Adjustment for missing confounders in studies based on observational databases: 2-stage calibration combining propensity scores from primary and validation data

Inverse Probability of Treatment Weighting and Confounder Missingness in Electronic Health Record-based Analyses: A Comparison of Approaches Using Plasmode Simulation

BACKGROUND

Electronic health record (EHR) data represent a critical resource for comparative effectiveness research, allowing investigators to study intervention effects in real-world settings with large patient samples. However, high levels of missingness in confounder variables is common, challenging the perceived validity of EHR-based investigations.

METHODS

We investigated performance of multiple imputation and propensity score (PS) calibration when conducting inverse probability of treatment weights (IPTW)-based comparative effectiveness research using EHR data with missingness in confounder variables and outcome misclassification. Our motivating example compared effectiveness of immunotherapy versus chemotherapy treatment of advanced bladder cancer with missingness in a key prognostic variable. We captured complexity in EHR data structures using a plasmode simulation approach to spike investigator-defined effects into resamples of a cohort of 4361 patients from a nationwide deidentified EHR-derived database. We characterized statistical properties of IPTW hazard ratio estimates when using multiple imputation or PS calibration missingness approaches.

RESULTS

Multiple imputation and PS calibration performed similarly, maintaining ≤0.05 absolute bias in the marginal hazard ratio even when ≥50% of subjects had missing at random or missing not at random confounder data. Multiple imputation required greater computational resources, taking nearly 40 times as long as PS calibration to complete. Outcome misclassification minimally increased bias of both methods.

CONCLUSION

Our results support multiple imputation and PS calibration approaches to missingness in missing completely at random or missing at random confounder variables in EHR-based IPTW comparative effectiveness analyses, even with missingness ≥50%. PS calibration represents a computationally efficient alternative to multiple imputation.

Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer's disease

BACKGROUND

Causal feature selection is essential for estimating effects from observational data. Identifying confounders is a crucial step in this process. Traditionally, researchers employ content-matter expertise and literature review to identify confounders. Uncontrolled confounding from unidentified confounders threatens validity, conditioning on intermediate variables (mediators) weakens estimates, and conditioning on common effects (colliders) induces bias. Additionally, without special treatment, erroneous conditioning on variables combining roles introduces bias. However, the vast literature is growing exponentially, making it infeasible to assimilate this knowledge. To address these challenges, we introduce a novel knowledge graph (KG) application enabling causal feature selection by combining computable literature-derived knowledge with biomedical ontologies. We present a use case of our approach specifying a causal model for estimating the total causal effect of depression on the risk of developing Alzheimer's disease (AD) from observational data.

METHODS

We extracted computable knowledge from a literature corpus using three machine reading systems and inferred missing knowledge using logical closure operations. Using a KG framework, we mapped the output to target terminologies and combined it with ontology-grounded resources. We translated epidemiological definitions of confounder, collider, and mediator into queries for searching the KG and summarized the roles played by the identified variables. We compared the results with output from a complementary method and published observational studies and examined a selection of confounding and combined role variables in-depth.

RESULTS

Our search identified 128 confounders, including 58 phenotypes, 47 drugs, 35 genes, 23 collider, and 16 mediator phenotypes. However, only 31 of the 58 confounder phenotypes were found to behave exclusively as confounders, while the remaining 27 phenotypes played other roles. Obstructive sleep apnea emerged as a potential novel confounder for depression and AD. Anemia exemplified a variable playing combined roles.

CONCLUSION

Our findings suggest combining machine reading and KG could augment human expertise for causal feature selection. However, the complexity of causal feature selection for depression with AD highlights the need for standardized field-specific databases of causal variables. Further work is needed to optimize KG search and transform the output for human consumption.

Estimation of treatment effects in observational stroke care data: comparison of statistical approaches

Introduction

Various statistical approaches can be used to deal with unmeasured confounding when estimating treatment effects in observational studies, each with its own pros and cons. This study aimed to compare treatment effects as estimated by different statistical approaches for two interventions in observational stroke care data.

Patients and methods

We used prospectively collected data from the MR CLEAN registry including all patients (n = 3279) with ischemic stroke who underwent endovascular treatment (EVT) from 2014 to 2017 in 17 Dutch hospitals. Treatment effects of two interventions – i.e., receiving an intravenous thrombolytic (IVT) and undergoing general anesthesia (GA) before EVT – on good functional outcome (modified Rankin Scale ≤2) were estimated. We used three statistical regression-based approaches that vary in assumptions regarding the source of unmeasured confounding: individual-level (two subtypes), ecological, and instrumental variable analyses. In the latter, the preference for using the interventions in each hospital was used as an instrument.

