2-positive if ≥5% of tumor cells had immunoreactivity.32 The sections were PD-1-positive if ≥1% of tumor cells had immunoreactivity.33 The cell densities were measured with Cellsens standard software (Olympus, Japan) at 200× and defined as the number of cells positive for each marker (for example, CD3, CD8 and FOXP3) per field. The upper quartile was defined as the T cell density cutoff point. The cutoff values for intratumoural counts of CD3+, CD8+ and FOXP3+ T cells were 47, 74 and 27, respectively, while those of stromal CD3+, CD8+ and FOXP3+ T cells were 286, 261 and 88, respectively. All scores were independently assessed by two experienced pathologists.