The Experts below are selected from a list of 29502 Experts worldwide ranked by ideXlab platform
David Ellenberger - One of the best experts on this subject based on the ideXlab platform.
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Validation of automatic passenger counting: introducing the t-Test-induced Equivalence Test
Transportation, 2019Co-Authors: Michael Siebert, David EllenbergerAbstract:Automatic passenger counting (APC) in public transport has been introduced in the 1970s and has been rapidly emerging in recent years. Still, real-world applications continue to face events that are difficult to classify. The induced imprecision needs to be handled as statistical noise and thus methods have been defined to ensure that measurement errors do not exceed certain bounds. Various recommendations for such an APC validation have been made to establish criteria that limit the bias and the variability of the measurement errors. In those works, the misinterpretation of non-significance in statistical hypothesis Tests for the detection of differences (e.g. Student’s t-Test) proves to be prevalent, although existing methods which were developed under the term Equivalence Testing in biostatistics (i.e. bioEquivalence trials, Schuirmann in J Pharmacokinet Pharmacodyn 15(6):657–680, 1987) would be appropriate instead. This heavily affects the calibration and validation process of APC systems and has been the reason for unexpected results when the sample sizes were not suitably chosen: Large sample sizes were assumed to improve the assessment of systematic measurement errors of the devices from a user’s perspective as well as from a manufacturers perspective, but the regular t-Test fails to achieve that. We introduce a variant of the t-Test, the revised t-Test, which addresses both type I and type II errors appropriately and allows a comprehensible transition from the long-established t-Test in a widely used industrial recommendation. This Test is appealing, but still it is susceptible to numerical instability. Finally, we analytically reformulate it as a numerically stable Equivalence Test, which is thus easier to use. Our results therefore allow to induce an Equivalence Test from a t-Test and increase the comparability of both Tests, especially for decision makers.
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Automatic Passenger Counting: Introducing the t-Test Induced Equivalence Test
Transportation, 2019Co-Authors: Michael Siebert, David EllenbergerAbstract:Automatic passenger counting (APC) in public transport has been introduced in the 1970s and has been rapidly emerging in recent years. Still, real-world applications continue to face events that are difficult to classify. The induced imprecision needs to be handled as statistical noise and thus methods have been defined to ensure that measurement errors do not exceed certain bounds. Various recommendations for such an APC validation have been made to establish criteria that limit the bias and the variability of the measurement errors. In those works, the misinterpretation of non-significance in statistical hypothesis Tests for the detection of differences (e.g. Student's t-Test) proves to be prevalent, although existing methods which were developed under the term Equivalence Testing in biostatistics (i.e. bioEquivalence trials, Schuirmann in J Pharmacokinet Pharmacodyn 15(6):657-680, 1987) would be appropriate instead. This heavily affects the calibration and validation process of APC systems and has been the reason for unexpected results when the sample sizes were not suitably chosen: Large sample sizes were assumed to improve the assessment of systematic measurement errors of the devices from a user's perspective as well as from a manufacturer's perspective, but the regular t-Test fails to achieve that. We introduce a variant of the t-Test, the revised t-Test, which addresses both type I and type II errors appropriately and allows a comprehensible transition from the long-established t-Test in a widely used industrial recommendation. This Test is appealing, but still it is susceptible to numerical instability. Finally, we analytically reformulate it as a numerically stable Equivalence Test, which is thus easier to use. Our results therefore allow to induce an Equivalence Test from a t-Test and increase the comparability of both Tests, especially for decision makers.
Harald Heinzl - One of the best experts on this subject based on the ideXlab platform.
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Combining difference and Equivalence Test results in spatial maps
International journal of health geographics, 2011Co-Authors: Thomas Waldhoer, Harald HeinzlAbstract:Background: Regionally partitioned health indicator values are commonly presented in choropleth maps. Policymakers and health authorities use them among others for health reporting, demand planning and quality assessment. Quite often there are concerns whether the health situation in certain areas can be considered different or equivalent to a reference value. Results: Highlighting statistically significant areas enables the statement that these areas differ from the reference value. However, this approach does not allow conclusions which areas are sufficiently close to the reference value, although these are crucial for health policy making as well. In order to overcome this weakness a combined integration of statistical difference and Equivalence Tests into choropleth maps is suggested and the approach is exemplified with health data of Austrian newborns. Conclusions: The suggested method will improve the interpretability of choropleth maps for policymakers and health authorities.