Results

Use of IVT (range 66–87%) and GA (range 0–93%) varied substantially between hospitals. For IVT, the individual-level (OR ~ 1.33) resulted in significant positive effect estimates whereas in instrumental variable analysis no significant treatment effect was found (OR 1.11; 95% CI 0.58–1.56). The ecological analysis indicated no statistically significant different likelihood (β = − 0.002%; P = 0.99) of good functional outcome at hospitals using IVT 1% more frequently. For GA, we found non-significant opposite directions of points estimates the treatment effect in the individual-level (ORs ~ 0.60) versus the instrumental variable approach (OR = 1.04). The ecological analysis also resulted in a non-significant negative association (0.03% lower probability).

Discussion and conclusion

Both magnitude and direction of the estimated treatment effects for both interventions depend strongly on the statistical approach and thus on the source of (unmeasured) confounding. These issues should be understood concerning the specific characteristics of data, before applying an approach and interpreting the results. Instrumental variable analysis might be considered when unobserved confounding and practice variation is expected in observational multicenter studies.

Association between prescribed opioid use for acute pain in adolescents and the subsequent development of opioid misuse and substance use disorders: a systematic review protocol

OBJECTIVE

This review aims to examine prescribed short-term opioid use in adolescents to treat acute pain. The review will analyze the influence of opioid use on future non-medical opioid use (misuse) or substance use disorders (addiction) in adolescents and young adults.

INTRODUCTION

Prescription opioids are medically indicated for acute pain. Descriptive studies of administrative datasets and surveys implicate adolescent opioid exposure as a risk factor for subsequent opioid misuse and addiction. This review will provide a synthesis of the literature on the association between prescribed opioid exposure to treat acute pain in adolescents and the subsequent development of opioid misuse or substance use disorders in adolescents and young adults.

INCLUSION CRITERIA

This review will consider quantitative studies on opioid misuse or substance use disorders in Canadian and US adolescents and young adults (12 to 25 years of age). Studies must include exposure during adolescence (12 to 17 years of age) to legitimately prescribed short-term opioid use to treat acute pain. Studies on chronic pain or exposure to opioids for longer duration (more than 30 doses or more than 7 days) will be excluded.

METHODS

This review will follow the JBI methodology for systematic reviews of etiology and risk. Published and unpublished studies will be sourced from multiple databases and resources. Two independent reviewers will screen, appraise, and extract data from studies that meet the inclusion criteria. Data synthesis will be conducted and a Summary of Findings will be presented.

SYSTEMATIC REVIEW REGISTRATION NUMBER

PROSPERO CRD42020179635.

A causal data fusion method for the general exposure and outcome

With the advent of the big data era, the need to combine multiple individual data sets to draw causal effects arises naturally in many medical and biological applications. Especially each data set cannot measure enough confounders to infer the causal effect of an exposure on an outcome. In this article, we extend the method proposed by a previous study to causal data fusion of more than two data sets without external validation and to a more general (continuous or discrete) exposure and outcome. Theoretically, we obtain the condition for identifiability of exposure effects using multiple individual data sources for the continuous or discrete exposure and outcome. The simulation results show that our proposed causal data fusion method has unbiased causal effect estimate and higher precision than traditional regression, meta-analysis and statistical matching methods. We further apply our method to study the causal effect of BMI on glucose level in individuals with diabetes by combining two data sets. Our method is essential for causal data fusion and provides important insights into the ongoing discourse on the empirical analysis of merging multiple individual data sources.

Estimation of causal effect in integrating randomized clinical trial and observational data - An example application to cardiovascular outcome trial

Safety evaluation of drug development is a comprehensive process across the product lifecycle. While a randomized clinical trial (RCT) can provide high-quality data to assess the efficacy and safety of a new intervention, the pre-marketing trials are limited in statistical power to detect causal elevation of rare but potentially serious adverse events. On the other hand, real-world data (RWD) sources play a critical role in further understanding the safety profile of the new intervention. Bringing together the breadth and strength of RWD and RCT data, we can maximize the utility of RWD and answer broader questions. In this manuscript, we propose a three-step statistical framework to corroborate findings from both RCT and RWD for evaluating important safety concerns identified in the pre-marketing setting. By the proposed approach, we first match the observational study to RCT, then the causal estimation is validated via the matched observational study with the target RCT by targeted maximum likelihood estimation (TMLE) method, and lastly the evidence from RCT and RWD can be combined in an integrative analysis. A potential application to cardiovascular outcome trials for type 2 diabetes mellitus is illustrated. Finally, simulation results suggest that the heterogeneity of patient population from RCT and RWD can lead to varying degrees of treatment effect estimation and the proposed approach may be able to mitigate such difference in the integrative analysis.

