Comparative Effectiveness of Valoctocogene Roxaparvovec and Prophylactic Factor VIII Replacement in Severe Hemophilia A

INTRODUCTION

A prospective, non-interventional study (270-902) followed 294 adults with severe hemophilia A (SHA) receiving prophylactic factor VIII (FVIII). From these participants, 112 rolled over into a single-arm, multicenter, phase 3 trial (GENEr8-1; NCT03370913) that evaluated efficacy and safety of valoctocogene roxaparvovec, a gene therapy that provides endogenous FVIII in individuals with SHA. Participants from 270-902 who did not roll over provide an opportunity for a contemporaneous external control. Therefore, the comparative effectiveness of valoctocogene roxaparvovec vs FVIII prophylaxis was evaluated using propensity scoring (PS).

METHODS

This post hoc analysis compared 112 participants from GENEr8-1 (treated cohort) to 73 participants in 270-902 who did not enroll in GENEr8-1 (control cohort). The primary analysis used standardized mortality ratio weighting to re-weight baseline characteristics of the control cohort to better match the treated cohort. Mean annualized bleeding rates (ABR) for treated and all bleeds were compared between cohorts along with the proportion of participants with zero bleeds (treated and all bleeds). Sensitivity and scenario analyses were also conducted.

RESULTS

PS adjustments reduced differences in baseline characteristics between cohorts. Mean treated (4.40 vs 0.85; P < 0.001) and all (5.01 vs 1.54; P < 0.001) ABR were significantly lower, and the proportions of participants with zero treated bleeds (82.1% vs 32.9%; P < 0.001) and all bleeds (58.0% vs 28.5%; P < 0.001) were significantly higher in GENEr8-1.

CONCLUSIONS

PS-adjusted analyses were consistent with prior intra-individual comparisons. Compared with participants receiving prophylactic FVIII, the participants receiving valoctocogene roxaparvovec experienced lower ABR, and a higher proportion had zero bleeds.

TRAIL REGISTRATION

ClinicalTrials.gov identifier, NCT03370913.

Association of spontaneous abortion with bipolar disorder and major depression based on inverse probability treatment weighting of multigroup propensity scores: Evidence from the UK Biobank

BACKGROUND

Few studies have explored the association between the number of SAs and bipolar disorder and major depression (BDMD). This study aims to investigate the association between SA and BDMD, and the possible dose-response relationship between them.

METHODS

We conducted a cross-sectional study of 13,200 female UK Biobank participants. Participants were classified into BDMD and no-BDMD groups based on their BDMD status. The number of SAs was grouped into non-SA, occasional SA (OSA), and recurrent SA (RSA). Baseline characteristics of the three groups were balanced using inverse probability treatment weighting (IPTW) based on propensity scores. The three-knots restricted cubic spline regression model was utilized to assess the dose-response relationship between the number of SAs and BDMD.

RESULTS

The IPTW-adjusted multivariate logistic regression revealed that SA was an independent risk factor for BDMD, with adjusted OR of 1.12 (95 % CI: 1.07-1.19) and 1.32 (95 % CI: 1.25-1.40) in the OSA and RSA groups, respectively. The strength of this association amplified as the number of SAs (P for trend <0.001). There was a nonlinear relationship between the number of SAs and the risk of BDMD, with an approximately inverted L-shaped curve.

LIMITATIONS

The information of the SA and BDMD status relied on self-reported by volunteers, and the study sample was mostly of European descent.

CONCLUSIONS

Women who reported experiencing multiple SAs are more likely to have BDMD. Therefore, it is imperative to provide psychological care and interventions for women in the postpartum period.

Understanding the effects of Haemophilus influenzae colonization on bronchiectasis: a retrospective cohort study

BACKGROUND

Bacterial colonization is an essential aspect of bronchiectasis. Although Haemophilus influenzae is a frequent colonizer in some regions, its clinical impacts are poorly understood. This study aimed to elucidate the impact of H. influenzae colonization in patients with bronchiectasis.

METHODS

This retrospective study screened adult patients diagnosed with bronchiectasis at a tertiary referral center between April 1, 2003, and May 16, 2021, in South Korea. Propensity score matching was used to match patients with and without H. influenzae colonization. We assessed the severity of bronchiectasis as per the bronchiectasis severity index, the incidence of exacerbation, differences in lung function, and all-cause mortality.

RESULTS

Out of the 4,453 patients with bronchiectasis, 79 (1.8%) were colonized by H. influenzae. After 1:2 propensity score matching, 78 and 154 patients were selected from the H. influenzae colonizer and non-colonizer groups, respectively. Although there were no significant differences between the groups regarding baseline demographics, patients colonized with H. influenzae had a higher bronchiectasis severity index (median 6 [interquartile range 4-8] vs. 4 [2-7], p = 0.002), associated with extensive radiographic involvement (52.2% vs. 37.2%, p = 0.045) and mild exacerbation without hospitalization (adjusted incidence rate ratio 0.15; 95% confidence interval 0.12-0.24). Lung function and mortality rates did not reveal significant differences, regardless of H. influenzae colonization.

CONCLUSION

H. influenzae colonization in bronchiectasis was associated with more severe disease and greater incidence of mild exacerbation, but not lung function and mortality. Attention should be paid to patients with bronchiectasis with H. influenzae colonization.

Confounding-adjustment methods for the causal difference in medians

BACKGROUND

With continuous outcomes, the average causal effect is typically defined using a contrast of expected potential outcomes. However, in the presence of skewed outcome data, the expectation (population mean) may no longer be meaningful. In practice the typical approach is to continue defining the estimand this way or transform the outcome to obtain a more symmetric distribution, although neither approach may be entirely satisfactory. Alternatively the causal effect can be redefined as a contrast of median potential outcomes, yet discussion of confounding-adjustment methods to estimate the causal difference in medians is limited. In this study we described and compared confounding-adjustment methods to address this gap.

METHODS

The methods considered were multivariable quantile regression, an inverse probability weighted (IPW) estimator, weighted quantile regression (another form of IPW) and two little-known implementations of g-computation for this problem. Methods were evaluated within a simulation study under varying degrees of skewness in the outcome and applied to an empirical study using data from the Longitudinal Study of Australian Children.

RESULTS

Simulation results indicated the IPW estimator, weighted quantile regression and g-computation implementations minimised bias across all settings when the relevant models were correctly specified, with g-computation additionally minimising the variance. Multivariable quantile regression, which relies on a constant-effect assumption, consistently yielded biased results. Application to the empirical study illustrated the practical value of these methods.

CONCLUSION

The presented methods provide appealing avenues for estimating the causal difference in medians.

Semi-supervised mixture multi-source exchangeability model for leveraging real-world data in clinical trials

The traditional trial paradigm is often criticized as being slow, inefficient, and costly. Statistical approaches that leverage external trial data have emerged to make trials more efficient by augmenting the sample size. However, these approaches assume that external data are from previously conducted trials, leaving a rich source of untapped real-world data (RWD) that cannot yet be effectively leveraged. We propose a semi-supervised mixture (SS-MIX) multisource exchangeability model (MEM); a flexible, two-step Bayesian approach for incorporating RWD into randomized controlled trial analyses. The first step is a SS-MIX model on a modified propensity score and the second step is a MEM. The first step targets a representative subgroup of individuals from the trial population and the second step avoids borrowing when there are substantial differences in outcomes among the trial sample and the representative observational sample. When comparing the proposed approach to competing borrowing approaches in a simulation study, we find that our approach borrows efficiently when the trial and RWD are consistent, while mitigating bias when the trial and external data differ on either measured or unmeasured covariates. We illustrate the proposed approach with an application to a randomized controlled trial investigating intravenous hyperimmune immunoglobulin in hospitalized patients with influenza, while leveraging data from an external observational study to supplement a subgroup analysis by influenza subtype.

Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables

To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation and double machine learning. To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment. We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial.

Propensity scores as a novel method to guide sample allocation and minimize batch effects during the design of high throughput experiments

BACKGROUND

We developed a novel approach to minimize batch effects when assigning samples to batches. Our algorithm selects a batch allocation, among all possible ways of assigning samples to batches, that minimizes differences in average propensity score between batches. This strategy was compared to randomization and stratified randomization in a case-control study (30 per group) with a covariate (case vs control, represented as β1, set to be null) and two biologically relevant confounding variables (age, represented as β2, and hemoglobin A1c (HbA1c), represented as β3). Gene expression values were obtained from a publicly available dataset of expression data obtained from pancreas islet cells. Batch effects were simulated as twice the median biological variation across the gene expression dataset and were added to the publicly available dataset to simulate a batch effect condition. Bias was calculated as the absolute difference between observed betas under the batch allocation strategies and the true beta (no batch effects). Bias was also evaluated after adjustment for batch effects using ComBat as well as a linear regression model. In order to understand performance of our optimal allocation strategy under the alternative hypothesis, we also evaluated bias at a single gene associated with both age and HbA1c levels in the 'true' dataset (CAPN13 gene).