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combining difference and Equivalence Test results in spatial maps
International Journal of Health Geographics, 2011Co-Authors: Thomas Waldhoer, Harald HeinzlAbstract:Regionally partitioned health indicator values are commonly presented in choropleth maps. Policymakers and health authorities use them among others for health reporting, demand planning and quality assessment. Quite often there are concerns whether the health situation in certain areas can be considered different or equivalent to a reference value. Highlighting statistically significant areas enables the statement that these areas differ from the reference value. However, this approach does not allow conclusions which areas are sufficiently close to the reference value, although these are crucial for health policy making as well. In order to overcome this weakness a combined integration of statistical difference and Equivalence Tests into choropleth maps is suggested and the approach is exemplified with health data of Austrian newborns. The suggested method will improve the interpretability of choropleth maps for policymakers and health authorities.
Michael Siebert - One of the best experts on this subject based on the ideXlab platform.
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Validation of automatic passenger counting: introducing the t-Test-induced Equivalence Test
Transportation, 2019Co-Authors: Michael Siebert, David EllenbergerAbstract:Automatic passenger counting (APC) in public transport has been introduced in the 1970s and has been rapidly emerging in recent years. Still, real-world applications continue to face events that are difficult to classify. The induced imprecision needs to be handled as statistical noise and thus methods have been defined to ensure that measurement errors do not exceed certain bounds. Various recommendations for such an APC validation have been made to establish criteria that limit the bias and the variability of the measurement errors. In those works, the misinterpretation of non-significance in statistical hypothesis Tests for the detection of differences (e.g. Student’s t-Test) proves to be prevalent, although existing methods which were developed under the term Equivalence Testing in biostatistics (i.e. bioEquivalence trials, Schuirmann in J Pharmacokinet Pharmacodyn 15(6):657–680, 1987) would be appropriate instead. This heavily affects the calibration and validation process of APC systems and has been the reason for unexpected results when the sample sizes were not suitably chosen: Large sample sizes were assumed to improve the assessment of systematic measurement errors of the devices from a user’s perspective as well as from a manufacturers perspective, but the regular t-Test fails to achieve that. We introduce a variant of the t-Test, the revised t-Test, which addresses both type I and type II errors appropriately and allows a comprehensible transition from the long-established t-Test in a widely used industrial recommendation. This Test is appealing, but still it is susceptible to numerical instability. Finally, we analytically reformulate it as a numerically stable Equivalence Test, which is thus easier to use. Our results therefore allow to induce an Equivalence Test from a t-Test and increase the comparability of both Tests, especially for decision makers.
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Automatic Passenger Counting: Introducing the t-Test Induced Equivalence Test
Transportation, 2019Co-Authors: Michael Siebert, David EllenbergerAbstract:Automatic passenger counting (APC) in public transport has been introduced in the 1970s and has been rapidly emerging in recent years. Still, real-world applications continue to face events that are difficult to classify. The induced imprecision needs to be handled as statistical noise and thus methods have been defined to ensure that measurement errors do not exceed certain bounds. Various recommendations for such an APC validation have been made to establish criteria that limit the bias and the variability of the measurement errors. In those works, the misinterpretation of non-significance in statistical hypothesis Tests for the detection of differences (e.g. Student's t-Test) proves to be prevalent, although existing methods which were developed under the term Equivalence Testing in biostatistics (i.e. bioEquivalence trials, Schuirmann in J Pharmacokinet Pharmacodyn 15(6):657-680, 1987) would be appropriate instead. This heavily affects the calibration and validation process of APC systems and has been the reason for unexpected results when the sample sizes were not suitably chosen: Large sample sizes were assumed to improve the assessment of systematic measurement errors of the devices from a user's perspective as well as from a manufacturer's perspective, but the regular t-Test fails to achieve that. We introduce a variant of the t-Test, the revised t-Test, which addresses both type I and type II errors appropriately and allows a comprehensible transition from the long-established t-Test in a widely used industrial recommendation. This Test is appealing, but still it is susceptible to numerical instability. Finally, we analytically reformulate it as a numerically stable Equivalence Test, which is thus easier to use. Our results therefore allow to induce an Equivalence Test from a t-Test and increase the comparability of both Tests, especially for decision makers.
Stavros A Kavouras - One of the best experts on this subject based on the ideXlab platform.