Time-dependent association between cancer and risk of tuberculosis: A population-based cohort study

BACKGROUND

We aimed to investigate the time-dependent association between cancer and the risk of tuberculosis (TB) before and after cancer diagnosis.

METHODS

This population-based cohort study incorporated the National Health Insurance Research Database and the National Health Interview Survey in Taiwan to estimate TB risk in cancer and noncancer populations. We estimated the period-specific incidence rate ratio (IRR) between cancer and risk of TB and used Cox proportional hazards models to estimate the average hazard ratio between cancer and TB during the peridiagnostic period.

RESULTS

From 2001 to 2015, 457 673 cancer and 3 738 122 noncancer individuals were enrolled. After stratifying the IRR of TB by year relative to the date of cancer diagnosis, the peak IRRs clustered in the year before and after the index date. In the peridiagnostic period of cancer, the adjusted hazard ratio was 2.29 (95% CI, 2.22-2.35) using the Cox model and 2.20 (95% CI, 2.09-2.32) after adjustment for missing confounders. Patients with cancers in the respiratory tract, upper digestive tract, and hematologic system were at the highest risk for TB.

CONCLUSIONS

Cancer is an independent risk factor for TB, with the highest risk observed around the time of cancer diagnosis.

No inexplicable disagreements between real-world data-based nonrandomized controlled studies and randomized controlled trials were found

OBJECTIVES

We assessed disagreements between nonrandomized controlled studies based on real-world data (NRCS-RWDs) and randomized controlled trials (RCTs).

STUDY DESIGN AND SETTING

We systematically searched for studies that compared treatment effect estimates from NRCS-RWDs and RCTs on the same clinical question. We assessed the potential difference between NRCS-RWDs and RCTs related to internal and external validity. We calculated various meta-epidemiological measures to assess agreement. In case of disagreements, we tried to identify the probable causes of disagreements.

RESULTS

We included 12 studies comparing 15 treatment effect estimates of NRCS-RWDs and RCTs. There were many potential causes of disagreement. Ninety-five percent confidence intervals overlapped for 12 of 15 treatment effect estimates. Our analysis on predicted vs. observed overlap showed that there were no more disagreements than expected by chance. We observed only two substantial differences between the 15 treatment effect estimates. In both cases, we identified risk of bias in the NRCS-RWDs as the most probable cause of disagreement.

CONCLUSION

Our findings suggest that there are clinical questions where the difference in risk of bias between a well-conducted NRCS-RWD and an RCT is negligible. In our analysis, threats to external validity appeared to have no or only a weak impact on the disagreements of treatment effect estimates.

Bias Reduction Methods for Propensity Scores Estimated from Error-Prone EHR-Derived Covariates

As the use of electronic health records (EHR) to estimate treatment effects has become widespread, concern about bias introduced by error in EHR-derived covariates has also grown. While methods exist to address measurement error in individual covariates, little prior research has investigated the implications of using propensity scores for confounder control when the propensity scores are constructed from a combination of accurate and error-prone covariates. We reviewed approaches to account for error in propensity scores and used simulation studies to compare their performance. These comparisons were conducted across a range of scenarios featuring variation in outcome type, validation sample size, main sample size, strength of confounding, and structure of the error in the mismeasured covariate. We then applied these approaches to a real-world EHR-based comparative effectiveness study of alternative treatments for metastatic bladder cancer. This head-to-head comparison of measurement error correction methods in the context of a propensity score-adjusted analysis demonstrated that multiple imputation for propensity scores performs best when the outcome is continuous and regression calibration-based methods perform best when the outcome is binary.

Using real-world evidence for pharmacovigilance and drug safety-related decision making by a resource-limited health authority: 10 years of experience in Taiwan

PURPOSE

Real-world evidence has become increasingly relevant in regulatory decision making. Compared to large regulatory bodies, the national pharmacovigilance system in Taiwan is still under development, and the aim of this study is to demonstrate how a resource-limited health authority utilizes real-world evidence in decision making.

METHODS

We described different sources of real-world data available in Taiwan and illustrated the structural framework that integrates real-world evidence into Taiwan's national pharmacovigilance system. Additionally, we reviewed real-world studies conducted in the past 10 years and provided examples to show how these studies influenced drug safety-related decision making in Taiwan.

RESULTS

During the past 10 years, real-world evidence used when making drug safety-related regulatory decisions in Taiwan was mainly generated from nationwide claims databases, but other sources of real-world data, such as national registries and large electronic hospital databases, also became available recently. Different types of real-world evidence, including drug utilization studies, risk evaluation studies, and risk minimization measure evaluation studies, have been used to support regulatory decisions in Taiwan.