RESULTS

Pre-batch correction, under the null hypothesis (β1), maximum absolute bias and root mean square (RMS) of maximum absolute bias, were minimized using the optimal allocation strategy. Under the alternative hypothesis (β2 and β3 for the CAPN13 gene), maximum absolute bias and RMS of maximum absolute bias were also consistently lower using the optimal allocation strategy. ComBat and the regression batch adjustment methods performed well as the bias estimates moved towards the true values in all conditions under both the null and alternative hypotheses. Although the differences between methods were less pronounced following batch correction, estimates of bias (average and RMS) were consistently lower using the optimal allocation strategy under both the null and alternative hypotheses.

CONCLUSIONS

Our algorithm provides an extremely flexible and effective method for assigning samples to batches by exploiting knowledge of covariates prior to sample allocation.

Patient-reported health outcomes of SARS-CoV-2-tested patients presenting to emergency departments: a propensity score-matched prospective cohort study

OBJECTIVE

This study aimed to compare the long-term physical and mental health outcomes of matched severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive and SARS-CoV-2-negative patients controlling for seasonal effects.

STUDY DESIGN

This was a retrospective cohort study.

METHODS

This study enrolled patients presenting to emergency departments participating in the Canadian COVID-19 Emergency Department Rapid Response Network. We enrolled consecutive eligible consenting patients who presented between March 1, 2020, and July 14, 2021, and were tested for SARS-CoV-2. Research assistants randomly selected four site and date-matched SARS-CoV-2-negative controls for every SARS-CoV-2-positive patient and interviewed them at least 30 days after discharge. We used propensity scores to match patients by baseline characteristics and used linear regression to compare Veterans RAND 12-item physical health component score (PCS) and mental health component scores (MCS), with higher scores indicating better self-reported health.

RESULTS

We included 1170 SARS-CoV-2-positive patients and 3716 test-negative controls. The adjusted mean difference for PCS was 0.50 (95% confidence interval [CI]: -0.36, 1.36) and -1.01 (95% CI: -1.91, -0.11) for MCS. Severe disease was strongly associated with worse PCS (β = -7.4; 95% CI: -9.8, -5.1), whereas prior mental health illness was strongly associated with worse MCS (β = -5.4; 95% CI: -6.3, -4.5).

CONCLUSION

Physical health, assessed by PCS, was similar between matched SARS-CoV-2-positive and SARS-CoV-2-negative patients, whereas mental health, assessed by MCS, was worse during a time when the public experienced barriers to care. These results may inform the development and prioritization of support programs for patients.

Logistic regression frequently outperformed propensity score methods, especially for large datasets: a simulation study

OBJECTIVES

In observational studies, researchers must select a method to control for confounding. Options include propensity score (PS) methods and regression. It remains unclear how dataset characteristics (size, overlap in PSs, and exposure prevalence) influence the relative performance of the methods.

STUDY DESIGN AND SETTING

A simulation study to evaluate the role of dataset characteristics on the performance of PS methods, compared to logistic regression, for estimating a marginal odds ratio was conducted. Dataset size, overlap in PSs, and exposure prevalence were varied.

RESULTS

Regression showed poor coverage for small sample sizes, but with large sample sizes was relatively robust to imbalance in PSs and low exposure prevalence. PS methods displayed suboptimal coverage as overlap in PSs decreased, which was exacerbated at larger sample sizes. Power of matching methods was particularly affected by a lack of overlap, low exposure prevalence, and small sample size. The advantage of regression for large data size was reduced in sensitivity analysis with a complementary log-log outcome generation mechanism and unmeasured confounding, with superior bias and error but inferior coverage to matching methods.

CONCLUSION

Dataset characteristics influence performance of methods for confounder adjustment. In many scenarios, regression may be the preferable option.

CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis

PURPOSE

To evaluate the diagnostic performance of the radiomics score (rad-score) for differentiating focal-type autoimmune pancreatitis (fAIP) from pancreatic ductal adenocarcinoma (PDAC).

METHODS

This retrospective review included 42 consecutive patients with fAIP diagnosed according to the International Consensus Diagnostic Criteria between January 2011 and December 2018. Furthermore, 334 consecutive patients with PDAC confirmed by pathology were also reviewed during the same period. Patients with PDAC and fAIP were matched via propensity score matching (PSM). All patients underwent multidetector computed tomography (MDCT). For each patient, 1409 radiomics features of the portal phase were extracted and reduced using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The portal rad-score performance was assessed based on its discriminative ability.

RESULTS

After PSM, we matched 55 patients with PDAC to 42 patients with fAIP, based on clinical and CT characteristics (e.g., patient age, sex, body mass index, location, size, enhanced mode). A rad-score for discriminating fAIP from PDAC, which contained four CT derived radiomic features, was developed (area under the curve = 0.97). The sensitivity, specificity, and accuracy of the radiomics model were 95.24%, 92.73% and 0.94, respectively.

CONCLUSION

The portal rad-score can accurately and noninvasively differentiate fAIP from PDAC.

Volunteer Recovery Support for Adolescents: Using propensity score based methods to understand dosage effects within a randomized controlled trial

BACKGROUND

In a recently published randomized controlled trial (RCT) of Volunteer Recovery Support for Adolescents (VRSA), a secondary finding indicated that better adherence to planned VRSA telephone session frequency resulted in significantly higher remission rates relative to lower session adherence. However, interpretation of this dose-response relationship may have been confounded by participant characteristics such as baseline levels of substance use and mental health problems.

METHODS

The present study used statistical methods designed to approximate RCTs when comparing more than two nonequivalent groups that include an assessment of the potential impact of omitted variables. Classification and Regression Tree (CRT) analysis was used to establish the cut-point between high (H) and low (L) VRSA dosage groups. Because we were interested in generalizing to youth with poor attendance, the L-VRSA group served as the reference group. Balancing weights for H-VRSA and a services as usual (SAU) control group were calculated to ensure similarity of baseline pretreatment characteristics to the reference group, and sensitivity of findings to unobserved confounding variables was assessed.

RESULTS

Findings suggested that superior remission rates at the end of the intervention phase were the result of high adherence to planned VRSA session frequency. Recommendations to achieve high VRSA participation among a larger segment of youth and to test whether longer VRSA duration improves the stability of recovery outcomes are provided.

CONCLUSION

Few published dose-response studies have adequately controlled for selection confounds from both observed and unobserved confounding. As such, the present study aims to both assess the impact of different dosage levels of VRSA and provide a template for how to apply state-of-the-art statistical methods designed to approximate randomized controlled trials to such studies.

COVID-19 and the impact of cash transfers on health care use in Togo

Background

Cash transfer program during pandemics provide a social protection mechanism to improve the health of the most vulnerable households. This article analysis the impact of cash transfers on household demand for health care during Covid-19.

Methods

Using data from the survey conducted from 8th to 17th July 2020 covering all 44 districts in the 6 health regions of Togo under the direction of the United Nations Office for Project Services (UNOPS), we used propensity score matching and the ESR model. These models allow us to analysis the impact of cash transfers on health care use during Covid-19.

Results

Analysis of the results shows a positive impact of cash transfers on the use of health care services in Togo for beneficiary households. In addition, the health insurance promotes the use of health care among households’ socio-economic factors.

Conclusion

This cash transfer program is an effective approach to improving access to health care services for the most vulnerable households, particularly in the most disadvantaged settings. Thus, policy makers need to extend these cash transfers to a large part of the population during this Covid-19 health crisis as it has a positive impact on the demand for health care.

Iodized oil computed tomography versus ultrasound-guided radiofrequency ablation for early hepatocellular carcinoma

BACKGROUND AND PURPOSE

Radiofrequency ablation (RFA) is the standard of care for early stage hepatocellular carcinoma (HCC). However, the clinical outcomes of iodized oil computed tomography (IoCT) versus ultrasound (US)-guided RFA for HCC remain unclear.

