Propensity Score Adjustment Method Research Articles

The Association Between Vitamin D Deficiency and Diabetes in Adult African Americans and Whites: An NHANES Study

Abstract Objective The primary objective of this cross-sectional study is to investigate the association between vitamin D deficiency (VDD) and diabetes and see if this association is the same for adult (age ≥ 20) African Americans (AAs) and Whites. The secondary objective is to examine the distribution of the 25-hydroxyvitamin D test among AAs and Whites and to evaluate the appropriateness of using the same cut-off point for both groups to diagnose VDD. Methods Our analysis is based on the 2011–2014 National Health and Nutrition Examination Surveys (NHANES). We used two common propensity score adjustment methods to analyze the data—propensity score matching (PSM) and the inverse probability of treatment weighting (IPTW). Results The prevalence of diabetes for AAs and Whites was 12.27% (95% CI, 10.47–14.07%) and 7.24% (95% CI, 6.35–8.13%), respectively. The prevalence of VDD for AAs and Whites was 65.29% (95% CI, 62.01–68.58%) and 19.49% (95% CI, 16.53–22.45%), respectively. Under PSM, the odds ratios for the diabetes-VDD association for AAs and Whites were 0.94 (95% CI, 0.70–1.27) and 2.16 (95% CI, 1.49–3.13), respectively. Under IPTW, the VDD-diabetes odds ratios for AAs and Whites were 0.83 (95% CI, 0.64–1.10) and 2.35 (95% CI, 1.67–3.30), respectively. Our results further demonstrate that the 25-hydroxyvitamin D measurements are significantly different for AAs and Whites across the general population, as well as the vitamin D–sufficient and vitamin D–deficient populations. Conclusion The prevalence of VDD and diabetes was higher for AAs compared to Whites. However, VDD was associated with increased diabetes risk for Whites but not for AAs. Though more research is needed to explain why this is the case, a reason for this may be that the 25-hydroxyvitamin D test or its associated cut-off point for defining VDD may not accurately reflect the vitamin D status among AAs.

Long-Term Maternal and Child Outcomes Following Postnatal SSRI Treatment

Although selective serotonin reuptake inhibitors (SSRIs) are recommended for postnatal depression treatment, there is a lack of evidence regarding long-term maternal and child outcomes following postnatal SSRI treatment. To examine whether postnatal SSRI treatment moderated postnatal depression-associated maternal and child outcomes across early childhood years. This cohort study used longitudinal data from the Norwegian Mother, Father and Child Cohort Study. Participating women were recruited in weeks 17 to 18 of pregnancy from 1999 to 2008 and were prospectively followed up after childbirth. Data analysis was performed between December 2021 to October 2022. Postnatal depression diagnosis (a binary indicator of eligibility for treatment) was defined as a score of 7 or greater on the 6-item version of the Edinburgh Postnatal Depression Scale. The Hopkins Symptom Checklist was used as a continuous indicator of and postnatal depressive symptomology at postpartum month 6. Postnatal SSRI treatment was identified using self-reported data at postpartum month 6. Maternal outcomes included self-reported depression symptomology and relationship satisfaction from childbirth to postpartum year 5. Child outcomes included maternal-report internalizing and externalizing problems, attention-deficit/hyperactivity disorder symptoms, and motor and language development at ages 1.5, 3, and 5 years. A propensity score adjustment method was used to control for prenatal factors associated with postnatal SSRI exposure probability. Among a total of 61 081 mother-child dyads, 8671 (14.2%) (mean [SD] age, 29.93 [4.76] years) met the criteria for postnatal depression diagnosis, 177 (2.0%) (mean [SD] age, 30.20 [5.01] years) of whom received postnatal SSRI treatment. More severe postnatal depression symptomology was associated with a range of adverse maternal and child outcomes. Focusing analyses only on the postnatal depression dyads indicated that postnatal SSRI treatment attenuated negative associations between postnatal depression and maternal relationship satisfaction at postpartum month 6 (moderation β, 0.13; 95% CI, 0.07-0.19), years 1.5 (moderation β, 0.11; 95% CI, 0.05-0.18) and 3 (moderation β, 0.12; 95% CI, 0.04-0.19), and for child ADHD at age 5 years (moderation β, -0.15; 95% CI, -0.24 to -0.05). Postnatal SSRI treatment mitigated the negative associations between postnatal depression and maternal depression, partner relationship satisfaction, child externalizing problems, and attention-deficit/hyperactivity disorder up to 5 years after childbirth. The results of this large prospective cohort study suggest that postnatal SSRI treatment was associated with a reduced risk of postnatal depression-associated maternal mental health problems and child externalizing behaviors across early childhood years. These findings suggest that postnatal SSRI treatment may bring benefits in the long term to women with postnatal depression and their offspring. This study potentially provides valuable information for clinicians and women with postnatal depression to make informed treatment decisions.

