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Propensity score matching is commonly used to draw causal inference from observational survival data. However, its asymptotic properties have yet to be established, and variance estimation is still open to debate. We derive the statistical properties of the propensity score matching estimator of the marginal causal hazard ratio based on matching with replacement and a fixed number of matches. We also propose a double‐resampling technique for variance estimation that takes into account the uncertainty due to propensity score estimation prior to matching. [ABSTRACT FROM AUTHOR]

Unlike in randomized clinical trials (RCTs), confounding control is critical for estimating the causal effects from observational studies due to the lack of treatment randomization. Under the unconfoundedness assumption, matching methods are popular because they can be used to emulate an RCT that is hidden in the observational study. To ensure the key assumption hold, the effort is often made to collect a large number of possible confounders, rendering dimension reduction imperative in matching. Three matching schemes based on the propensity score (PSM), prognostic score (PGM), and double score (DSM, ie, the collection of the first two scores) have been proposed in the literature. However, a comprehensive comparison is lacking among the three matching schemes and has not made inroads into the best practices including variable selection, choice of caliper, and replacement. In this article, we explore the statistical and numerical properties of PSM, PGM, and DSM via extensive simulations. Our study supports that DSM performs favorably with, if not better than, the two single score matching in terms of bias and variance. In particular, DSM is doubly robust in the sense that the matching estimator is consistent requiring either the propensity score model or the prognostic score model is correctly specified. Variable selection on the propensity score model and matching with replacement is suggested for DSM, and we illustrate the recommendations with comprehensive simulation studies. An R package is available at https://github.com/Yunshu7/dsmatch. [ABSTRACT FROM AUTHOR]

Objective: The ObserVational survey of the Epidemiology, tReatment and Care of MigrainE (OVERCOME; United States) study is a multicohort, longitudinal web survey that assesses symptomatology, consulting, diagnosis, treatment, and impact of migraine in the United States. Background: Regularly updating population‐based views of migraine in the United States provides a method for assessing the quality of ongoing migraine care and identifying unmet needs. Methods: The OVERCOME (US) 2018 migraine cohort involved: (I) creating a demographically representative sample of US adults using quota sampling (n = 97,478), (II) identifying people with active migraine in the past year via a validated migraine diagnostic questionnaire and/or self‐reported medical diagnosis of migraine (n = 24,272), and (III) assessing consultation, diagnosis, and treatment of migraine (n = 21,143). The current manuscript evaluated whether those with low frequency episodic migraine (LFEM; 0–3 monthly headache days) differed from other categories on outcomes of interest. Results: Among the migraine cohort (n = 21,143), 19,888 (94.1%) met our International Classification of Headache Disorders, 3rd edition‐based case definition of migraine and 12,905 (61.0%) self‐reported a medical diagnosis of migraine. Respondents' mean (SD) age was 42.2 (15.0) years; 15,697 (74.2%) were women. Having at least moderate disability was common (n = 8965; 42.4%) and around half (n = 10,783; 51.0%) had consulted a medical professional for migraine care in the past year. Only 4792 (22.7%) of respondents were currently using a triptan. Overall, 8539 (40.4%) were eligible for migraine preventive medication and 3555 (16.8%) were currently using migraine preventive medication. Those with LFEM differed from moderate and high frequency episodic migraine and chronic migraine on nearly all measures of consulting, diagnosis, and treatment. Conclusion: The OVERCOME (US) 2018 cohort revealed slow but steady progress in diagnosis and preventive treatment of migraine. However, despite significant impact among the population, many with migraine have unmet needs related to consulting for migraine, migraine diagnosis, and getting potentially beneficial migraine treatment. Moreover, it demonstrated the heterogeneity and varying unmet needs within episodic migraine. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: Observational pharmacoepidemiological studies can provide valuable information on the effectiveness or safety of interventions in the real world, but one major challenge is the existence of unmeasured confounder(s). While many analytical methods have been developed for dealing with this challenge, they appear under‐utilized, perhaps due to the complexity and varied requirements for implementation. Thus, there is an unmet need to improve understanding the appropriate course of action to address unmeasured confounding under a variety of research scenarios. Methods: We implemented a stepwise search strategy to find articles discussing the assessment of unmeasured confounding in electronic literature databases. Identified publications were reviewed and characterized by the applicable research settings and information requirements required for implementing each method. We further used this information to develop a best practice recommendation to help guide the selection of appropriate analytical methods for assessing the potential impact of unmeasured confounding. Results: Over 100 papers were reviewed, and 15 methods were identified. We used a flowchart to illustrate the best practice recommendation which was driven by 2 critical components: (1) availability of information on the unmeasured confounders; and (2) goals of the unmeasured confounding assessment. Key factors for implementation of each method were summarized in a checklist to provide further assistance to researchers for implementing these methods. Conclusion: When assessing comparative effectiveness or safety in observational research, the impact of unmeasured confounding should not be ignored. Instead, we suggest quantitatively evaluating the impact of unmeasured confounding and provided a best practice recommendation for selecting appropriate analytical methods. [ABSTRACT FROM AUTHOR]