METHODS

We retrospectively analyzed consecutive treatment-naïve patients who received curative RFA for HCC within Milan criteria from January 2016 to December 2018. Patients who underwent either IoCT-guided RFA (IoCT group) or US-guided RFA (US group) were included. Various clinical factors, including tumor location, were adjusted with a 1:1 propensity score matching. Subsequently, the cumulative incidence rates for recurrence and hazard ratios for survival were calculated.

RESULTS

We included 184 (37.9%) and 301 (62.1%) patients who received IoCT- and US-guided RFA, respectively. Before propensity score matching, IoCT guidance was significantly associated with multiple tumors, higher body mass index, lower albumin level, and tumors located at S8. After matching, the 1-, 2-, and 3-year local tumor progression rates of the IoCT group were significantly lower than those of the US group (4.4%, 6.9%, and 7.5% vs. 14.4%, 16.3%, and 16.3%, respectively, at p = 0.002, 0.009, and 0.016, respectively). In univariate analyses and multivariate analyses that adjusted for clinical and tumor location-related parameters, the IoCT group had better recurrence-free survival (hazard ratio = 0.581, 95% confidence interval 0.375-0.899) than those with US guidance but not overall survival.

CONCLUSION

IoCT-guided RFA had a lower local tumor progression rate and better recurrence-free survival than did US-guided RFA for HCC within the Milan criteria. CT-guide RFA is a safe and effective alternative to US-guided with similar overall survival. IoCT-guided RFA might have a better local tumor control than US-guided. IoCT-guided RFA may be more suitable for male patients, aged < 70 years, a single tumor measuring 2-5 cm, and a tumor located at the subdiaphragmatic/subcardiac region.

Methods for evaluating the benefit and harms of deprescribing in observational research using routinely collected data

Deprescribing is defined as "the planned and supervised process of dose reduction or stopping of medication that might be causing harm, or no longer be of benefit". Barriers to deprescribing include healthcare professional fear and lack of guidance. These may stem from limited available evidence on benefits and harms of deprescribing medications commonly used among older persons. Advances in pharmacoepidemiology and causal inference methods to evaluate comparative effectiveness and safety of prescribing medications have yet to be considered for deprescribing medication. This paper discusses select methods and how they can be applied to deprescribing research, using case studies of benzodiazepines and low-dose acetylsalicylic acid (aspirin). Target trial emulation involves the explicit application of design principles from randomised controlled trials to observational studies. Several design aspects, including defining eligibility criteria and time zero, require additional considerations for deprescribing studies. The active comparator new user design also presents challenges, including selection of an appropriate comparator. This paper discusses these aspects, and others, in relation to deprescribing studies. Furthermore, methods proposed to control for confounding, in particular, the prior event rate ratio and propensity scores, are discussed. Introduction of billing codes or mechanisms for accurately determining when deprescribing has occurred would enhance the ability to conduct research using routinely collected data. Although the approaches discussed in this paper may strengthen observational studies of deprescribing, their use may be best suited to certain scenarios or research questions, where randomised controlled trials may be less feasible.

The effects of competition and bundled payment on patient reported outcome measures after hip replacement surgery

BACKGROUND

Competition-promoting reforms and economic incentives are increasingly being introduced worldwide to improve the performance of healthcare delivery. This study considers such a reform which was initiated in 2009 for elective hip replacement surgery in Stockholm, Sweden. The reform involved patient choice of provider, free establishment of new providers and a bundled payment model. The study aimed to examine its effects on hip replacement surgery quality as captured by patient reported outcome measures (PROMs) of health gain (as indicated by the EQ-5D index and a visual analogue scale (VAS)), pain reduction (VAS) and patient satisfaction (VAS) one and six years after the surgery.

METHODS

Using patient-level data collected from multiple national registers, we applied a quasi-experimental research design. Data were collected for elective primary total hip replacements that were carried out between 2008 and 2012, and contain information on patient demography, the surgery and PROMs at baseline and at one- and six-years follow-up. In total, 36,627 observations were included in the analysis. First, entropy balancing was applied in order to reduce differences in observable characteristics between treatment groups. Second, difference-in-difference analyses were conducted to eliminate unobserved time-invariant differences between treatment groups and to estimate the causal treatment effects.

RESULTS

The entropy balancing was successful in creating balance in all covariates between treatment groups. No significant effects of the reform were found on any of the included PROMs at one- and six-years follow-up. The sensitivity analyses showed that the results were robust.

CONCLUSIONS

Competition and bundled payment had no effects on the quality of hip replacement surgery as captured by post-surgery PROMs of health gain, pain reduction and patient satisfaction. The study provides important insights to the limited knowledge on the effects of competition and economic incentives on PROMs.

Post-tuberculosis airway disease: A population-based cohort study of people immigrating to British Columbia, Canada, 1985-2015

BACKGROUND

Current epidemiological evidence of post-TB airway disease is largely cross-sectional and derived from high-TB-incidence settings. We present the first cohort study of post-TB airway disease in a low-TB-incidence setting.

AIMS

(1) analyze the risk of airway disease by respiratory TB, (2) assess potential unmeasured confounding between TB and airway disease, and (3) investigate TB effect measure modification.

METHODS

A population-based cohort study using healthcare claims data for immigrants to British Columbia (BC), Canada, 1985-2015. Airway disease included chronic airway obstruction, asthma, bronchitis, bronchiolitis, and emphysema. Respiratory TB was defined from TB registry data. Cox proportional hazards (PH) regressions were used to analyze time-to-airway disease by respiratory TB. Sensitivity analyses included varying definitions of TB and airway disease. Potential unmeasured confounding by smoking was evaluated by E-value and hybrid least absolute shrinkage and selection operator (LASSO)-high-dimensional propensity score (hdPS).

FINDINGS

In our cohort (N = 1 005 328; n_TB=1141) there were 116 840 incident cases of airway disease during our 30-year study period (10.43 per 1,000 person-years of follow-up), with cumulative incidence of 42·5% among respiratory TB patients compared with 11·6% among non-TB controls. The covariate-adjusted hazard ratio (aHR) for airway disease by respiratory TB was 2·08 (95% CI: 1·91-2·28) with E-value=3·58. The LASSO-hdPS analysis produced aHR=2·26 (95% CI: 2·07-2·47).

INTERPRETATION

A twofold higher risk of airway disease was observed among immigrants diagnosed with respiratory TB, compared with non-TB controls, in a low-TB-incidence setting. Unmeasured confounding is unlikely to explain this relationship. Models of post-TB care are needed.

FUNDING

Canadian Institutes of Health Research.

Estimating the causal effects of modifiable, non-genetic factors on Huntington disease progression using propensity score weighting

INTRODUCTION

Despite being genetically inherited, it is unclear how non-genetic factors (e.g., substance use, employment) might contribute to the progression and severity of Huntington's disease (HD).

METHODS

We used propensity score (PS) weighting in a large (n = 2914) longitudinal dataset (Enroll-HD) to examine the impact of education, employment status, and use of tobacco, alcohol, and recreational and therapeutic drugs on HD progression. Each factor was investigated in isolation while controlling for 19 other factors to ensure that groups were balanced at baseline on potential confounders using PS weights. Outcomes were compared several years later using doubly robust models.

RESULTS

Our results highlighted cases where modifiable (non-genetic) factors - namely light and moderate alcohol use and employment - would have been associated with HD progression in models that did not use PS weights to control for baseline imbalances. These associations did not hold once we applied PS weights to balance baseline groups. We also found potential evidence of a protective effect of substance use (primarily marijuana use), and that those who needed antidepressant treatment were likely to progress faster than non-users.

CONCLUSIONS

Our study is the first to examine the effect of non-genetic factors on HD using a novel application of PS weighting. We show that previously-reported associated factors - including light and moderate alcohol use - are reduced and no longer significantly linked to HD progression after PS weighting. This indicates the potential value of PS weighting in examining non-genetic factors contributing to HD as well as in addressing the known biases that occur with observational data.

Increasing antiviral treatment uptake improves survival in patients with HBV-related HCC

Background & Aims

Antiviral treatment is known to improve survival in patients with chronic hepatitis B (CHB)-related hepatocellular carcinoma (HCC). Yet, the treatment uptake in CHB patients remains low. We aimed to report the secular trend in antiviral treatment uptake from 2007-2017, and to compare the effect of different nucleos(t)ide analogue (NA) initiation times (before vs. after HCC diagnosis) on survival.