CONCLUSIONS

Through collaborations between the government and academics, Taiwan has started to integrate real-world evidence into the national pharmacovigilance system. However, future efforts, including linkages between different sources of real-world data and improvements in procedural and methodological practices, are needed to generate more regulatory-quality real-world evidence.

Association between physical fitness parameters and risk of in-season injury among adult male rugby players: a systematic review protocol

OBJECTIVE

The objective of this review is to assess whether pre-season physical fitness parameters are associated with in-season injury risk among adult male rugby players.

INTRODUCTION

Pre-season neuromusculoskeletal screening protocols (which include tests related to different physical fitness parameters) are injury prevention strategies employed to manage athletes' in-season injury risk. A systematic review exploring the association between in-season injury and specific physical fitness parameters may justify the inclusion or exclusion of these tests in official screening protocols.

INCLUSION CRITERIA

This review will consider prospective, observational cohort studies that investigate injury-free adult (aged 18 years or above) male rugby players, from all levels of participation (recreational, sub-elite and elite). Studies investigating physical fitness parameters and their association to rugby-related neuromusculoskeletal injury will be included.

METHODS

The proposed systematic review will be conducted in accordance with the JBI methodology for systematic reviews of etiology and risk. Published and unpublished studies will be sourced from several databases and resources. Two independent researchers will screen, appraise and extract data from studies meeting the inclusion criteria using standardized critical appraisal and data extraction tools. Data synthesis will be conducted and a Summary of Findings constructed to summarize data and draw conclusions.

SYSTEMATIC REVIEW REGISTRATION NUMBER

PROSPERO CRD42020130420.

Adjusting for unmeasured confounding using validation data: Simplified two-stage calibration for survival and dichotomous outcomes

In epidemiology, one typically wants to estimate the risk of an outcome associated with an exposure after adjusting for confounders. Sometimes, outcome and exposure and maybe some confounders are available in a large data set, whereas some important confounders are only available in a validation data set that is typically a subset of the main data set. A generally applicable method in this situation is the two-stage calibration (TSC) method. We present a simplified easy-to-implement version of the TSC for the case where the validation data are a subset of the main data. We compared the simplified version to the standard TSC version for incidence rate ratios, odds ratios, relative risks, and hazard ratios using simulated data, and the simplified version performed better than our implementation of the standard version. The simplified version was also tested on real data and performed well.

Combining Multiple Observational Data Sources to Estimate Causal Effects

The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with supplementary information on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. Our framework applies to asymptotically normal estimators, including the commonly used regression imputation, weighting, and matching estimators, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables. We also propose appropriate bootstrap procedures, which makes our method straightforward to implement using software routines for existing estimators. Supplementary materials for this article are available online.

Assessment of patient experience profiles and satisfaction with expectations of treatment effects by using latent class analysis based on a national patient experience survey in Taiwan

OBJECTIVES

Health system responsiveness is a complicated issue that guides researchers wishing to design an efficient methodology for enhancing understanding of perspectives regarding healthcare systems. This study examined the relationship between patient experience profiles and satisfaction with expectations of treatment effects.

DESIGN

This was a cross-sectional study. We used eight items obtained from latent class analysis to develop patient experience profiles.

SETTING

Primary care users in Taiwan.

PARTICIPANTS

This study conducted an annual National Health Insurance survey in Taiwan and sampled from those who had experience with the medical service in primary care clinics in 2015.

PRIMARY OUTCOME MEASURE

Respondents were asked to indicate the extent of their satisfaction with their expectation of treatment effects (or symptom improvement).

RESULTS

The proportions of participants in groups 1-4 were 34%, 24%, 29% and 12%, respectively. Patients in good health were more satisfied with their expectations of treatment effects (OR 1.639, p=0.007). Furthermore, group 4 (-eAll) were less satisfied with their expectations of treatment effects than those in the other three groups (ORs: group 1 (+eAll): 9.81, group 2 (-CwR): 4.14 and group 3 (-CnR): 4.20).

CONCLUSIONS

The results revealed that experiences of poor accessibility and physician-patient relationships affected the patients' expectations. Therefore, greater accessibility and more positive physician-patient relationships could lead to higher patient satisfaction with their expectations of treatment effects. Furthermore, the findings could assist authorities in targeting specific patients, with the objective of improving their healthcare service experience. They could also serve as a mechanism for improving the quality of healthcare services and increase accountability in healthcare practices.

Health administrative data enrichment using cohort information: Comparative evaluation of methods by simulation and application to real data

Background

Studies using health administrative databases (HAD) may lead to biased results since information on potential confounders is often missing. Methods that integrate confounder data from cohort studies, such as multivariate imputation by chained equations (MICE) and two-stage calibration (TSC), aim to reduce confounding bias. We provide new insights into their behavior under different deviations from representativeness of the cohort.