Purpose Observational studies are frequently used to assess the effectiveness of medical interventions in routine clinical practice. However, the use of observational data for comparative effectiveness is challenged by selection bias and the potential of unmeasured confounding. This is especially problematic for analyses using a health care administrative database, in which key clinical measures are often not available. This paper provides an approach to conducting a sensitivity analyses to investigate the impact of unmeasured confounding in observational studies. Methods In a real world osteoporosis comparative effectiveness study, the bone mineral density (BMD) score, an important predictor of fracture risk and a factor in the selection of osteoporosis treatments, is unavailable in the data base and lack of baseline BMD could potentially lead to significant selection bias. We implemented Bayesian twin-regression models, which simultaneously model both the observed outcome and the unobserved unmeasured confounder, using information from external sources. A sensitivity analysis was also conducted to assess the robustness of our conclusions to changes in such external data. Results The use of Bayesian modeling in this study suggests that the lack of baseline BMD did have a strong impact on the analysis, reversing the direction of the estimated effect (odds ratio of fracture incidence at 24 months: 0.40 vs. 1.36, with/without adjusting for unmeasured baseline BMD). Conclusions The Bayesian twin-regression models provide a flexible sensitivity analysis tool to quantitatively assess the impact of unmeasured confounding in observational studies. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Introduction Recent developments in diagnostic technology can support earlier, more accurate diagnosis of non-Alzheimer's disease (AD) dementias. Methods To evaluate potential economic benefits of early rule-out of AD, annual medical resource use and costs for Medicare beneficiaries potentially misdiagnosed with AD prior to their diagnosis of vascular dementia (VD) or Parkinson's disease (PD) were compared with that of similar patients never diagnosed with AD. Results Patients with prior AD diagnosis used substantially more medical services every year until their VD/PD diagnosis, resulting in incremental annual medical costs of approximately $9,500-$14,000. However, following their corrected diagnosis, medical costs converged with those of patients never diagnosed with AD. Discussion The observed correlation between timing of correct diagnosis and subsequent reversal in excess costs is strongly suggestive of the role of misdiagnosis of AD - rather than AD comorbidity - in this patient population. Our findings suggest potential benefits from earlier, accurate diagnosis. [ABSTRACT FROM AUTHOR]

Objectives To assess and compare direct medical costs and medication compliance between patients with fibromyalgia who initiated duloxetine and patients with fibromyalgia who initiated pregabalin in 2008. Methods A retrospective cohort study design was used based on a large US national commercial claims database (2006 to 2009). Patients with fibromyalgia aged 18 to 64 who initiated duloxetine or pregabalin in 2008 and who had continuous health insurance 1 year preceding and 1 year following the initiation were selected into duloxetine cohort or pregabalin cohort based on their initiated agent. Medication compliance was measured by total supply days, medication possession ratio ( MPR), and proportion of patients with MPR ≥ 0.8. Direct medical costs were measured by annual costs per patient and compared between the cohorts in the year following the initiation. Propensity score stratification and bootstrapping methods were used to adjust for distribution bias, as well as cross-cohort differences in demographic, clinical and economic characteristics, and medication history prior to the initiation. Results Both the duloxetine ( n = 3,033) and pregabalin ( n = 4,838) cohorts had a mean initiation age around 49 years, 89% were women. During the postindex year, compared to the pregabalin cohort, the duloxetine cohort had higher totally annual supply days (273.5 vs. 176.6, P < 0.05), higher MPR (0.7 vs. 0.5, P < 0.05), and more patients with MPR ≥ 0.8 (45.1% vs. 29.4%, P < 0.05). Further, relative to pregabalin cohort, duloxetine cohort had lower inpatient costs ($2,994.9 vs. $4,949.6, P < 0.05), lower outpatient costs ($8,259.6 vs. $10,312.2, P < 0.05), similar medication costs ($5,214.6 vs. $5,290.8, P > 0.05), and lower total medical costs ($16,469.1 vs. $20,552.6, P < 0.05) in the postinitiation year. Conclusions In a real-world setting, patients with fibromyalgia who initiated duloxetine in 2008 had better medication compliance and consumed less inpatient, outpatient, and total medical costs than those who initiated pregabalin. [ABSTRACT FROM AUTHOR]