Association Between Hypertension and New-Onset Non-Alcoholic Fatty Liver Disease in Chinese Non-Obese People: A Longitudinal Cohort Study.

Quantification of the relationship between hypertension and non-alcoholic fatty liver disease (NAFLD) risk is limited and controversial. This study aimed to investigate the relationship between hypertension and NAFLD in non-obese Chinese and to use different methods to demonstrate that hypertension is an independent risk factor for NAFLD. On 16,153 nonobese individuals, a retrospective cohort study was conducted in China to examine the impact of hypertension on incident NAFLD. We compared five methods: multivariable Cox proportional-hazards regression, propensity score-matched (PSM) analysis, propensity score adjustment method (considering the propensity score as a covariate in a multivariable Cox proportional-hazard regression), and two propensity score-based weighted methods-The first one estimated the hypertension effect in the overall study population-inverse probability of treatment weights (IPTW), the other in the hypertensive population-standardized mortality ratio (SMR) weights. We also used a genetic matching (GenMatch) algorithm to match the participants for sensitive analysis. Between 2010 and 2014, 16,153 participants met our inclusion criteria, including 2427 (15.03%) with hypertension. A total of 2321 (14.37%) participants developed NAFLD during the median follow-up of 2.98 years. The crude hazard ratio (HR) between hypertension and incident NAFLD was 2.05 (95% confidence interval (CI): 1.87, 2.25). The adjusted HR depended on the different methods, ranging from 1.09 (95% CI: 0.77, 1.23) for the PSM method to 2.24 (95% CI: 2.05, 2.44) for the SMR weighted analysis. Hypertensive participants with high propensity scores had a higher risk of developing NAFLD in the future. Excluding participants with propensity scores <8% yielded comparable hazard ratios with a narrower range, from 1.04 to 1.80. After adjusting for the confounding variables, the relationship also existed in the GenMatch cohort as a sensitivity analysis (HR=1.06, 95% CI 1.01-1.13). Hypertension is a significant cause of NAFLD in Chinese adults in non-obese Chinese adults, with the hazard ratio ranging from 1.09 to 2.24.

Risk of nonpulmonary infections requiring hospitalization in spondyloarthritis.

ObjectivesTo compare the risk of five nonpulmonary infections leading to hospitalization between spondyloarthritis (SpA) and nonspecific back pain (NSBP), and to identify the risk factors.MethodsA total of 3018 patients with SpA and 2527 patients with NSBP were identified. Data from December 1995 to June 2019 was retrieved from a centralized electronic medical record system. The date of onset of five types of nonpulmonary infections including: urinary tract infection (UTI), skin infection, gastroenteritis (GE), septic arthritis, and pancreato‐hepatobiliary tract infection were identified. Demographic data, comorbidities, and medications used were also retrieved. Comparative risk of each type of infection between SpA and NSBP was determined using propensity score adjustment method. Cox regression model was used to identified risk factors.ResultsPatients with SpA were younger in age, predominantly male, with fewer comorbid diabetes mellitus (DM), renal impairment, and depression. Compared with NSBP, patients with SpA had higher risk of UTI (hazard ratio [HR] 1.91; p < .001), skin infection (HR 1.79; p < .001), and septic arthritis (HR 4.57; p = .04). Risk of GE (HR 1.42; p = 1.00), and pancreato‐hepatobiliary tract infection (HR 1.67; p = .06) were not increased. Infliximab was an independent risk factor for UTI (HR 2.21; p = .04). Duration of steroid therapy >6 months (HR 2.22; p < .001), smoker (HR 1.81; p < .001), and psoriasis (HR 2.47; p < .001) were risk factors for skin infection.ConclusionSpA was associated with increased risk of UTI, skin infection, and septic arthritis. Infliximab, prolonged steroid therapy, smoking, and psoriasis were associated risk factors.