Methods

We conducted an extensive simulation study to assess the performance of these two methods under different deviations from representativeness of the cohort. We illustrate these approaches by studying the association between benzodiazepine use and fractures in the elderly using the general sample of French health insurance beneficiaries (EGB) as main database and two French cohorts (Paquid and 3C) as validation samples.

Results

When the cohort was representative from the same population as the HAD, the two methods are unbiased. TSC was more efficient and faster but its variance could be slightly underestimated when confounders were non-Gaussian. If the cohort was a subsample of the HAD (internal validation) with the probability of the subject being included in the cohort depending on both exposure and outcome, MICE was unbiased while TSC was biased. The two methods appeared biased when the inclusion probability in the cohort depended on unobserved confounders.

Conclusion

When choosing the most appropriate method, epidemiologists should consider the origin of the cohort (internal or external validation) as well as the (anticipated or observed) selection biases of the validation sample.

Using Sensitivity Analyses for Unobserved Confounding to Address Covariate Measurement Error in Propensity Score Methods

Propensity score methods are a popular tool with which to control for confounding in observational data, but their bias-reduction properties-as well as internal validity, generally-are threatened by covariate measurement error. There are few easy-to-implement methods of correcting for such bias. In this paper, we describe and demonstrate how existing sensitivity analyses for unobserved confounding-propensity score calibration, VanderWeele and Arah's bias formulas, and Rosenbaum's sensitivity analysis-can be adapted to address this problem. In a simulation study, we examine the extent to which these sensitivity analyses can correct for several measurement error structures: classical, systematic differential, and heteroscedastic covariate measurement error. We then apply these approaches to address covariate measurement error in estimating the association between depression and weight gain in a cohort of adults in Baltimore, Maryland. We recommend the use of VanderWeele and Arah's bias formulas and propensity score calibration (assuming it is adapted appropriately for the measurement error structure), as both approaches perform well for a variety of propensity score estimators and measurement error structures.

Propensity Scores in Pharmacoepidemiology: Beyond the Horizon

Purpose of review

Propensity score methods have become commonplace in pharmacoepidemiology over the past decade. Their adoption has confronted formidable obstacles that arise from pharmacoepidemiology's reliance on large healthcare databases of considerable heterogeneity and complexity. These include identifying clinically meaningful samples, defining treatment comparisons, and measuring covariates in ways that respect sound epidemiologic study design. Additional complexities involve correctly modeling treatment decisions in the face of variation in healthcare practice, and dealing with missing information and unmeasured confounding. In this review, we examine the application of propensity score methods in pharmacoepidemiology with particular attention to these and other issues, with an eye towards standards of practice, recent methodological advances, and opportunities for future progress.

Recent findings

Propensity score methods have matured in ways that can advance comparative effectiveness and safety research in pharmacoepidemiology. These include natural extensions for categorical treatments, matching algorithms that can optimize sample size given design constraints, weighting estimators that asymptotically target matched and overlap samples, and the incorporation of machine learning to aid in covariate selection and model building.

Summary

These recent and encouraging advances should be further evaluated through simulation and empirical studies, but nonetheless represent a bright path ahead for the observational study of treatment benefits and harms.

Adjustment for time-dependent unmeasured confounders in marginal structural Cox models using validation sample data

Large databases used in observational studies of drug safety often lack information on important confounders. The resulting unmeasured confounding bias may be avoided by using additional confounder information, frequently available in smaller clinical “validation samples”. Yet, no existing method that uses such validation samples is able to deal with unmeasured time-varying variables acting as both confounders and possible mediators of the treatment effect. We propose and compare alternative methods which control for confounders measured only in a validation sample within marginal structural Cox models. Each method corrects the time-varying inverse probability of treatment weights for all subject-by-time observations using either regression calibration of the propensity score, or multiple imputation of unmeasured confounders. Two proposed methods rely on martingale residuals from a Cox model that includes only confounders fully measured in the large database, to correct inverse probability of treatment weight for imputed values of unmeasured confounders. Simulation demonstrates that martingale residual-based methods systematically reduce confounding bias over naïve methods, with multiple imputation including the martingale residual yielding, on average, the best overall accuracy. We apply martingale residual-based imputation to re-assess the potential risk of drug-induced hypoglycemia in diabetic patients, where an important laboratory test is repeatedly measured only in a small sub-cohort.

