scholarly journals Comparison of Parametric and Nonparametric Estimators for the Association Between Incident Prepregnancy Obesity and Stillbirth in a Population-Based Cohort Study

2019 ◽  
Vol 188 (7) ◽  
pp. 1328-1336 ◽  
Author(s):  
Ya-Hui Yu ◽  
Lisa M Bodnar ◽  
Maria M Brooks ◽  
Katherine P Himes ◽  
Ashley I Naimi
Keyword(s):  
Inverse Probability Weighting ◽  
Population Based ◽  
Parametric Models ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Nonparametric Estimators ◽  
Inverse Probability Weights ◽  
Highly Sensitive ◽  
Prepregnancy Obesity ◽  
Index Weight

AbstractWhile prepregnancy obesity increases risk of stillbirth, few studies have evaluated the role of newly developed obesity independent of long-standing obesity. Additionally, researchers have relied almost exclusively on parametric models, which require correct specification of an unknown function for consistent estimation. We estimated the association between incident obesity and stillbirth in a cohort constructed from linked birth and death records in Pennsylvania (2003–2013). Incident obesity was defined as body mass index (weight (kg)/height (m)2) greater than or equal to 30. We used parametric G-computation, semiparametric inverse-probability weighting, and parametric/nonparametric targeted minimum loss-based estimation (TMLE) to estimate the association between incident prepregnancy obesity and stillbirth. Compared with pregnancies from women who stayed nonobese, women who became obese prior to their next pregnancy were estimated to have 2.0 (95% confidence interval (CI): 0.5, 3.5) more stillbirths per 1,000 pregnancies using parametric G-computation. However, despite well-behaved stabilized inverse probability weights, risk differences estimated from inverse-probability weighting, nonparametric TMLE, and parametric TMLE represented 6.9 (95% CI: 3.7, 10.0), 0.4 (95% CI: 0.1, 0.7), and 2.9 (95% CI: 1.5, 4.2) excess stillbirths per 1,000 pregnancies, respectively. These results, particularly those derived from nonparametric TMLE, were highly sensitive to covariates included in the propensity score models. Our results suggest that caution is warranted when using nonparametric estimators to quantify exposure effects.

scholarly journals Can the Adoption of Protected Cultivation Facilities Affect Farm Sustainability?

2020 ◽  
Vol 12 (23) ◽  
pp. 9970
Author(s):  
Pei-An Liao ◽  
Jhih-Yun Liu ◽  
Lih-Chyun Sun ◽  
Hung-Hao Chang
Keyword(s):  
Inverse Probability Weighting ◽  
Population Based ◽  
Small Scale ◽  
Farm Labor ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Protected Cultivation ◽  
Farm Profit ◽  
Farm Sustainability ◽  
On Farm

Given the increasing threat of climate change to agriculture, determining how to achieve farm sustainability is important for researchers and policy makers. Among others, protected cultivation has been proposed as a possible adaptive solution at the farm level. This study contributes to this research topic by quantifying the effects of the use of protected cultivation facilities on farm sustainability. In contrast to previous studies that relied on small-scale random surveys, a population-based sample of fruit, flower and vegetable farms was drawn from the Agricultural Census Survey in Taiwan. Propensity score matching, inverse probability weighting and inverse probability weighting regression adjustment methods were applied. Empirical results show that the use of protected cultivation facilities increases farm profit by 68–73%, other things being equal. This finding is persistent when farms suffer from disaster shocks. Moreover, the changes in farm labor use can be seen as a mechanism behind the positive effect of the protected cultivation facility use on farm profit. Our findings suggest that agricultural authority can consider subsidizing farms to increase the adoption of protected cultivation facilities to mitigate the risks resulting from natural disaster shocks.

scholarly journals POS0309 SEROPOSITIVITY INCREASES RISK OF INCIDENT DEMENTIA IN INDIVIDUALS WITH RHEUMATOID ARTHRITIS: A POPULATION-BASED COHORT STUDY

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 380.2-381
Author(s):  
V. Kronzer ◽  
T. Gunderson ◽  
C. S. Crowson ◽  
J. M. Davis ◽  
M. Vassilaki ◽  
...  
Keyword(s):  
Cumulative Incidence ◽  
Smoking Status ◽  
Inverse Probability Weighting ◽  
Population Based ◽  
Probability Weighting ◽  
Research Support ◽  
Inverse Probability ◽  
Incident Dementia ◽  
Increased Risk ◽  
Incidence Of Dementia