MRI lesions in SpA: a comparison with noninflammatory back pain using propensity score adjustment method.

Background:Magnetic resonance imaging (MRI) is important in the management of axial spondyloarthritis (SpA). However, many MRI lesions are not exclusive to axial SpA. Further characterization of these lesions may lead to better clinical decisions.Objective:The objective of this study was to compare the frequency of individual spinal MRI lesions between axial SpA and noninflammatory back pain. The factors associated with individual lesions in participants with axial SpA were also determined.Design:This was a cross-sectional observational study.Methods:MRI lesions in 447 participants with axial SpA and 122 participants with noninflammatory back pain were compared using the propensity score adjustment method. Individual lesions included discovertebral lesions (DVL), Modic type 1 lesions, DVL without Modic type 1 lesions, facet joint lesions, costovertebral joint lesions, corner inflammatory lesions (CIL), and fatty corner lesions (FCL). The factors associated with the lesions were determined using regression analyses.Results:Among participants with axial SpA, 81.9% were HLA-B27-positive, 55.0% had radiographic axial SpA, and 60.5% had radiographic features of spinal damage (mSASSS >2). Almost half (48.6% in axial SpA versus 31.1% in noninflammatory back pain) had inflammatory lesions on spinal MRI. In propensity score matching with noninflammatory back pain, axial SpA had an increased occurrence of DVL without Modic type 1 lesion (OR = 3.43, p = 0.01), costovertebral lesion (OR = 11.89, p = 0.02), number of CIL (B = 1.19, p < 0.001), and number of FCL (B = 3.33, p < 0.001). Similar associations were found in the regression models in the radiographic axial SpA subgroup: DVL without Modic type 1 lesion (OR = 2.46, p = 0.001), costovertebral lesion (OR = 3.86, p < 0.001), number of CIL (B = 1.13, p < 0.001), and FCL (B = 2.29, p < 0.01).Conclusion:MRI lesions including DVL without Modic type 1, costovertebral joint lesions, CIL, and FCL were more specific in axial SpA.

Propensity score-integrated Bayesian prior approaches for augmented control designs: a simulation study

ABSTRACT Drug development can be costly, and the availability of clinical trial participants may be limited either due to the disease setting (rare or pediatric diseases) or due to many sponsors evaluating multiple drugs or combinations in the same patient population. To maximize resource utilization, sponsors may leverage patient-level control data from historical trials. However, in a study with no control arm, it is impossible to evaluate if the historical controls are an appropriate comparator for the current study. Here, instead of conducting a single-arm trial and relying solely on historical controls, we evaluate the situation where a minimal number of patients are enrolled into a control arm, which is augmented by borrowing historical control data. Propensity score (PS) methods are commonly used to minimize bias for non-randomized data. In addition, Bayesian information borrowing with PS adjustments has been proposed when it may not be reasonable to include all available historical data. This paper proposes using PS adjustment integrated with Bayesian commensurate priors to adaptively borrow information. We then evaluate the performance of different PS adjustment methods and different Bayesian priors for augmented control using simulation studies to help inform the design of future trials. In general, we find that propensity weighting or matching combined with the commensurate prior yield reasonable statistical properties across a range of scenarios. Finally, our proposed methods are applied to a real trial with a binary outcome. Abbreviations: PS: propensity score; IPTW: inverse probability of treatment weighting; ATT: average treatment effect on those who received treatment; RCT: randomized controlled trial; CDD: covariate distribution difference; ESS: effective sample size

A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse

A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse

Comparisons of breast conserving therapy versus mastectomy in young and old women with early-stage breast cancer: long-term results using propensity score adjustment method