Methods

A 3-month landmark analysis was used to compare overall survival in patients not receiving NA treatment (i.e. no NA), patients receiving NAs after their first HCC treatment (i.e. post-HCC NA), and patients receiving NAs ≤3 months before their first HCC treatment (i.e. pre-HCC NA). A propensity score-weighted Cox proportional hazards model was used to balance clinical characteristics between the 3 groups and to estimate hazard ratios (HRs).

Results

The uptake of antiviral treatment in HCC patients increased from 47.3% in 2007 to 98.3% in 2017. The pre-HCC NA group contributed mostly to the uptake rate, which increased from 72.7% to 96.0% in the past decade. In addition, 3,843 CHB patients (407 no NA; 2,932 pre-HCC NA; 504 post-HCC NA) with HCC, receiving at least 1 type of HCC treatment, were included in the analysis. Lack of NA treatment at the time of HCC diagnosis increased the risk of death (weighted HR 3.05; 95% CI 2.70-3.44; p <0.001). The impact of the timing of NA treatment was insignificant (weighted HR 0.90; 95% CI 0.78-1.04; p = 0.161).

Conclusions

The uptake of antiviral treatment in HCC patients increased over the past decade. NA treatment, regardless of whether it was initiated before or after HCC diagnosis, improved survival. It is never too late to initiate NA treatment, even after HCC diagnosis.

Lay summary

More and more patients who have hepatitis B-related liver cancer received antiviral treatment over the past decade. The timing of starting antiviral treatment, regardless of whether it was before or after liver cancer happens, does not really matter in terms of survival benefits.

Continuous versus intermittent short-acting β2-agonists nebulization as first-line therapy in hospitalized children with severe asthma exacerbation: a propensity score matching analysis

Background

Short-acting β2-agonist (SABA) nebulization is commonly prescribed for children hospitalized with severe asthma exacerbation. Either intermittent or continuous delivery has been considered safe and efficient. The comparative efficacy of these two modalities is inconclusive. We aimed to compare these two modalities as the first-line treatments.

Methods

An efficacy research with a retrospective cohort study design was conducted. Hospital records of children with severe asthma exacerbation admitted to Hat Yai Hospital between 2015 and 2017 were retrospectively collected. Children initially treated with continuous salbutamol 10 mg per hour or intermittent salbutamol 2.5 mg per dose over 1–4 h nebulization were matched one-to-one using the propensity score. Competing risk and risk difference regression was applied to evaluate the proportion of children who succeeded and failed the initial treatment. Restricted mean survival time regression was used to compare the length of stay (LOS) between the two groups.

Results

One-hundred and eighty-nine children were included. Of these children, 112 were matched for analysis (56 with continuous and 56 with intermittent nebulization). Children with continuous nebulization experienced a higher proportion of success in nebulization treatment (adjusted difference: 39.5, 95% CI 22.7, 56.3, p < 0.001), with a faster rate of success (adjusted SHR: 2.70, 95% CI 1.73, 4.22, p < 0.001). There was a tendency that LOS was also shorter (adjusted mean difference − 9.9 h, 95% CI -24.2, 4.4, p = 0.176).

Conclusion

Continuous SABA nebulization was more efficient than intermittent nebulization in the treatment of children with severe asthma exacerbation.

The Comparison of Latent Variable Propensity Score Models to Traditional Propensity Score Models under Conditions of Covariate Unreliability

Propensity score (PS) methods are implemented by researchers to balance the differences between participants in control and treatment groups that exist in observational studies using a set of baseline covariates. Propensity scores are most commonly calculated using baseline covariates in a logistic regression model to predict the binary grouping variable (control versus treatment). Low reliability associated with the covariates can adversely impact the calculation of treatment effects in propensity score models. The incorporation of latent variables when calculating propensity scores has been suggested to offset the negative impact of covariate unreliability. Simulation studies were conducted to compare the performance of latent variable methods with traditional propensity score methods when estimating the treatment effect under conditions of covariate unreliability. The results indicated that using factor scores or composite variables to compute propensity scores resulted in biased estimates and inflated Type I error rates as compared to using latent factors to compute propensity scores in certain conditions. This was largely dependent upon the number of infallible covariates also included in the PS model and the outcome analysis model analyzed. Implications of the findings are discussed.

Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure

Background

Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values in covariates.

Method

Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP), and treatment mean imputation. However, there are other potentially useful approaches that have not been evaluated, including single imputation (SI) + prediction error (PE), SI + PE + parameter uncertainty (PU), and Generalized Boosted Modeling (GBM), which is a nonparametric approach for estimating propensity scores in which missing values are automatically handled in the estimation using a surrogate split method. To evaluate the performance of these approaches, a simulation study was conducted.

Results

Results suggested that SI + PE, SI + PE + PU, MI, and MIMP perform almost equally well and better than treatment mean imputation and GBM in terms of bias; however, MI and MIMP account for the additional uncertainty of imputing the missingness.

Conclusions

Applying GBM to the incomplete data and relying on the surrogate split approach resulted in substantial bias. Imputation prior to implementing GBM is recommended.

Examining Intervention Component Dosage Effects on Substance Use Initiation in the Strengthening Families Program: for Parents and Youth Ages 10-14

Family-based prevention programs increasingly are being disseminated and can be effective for an array of adolescent problem behaviors, including substance use initiation. Yet, we continue to have little understanding of how and why these programs work. Increased specificity in our understanding of what components drive program effects can facilitate refinement of programs, with potential for greater impact at a lower cost. Using attendance data, previously coded intervention components, and a previously developed propensity model to adjust for potential bias, this study evaluated content component-specific dosage effects of the Strengthening Families Program: for Parents and Youth Ages 10-14 on three substance use initiation outcomes by grade 12. Results indicated that greater dosages of program content related to (a) parental monitoring and behavior management strategies and (b) promoting positive family relationships had potent and robust effects on reduction of risk for initiating drunkenness and marijuana use and (c) self-regulation and stress management had potent and robust effects on reduction of risk for initiating cigarette and marijuana use. Results indicate potential critical components within SFP 10-14 and offer a path forward for continuing work in efforts to optimize this widely disseminated program.

COMBINING INFORMATION FROM MULTIPLE DATA SOURCES TO ASSESS POPULATION HEALTH

Information about an extensive set of health conditions on a well-defined sample of subjects is essential for assessing population health, gauging the impact of various policies, modeling costs, and studying health disparities. Unfortunately, there is no single data source that provides accurate information about health conditions. We combine information from several administrative and survey data sets to obtain model-based dummy variables for 107 health conditions (diseases, preventive measures, and screening for diseases) for elderly (age 65 and older) subjects in the Medicare Current Beneficiary Survey (MCBS) over the fourteen-year period, 1999-2012. The MCBS has prevalence of diseases assessed based on Medicare claims and provides detailed information on all health conditions but is prone to underestimation bias. The National Health and Nutrition Examination Survey (NHANES), on the other hand, collects self-reports and physical/laboratory measures only for a subset of the 107 health conditions. Neither source provides complete information, but we use them together to derive model-based corrected dummy variables in MCBS for the full range of existing health conditions using a missing data and measurement error model framework. We create multiply imputed dummy variables and use them to construct the prevalence rate and trend estimates. The broader goal, however, is to use these corrected or modeled dummy variables for a multitude of policy analysis, cost modeling, and analysis of other relationships either using them as predictors or as outcome variables.

A review of the use of propensity score diagnostics in papers published in high-ranking medical journals

Background

Propensity scores are widely used to deal with confounding bias in medical research. An incorrectly specified propensity score model may lead to residual confounding bias; therefore it is essential to use diagnostics to assess propensity scores in a propensity score analysis. The current use of propensity score diagnostics in the medical literature is unknown. The objectives of this study are to (1) assess the use of propensity score diagnostics in medical studies published in high-ranking journals, and (2) assess whether the use of propensity score diagnostics differs between studies (a) in different research areas and (b) using different propensity score methods.

Methods

A PubMed search identified studies published in high-impact journals between Jan 1st 2014 and Dec 31st 2016 using propensity scores to answer an applied medical question. From each study we extracted information regarding how propensity scores were assessed and which propensity score method was used. Research area was defined using the journal categories from the Journal Citations Report.

Results

A total of 894 papers were included in the review. Of these, 187 (20.9%) failed to report whether the propensity score had been assessed. Commonly reported diagnostics were p-values from hypothesis tests (36.6%) and the standardised mean difference (34.6%). Statistical tests provided marginally stronger evidence for a difference in diagnostic use between studies in different research areas (p = 0.033) than studies using different propensity score methods (p = 0.061).

