The Role of Distributional Overlap on the Precision Gain of Bounds for Generalization

Over the past ten years, propensity score methods have made an important contribution to improving generalizations from studies that do not select samples randomly from a population of inference. However, these methods require assumptions and recent work has considered the role of bounding approaches that provide a range of treatment impact estimates that are consistent with the observable data. An important limitation to bound estimates is that they can be uninformatively wide. This has motivated research on the use of propensity score stratification to narrow bounds. This article assesses the role of distributional overlap in propensity scores on the effectiveness of stratification to tighten bounds. Using the results of two simulation studies and two case studies, I evaluate the relationship between distributional overlap and precision gain and discuss the implications when propensity score stratification is used as a method to improve precision in the bounding framework.

Unicompartmental compared with total knee replacement for patients with multimorbidities: a cohort study using propensity score stratification and inverse probability weighting

Background Although routine NHS data potentially include all patients, confounding limits their use for causal inference. Methods to minimise confounding in observational studies of implantable devices are required to enable the evaluation of patients with severe systemic morbidity who are excluded from many randomised controlled trials. Objectives Stage 1 – replicate the Total or Partial Knee Arthroplasty Trial (TOPKAT), a surgical randomised controlled trial comparing unicompartmental knee replacement with total knee replacement using propensity score and instrumental variable methods. Stage 2 – compare the risk benefits and cost-effectiveness of unicompartmental knee replacement with total knee replacement surgery in patients with severe systemic morbidity who would have been ineligible for TOPKAT using the validated methods from stage 1. Design This was a cohort study. Setting Data were obtained from the National Joint Registry database and linked to hospital inpatient (Hospital Episode Statistics) and patient-reported outcome data. Participants Stage 1 – people undergoing unicompartmental knee replacement surgery or total knee replacement surgery who met the TOPKAT eligibility criteria. Stage 2 – participants with an American Society of Anesthesiologists grade of ≥ 3. Intervention The patients were exposed to either unicompartmental knee replacement surgery or total knee replacement surgery. Main outcome measures The primary outcome measure was the postoperative Oxford Knee Score. The secondary outcome measures were 90-day postoperative complications (venous thromboembolism, myocardial infarction and prosthetic joint infection) and 5-year revision risk and mortality. The main outcome measures for the health economic analysis were health-related quality of life (EuroQol-5 Dimensions) and NHS hospital costs. Results In stage 1, propensity score stratification and inverse probability weighting replicated the results of TOPKAT. Propensity score adjustment, propensity score matching and instrumental variables did not. Stage 2 included 2256 unicompartmental knee replacement patients and 57,682 total knee replacement patients who had severe comorbidities, of whom 145 and 23,344 had linked Oxford Knee Scores, respectively. A statistically significant but clinically irrelevant difference favouring unicompartmental knee replacement was observed, with a mean postoperative Oxford Knee Score difference of < 2 points using propensity score stratification; no significant difference was observed using inverse probability weighting. Unicompartmental knee replacement more than halved the risk of venous thromboembolism [relative risk 0.33 (95% confidence interval 0.15 to 0.74) using propensity score stratification; relative risk 0.39 (95% confidence interval 0.16 to 0.96) using inverse probability weighting]. Unicompartmental knee replacement was not associated with myocardial infarction or prosthetic joint infection using either method. In the long term, unicompartmental knee replacement had double the revision risk of total knee replacement [hazard ratio 2.70 (95% confidence interval 2.15 to 3.38) using propensity score stratification; hazard ratio 2.60 (95% confidence interval 1.94 to 3.47) using inverse probability weighting], but half of the mortality [hazard ratio 0.52 (95% confidence interval 0.36 to 0.74) using propensity score stratification; insignificant effect using inverse probability weighting]. Unicompartmental knee replacement had lower costs and higher quality-adjusted life-year gains than total knee replacement for stage 2 participants. Limitations Although some propensity score methods successfully replicated TOPKAT, unresolved confounding may have affected stage 2. Missing Oxford Knee Scores may have led to information bias. Conclusions Propensity score stratification and inverse probability weighting successfully replicated TOPKAT, implying that some (but not all) propensity score methods can be used to evaluate surgical innovations and implantable medical devices using routine NHS data. Unicompartmental knee replacement was safer and more cost-effective than total knee replacement for patients with severe comorbidity and should be considered the first option for suitable patients. Future work Further research is required to understand the performance of propensity score methods for evaluating surgical innovations and implantable devices. Trial registration This trial is registered as EUPAS17435. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 66. See the NIHR Journals Library website for further project information.

Applying Propensity Score Methods in Clinical Research in Neurology

Propensity score-based analysis is increasingly being used in observational studies to estimate the effects of treatments, interventions, and exposures. We introduce the concept of the propensity score and how it can be used in observational research. We describe four different ways of using the propensity score: matching on the propensity score, inverse probability of treatment weighting using the propensity score, stratification on the propensity score, and covariate adjustment on the propensity score (with a focus on the first two). We provide recommendations for the use and reporting of propensity score methods for the conduct of observational studies in neurological research.