This study aimed to compare the effect of BCT versus mastectomy on the recurrence and survival of different-aged patients, and to investigate whether effects of BCT versus mastectomy on survival of young patients were consistent with those of old patients. Data on women with primary invasive breast cancer between 2007 and 2011 were extracted from the institutional database of Breast Center. Disparities in hormone receptor, tumor size, lymph node status, and Her-2 status between BCT and mastectomy groups were adjusted using the propensity score (PS) adjustment method. Patients were divided by age into two groups (≤ 40years and > 40years). We assessed proportions of local recurrence (LR), distant disease-free survival (DDFS), disease-free survival (DFS), and breast cancer-specific survival (BCSS) in different-aged groups; this assessment was further stratified by surgical treatment. A total of 2964 patients were included; 565 (19%) were aged ≤ 40years. In the entire cohort, hazard ratios (HR) of BCT versus mastectomy for DDFS and DFS were 0.56 (P = 0.029) and 0.55 (P = 0.008), respectively. After PS adjustment, there was no significant difference between BCT and mastectomy in LR, DDFS, DFS and BCSS in the young age group. In the old age group, women who underwent BCT exhibited improved DDFS (HR 0.57, 95% CI 0.39-0.84, P = 0.004). BCT did not significantly affect survival outcomes of young patients with breast cancer. Superior survival of BCT compared to mastectomy was observed only in old patients.

A new approach to assess the normalization of differential rates of protest participation

Research that compares those who do and do not participate in protest over time purports that protesters are becoming increasingly similar to the non-protesting population. Using a protest survey that includes the frequency of protest participation, we consider the extent to which those who protest to different degrees are similar to non-protesters. Selection bias in non-probability protest survey data is compensated for by combining the data with random reference samples from the European Social Survey under a quasi-randomisation approach. We test hypotheses on the normalization of protesters and compare two methods for compensating for selection bias: a proportional weighting method and a propensity score adjustment method. The propensity score adjustment method is more effective in mitigating selection bias by balancing on variables that explain the selection and outcome, and enables the comparison of groups of protesters to non-protesters. We find that protesters become increasingly differentiated from non-protesters according to their left-wing self-placement and education as their extent of protest participation increases.

PNS25 METHODS FOR DETECTING TREATMENT EFFECT MODIFIERS IN ANCHORED INDIRECT TREATMENT COMPARISONS: AN OVERVIEW AND SIMULATION STUDY OF MACHINE LEARNING ALGORITHMS

Anchored matching-adjusted indirect treatment comparisons (ITC) or propensity score adjustment methods (e.g. matching, reweighting) leverage individual patient data (IPD) to compare treatments across trials by anchoring through a common comparator. Since anchored ITCs adjust for imbalances in prognostic factors via the common comparator, health technology agencies (HTAs) such as NICE have recommended adjusting only for treatment effect modifiers that exhibit large imbalances between studies. Although many automated and semi-automated methods (e.g. high dimensionality propensity score, random forest, Bayesian Additive Regression Trees) for variable selection are available in open-source software packages, most practical applications of these methods have focused on detecting prognostic factors rather than treatment effect modifiers, and do not factor in imbalances between studies. This study provides a general overview of common machine-learning variable selection routines often employed for prognostic applications and explore their feasibility for identifying imbalanced treatment effect modifiers. We use Monte Carlo simulation studies to evaluate the finite sample performance of several supervised machine learning algorithms, in terms of their ability to detect true treatment effect modifiers with large imbalances, bias, and efficiency. Simulations are performed using simulated IPD that include one or more of the following characteristics: a) small samples; b) data sparsity relative to the number of potential treatment effect modifiers; and c) collinearity among variables. The findings from this overview and simulation study can inform data-driven variable selection methods for HTA and regulatory submissions involving indirect treatment comparisons which require the identification of variables that are important effect modifiers and differ between studies.

Performance evaluation of regression splines for propensity score adjustment in post-market safety analysis with multiple treatments

ABSTRACTObservational studies provide a core resource in assessing post-market drug safety and effectiveness. Propensity scores are a predominant method for confounding adjustment to achieve unbiased estimation of average treatment effects in observational data. However, the use of propensity score methods has been limited to comparing two treatment groups, while medical situations frequently present with multiple treatment options. Inverse probability of treatment weighting (IPTW) is a popular propensity score adjustment method, but its performance degrades with decreased positivity leading to extreme weights, a problem that can be amplified with multiple treatment groups. Meanwhile, regression on a spline of the propensity score has shown favorable performance compared to other propensity score methods in recent studies involving two treatments. This project utilizes a simulation study to compare IPTW and propensity score splines as adjustment methods in a three-treatment setting. We test a variety of spline methods, including natural cubic splines with varying numbers of interior knots, and thin-plate regression splines. We vary several parameters across simulations, including the degree of propensity score overlap among treatment groups, treatment prevalence, outcome prevalence, and true marginal relative risk. We assess methods based on their bias, root mean squared error, and coverage of the true marginal relative risk across simulations. We find that all methods perform similarly well when there is good propensity score distribution overlap. However, with even moderate decrease in overlap or low outcome prevalence, IPTW produces more biased estimates and higher variance than propensity score splines. Low treatment prevalence or unequal treatment prevalences across groups also worsens IPTW performance. Overall, a natural cubic spline with a relatively small number of interior knots provides good performance across a range of simulations.