Conclusions

The use of diagnostics in the propensity score medical literature is far from optimal, with different diagnostics preferred in different areas of medicine. The propensity score literature may improve with focused efforts to change practice in areas where suboptimal practice is most common.

Inverse Propensity Score Weighting with a Latent Class Exposure: Estimating the Causal Effect of Reported Reasons for Alcohol Use on Problem Alcohol Use 16 Years Later

Latent class analysis (LCA) has proven to be a useful tool for identifying qualitatively different population subgroups who may be at varying levels of risk for negative outcomes. Recent methodological work has improved techniques for linking latent class membership to distal outcomes; however, these techniques do not adjust for potential confounding variables that may provide alternative explanations for observed relations. Inverse propensity score weighting provides a way to account for many confounders simultaneously, thereby strengthening causal inference of the effects of predictors on outcomes. Although propensity score weighting has been adapted to LCA with covariates, there has been limited work adapting it to LCA with distal outcomes. The current study proposes a step-by-step approach for using inverse propensity score weighting together with the "Bolck, Croon, and Hagenaars" approach to LCA with distal outcomes (i.e., the BCH approach), in order to estimate the causal effects of reasons for alcohol use latent class membership during the year after high school (at age 19) on later problem alcohol use (at age 35) with data from the longitudinal sample in the Monitoring the Future study. A supplementary appendix provides evidence for the accuracy of the proposed approach via a small-scale simulation study, as well as sample programming code to conduct the step-by-step approach.

The burden of high workload on the health-related quality of life among home care workers in Northern Sweden

Objective

Previous studies have shown that high workload affects health negatively. However, studies are lacking among home care workers. The aim of this study is to examine the burden of perceived workload on health-related quality of life (HRQoL) among home care workers and to determine whether psychosocial factors modify such a relationship.

Methods

A cross-sectional study was conducted in which 1162 (58% response rate) home care workers participated. The psychosocial factors were measured by QPSnordic. HRQoL was measured by EuroQol 5 dimensions, from which responses were translated into quality-adjusted life year scores (QALY). Propensity scores were used with absolute risk differences (RD). Stratified analysis was used to test the buffer hypothesis of the demand–control–support model.

Results

Personnel with a high workload had a statistically significant 0.035 lower QALY than personnel with a normal workload. This difference was also statistically significant for the Visual Analogue Scale (RD 5.0) and the mobility (RD 0.033) and anxiety/depression scales (RD 0.20) dimensions of EQ-5D. For QALY, the effect of a high workload compared to a normal workload was higher, with low (RD 0.045, significant) compared with high (RD 0.015, non-significant) social support; while it was similar, and non-significant results, for low and high control.

Conclusions

Our study shows that lowered work burden would be beneficial for home care personnel. Furthermore, our results suggest that interventions aimed at increasing social support could reduce work-related illness.

School suspension predicts trichomoniasis five years later in a matched sample

BACKGROUND

Young adults who were suspended from school during adolescence are more likely than matched non-suspended youth to be arrested, on probation, or not graduate from high school, which are STI risk factors. This study evaluates whether suspension is a marker for STI risk among young adults who avoid subsequent negative effects.

METHODS

This study evaluated whether suspension predicts a positive test for chlamydia, gonorrhea, or trichomoniasis in a urine sample using matched sampling in the National Longitudinal Study of Adolescent and Adult Health (Add Health), and evaluated potential mediators between suspension and STI status using causal mediation analysis. We used Mahalanobis and exact matched sampling within propensity score calipers to compare 381 youth suspended for the first time in a 1-year period with 980 non-suspended youth. The suspended and non-suspended youth were similar on 67 pre-suspension variables. We evaluated STI outcomes 5 years after suspension.

RESULTS

Before matching, suspended youth were more likely to test positive for trichomoniasis and gonorrhea, but not chlamydia, than non-suspended youth. Suspended youth were more likely to test positive for trichomoniasis 5 years after suspension than matched non-suspended youth (OR = 2.87 (1.40, 5.99)). Below-median household income before suspension explained 9% of the suspension-trichomoniasis association (p = 0.02), but criminal justice involvement and educational attainment were not statistically significantly mediators.

CONCLUSIONS

School suspension is a marker for STI risk. Punishing adolescents for initial deviance may cause them to associate with riskier sexual networks even if they graduate high school and avoid criminal justice system involvement. Suspension may compound disadvantages for youth from below-median-income families, who have fewer resources for recovering from setbacks.

Recruiting Control Participants into Stroke Biomarker Studies

The number of scientists using -omics technologies to investigate biomarkers with the potential to gauge risk and aid in the diagnosis, treatment, and prognosis of stroke continues to rise, yet there are few resources to aid investigators in recruiting control participants. In this review, we describe two major strategies to match control participants to a stroke cohort-propensity score matching and one-to-one matching-including statistical approaches to gauge the balance between groups. We then explore the advantages and disadvantages of traditional recruitment methods including approaching spouses of enrolled stroke participants, direct recruitment from clinics, community outreach events, approaching retirement communities, and buying samples from a 3rd party vendor. Newer methods to identify controls by screening the electronic health record and using an online screening questionnaire are also described. Finally, we cover compensation for control participants and special considerations. The hope is that this review will serve as a roadmap whereby an investigator can successfully tailor their control recruitment strategy to the research question at hand and the local research environment. While this review is focused on blood-based biomarker studies, the principles will apply to investigators studying a broad range of biological materials.

Assessing causal treatment effect estimation when using large observational datasets

Background

Recently, there has been a heightened interest in developing and evaluating different methods for analysing observational data. This has been driven by the increased availability of large data resources such as Electronic Health Record (EHR) data alongside known limitations and changing characteristics of randomised controlled trials (RCTs). A wide range of methods are available for analysing observational data. However, various, sometimes strict, and often unverifiable assumptions must be made in order for the resulting effect estimates to have a causal interpretation. In this paper we will compare some common approaches to estimating treatment effects from observational data in order to highlight the importance of considering, and justifying, the relevant assumptions prior to conducting an observational analysis.

Methods

A simulation study was conducted based upon a small cohort of patients with chronic obstructive pulmonary disease. Two-stage least squares instrumental variables, propensity score, and linear regression models were compared under a range of different scenarios including different strengths of instrumental variable and unmeasured confounding. The effects of violating the assumptions of the instrumental variables analysis were also assessed. Sample sizes of up to 200,000 patients were considered.

Results

Two-stage least squares instrumental variable methods can yield unbiased treatment effect estimates in the presence of unmeasured confounding provided the sample size is sufficiently large. Adjusting for measured covariates in the analysis reduces the variability in the two-stage least squares estimates. In the simulation study, propensity score methods produced very similar results to linear regression for all scenarios. A weak instrument or strong unmeasured confounding led to an increase in uncertainty in the two-stage least squares instrumental variable effect estimates. A violation of the instrumental variable assumptions led to bias in the two-stage least squares effect estimates. Indeed, these were sometimes even more biased than those from a naïve linear regression model.

Conclusions

Instrumental variable methods can perform better than naïve regression and propensity scores. However, the assumptions need to be carefully considered and justified prior to conducting an analysis or performance may be worse than if the problem of unmeasured confounding had been ignored altogether.

Does height catch-up in schoolchildren have an effect on bone mass in adolescents? Analysis in a Brazilian birth cohort

OBJECTIVE

To analyze the effect of height catch-up at school age on the bone mass of adolescents of a Brazilian birth cohort.

METHODS

A cohort study using data obtained from the three moments (birth, 7/9 years and 18/19 years of age) of the Cohorts-RPS study. Height catch-up was defined based on the difference between the schoolchild's z-score and birth z-score. The adolescents' bone mineral mass was analyzed using the z-score index for the lumbar spine measured by dual emission X-ray absorptiometry. A theoretical model was developed for the proposed analysis using directed acyclic graphs paired through the nearest-neighbor matching propensity score using the STATA software, version 14.0. The level of significance was set at 5%.

RESULTS

Of the 297 studied children, 24.5% achieved height catch-up. The bone mass below the expected for age was observed in 5.39% of the subjects. The mean lumbar spine z-score was -0.34 (±1.01). After the adjustment, no effect was observed between height catch-up at school age and bone mass in adolescents (Coeff=0.598; 95% CI -0.117 to 1.313; p=0.101).

CONCLUSION

Even using the directed acyclic graphs and the causal inference method by adjusting the propensity score, the height catch-up did not seem to affect bone mass in adolescents, a result perhaps related to the sample size.