Impact of Weekday of Esophagectomy on Short-term and Long-term Oncological Outcomes: A Nationwide Population-based Cohort Study in the Netherlands

OBJECTIVE

The aim of this study was to determine whether weekday of esophagectomy impacts 30-day mortality, and short- and long-term oncologic outcomes in esophageal cancer.

SUMMARY OF BACKGROUND DATA

Recent literature suggests a relationship between the weekday of esophagectomy and overall survival. This finding could impact clinical practice, but has not yet been validated in other studies.

METHODS

The Netherlands Cancer Registry database (2005-2013) identified all patients who underwent esophagectomy for esophageal cancer. The impact of weekday on 30-day mortality, the total number of resected lymph nodes, and R0 resection rates was evaluated with multivariable logistic regression analyses and for overall survival with Cox regression analyses.

RESULTS

In total, 3840 patients were included. Weekday was not significantly associated with 30-day mortality (P > 0.05), nor the total number of resected lymph nodes (P > 0.05), nor with R0 resection rates (P > 0.05). Also, weekday did not significantly influence overall survival using weekday as discrete variable [Monday-Friday, hazard ratio (HR) 0.98, P = 0.140), as 2 weekday categories (Wednesday-Friday vs Monday-Tuesday, HR 0.97, P = 0.434), or with separate weekday categories (Tuesday vs Monday, HR 0.99, P = 0.826; Wednesday vs Monday, HR 1.06, P = 0.430; Thursday vs Monday, HR 0.92, P = 0.206; Friday vs Monday, HR 0.91, P = 0.140).

CONCLUSIONS

This large population-based cohort study in the Netherlands refutes the finding from a previous report that suggests that the weekday of esophagectomy in patients diagnosed with potentially curable esophageal cancer impacts overall survival. In addition, this study demonstrates that weekday of esophagectomy does not influence other outcomes including the 30-day mortality, total number of resected lymph nodes, and R0 resection rates.

Gout Can Increase the Risk of Receiving Rotator Cuff Tear Repair Surgery

BACKGROUND

Gout commonly involves joint inflammation, and clinical epidemiological studies on involved tendons are scant. Rotator cuff tears are the most common cause of shoulder disability, and surgery is one of the choices often adopted to regain previous function.

PURPOSE

To investigate the risk of receiving rotator cuff repair surgery among patients with gout and to analyze possible risk factors to design an effective prevention strategy.

STUDY DESIGN

Cohort study; Level of evidence, 3.

METHODS

The authors studied a 7-year longitudinal follow-up of patients from the Taiwan Longitudinal Health Insurance Database 2005 (LHID2005). This included a cohort of patients who received a diagnosis of gout during 2004-2008 (gout cohort) and a cohort matched by propensity scores (control cohort). A 2-stage approach that used the National Health Interview Survey 2005 was used to obtain missing confounding variables from the LHID2005. The crude hazard ratio (HR) and adjusted HR were estimated between the gout and control cohorts.

RESULTS

The gout and control cohorts comprised 32,723 patients with gout and 65,446 people matched at a ratio of 1:2. The incidence of rotator cuff repair was 31 and 18 per 100,000 person-years in the gout and control cohorts, respectively. The crude HR for rotator cuff repair in the gout cohort was 1.73 (95% confidence interval [CI], 1.23-2.44; P < .01) during the 7-year follow-up period. After adjustment for covariates by use of the 2-stage approach, the propensity score calibration-adjusted HR was 1.60 (95% CI, 1.12-2.29; P < .01) in the gout cohort. Further analysis revealed that the adjusted HR was 1.73 (95% CI, 1.20-2.50; P < .001) among patients with gout who did not take hypouricemic medication and 2.70 (95% CI, 1.31-5.59; P < .01) for patients with gout aged 50 years or younger.

CONCLUSION

Patients with gout, particularly those aged 50 years or younger and without hypouricemic medication control, are at a relatively higher risk of receiving rotator cuff repair surgery. Strict control of uric acid levels with hypouricemic medication may effectively reduce the risk of rotator cuff repair.

A High Lymph Node Yield is Associated with Prolonged Survival in Elderly Patients Undergoing Curative Gastrectomy for Cancer: A Dutch Population-Based Cohort Study

Purpose

The aim of this study was to evaluate the influence of lymph node yield (LNY) on postoperative mortality and overall survival in elderly patients with gastric cancer.

Methods

This population-based study included data from The Netherlands Cancer Registry of patients who underwent curative gastrectomy for adenocarcinoma between 2006 and 2014. Patients were divided into two groups based on age (<75 years, young; ≥75 years, elderly). LNY was analyzed as both a categorical variable (low, <15 nodes; intermediate, 15–25 nodes; high, >25 nodes), and a discrete variable. Multivariable analysis was used to evaluate the influence of LNY on 30- and 90-day mortality, as well as overall survival.