Misoprostol as an adjunct to oxytocin can reduce postpartum-haemorrhage: a propensity score\u2013matched retrospective chart review in Bamenda-Cameroon, 2015\u20132016

BackgroundThere is some evidence that suggests misoprostol may supplement the action of oxytocin in preventing post-partum haemorrhage (PPH). The primary objective of this study was to determine the effect of the administration of 600 μg misoprostol in addition to oxytocin versus oxytocin alone, on the risk of PPH among pregnant women after delivery. The secondary objectives were to determine the effects of the above combination on maternal death and blood transfusion among pregnant women after delivery; and to determine the incidence of PPH, its case fatality, and the maternal mortality ratio in our hospital.MethodsDesign and setting: Retrospective chart review of 1736 women delivering at the Regional Hospital Bamenda Cameroon, between 2015 and 2016. This was a pre versus post study following a policy change in the prevention of PPH.Exposure groups: One group received oxytocin-misoprostol (January–April 2016: period after policy change), and the second group received oxytocin-only (January–April 2015: period before policy change) after delivery.Outcomes: The primary outcome was PPH, and the secondary outcomes were maternal death and blood transfusion.Statistical analysis: A 1:1 matching with replacement was done with the propensity score (PS). The groups were compared using PS matching with conditional logistic regression on the matched pairs as the main analysis. A sensitivity analysis was done using other PS adjustment methods and multiple regression.ResultsOf the 1736 women included in this study, 1238 were matched and compared. Women who received oxytocin-misoprostol were less likely to have PPH as compared to those receiving oxytocin-only (odds ratio [OR] 0.22, 95% confidence interval [CI] 0.08, 0.59, p = 0.003). This reduced odds of PPH was upheld in the different sensitivity analyses. There were no significant differences in the odds of maternal death and the use of blood transfusions between the two groups: OR 3.91, 95% CI [0.44, 35.08], p = 0.22, and OR 0.89, 95% CI [0.14–5.63], p = 0.91, respectively. Sensitivity analyses showed similar results. The incidence of PPH was 2.9% (before adding misoprostol the incidence was 4.4% and after adding misoprostol it was 1.5%), the case fatality rate of PPH was 1.96%, and the overall maternal mortality ratio in the hospital was 293 maternal deaths/100000 life births.ConclusionOur evidence suggests that using 600 μg misoprostol as an add-on to oxytocin in the prevention of post-partum haemorrhage significantly reduces the odds of PPH without affecting other maternal outcomes.

경향점수를 활용한 인과효과 추정 방법 비교1) : 대응, 가중, 층화, 이중경향점수 보정

This study aimed to introduce the double propensity score adjustment method and compare it with matching, weighting, and subclassification methods using estimated propensity scores. Based on empirical data analysis, the study emphasized the recognition of the estmand (ATT vs ATE) a researcher wants to investigate, considering the data structure, the sample sizes of the treatment and comparison groups, and the distributions of covariates between the groups to draw a valid and meaningful inference with respect to the effect of a treatment on the population of interest. To do so, we compared the results of four different propensity score utilization methods by analyzing the effects of private education on mathematic achievement with Gyeonggi educational longitudinal research data(GEPS2012). The results showed that matching was relatively limited to use when the sample size of the comparison group was not large enough or the distributions of propensity score between the treament and comparison groups were not sufficiently overlapped. On the other hand, the double propensity score adjustment method could be useful to estimate the average treatment effect for the treated when research targeted a special population and the treatment and comparison groups were different in various aspects.