Background:Several studies show increased risk of dementia among individuals with rheumatoid arthritis (RA), while others show no association. One reason for this discrepancy might be differential association by serostatus. No prior studies have investigated the association between RA and dementia by serostatus.Objectives:We aimed to evaluate the risk of incident dementia among individuals with RA, stratified by serostatus.Methods:This population-based cohort study included all cases of incident RA within Olmsted County, Minnesota with index date of RA onset between 1 January 1999 and 31 December 2013. We matched RA cases to non-RA comparators 1:1 on age and sex. All RA cases met 1987 ACR criteria for RA. We defined seropositivity as positive rheumatoid factor or anti-cyclic citrullinated peptide antibodies. We defined incident dementia as having two ICD-9/10 codes for dementia at least 30 days apart, with the date of the second code representing the time of dementia onset. We excluded individuals with dementia prior to index date. We estimated the cumulative incidence of dementia adjusting for the competing risk of death. For the main analysis, cox proportional hazard models estimated adjusted hazard ratios (aHR) with 95% confidence intervals (CI) for incident dementia, adjusting for age, sex, index year, body mass index, and smoking status (never, former, current). These models compared the incidence of dementia for RA versus non-RA, seropositive and seronegative RA versus their matches, and seropositive versus seronegative RA. To validate these results, we also performed sensitivity analyses using groups matched via inverse probability weighting on age, sex, index year, obesity, smoking status, and race.Results:We identified 597 RA cases (mean age 56, 70% female), and 594 non-RA comparators. Of the RA cases, 388 (65%) were seropositive, and 209 (35%) were seronegative. The ten-year cumulative incidence of dementia in patients with RA was 3.3 (95%CI 2.0,5.5) per 100,000 compared to 2.4 (95%CI 1.3,4.2) in non-RA comparators, for aHR of 1.26 (95%CI 0.7,2.3). When stratifying by serostatus, the ten-year incidence of dementia for seropositive RA cases was 3.6 (95%CI 2.0,6.5), corresponding to aHR of 1.45 (95%CI 0.73,2.9) compared to matched non-RA cases. In contrast, the incidence of dementia in seronegative RA cases was 2.6 (95%CI 1.0,7.0), for an aHR of 1.0 (95%CI 0.29,3.5). Overall, the incidence of dementia in seropositive RA cases was significantly higher than seronegative cases (aHR 3.2, 95%CI 1.2,8.5). Indeed, sensitivity analysis using inverse probability weighting confirmed that among participants aged 50 and older, dementia incidence was higher for seropositive than seronegative RA (aHR 2.9, 95%CI1.1,7.8) (Figure 1).Conclusion:Individuals with seropositive RA have an increased risk for incident dementia than those with seronegative RA. Future studies should replicate these findings and investigate the mechanism for this association.Figure 1.Cumulative incidence of dementia for individuals with seropositive versus seronegative RA aged 50 and older, balanced by inverse probability weightingAcknowledgements:This work was funded by grants from the National Institutes of Health, NIAMS (R01 AR46849) and NIA (R01 AG068192, R01 AG034676).Disclosure of Interests:Vanessa Kronzer: None declared, Tina Gunderson: None declared, Cynthia S. Crowson: None declared, John M Davis III Grant/research support from: Pfizer, Maria Vassilaki Shareholder of: equity ownership in Abbott Laboratories, Johnson and Johnson, Medronic and Amgen;, Grant/research support from: Roche and Biogen, Michelle Mielke Consultant of: Biogen and Brain Protection Company, Elena Myasoedova: None declared

scholarly journals Impact of Adopting Improved Seeds on Access to Broader Food Groups Among Small-Scale Sorghum Producers in Tanzania

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402097999
Author(s):  
Aloyce R. Kaliba ◽  
Anne G. Gongwe ◽  
Kizito Mazvimavi ◽  
Ashagre Yigletu
Keyword(s):  
Food Security ◽  
Dietary Diversity ◽  
Sampling Technique ◽  
Inverse Probability Weighting ◽  
Reproductive Age ◽  
Small Scale ◽  
Probability Weighting ◽  
Food Groups ◽  
Inverse Probability ◽  
Double Robust

In this study, we use double-robust estimators (i.e., inverse probability weighting and inverse probability weighting with regression adjustment) to quantify the effect of adopting climate-adaptive improved sorghum varieties on household and women dietary diversity scores in Tanzania. The two indicators, respectively, measure access to broader food groups and micronutrient and macronutrient availability among children and women of reproductive age. The selection of sample households was through a multistage sampling technique, and the population was all households in the sorghum-producing regions of Central, Northern, and Northwestern Tanzania. Before data collection, enumerators took part in a 1-week training workshop and later collected data from 822 respondents using a structured questionnaire. The main results from the study show that the adoption of improved sorghum seeds has a positive effect on both household and women dietary diversity scores. Access to quality food groups improves nutritional status, food security adequacy, and general welfare of small-scale farmers in developing countries. Agricultural projects that enhance access to improved seeds are, therefore, likely to generate a positive and sustainable effect on food security and poverty alleviation in sorghum-producing regions of Tanzania.

scholarly journals Inverse probability weighting for clustered nonresponse

Biometrika ◽  
2011 ◽  
Vol 98 (4) ◽  
pp. 953-966 ◽  
Author(s):  
C. J. Skinner ◽  
D'arrigo
Keyword(s):  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Inverse Probability

scholarly journals Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?