Results

A total of 3764 patients were included in the study; 2387 (63%) were classified as ‘young’, and 1377 (37%) were classified as ‘elderly’. The median LNY was 14 in the young group, compared with 11 in the elderly group (p < 0.001). In the elderly group, 851 (62%) patients had a low LNY, 333 (24%) had an intermediate LNY, and 174 (13%) had a high LNY. Multivariable analysis demonstrated that in the elderly patients, a higher LNY was associated with a prolonged overall survival (low: reference; intermediate: hazard ratio [HR] 0.74, 95% confidence interval [CI] 0.62–0.88, p < 0.001; high: HR 0.59, 95% CI 0.45–0.78, p < 0.001), but not with 30-day (p = 0.940) and 90-day mortality (p = 0.573). For young patients, these results were comparable.

Conclusion

In both young and elderly patients, a high LNY is associated with prolonged survival but not with an increase in postoperative mortality. Therefore, an extensive lymphadenectomy is the preferred strategy for all patients during gastrectomy in order to provide an optimal oncological result.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

BACKGROUNDS

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.

METHOD

We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.

RESULTS

Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.

CONCLUSIONS

Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Martingale residual-based method to control for confounders measured only in a validation sample in time-to-event analysis

Unmeasured confounding remains an important problem in observational studies, including pharmacoepidemiological studies of large administrative databases. Several recently developed methods utilize smaller validation samples, with information on additional confounders, to control for confounders unmeasured in the main, larger database. However, up-to-date applications of these methods to survival analyses seem to be limited to propensity score calibration, which relies on a strong surrogacy assumption. We propose a new method, specifically designed for time-to-event analyses, which uses martingale residuals, in addition to measured covariates, to enhance imputation of the unmeasured confounders in the main database. The method is applicable for analyses with both time-invariant data and time-varying exposure/confounders. In simulations, our method consistently eliminated bias because of unmeasured confounding, regardless of surrogacy violation and other relevant design parameters, and almost always yielded lower mean squared errors than other methods applicable for survival analyses, outperforming propensity score calibration in several scenarios. We apply the method to a real-life pharmacoepidemiological database study of the association between glucocorticoid therapy and risk of type II diabetes mellitus in patients with rheumatoid arthritis, with additional potential confounders available in an external validation sample. Compared with conventional analyses, which adjust only for confounders measured in the main database, our estimates suggest a considerably weaker association. Copyright © 2016 John Wiley & Sons, Ltd.

Chronic Obstructive Pulmonary Disease Increases the Risk of Hip Fracture: A Nationwide Population-Based Cohort Study

Hip fractures can lead to functional disability and high mortality rates among elderly patients. The aim of this study was to investigate whether chronic obstructive pulmonary disease (COPD) is a risk factor for hip fracture. A retrospective population-based 4-year cohort study was conducted using case–control matched analysis of data from the Taiwan Longitudinal Health Insurance Database 2005 (LHID2005). Patients with a diagnosis of COPD between January 1, 2004 and December 31, 2007 were enrolled. A 2-stage approach and data from the National Health Interview Survey 2005 were applied to adjust for missing confounders in the LHID2005 cohort. Hazard ratios (HRs) and adjusted HRs were estimated hip fracture risk for the COPD. We enrolled 16,239 patients in the COPD cohort and 48,747 (1:3) patients in non-COPD cohort. The hip fracture incidences were 649 per 100,000 person-years in the study cohort and 369 per 100,000 person-years in non-COPD cohort. The hip fracture HR during the follow-up period was 1.78 (P < 0.001) and the adjusted hip fracture HR was 1.57 (P < 0.001) after adjustment for covariates by using the 2-stage approach method. Patients with COPD were at hip fracture risk and fracture-prevention strategies are essential for better quality of care.

Incorporating linked healthcare claims to improve confounding control in a study of in-hospital medication use

INTRODUCTION

The Premier Perspective hospital billing database provides a promising data source for studies of inpatient medication use. However, in-hospital recording of confounders is limited, and incorporating linked healthcare claims data available for a subset of the cohort may improve confounding control. We investigated methods capable of adjusting for confounders measured in a subset, including complete case analysis, multiple imputation of missing data, and propensity score (PS) calibration.

METHODS

Methods were implemented in an example study of adults in Premier undergoing percutaneous coronary intervention (PCI) in 2004-2008 and exposed to either bivalirudin or heparin. In a subset of patients enrolled in UnitedHealth for at least 90 days before hospitalization, additional confounders were assessed from healthcare claims, including comorbidities, prior medication use, and service use intensity. Diagnostics for each method were evaluated, and methods were compared with respect to the estimates and confidence intervals of treatment effects on repeat PCI, bleeding, and in-hospital death.

