Joint regression analysis of mixed-type outcome data via efficient scores

Cotton-breeding trials are conducted annually throughout the commercial growing regions of eastern Australia. Accumulated yield data for the period 1974-85 were assembled into an incomplete cultivar x location x year table. This table was then analysed in order to compare test locations. The method involved analysing cultivar x location tables separately for each year, using symmetric joint regression analysis. Results were then collected into location x year tables and further analysed. Four criteria for comparing test locations were developed. The discrimination criterion is important when locations are evaluated in terms of their ability to display cultivar differences. The representation criterion measures the ability of a location to mirror the relative performance of cultivars over all locations. The other 2 criteria are concerned with the mean yield at test locations and the stability of location yields over years. Based on the 4 criteria, preferred test locations are recommended.

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Joint regression analysis and AMMI model applied to oat improvement

10.1063/1.4756504 ◽

2012 ◽

Author(s):

A. Oliveira ◽

T. A. Oliveira ◽

S. Mejza

Keyword(s):

Regression Analysis ◽

Ammi Model ◽

Joint Regression Analysis

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Trimmed joint regression: a new approach to the joint regression analysis for cultivar relative-performance evaluation

Theoretical and Applied Genetics ◽

10.1007/bf00224178 ◽

1992 ◽

Vol 84-84 (5-6) ◽

pp. 735-738 ◽

Cited By ~ 1

Author(s):

L. Gusmão ◽

J. T. Mexia ◽

J. Baeta

Keyword(s):

Regression Analysis ◽

Performance Evaluation ◽

Relative Performance ◽

Relative Performance Evaluation ◽

New Approach ◽

Joint Regression Analysis

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Perinatal Outcome in Women with a Vietnamese Migration Background – Retrospective Comparative Data Analysis of 3000 Deliveries

Geburtshilfe und Frauenheilkunde ◽

10.1055/a-0636-4224 ◽

2018 ◽

Vol 78 (07) ◽

pp. 697-706

Author(s):

Nicole Boxall ◽

Matthias David ◽

Elisabeth Schalinski ◽

Jürgen Breckenkamp ◽

Oliver Razum ◽

...

Keyword(s):

Regression Analysis ◽

Child Outcomes ◽

Outcome Data ◽

Migration Background ◽

Child Outcome ◽

Perineal Tear ◽

Arterial Ph ◽

Background Exposure ◽

Perinatal Data ◽

Grade 3

Abstract Introduction Perinatal data of women with a Vietnamese migration background have not been systematically studied in Germany to date. Numerous details of important maternal and child outcomes were compared and analysed. The studyʼs primary parameters were the frequency of and indication for c-section. Methodology The perinatal data from a Berlin hospital were analysed retrospectively. The women (Vietnamese migration background vs. autochthonous) were grouped using name analysis. Datasets of 3002 women giving birth, including 999 women with a Vietnamese migration background, were included. The associations between primary or secondary cesarean delivery and different child outcomes depending on the migration background (exposure) were studied using logistical regression analysis. Results Women with a Vietnamese migration background have a lower c-section rate of 8.0% for primary and 12.6% for secondary c-section than women without a migration background (11.1% primary and 16.4% secondary c-section respectively). Regression analysis shows that the odds that women with a Vietnamese migration background will have a primary (OR 0.75; p = 0.0884) or secondary c-section (OR 0.82; p = 0.1137) are not significantly lower. A Vietnamese migration background was associated with higher odds for an episiotomy but not for a grade 3 – 4 perineal tear. A Vietnamese migration background does not have a significant influence on poor 5-min Apgar scores ≤ 7 and low umbilical cord arterial pH values ≤ 7.10. Newborns of mothers with a Vietnamese migration background have higher odds of a relatively higher birth weight (> 3110 g). Summary There was no evidence that women with a Vietnamese migration background are delivered more often by caesarean section. There were also no differences as regards important child outcome data from women in the comparator group. Overall, the results do not provide any evidence for poorer quality of care of women with a Vietnamese migration background in Berlin despite the cultural and communication barriers in the reality of care provision.

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Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Science Advances ◽

10.1126/sciadv.abd4049 ◽

2020 ◽

Vol 6 (45) ◽

pp. eabd4049 ◽

Cited By ~ 23

Author(s):

X. Wu ◽

R. C. Nethery ◽

M. B. Sabath ◽

D. Braun ◽

F. Dominici

Keyword(s):

United States ◽

Air Pollution ◽

Regression Analysis ◽

The United States ◽

Outcome Data ◽

Data Availability ◽

Future Research ◽

Individual Level ◽

Ecological Regression

Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

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