2018 ◽  
Vol 48 (3) ◽  
pp. 691-701 ◽  
Author(s):  
Apostolos Gkatzionis ◽  
Stephen Burgess
Keyword(s):  
Risk Factor ◽  
Selection Bias ◽  
Simulation Study ◽  
Cardiovascular Mortality ◽  
Mendelian Randomization ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Type 1 Error

Abstract Background Selection bias affects Mendelian randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor–outcome association. However, the relative importance of selection bias for Mendelian randomization compared with other potential biases is unclear. Methods We performed an extensive simulation study to assess the impact of selection bias on a typical Mendelian randomization investigation. We considered inverse probability weighting as a potential method for reducing selection bias. Finally, we investigated whether selection bias may explain a recently reported finding that lipoprotein(a) is not a causal risk factor for cardiovascular mortality in individuals with previous coronary heart disease. Results Selection bias had a severe impact on bias and Type 1 error rates in our simulation study, but only when selection effects were large. For moderate effects of the risk factor on selection, bias was generally small and Type 1 error rate inflation was not considerable. Inverse probability weighting ameliorated bias when the selection model was correctly specified, but increased bias when selection bias was moderate and the model was misspecified. In the example of lipoprotein(a), strong genetic associations and strong confounder effects on selection mean the reported null effect on cardiovascular mortality could plausibly be explained by selection bias. Conclusions Selection bias can adversely affect Mendelian randomization investigations, but its impact is likely to be less than other biases. Selection bias is substantial when the effects of the risk factor and confounders on selection are particularly large.

scholarly journals Likelihood Approach for Bayesian Logistic Weighted Model

2020 ◽  
Vol 4 (2) ◽  
pp. 9-12
Author(s):  
Dler H. Kadir
Keyword(s):  
Cognitive Development ◽  
Case Analysis ◽  
Inverse Probability Weighting ◽  
Complete Case Analysis ◽  
Parameter Estimates ◽  
Probability Weighting ◽  
Complete Case ◽  
Inverse Probability ◽  
Very Preterm ◽  
Weighted Model

Increasing the response rate and minimizing non-response rates represent the primary challenges to researchers in performing longitudinal and cohort research. This is most obvious in the area of paediatric medicine. When there are missing data, complete case analysis makes findings biased. Inverse Probability Weighting (IPW) is one of many available approaches for reducing the bias using a complete case analysis. Here, a complete case is weighted by probability inverse of complete cases. The data of this work is collected from the neonatal intensive care unit at Erbil maternity hospital for the years 2012 to 2017. In total, 570 babies (288 male and 282 females) were born very preterm. The aim of this paper is to use inverse probability weighting on the Bayesian logistic model developmental outcome. The Mental Development Index (MDI) approach is used for assessing the cognitive development of those born very preterm. Almost half of the information for the babies was missing, meaning that we do not know whether they have cognitive development issues or they have not. We obtained greater precision in results and standard deviation of parameter estimates which are less in the posterior weighted model in comparison with frequent analysis.

Association Effects Of Comorbid Versus Uncomorbid Diabetes And Blood Glucose Concentrations On Short-term Outcomes In Patients With Critical Illness: A Real-World Study Based On Propensity Score Analyses

2020 ◽  
Author(s):  
Shan Lin ◽  
Shanhui Ge ◽  
Wanmei He ◽  
Mian Zeng
Keyword(s):  
Critical Illness ◽  
Glycemic Control ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Cox Model ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Optimal Range ◽  
Short Term ◽  
Inverse Probability

Abstract Background: The effects of combined diabetes and glycemic control strategies on the short-term prognosis in patients with a critical illness are currently ambiguous. The objectives of our study were to determine whether comorbid diabetes affects short-term prognosis and the optimal range of glycemic control in critically ill patients.Methods: We performed this study with the critical care database. The primary outcomes were 28-day mortality in critically ill patients with comorbid diabetes and the optimal range of glycemic control. Association of comorbid diabetes with 28-day mortality was assessed by multivariable Cox regression model with inverse probability weighting. Smooth curves were applied to fit the association for glucose and 28-day mortality.Results: Of the 33,680 patients enrolled in the study, 8,701 (25.83%) had diabetic comorbidity. Cox model with inverse probability weighting showed that the 28-day mortality rate was reduced by 29% (HR=0.71, 95% CI 0.67-0.76) in the group with diabetes in comparison to the group without diabetes. The E value of 2.17 indicated robustness to unmeasured confounders. The effect of the association between comorbid diabetes and 28-day mortality was generally in line for all subgroup variables, significant interactions were observed for glucose on first day, admission type, and use of insulin or not (Interaction P <0.05). A V-shaped relationship was observed between glucose concentrations and 28-day mortality in patients without diabetes, with the lowest 28-day mortality corresponding to the glucose level was 101.75 mg/dl (95% CI 94.64-105.80 mg/dl); whereas in patients with comorbid diabetes, the effect of glucose concentration on 28-day mortality was structurally softer than in those with uncomorbid diabetes. Lastly, of all patients, hyperglycemia had the greatest deleterious effect on patients admitted to CSRU.Conclusions: Our study further confirmed the protective effect of comorbid diabetes on the short-term prognosis of critically ill patients, resulting in an approximately 29% reduction in 28-day mortality. Besides, we also demonstrated the personalized glycemic control strategy for critically ill patients. Lastly, clinicians should be aware of the occurrence and the prompt management of hyperglycemia in critically ill patients admitted to the CSRU.

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