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afternoon urine osmolality is equivalent to 24 h for hydration assessment in healthy children
European Journal of Clinical Nutrition, 2020Co-Authors: Hyungyu Suh, Lynn Dee G Summers, Adam D Seal, Abigail T Colburn, Andy Mauromoustakos, Erica T Perrier, Jeanne H Bottin, Stavros A KavourasAbstract:While daily hydration is best assessed in 24-h urine sample, spot sample is often used by health care professionals and researchers due to its practicality. However, urine output is subject to circadian variation, with urine being more concentrated in the morning. It has been demonstrated that afternoon spot urine samples are most likely to provide equivalent urine concentration to 24-h urine samples in adults. The aim of the present study was to examine whether urine osmolality (UOsm) assessed from a spot urine sample in specific time-windows was equivalent to 24-h UOsm in free-living healthy children. Among 541 healthy children (age: 3–13 years, female: 45%, 77% non-Hispanic white, BMI:17.7 ± 4.0 kg m−2), UOsm at specific time-windows [morning (0600–1159), early afternoon (1200–1559), late afternoon (1600–1959), evening (2000–2359), overnight (2400–0559), and first morning] was compared with UOsm from the corresponding pooled 24-h urine sample using an Equivalence Test. Late afternoon (1600–1959) spot urine sample UOsm value was equivalent to the 24-h UOsm value in children (P < 0.05; mean difference: 62 mmol kg−1; 95% CI: 45–78 mmol kg−1). The overall diagnostic ability of urine osmolality assessed at late afternoon (1600–1959) to diagnose elevated urine osmolality on the 24-h sample was good for both cutoffs of 800 mmol kg−1 [area under the curve (AUC): 87.4%; sensitivity: 72.6%; specificity: 90.5%; threshold: 814 mmol kg−1] and 500 mmol kg−1 (AUC: 83.5%; sensitivity: 75.0%; specificity: 80.0%; threshold: 633 mmol kg−1). These data suggest that in free-living healthy children, 24-h urine concentration may be approximated from a late afternoon spot urine sample. This data will have practical implication for health care professionals and researchers.
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Afternoon urine osmolality is equivalent to 24 h for hydration assessment in healthy children.
European Journal of Clinical Nutrition, 2019Co-Authors: Hyungyu Suh, Lynn Dee G Summers, Adam D Seal, Abigail T Colburn, Andy Mauromoustakos, Erica T Perrier, Jeanne H Bottin, Stavros A KavourasAbstract:While daily hydration is best assessed in 24-h urine sample, spot sample is often used by health care professionals and researchers due to its practicality. However, urine output is subject to circadian variation, with urine being more concentrated in the morning. It has been demonstrated that afternoon spot urine samples are most likely to provide equivalent urine concentration to 24-h urine samples in adults. The aim of the present study was to examine whether urine osmolality (UOsm) assessed from a spot urine sample in specific time-windows was equivalent to 24-h UOsm in free-living healthy children. Among 541 healthy children (age: 3–13 years, female: 45%, 77% non-Hispanic white, BMI:17.7 ± 4.0 kg m−2), UOsm at specific time-windows [morning (0600–1159), early afternoon (1200–1559), late afternoon (1600–1959), evening (2000–2359), overnight (2400–0559), and first morning] was compared with UOsm from the corresponding pooled 24-h urine sample using an Equivalence Test. Late afternoon (1600–1959) spot urine sample UOsm value was equivalent to the 24-h UOsm value in children (P
Thomas Waldhoer - One of the best experts on this subject based on the ideXlab platform.
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Combining difference and Equivalence Test results in spatial maps
International journal of health geographics, 2011Co-Authors: Thomas Waldhoer, Harald HeinzlAbstract:Background: Regionally partitioned health indicator values are commonly presented in choropleth maps. Policymakers and health authorities use them among others for health reporting, demand planning and quality assessment. Quite often there are concerns whether the health situation in certain areas can be considered different or equivalent to a reference value. Results: Highlighting statistically significant areas enables the statement that these areas differ from the reference value. However, this approach does not allow conclusions which areas are sufficiently close to the reference value, although these are crucial for health policy making as well. In order to overcome this weakness a combined integration of statistical difference and Equivalence Tests into choropleth maps is suggested and the approach is exemplified with health data of Austrian newborns. Conclusions: The suggested method will improve the interpretability of choropleth maps for policymakers and health authorities.
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combining difference and Equivalence Test results in spatial maps
International Journal of Health Geographics, 2011Co-Authors: Thomas Waldhoer, Harald HeinzlAbstract:Regionally partitioned health indicator values are commonly presented in choropleth maps. Policymakers and health authorities use them among others for health reporting, demand planning and quality assessment. Quite often there are concerns whether the health situation in certain areas can be considered different or equivalent to a reference value. Highlighting statistically significant areas enables the statement that these areas differ from the reference value. However, this approach does not allow conclusions which areas are sufficiently close to the reference value, although these are crucial for health policy making as well. In order to overcome this weakness a combined integration of statistical difference and Equivalence Tests into choropleth maps is suggested and the approach is exemplified with health data of Austrian newborns. The suggested method will improve the interpretability of choropleth maps for policymakers and health authorities.