RESULTS

Of 210,268 patients in the hospital-based cohort, 3240 (1.5 %) had linked healthcare claims. This subset was younger and healthier than the overall study population. The linked subset was too small for complete case evaluation of two of the three outcomes of interest. Multiple imputation and PS calibration did not meaningfully impact treatment effect estimates and associated confidence intervals.

CONCLUSIONS

Despite more than 98 % missingness on 24 variables, PS calibration and multiple imputation incorporated confounders from healthcare claims without major increases in estimate uncertainty. Additional research is needed to determine the relative bias of these methods.

Better big data

By 2018, Medicare payments will be tied to quality of care. The Centers for Medicare and Medicaid Services currently use quality-based metric for some reimbursements through their different programs. Existing and future quality metrics will rely on risk adjustment to avoid unfairly punishing those who see the sickest, highest-risk patients. Despite the limitations of the data used for risk adjustment, there are potential solutions to improve the accuracy of these codes by calibrating data by merging databases and compiling information collected for multiple reporting programs to improve accuracy. In addition, healthcare staff should be informed about the importance of risk adjustment for quality of care assessment and reimbursement. As the number of encounters tied to value-based reimbursements increases in inpatient and outpatient care, coupled with accurate data collection and utilization, the methods used for risk adjustment could be expanded to better account for differences in the care delivered in diverse settings.

Inhaled Pharmacotherapy and Stroke Risk in Patients with Chronic Obstructive Pulmonary Disease: A Nationwide Population Based Study Using Two-Stage Approach

Background and Purpose

Patients with chronic obstructive pulmonary disease (COPD) are at higher risk of stroke than those without COPD. This study aims to explore the impact of inhaled pharmacotherapy on stroke risk in COPD patients during a three-year follow-up, using a nationwide, population-based study and a matched cohort design.

Methods

The study cohort comprised 10,413 patients who had received COPD treatment between 2004 and 2006; 41,652 randomly selected subjects comprised the comparison cohort. Cox proportional hazard regressions and two-stage propensity score calibration were performed to determine the impact of various inhaled therapies including short-acting muscarinic antagonists, long-acting muscarinic antagonists, short-acting β-agonists (SABAs), long-acting β-agonists (LABAs), and LABA plus inhaled corticosteroid (ICS), on the risk after adjustment for patient demographic characteristics and comorbid disorders.

Results

Of the 52,065 sampled patients, 2,689 (5.2%) developed stroke during follow-up, including 727 (7.0%) from the COPD cohort and 1,962 (4.7%) from the comparison cohort (p < 0.001). Treatment with SABA was associated with 1.67-fold (95% CI 1.45–1.91; p < 0.001) increased risk of stroke in COPD patients. By contrast, the cumulative incidence of stroke was significantly lower in those treated with LABA plus ICS than those treated without (adjusted hazard ratio 0.75, 95% CI 0.60–0.94, p = 0.014).

Conclusions

Among COPD patients, the use of inhaled SABA is associated with an increased risk of stroke, and combination treatment with inhaled LABA and ICS relates to a risk reduction. Further prospective research is needed to verify whether LABA plus ICS confers protection against stroke in patients with COPD.

Can you party your way to better health? A propensity score analysis of block parties and health

While other indicators of social capital have been linked to health, the role of block parties on health in Black neighborhoods and on Black residents is understudied. Block parties exhibit several features of bonding social capital and are present in nearly 90% of Philadelphia's predominantly Black neighborhoods. This analysis investigated: (1) whether or not block parties are an indicator of bonding social capital in Black neighborhoods; (2) the degree to which block parties might be related to self-rated health in the ways that other bonding social indicators are related to health; and (3) whether or not block parties are associated with average self-rated health for Black residents particularly. Using census tract-level indicators of bonding social capital and records of block parties from 2003 to 2008 for 381 Philadelphia neighborhoods (defined by census tracts), an ecological-level propensity score was generated to assess the propensity for a block party, adjusting for population demographics, neighborhood characteristics, neighborhood resources and violent crime. Results indicate that in multivariable regression, block parties were associated with increased bonding social capital in Black neighborhoods; however, the calculation of the average effect of the treatment on the treated (ATT) within each propensity score strata showed no effect of block parties on average self-rated health for Black residents. Block parties may be an indicator of bonding social capital in Philadelphia's predominantly Black neighborhoods, but this analysis did not show a direct association between block parties and self-rated health for Black residents. Further research should consider what other health outcomes or behaviors block parties may be related to and how interventionists can leverage block parties for health promotion.