Does unemployment contribute to poorer health-related quality of life among Swedish adults?

Background

Previous studies have shown that unemployment has negative impacts on various aspects of health. However, little is known about the effect of unemployment on health-related quality of life. Our aim was to examine how unemployment impacts upon health-related quality of life among Swedish adults, and to investigate these effects on population subgroups defined by education level, marital status, previous health, and gender.

Methods

As part of a cross-sectional study, a questionnaire was sent to 2500 randomly selected individuals aged 20 to 64 years living in Sweden in 2016. The questionnaire included the EuroQol 5 dimensions (EQ-5D) instrument and was answered by 967 individuals (39%). Quality-adjusted life year (QALY) scores were derived from the EQ-5D responses. Of the respondents, 113 were unemployed and 724 were employed. We used inverse probability-weighted propensity scores in our analyses to estimate a risk difference. Gender, age, education level, marital status, and previous health were used as covariates in our analyses.

Results

There was a statistically significant lower QALY score by 0.096 points for the unemployed compared to the employed. There were also statistically significant more problems due to unemployment for usual activities (6.6% more), anxiety/depression (23.6% more), and EQ-5D’s Visual Analogue Scale (7.5 point lower score). Grouped analyses indicated a larger negative health effect from becoming unemployed for men, those who are married, and young individuals.

Conclusions

In our study, we show that the health deterioration from unemployment is likely to be large, as our estimated effect implies an almost 10% worse health (in absolute terms) from being unemployed compared to being employed. This further highlights that unemployment is a public health problem that needs more focus. Our study also raises further demands for determining for whom unemployment has the most negative effects and thus suggesting groups of individuals who are in greatest need for labor market measures.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-6825-y) contains supplementary material, which is available to authorized users.

Different statistical techniques dealing with confounding in observational research: measuring the effect of breast-conserving therapy and mastectomy on survival

PURPOSE

Propensity trimming, hierarchical modelling and instrumental variable (IV) analysis are statistical techniques dealing with confounding, cluster-related variation or confounding by severity. This study aimed to explain (dis)advantages of these techniques in estimating the effect of breast-conserving therapy (BCT) and mastectomy on 10-year distant metastasis-free survival (DMFS).

METHODS

All women diagnosed in 2005 with primary T1-2N0-1 breast cancer treated with BCT or mastectomy were selected from the Netherlands Cancer Registry. We used multivariable Cox regression to correct for confounding. Propensity trimming was used to create a more homogeneous population for which the treatment choice was not self-evident. Hospital of surgery was used as hierarchical level to handle hospital-related variation, and as IV to deal with unmeasured confounding.

RESULTS

Multivariable Cox regression showed higher 10-year DMFS for BCT than mastectomy [HR 0.70 (95% CI 0.60-82)]. Propensity trimming on the 10-90th and the 20-80th percentile of the propensity score distribution and hierarchical modelling showed similar HRs. IV analysis showed no significant difference between BCT and mastectomy.

CONCLUSION

Unmeasured confounding is very difficult to eliminate in observational research. We cannot conclude that BCT or mastectomy has a causal relationship with 10-year DMFS. It is crucial to critically evaluate all model's assumptions, and to be careful in drawing firm conclusions.

An overview of commonly used statistical methods in clinical research

Statistics plays an essential role in clinical research by providing a framework for making inferences about a population of interest. In order to interpret research datasets, clinicians involved in clinical research should have an understanding of statistical methodology. This article provides a brief overview of statistical methods that are frequently used in clinical research studies. Descriptive and inferential methods, including regression modeling and propensity scores, are discussed, with focus on the rationale, assumptions, strengths, and limitations to their application.

Improved Survival of Hepatocellular Carcinoma Patients Diagnosed with a Dedicated Screening Programme-a Propensity Score Adjusted Analysis

AIM

To assess the overall survival (OS) in those with hepatocellular carcinoma (HCC) diagnosed within a programmatic, centrally co-ordinated, regional screening programme.

METHODS

A retrospective cohort analysis of consecutive HCC patients diagnosed between 2004 and 2013. Patients were followed up till death or end of study period (30 April 2015). A dedicated screening programme was commenced in 2009 to screen high-risk patients for HCC. Primary objective is to compare the OS between HCC patients diagnosed within the screening group versus those diagnosed outside this group. Other objectives were to compare tumour stage at diagnosis and the proportion having curative treatments in the two groups. Propensity score adjustments were performed to assess the survival benefit.

RESULTS

HCC was diagnosed in 130 subjects during the study period (82.3% males, median [IQR] age 62 [± 19] years and median [IQR] follow-up of 11.3 (± 23.5) months). Ninety-six patients (73.8%) died during the follow-up, and the median (95%CI) OS was 15.7 (9.7-21.8) months. HCC diagnosed within the screening programme had a better OS compared to those diagnosed outside this programme (26.8 vs 11.5 months, p = 0.01). Further, those diagnosed within the programme had an earlier stage HCC ([58.3% vs 23.6%], Ӽ² = 11.3, p = 0.001), and a significant proportion were treated with curative intent ([62.5% vs 31.1%], Ӽ² = 8.3, p = 0.004). Propensity score adjustment showed a 58% reduction in mortality for HCC diagnosed within the screening programme (HR [95%CI] 0.42 [0.20-0.89], p = 0.02).

CONCLUSION

A programmatic, regional HCC screening programme improved the OS and detected tumours at an earlier stage enabling more patients to have curative therapies.

Propensity scores with misclassified treatment assignment: a likelihood-based adjustment

Propensity score methods are widely used in comparative effectiveness research using claims data. In this context, the inaccuracy of procedural or billing codes in claims data frequently misclassifies patients into treatment groups, that is, the treatment assignment ($T$) is often measured with error. In the context of a validation data where treatment assignment is accurate, we show that misclassification of treatment assignment can impact three distinct stages of a propensity score analysis: (i) propensity score estimation; (ii) propensity score implementation; and (iii) outcome analysis conducted conditional on the estimated propensity score and its implementation. We examine how the error in $T$ impacts each stage in the context of three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting (IPTW). Using validation data, we propose a two-step likelihood-based approach which fully adjusts for treatment misclassification bias under subclassification. This approach relies on two common measurement error-assumptions; non-differential measurement error and transportability of the measurement error model. We use simulation studies to assess the performance of the adjustment under subclassification, and also investigate the method's performance under matching or IPTW. We apply the methods to Medicare Part A hospital claims data to estimate the effect of resection versus biopsy on 1-year mortality among $10\,284$ Medicare beneficiaries diagnosed with brain tumors. The ICD9 billing codes from Medicare Part A inaccurately reflect surgical treatment, but SEER-Medicare validation data are available with more accurate information.

Choice of time period to identify confounders for propensity score matching, affected the estimate: a retrospective cohort study of drug effectiveness in asthmatic children

OBJECTIVES

To control for confounding by indication in comparative (drug) effectiveness studies, propensity score (PS) methods may be used. Since childhood diseases or outcomes often present as acute events, we compared the effect of using different look-back periods in electronic health-care data, to construct PSs. This was applied in our research on the effect of a combination of inhaled corticosteroids/long-acting beta-2 agonists (ICS + LABA), either as fixed combination or used as loose combination (2 separate inhaler devices) in the prevention of severe asthma exacerbations.

METHODS

We created a cohort of children (5-17 years) diagnosed with asthma from the Dutch Integrated Primary Care information database. Within this cohort, we identified new users of ICS + LABA, either as fixed combination or loose combination (2 separate inhaler devices). The outcome of interest was severe asthma exacerbations. PSs for type of treatment were created using comorbidity and drug use history in different time windows: 1 week, 1 month, 3 months, 1 year, and full history prior to the start of treatment. PSs were used for matching subjects in both exposure groups. Time to first asthma exacerbation was analyzed with Cox proportional hazard regression. The results were compared with published clinical trials.

RESULTS

Of 39,682 asthmatic children, 3,500 (8.8%) were new users of either ICS + LABA fixed (3,324 [95.0%]) or loose (176 [5.0%]). The crude hazard ratio (HR) for a severe asthma exacerbation, comparing ICS + LABA fixed to loose was 0.37 (95% confidence interval [CI]: 0.20-0.66). PS-matched HRs (1 week, 1 month, 3 month, 1 year, and full history) were 0.48 (95% CI: 0.22-1.04); 0.60 (95% CI: 0.26-1.38), 0.69 (95% CI: 0.31-1.57), 0.56 (CI: 0.25-1.24), and 0.58 (CI: 0.24-1.36), respectively.