Caregivers are regularly faced with decisions between competing treatments. Large observational health care databases provide a golden opportunity for research on heterogeneity in patient response to guide caregiver decisions, due to their sample size, diverse populations, and real-world setting. Local control is a promising tool for using observational data to detect patient subgroups with differential response on one treatment relative to another. While standard data mining approaches find subgroups with optimal responses for a particular population, detecting subgroups that reveal treatment differences while also adjusting for confounding in observational data is challenging. Local control utilizes unsupervised clustering to form non-parametric patient-level counterfactual treatment differences and displays them as an observed distribution of effect-size estimates. Classification and regression trees (CART) then find the factors that drive the greatest outcome differentiation between treatments. In this manuscript, we demonstrate the use of this two-step strategy using local control plus CART to identify depression patients most (least) likely to benefit from treatment with duloxetine relative to extended-release venlafaxine. Prior medication costs and age were found to be factors most associated with differential outcome, with prior medication costs remaining as an important factor after sensitivity analyses using a second dataset. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Adherence and persistence with osteoporosis treatments are essential for reducing fracture risk. Once-daily teriparatide is available in Japan for treating osteoporosis in patients with a high risk of fracture. The study objective was to describe real-world adherence and persistence with once-daily teriparatide 20 μg during the first year of treatment for patients who started treatment during the first eight months of availability in Japan. This prescription database study involved patients with an index date (first claim) between October 2010 and May 2011, a preindex period ⩾6 months, and a postindex period ⩾12 months and who were aged >45 years. Adherence (medication possession ratio (MPR)) and persistence (time from the start of treatment to discontinuation; a 60-day gap in supply) were calculated. A total of 287 patients started treatment during the specified time period; 123 (42.9%) were eligible for inclusion.Overall mean (standard deviation) adherence was 0.702 (0.366), with 61.0% of patients having high adherence (MPR > 0.8).The percentage of patients remaining on treatment was 65.9% at 180 days and 61.0% at 365 days. Our findings suggest that real-world adherence and persistence with once-daily teriparatide in Japan are similar to that with once-daily teriparatide in other countries and with other osteoporosis medications. [ABSTRACT FROM AUTHOR]

Assessing dose response from flexible-dose clinical trials is problematic. The true dose effect may be obscured and even reversed in observed data because dose is related to both previous and subsequent outcomes. To remove selection bias, we propose marginal structural models, inverse probability of treatment-weighting (IPTW) methodology. Potential clinical outcomes are compared across dose groups using a marginal structural model (MSM) based on a weighted pooled repeated measures analysis (generalized estimating equations with robust estimates of standard errors), with dose effect represented by current dose and recent dose history, and weights estimated from the data (via logistic regression) and determined as products of (i) inverse probability of receiving dose assignments that were actually received and (ii) inverse probability of remaining on treatment by this time. In simulations, this method led to almost unbiased estimates of true dose effect under various scenarios. Results were compared with those obtained by unweighted analyses and by weighted analyses under various model specifications. The simulation showed that the IPTW MSM methodology is highly sensitive to model misspecification even when weights are known. Practitioners applying MSM should be cautious about the challenges of implementing MSM with real clinical data. Clinical trial data are used to illustrate the methodology. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Chemotherapy regimens may have differential efficacy by histology in nonsmall cell lung cancer (NSCLC). We examined the impact of histology on survival of patients (N = 2,644) with stage IIIB/IV NSCLC who received first-line cisplatin/carboplatin plus gemcitabine (C/C+G) and cisplatin/carboplatin plus a taxane (C/C+T) identified retrospectively in the SEER cancer registry (1997-2002). Patients with squamous and nonsquamous cell carcinoma survived 8.5 months and 8.1 months, respectively (P = .018). No statistically significant difference was observed in survival between C/C+G and C/C+T in both histologies. Adjusting for clinical and demographic characteristics, the effect of treatment regimen on survival did not differ by histology (P for interaction = .257). There was no statistically significant difference in hazard of death by histology in both groups. These results contrast the predictive role of histology and improved survival outcomes observed for cisplatin-pemetrexed regimens in advanced nonsquamous NSCLC. [ABSTRACT FROM AUTHOR]