Critically ill COVID-19 associated trait genetics reveals CDK6 inhibitors as potential treatment

Despite the recent development of vaccines and monoclonal antibodies preventing SARS-CoV-2 infection, treating critically ill COVID-19 patients still remains a top goal. In principle, drug repurposing, the use of an already existing drug for a new indication, could provide a shortcut to a treatment. However, drug repurposing is often very speculative due to the lack of clinical evidence. We here report on a methodology to find and test gene drug target candidates for drug repurposing. We matched critically ill COVID-19 cases from the UK Biobank with healthy controls and screened for significant differences in 33 blood cell types, 30 blood biochemistries, and body mass index in cases and controls. Significant differences in traits that have previously been associated with critically ill COVID-19 status, such as alanine aminotransferase, body mass index, C-reactive protein, and neutrophil cell count were further investigated. In-depth statistical analysis of COVID-19 associated traits and their genetics using regression modeling and propensity score stratification identified cyclin-dependent kinase 6 (CDK6) as a more promising drug target to selectively treat critically ill COVID-19 patients than the previously reported interleukin 6. Four existing CDK6 inhibitors abemaciclib, ribociclib, trilaciclib, and palbociclib have been approved for breast cancer. Clinical evidence for CDK6 inhibitors in treating critically ill COVID-19 has been reported. Further clinical investigations are ongoing.

Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: a multinational network cohort study

Objectives Concern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA. Methods We performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2 &lt;40%. Results A total of 918 144 and 290 383 users of HCQ and SSZ, respectively, were included. No consistent risk of psychiatric events was observed with short-term HCQ (compared with SSZ) use, with meta-analytic HRs of 0.96 (95% CI 0.79, 1.16) for depression, 0.94 (95% CI 0.49, 1.77) for suicide/suicidal ideation and 1.03 (95% CI 0.66, 1.60) for psychosis. No consistent long-term risk was seen, with meta-analytic HRs of 0.94 (95% CI 0.71, 1.26) for depression, 0.77 (95% CI 0.56, 1.07) for suicide/suicidal ideation and 0.99 (95% CI 0.72, 1.35) for psychosis. Conclusion HCQ as used to treat RA does not appear to increase the risk of depression, suicide/suicidal ideation or psychosis compared with SSZ. No effects were seen in the short or long term. Use at a higher dose or for different indications needs further investigation. Trial registration Registered with EU PAS (reference no. EUPAS34497; http://www.encepp.eu/encepp/viewResource.htm? id=34498). The full study protocol and analysis source code can be found at https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine2.

PENGGUNAAN PROPENSITY SCORE STRATIFICATION-SUPPORT VECTOR MACHINE UNTUK MENGESTIMASI EFEK PERLAKUKAN AKTIVITAS OLAHRAGA PADA PENDERITA DIABETES MELITUS

In a study it is necessary to have a good randomization role between the treatment and control groups so there is no large differences in the observed covariates resulting in an estimate of the effect of unbiased treatment. However, in observational studies, especially in the field of health, because it is directly related to human life, it is not possible to do Randomized Controlled Trial (RCT). One method of propensity score (PS) is Propensity Score Stratification (PSS) with approach of Support Vector Machine (SVM) is used to overcome the problem of bias due to non-random observation and unbalanced covariate. The case used in this research is disease complication in patient of Diabetes Mellitus Type 2 at Regional Public Hospital of Pasuruan with respondent counted 96 patient. The result is obtained of the analysis is the variables that become confounding is a sport activity. The accuracy level of PSS SVM is the same for all strata that is equal to 65.6%. Estimation of treatment effect (ATE) gave the result that the variable of sports activity is a variable that influence the disease complication (Y) in patients of DM type 2. The number of strata that reduce the largest bias is in strata of 4 with the percent bias reduction (PBR) is 86.39% with the smallest standard error value is 0.103 and the estimated value of ATE is 0.597.

Effects of antiarrhythmic drugs on atrioventricular conduction in patients with preexisting bundle branch block

Background Theoretical concern exists regarding AV block (AVB) with class I antiarrhythmics (AADs) when bundle branch block (BBB) is present. Whether this is substantiated in real-world populations is unknown. Purpose To determine the relationship between type of AAD and incidence of AVB in patients with preexisting BBB. Methods We retrospectively studied all patients with BBB who received class I and III AADs between 1997–2019 to compare incidence of AVB. We defined index time as first exposure to either drug class and excluded patients with prior AVB or exposed to both classes. Time-at-risk window ended at first outcome occurrence or when patients were no longer observed in the database. We estimated hazard ratios for incident AVB using Cox proportional hazards models with propensity score stratification, adjusting for over 32,000 covariates from the electronic health record. Kaplan-Meier methods were used to determine treatment effects over time. Results Of 40,120 individuals with BBB, 148 were exposed to a class I AAD and 2401 to a class III AAD. Over nearly 4,200 person-years of follow up, there were 22 and 620 outcome events in the class I and class III cohorts, respectively (Figure). In adjusted analyses, AVB risk was markedly lower in patients exposed to class I AADs compared with class III (HR 0.48 [95% CI 0.30–0.75]). Conclusion Among patients with BBB, exposure to class III AADs was strongly associated with greater risk of incident AVB. This likely reflects differences in natural history of patients receiving class I vs class III AADs rather than adverse class III effects, however, the lack of worse outcomes acutely with class I AADs suggests that they may be safer in BBB than suspected. Funding Acknowledgement Type of funding source: None