Abstract 171: User Beware: Understanding the Adjusted Estimates of Propensity Score Methods

Background: Propensity score methods are used in observational studies to compensate for the lack of random allocation by balancing measured baseline characteristics between treated and untreated patients. Yet default implementations of propensity score methods may lead to contradictory findings. Our goal was to illustrate differences in treatment effect estimates produced using propensity score methods such as matching and inverse probability weighting (IPW). Methods: We used data from a retrospective analysis of the Northern New England Cardiovascular Disease Study Group registry that studied reintervention after single internal mammary artery (SIMA) versus bilateral internal mammary artery (BIMA) conduit use in 41,481 coronary artery bypass grafting (CABG) procedures from 1992-2014. Results: The mean duration of follow-up was 13.2 (IQR: 7.4-17.7) years. In our standard multivariable Cox regression analysis, the adjusted HR for reintervention was 0.83 (95% confidence interval (CI): 0.75-0.92) in patients receiving BIMA compared to SIMA (Table). The 1:1 propensity matched analysis (HR=0.79, 95% CI: 0.69-0.91) and IPW estimate among the treated (HR=0.83, 95% CI: 0.75-0.92) show a similar protective treatment effect of BIMA use on reintervention. However, the IPW approach for the overall population effect unusually showed no difference between BIMA and SIMA on reintervention-free survival (HR=1.08, 95% CI: 0.94-1.24). Conclusions and Relevance: The adjusted HR for reintervention after BIMA versus SIMA use in CABG was remarkably different for the two IPW estimates. This difference is attributed to the population represented by the two IPW approaches- the IPW among the treated estimate represents the effect in the average study population, whereas the IPW in the treated represents the effect in the treated population alone. Determining how the study population is balanced (weighted to look like the overall study population versus treated group) is a large driver of treatment effect, and a key element of propensity score methods. Ultimately, the treatment effect estimate desired should drive the choice of propensity score adjustment method.

Coronary artery disease screening and prognosis in incident dialysis patients

Guidelines have recommended that physicians screen for coronary artery disease (CAD) at the initiation of dialysis. The purpose of this study was to examine the effects of CAD screening at the initiation of dialysis on prognosis after starting dialysis This retrospective cohort study involved 224 consecutive incident dialysis patients without advanced cardiac disease. Pharmacological stress myocardial perfusion imaging (MPI) was performed to routinely screen incident dialysis patients within 3 months of starting dialysis therapy. The influence of MPI on prognosis after starting dialysis was explored by the propensity score adjustment method. The study outcome was defined as all-cause mortality and cardiac death. Of the 224 patients, 164 (73.2 %) underwent MPI screening at initiation of dialysis. During the median follow-up period of 5.5 years, 77 patients died, among whom 17 (22.1 %) cardiac deaths were observed. The patients who received MPI had a significantly higher survival rate for all-cause mortality and cardiac death compared with the patients without MPI (log-rank test, p < 0.001, p = 0.021, respectively). In multiple Cox regression analysis, MPI screening continued to have a statistically significant negative association with all-cause mortality after adjusting for confounding variables (hazard ratio 0.581, p = 0.034) and propensity score (hazard ratio 0.568, p = 0.025). MPI CAD screening at dialysis initiation could improve prognosis after starting dialysis.

Using propensity score adjustment method in genetic association studies

BackgroundThe statistical tests for single locus disease association are mostly under-powered. If a disease associated causal single nucleotide polymorphism (SNP) operates essentially through a complex mechanism that involves multiple SNPs or possible environmental factors, its effect might be missed if the causal SNP is studied in isolation without accounting for these unknown genetic influences. In this study, we attempt to address the issue of reduced power that is inherent in single point association studies by accounting for genetic influences that negatively impact the detection of causal variant in single point association analysis. In our method we use propensity score (PS) to adjust for the effect of SNPs that influence the marginal association of a candidate marker. These SNPs might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP and have a joint interactive influence on the disease under study. We therefore propose a propensity score adjustment method (PSAM) as a tool for dimension reduction to improve the power for single locus studies through an estimated PS to adjust for influence from these SNPs while regressing disease status on the target-genetic locus. The degree of freedom of such a test is therefore always restricted to 1. ResultsWe assess PSAM under the null hypothesis of no disease association to affirm that it correctly controls for the type-I-error rate (<0.05). PSAM displays reasonable power (>70%) and shows an average of 15% improvement in power as compared with commonly-used logistic regression method and PLINK under most simulated scenarios. Using the open-access multifactor dimensionality reduction dataset, PSAM displays improved significance for all disease loci. Through a whole genome study, PSAM was able to identify 21 SNPs from the GAW16 NARAC dataset by reducing their original trend-test p-values from within 0.001 and 0.05 to p-values less than 0.0009, and among which 6 SNPs were further found to be associated with immunity and inflammation. ConclusionsPSAM improves the significance of single-locus association of causal SNPs which have had marginal single point association by adjusting for influence from other SNPs in a dataset. This would explain part of the missing heritability without increasing the complexity of the model due to huge multiple testing scenarios. The newly reported SNPs from GAW16 data would provide evidences for further research to elucidate the etiology of rheumatoid arthritis. PSAM is proposed as an exploratory tool that would be complementary to other existing methods. A downloadable user friendly program, PSAM, written in SAS, is available for public use.