CONCLUSIONS

PS matching can be used to control for confounding in pediatric comparative (drug) effectiveness studies, the impact of different look-back periods to implement the PS is important. Controlling for confounders occurring in the 3 months preceding drug exposure may yield results comparable to clinical trial results.

A Bayesian nonparametric approach to causal inference on quantiles

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records.

Propensity scores - A brief introduction for resuscitation researchers

Propensity scores are commonly used in observational research. This article provides a brief introduction to propensity scores aimed for resuscitation researchers. We discuss the concept and calculation of the propensity score and how it can be used to adjust for confounding via regression adjustment, stratification, weighting or matching. The interpretation of these method is briefly discussed and the advantages and limitations of propensity scores are presented. Lastly, we provide some practical recommendations for the presentation of studies using propensity scores.

Comparison of surgical outcomes between open and robot-assisted minimally invasive pancreaticoduodenectomy

BACKGROUND

Robot surgery is a new method that maintains advantages and overcomes disadvantages of conventional methods, even in pancreatic surgery. This study aimed to evaluate safety and benefits of robot-assisted minimally invasive pancreaticoduodenectomy (robot PD).

METHODS

This study included 237 patients who underwent PD between 2015 and 2017. Demographics and surgical outcomes were evaluated.

RESULTS

Fifty-one patients underwent robot PD and 186 underwent open PD. Robot PD group had younger age (60.7 vs. 65.4 years, P = 0.006) and lower body mass index (22.7 vs. 24.0, P = 0.007). Robot PD group had lower proportion of patients with firm or hard pancreatic texture (15.7% vs. 38.2%, P = 0.004) and smaller pancreatic duct size (2.3 vs. 3.3 mm, P = 0.002). Two groups had similar operation time (robot vs. open: 335.6 vs. 330.1 min) and complications (15.7% vs. 21.0%), including postoperative pancreatic fistula rate (6.0% vs. 12.0%). Robot PD group had lower postoperative pain score (3.7 vs. 4.1 points, P = 0.008), and shorter postoperative stay (10.6 vs. 15.3 days, P = 0.001).

CONCLUSION

Robot PD is comparable to open PD in early outcomes. Robot PD is safe and feasible and enables early recovery; indication for robot PD is expected to expand in the near future.

Health related quality of life among myocardial infarction survivors in the United States: a propensity score matched analysis

Background

Little is known regarding the health-related quality of life among myocardial infarction (MI) survivors in the United States. The purpose of this population-based study was to identify differences in health-related quality of life domains between MI survivors and propensity score matched controls.

Methods

This retrospective, cross-sectional matched case-control study examined differences in health-related quality of life (HRQoL) among MI survivors of myocardial infarction compared to propensity score matched controls using data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) survey. Propensity scores were generated via logistic regression for MI survivors and controls based on gender, race/ethnicity, age, body mass index (BMI), smoking status, and comorbidities. Chi-square tests were used to compare differences between MI survivors to controls for demographic variables. A multivariate analysis of HRQoL domains estimated odds ratios. Life satisfaction, sleep quality, and activity limitations were estimated using binary logistic regression. Social support, perceived general health, perceived physical health, and perceived mental health were estimated using multinomial logistic regression. Significance was set at p < 0.05.

Results

The final sample consisted of 16,729 MI survivors matched to 50,187 controls (n = 66,916). Survivors were approximately 2.7 times more likely to report fair/poor general health compared to control (AOR = 2.72, 95% CI: 2.43–3.05) and 1.5 times more likely to report limitations to daily activities (AOR = 1.46, 95% CI: 1.34–1.59). Survivors were more likely to report poor physical health >15 days in the month (AOR = 1.63, 95% CI: 1.46–1.83) and poor mental health >15 days in the month (AOR = 1.25, 95% CI: 1.07–1.46) compared to matched controls. There was no difference in survivors compared to controls in level of emotional support (rarely/never: AOR = 0.75, 95% CI: 0.48–1.18; sometimes: AOR = 0.73, 95% CI: 0.41–1.28), hours of recommended sleep (AOR = 1.14, 95% CI: 0.94–1.38), or life satisfaction (AOR = 1.62, 95% CI: 0.99–2.63).

Conclusion

MI survivors experienced lower HRQoL on domains of general health, physical health, daily activity, and mental health compared to the general population.

Prenatal exposure to PM2.5 and birth weight: A pooled analysis from three North American longitudinal pregnancy cohort studies

A common practice when analyzing multi-site epidemiological data is to include a term for 'site' to account for unmeasured effects at each location. This practice should be carefully considered when site can have complex relationships with important demographic and exposure variables. We leverage data from three longitudinal North American pregnancy cohorts to demonstrate a novel method to assess study heterogeneity and potential combinability of studies for pooled analyses in order to better understand how to consider site in analyses. Results from linear regression and fixed effects meta-regression models run both prior to and following the proposed combinability analyses were compared. In order to exemplify this approach, we examined associations between prenatal exposure to particulate matter and birth weight. Analyses included mother-child dyads (N=1966) from the Asthma Coalition on Community Environment and Social Stress (ACCESS) Project and the PRogramming of Intergenerational Stress Mechanisms (PRISM) study in the northeastern United States, and the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study in Mexico City. Mothers' daily third trimester exposure to particulate matter≤2.5μm in diameter (PM_2.5) was estimated using a validated satellite-based spatio-temporally resolved model in all studies. Fenton birth weight for gestational age z-scores were calculated. Linear regression analyses within each cohort separately did not find significant associations between PM_2.5 averaged over the third trimester and Fenton z-scores. The initial meta-regression model also did not find significant associations between prenatal PM_2.5 and birthweight. Next, propensity scores and log linear models were used to assess higher order interactions and determine if sites were comparable with regard to sociodemographics and other covariates; these analyses demonstrated that PROGRESS and ACCESS were combinable. Adjusted linear regression models including a 2-level site variable according to the pooling indicated by the log linear models (ACCESS and PROGRESS as one level and PRISM as another) revealed that a 5μg/m³ increase in PM_2.5 was associated with a 0.075 decrease in Fenton z-score (p<0.0001); linear models including a 3-level site variable did not reveal significant associations. By assessing the combinability of heterogeneous populations prior to combining data using a method that more optimally accounts for underlying cohort differences, we were able to identify significant associations between prenatal PM_2.5 exposure and birthweight that were not detected using standard methods.

The perception of fairness in infant care and mothers' postpartum depression

This study investigates a potential causal effect of mothers' perceptions of the fairness of infant care on their postpartum depression. Based on the tenets of equity theory, it is hypothesized that, net of controls, mothers who see infant care as fairly apportioned between themselves and their husbands will be less depressed than others. We utilize data from a longitudinal study of a nonrandom sample of 178 heterosexual couples experiencing the birth of their first child together. The primary focus variable is the mothers' perception in the first couple of months postpartum that infant care is fair to them. Statistical analysis involved the careful chronological sequencing of response variable and controls, along with regression modeling using propensity scores. We find that a perception of fairness is associated with about a quarter of a standard deviation lower depressive symptomatology, controlling for key covariates. Depressive symptomatology is additionally elevated for mothers experiencing more pre-partum depression, and for those who more generally felt, before the birth, that they were overbenefiting in the marriage. This paper contributes to both equity theory and research on postpartum depression. In a scenario in which it is not practical or ethical to randomly assign people to fairness-in-infant-care conditions, we are able to utilize longitudinal data and a natural "experiment," along with propensity-score modeling to attempt to assess the causal impact of fairness in infant care on postpartum depression. The finding that fairness in this arena appears to reduce postpartum depression emphasizes the importance of encouraging father participation in this critical stage of parenting. Limitations of the study with respect to causal inference are also discussed.

Living in violence: Neighborhood domestic violence and small for gestational age births

OBJECTIVES

To determine the association between neighborhood domestic violence and small-for-gestational-age (SGA) birth and to examine if there is a differential impact of neighborhood domestic violence on SGA births by race in a high crime community.

METHODS

This analysis includes all birth records issued in New Orleans, Louisiana from 2011 to 2012 geocoded by census tract (N=177 census tracts, N=8322 women). Hierarchical modeling and ecologic spatial analysis were used to examine the area-effect of neighborhood domestic violence on SGA births, independent of individual-level predictors and accounting for the propensity to live in high domestic violence neighborhoods.

