scholarly journals A potential outcomes approach to defining and estimating gestational age-specific exposure effects during pregnancy

2022 ◽  
pp. 096228022110651
Author(s):  
Mireille E Schnitzer ◽  
Steve Ferreira Guerra ◽  
Cristina Longo ◽  
Lucie Blais ◽  
Robert W Platt
Keyword(s):  
Inhaled Corticosteroids ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
Inverse Probability Weighting ◽  
Time Dependent ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Immortal Time Bias ◽  
Targeted Maximum Likelihood ◽  
Time Dependent Effect

Many studies seek to evaluate the effects of potentially harmful pregnancy exposures during specific gestational periods. We consider an observational pregnancy cohort where pregnant individuals can initiate medication usage or become exposed to a drug at various times during their pregnancy. An important statistical challenge involves how to define and estimate exposure effects when pregnancy loss or delivery can occur over time. Without proper consideration, the results of standard analysis may be vulnerable to selection bias, immortal time-bias, and time-dependent confounding. In this study, we apply the “target trials” framework of Hernán and Robins in order to define effects based on the counterfactual approach often used in causal inference. This effect is defined relative to a hypothetical randomized trial of timed pregnancy exposures where delivery may precede and thus potentially interrupt exposure initiation. We describe specific implementations of inverse probability weighting, G-computation, and Targeted Maximum Likelihood Estimation to estimate the effects of interest. We demonstrate the performance of all estimators using simulated data and show that a standard implementation of inverse probability weighting is biased. We then apply our proposed methods to a pharmacoepidemiology study to evaluate the potentially time-dependent effect of exposure to inhaled corticosteroids on birthweight in pregnant people with mild asthma.

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.

The Individual-Level Deterrent Effect of “Call-In” Meetings on Time to Re-Arrest

2019 ◽  
Vol 66 (11) ◽  
pp. 1630-1651
Author(s):  
Giovanni Circo ◽  
Julie M. Krupa ◽  
Edmund McGarrell ◽  
Alaina DeBiasi
Keyword(s):  
Young Adults ◽  
Comparison Group ◽  
Cox Regression ◽  
Inverse Probability Weighting ◽  
Deterrent Effect ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Individual Level ◽  
The Individual ◽  
Level Effect

Focused on deterrence popular model to address community-level violence, however little research has examined the individual-level effect of deterrent messaging on subsequent offending. To answer this question, we utilize data on 254 gang- and group-involved probationers and parolees who attended offender “call-in” meetings as part of the Detroit Ceasefire. We employ inverse-probability weighting to construct a counterfactual comparison group from a sample of gang-involved young adults who were not subject to the Ceasefire call-in. We then use a Cox regression to estimate time to re-arrest. We find that individuals who were delivered a deterrent message at a call-in meeting had a longer time to re-arrest compared to a weighted comparison group for up to 3 years following the meeting.

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