The Effects of Body Mass Index on Complications and Mortality after Emergency Abdominal Operations: The Obesity Paradox

Dilektasli, Evren MD; Benjamin, Elizabeth R. MD, PhD, FACS; Haltmeier, Tobias MD; Beale, Elizabeth MD; Inaba, Kenji MD, FACS; Demetriades, Demetrios MD, PhD, FACS Author Information

A propensity score adjustment method for regression models with nonignorable missing covariates

In a linear regression model with nonignorable missing covariates, non-normal errors or outliers can lead to badly biased and misleading results with standard parameter estimation methods built on either least squares- or likelihood-based methods. A propensity score method with a robust and efficient regression procedure called composite quantile regression for parameter estimation of the linear regression model with nonignorable missing covariates is proposed. Semiparametric estimation of the propensity score is based on the exponentially tilted likelihood approach. Asymptotic properties of the proposed estimators are systematically investigated. The proposed method is resistant to heavy-tailed errors or outliers in the response. Simulation studies and real data applications are used to illustrate its potential impacts and benefits compared with conventional methods.

Entropy balancing: a maximum-entropy reweighting scheme to adjust for coverage error

This paper presents a newly available technique to adjust for bias in non-probabilistically selected samples. To date, applications of this innovative technique—termed entropy balancing—have been restricted to evaluation settings, where the goal is to reduce model dependence prior to the estimation of treatment effects. In a novel application, we demonstrate the technique’s utility in cases where the goal is to correct for sample bias originating in coverage error. The appeal of entropy balancing in this latter setting lies in its capacity to optimise the twin goals of improved balance in covariate distribution and maximum retention of information. Entropy balancing combines the opportunity to incorporate a large set of moment conditions in the calculation of weights, with the ability to directly implement exact balance. The technique thus builds upon the theoretical appeal of the more widely known and applied propensity score adjustment method, while addressing that method’s practical limitations. We demonstrate the utility of the entropy balancing technique empirically, through an example using the Young Lives Project survey data for rural Andhra Pradesh, South India. We conclude by summarising the potential of this procedure to contribute to robust survey-based research more widely.

Comparison of Instrumental Variable Analysis Using a New Instrument With Risk Adjustment Methods to Reduce Confounding by Indication

Confounding by indication is a vexing problem, especially in evaluating treatment effects using observational data, since treatment decisions are often related to disease severity, prognosis, and frailty. To compare the ability of the instrumental variable (IV) approach with a new instrument based on the local-area practice style and risk adjustment methods, including conventional multivariate regression and propensity score adjustment, to reduce confounding by indication, the authors investigated the effects of long-term control (LTC) therapy on the occurrence of acute asthma exacerbation events among children and young adults with incident and uncontrolled persistent asthma, using Iowa Medicaid claims files from 1997-1999. Established evidence from clinical trials has demonstrated the protective benefits of LTC therapy for persistent asthma. Among patients identified (n = 4,275), those with higher asthma severity at baseline were more likely to receive LTC therapy. The multivariate regression and propensity score adjustment methods suggested that LTC therapy had no effect on the occurrence of acute exacerbation events. Estimates from the new IV approach showed that LTC therapy significantly decreased the occurrence of acute exacerbation events, which is consistent with established clinical evidence. The authors discuss how to interpret estimates from the risk adjustment and IV methods when the treatment effect is heterogeneous.