RESULTS

Tests for spatial autocorrelation reveled area-level clustering and overlap of SGA and domestic violent rates. Pregnant women living in high domestic violence areas were more likely to give birth to an SGA infant compared to women in low-domestic violence areas (OR=1.04, 95%CI: 1.01, 1.08), net of the effects of individual-level factors and propensity scores.

CONCLUSION

Neighborhood-level attributes including rates of domestic violence may increase women's risk for SGA birth, highlighting a policy-relevant and potentially amenable exposure.

Adherence to combination DMARD therapy and treatment outcomes in rheumatoid arthritis: a longitudinal study of new and existing DMARD users

Medication adherence is believed to be a major contributor to treatment outcomes yet studies quantifying this relationship as rare in rheumatoid arthritis (RA). To determine the association of adherence to DMARD therapy with treatment outcomes among new and existing DMARD users over 2 years. Relevant clinical parameters were obtained from a longitudinal cohort of RA patients, most of who were treated with combination therapy. Patients were classified as adherent if the proportion of days covered for each DMARD was ≥80%. Outcome measures were the change in the disease activity score in 28 joints (DAS28), simplified disease activity index (SDAI), modified health assessment questionnaires (mHAQ) and proportion of patients who achieved response criteria. An inverse propensity-score weighting method was used to estimate the association of adherence with each outcome. Of 194 patients invited, a total of 111 patients (new = 45 and existing = 66 DMARD users) met study eligibility. DMARD-naive patients demonstrated relatively higher rates of adherence compared to existing users. After controlling for confounding variables, adherence was significantly associated with reduction in DAS28 (β = -1.5, 95% CI of β = - 2.17 to -0.83, p < 0.0001), SDAI (β = -9.44, 95% CI of β = -15.53 to -3.35, p = 0.002) and mHAQ (β = -0.269, 95% CI of β, -0.462 to -0.077, p = 0.017) over 2 years among new patients and adherent patients were more likely to achieve most response criteria compared to non-adherent patients. Such associations were not replicated among existing DMARD users. Adherence to combination DMARD therapy was associated with improvements in disease activity and functional outcomes in the first 2 years of therapy.

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides

Background

Bacterial tyrosine-kinases (BY-kinases), which play an important role in numerous cellular processes, are characterized as a separate class of enzymes and share no structural similarity with their eukaryotic counterparts. However, in silico methods for predicting BY-kinases have not been developed yet. Since these enzymes are involved in key regulatory processes, and are promising targets for anti-bacterial drug design, it is desirable to develop a simple and easily interpretable predictor to gain new insights into bacterial tyrosine phosphorylation. This study proposes a novel SCMBYK method for predicting and characterizing BY-kinases.

Results

A dataset consisting of 797 BY-kinases and 783 non-BY-kinases was established to design the SCMBYK predictor, which achieved training and test accuracies of 97.55 and 96.73%, respectively. Furthermore, the leave-one-phylum-out method was used to predict specific bacterial phyla hosts of target sequences, gaining 97.39% average test accuracy. After analyzing SCMBYK-derived propensity scores, four characteristics of BY-kinases were determined: 1) BY-kinases tend to be composed of α-helices; 2) the amino-acid content of extracellular regions of BY-kinases is expected to be dominated by residues such as Val, Ile, Phe and Tyr; 3) BY-kinases structurally resemble nuclear proteins; 4) different domains play different roles in triggering BY-kinase activity.

Conclusions

The SCMBYK predictor is an effective method for identification of possible BY-kinases. Furthermore, it can be used as a part of a novel drug repurposing method, which recognizes putative BY-kinases and matches them to approved drugs. Among other results, our analysis revealed that azathioprine could suppress the virulence of M. tuberculosis, and thus be considered as a potential antibiotic for tuberculosis treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1371-4) contains supplementary material, which is available to authorized users.

Parental health shocks and schooling: The impact of mutual health insurance in Rwanda

The goal of this study was to look at the educational spill-over effects of health insurance on schooling with a focus on the Rwandan Community Based Health Insurance Programme, the Mutual Health Insurance scheme. Using a two-person general equilibrium overlapping generations model, this paper theoretically analyses the possible effect of health insurance on the relationship between parental health shocks and child schooling. Individuals choose whether or not they want to incur a medical cost by seeking care in order to reduce the effect of health shocks on their labour market availability and productivity. The theoretical results show that, health shocks negatively affect schooling irrespective of insurance status. However, if the health shock is severe (incapacitating) or sudden in nature, there is a discernible mitigating effect of health insurance on the negative impact of parental ill health on child schooling. The results are tested empirically using secondary data from the third Integrated Household Living Conditions Survey (EICV) for Rwanda, collected in 2011. A total of 2401 children between the ages of 13 and 18 are used for the analysis. This age group is selected due to the age of compulsory education in Rwanda. Based on average treatment effect on treated we find a statistically significant difference in attendance between children with MHI affiliated parents and those with uninsured parents of about 0.044. The negative effect of a father being severely ill is significant only for uninsured household. For the case of the mother, this effect is felt by female children with uninsured parents only when the illness is sudden. The observed effects are more pronounced for older children. While the father's ill health (sever or sudden) significantly and negatively affects their working hours, health insurance plays appears to increase their working hours. The effects of health insurance extend beyond health outcomes.

Observational Research Using Propensity Scores

In most observational studies, treatments or other "exposures" (in an epidemiologic sense) do not occur at random. Instead, treatments or other such interventions depend on several patient-related and patient-independent characteristics. Such factors, associated with the receipt vs nonreceipt of treatment, may also be-independently-associated with outcomes. Thus, confounding exists making it difficult to ascertain the true association between treatments and outcomes. Propensity scores (PS) represent an intuitive set of approaches to reduce the influence of such "confounding" factors. PS is a computed probability of treatment, a value that is estimated for each patient in an observational study and then applied (in a variety of ways such as matching, stratification, weighting, etc.) to reduce distortion in the true nature of the association between treatment (or any similar exposure) and outcomes. Despite several advantages, PS-based methods cannot account for unmeasured confounding, ie, for factors that are not being included in the computation of PS.

The Impact of Degree of Exposure to Violent Video Games, Family Background, and Other Factors on Youth Violence

Despite decades of study, no scholarly consensus has emerged regarding whether violent video games contribute to youth violence. Some skeptics contend that small correlations between violent game play and violence-related outcomes may be due to other factors, which include a wide range of possible effects from gender, mental health, and social influences. The current study examines this issue with a large and diverse (49 % white, 21 % black, 18 % Hispanic, and 12 % other or mixed race/ethnicity; 51 % female) sample of youth in eighth (n = 5133) and eleventh grade (n = 3886). Models examining video game play and violence-related outcomes without any controls tended to return small, but statistically significant relationships between violent games and violence-related outcomes. However, once other predictors were included in the models and once propensity scores were used to control for an underlying propensity for choosing or being allowed to play violent video games, these relationships vanished, became inverse, or were reduced to trivial effect sizes. These results offer further support to the conclusion that video game violence is not a meaningful predictor of youth violence and, instead, support the conclusion that family and social variables are more influential factors.

Safety evaluation of continuous green T intersections: A propensity scores-genetic matching-potential outcomes approach

The continuous green T intersection is characterized by a channelized left-turn movement from the minor street approach onto the major street, along with a continuous through movement on the major street. The continuous flow through movement is not controlled by the three-phase traffic signal that is used to separate all other movements at the intersection. Rather, the continuous through movement typically has a green through arrow indicator to inform drivers that they do not have to stop. Past research has consistently shown that there are operational and environmental benefits to implementing this intersection form at three-leg locations, when compared to a conventional signalized intersection. These benefits include reduced delay, fuel consumption, and emissions. The safety effects of the conventional green T intersection are less clear. Past research has been limited to small sample sizes, or utilized only statistical comparisons reported crashes to evaluate the safety performance relative to similar intersection types. The present study overcomes past safety research evaluations by using a propensity scores-potential outcomes framework, with genetic matching, to compare the safety performance of the continuous green T to conventional signalized intersections, using treatment and comparison site data from Florida and South Carolina. The results show that the expected total, fatal and injury, and target crash (rear-end, angle, and sideswipe) frequencies are lower at the continuous green T intersection relative to the conventional signalized intersection (CMFs of 0.958 [95% CI=0.772-1.189], 0.846 [95% CI=0.651-1.099], and 0.920 [95% CI=0.714-1.185], respectively).