This study aims to summarize evidence on cross-cultural validation of postpartum patient-reported outcome measures (PROMs) and provide recommendations to improve measurement of cultural differences in their global administration.

While cell therapies (CTs) could result in lifelong clinical benefits for patients with relapsed/refractory diffuse large B-cell lymphoma (r/rDLBCL), there are concerns around their high upfront cost. There has been significant interest in and various cost-effectiveness analyses (CEAs) of CTs to inform decision makers and payers on the economic value of CTs for r/rDLBCL. We conducted a systematic literature review to describe the decision-analytic models used in CEAs of CTs for r/rDLBCL, in order to understand the modeling approaches, unsolved issues, and potential solutions. EMBASE, MEDLINE, EconLit, DARE, National Health System Economic Evaluations Database, and Health Technology Assessment body websites were systematically searched for CEAs of CTs for r/rDLBCL published in English between 1 January 2010 and 20 December 2020. Inclusion criteria: full economic evaluation, use of a decision-analytic model, target population of adult patients with r/rDLBCL after two prior systemic therapies, and availability in full-text. Studies were not excluded based on their methodological quality. Variations in study design were evaluated to inform future models of CT. Of 205 studies identified initially, four were included. Comparators included axicabtagene ciloleucel, tisagenlecleucel, and salvage chemotherapy. Variations were found in: modeling approaches, including Markov, partitioned survival, and hybrid decision tree plus partitioned survival; approaches to extrapolate overall and progression-free survival beyond clinical trial follow-up; definition of ‘cure’ and impact on survival, utilities, and costs; use of intent-to-treat versus ‘as-treated’ efficacy data. Main areas for improvement: report relevant health outcomes besides quality-adjusted life-years; conduct scenario analyses varying the perspective of analysis, extrapolation approaches, and treatment effect waning; report how the model was validated. The economic models used in CEAs of CTs for r/rDLBCL continue to evolve and have not yet converged. This study provides recommendations for future models of CTs for r/rDLBCL, which could be generalizable to other oncology indications.

Background: Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. Methods: Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum (R) De-identified Clinformatics (R) Data Mart Database using Ll -regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. Findings: Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated. Interpretation: We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use.

Aakjær, Mia 409 Aalst, Robertus van 408 Abanoz, Mustafa Naci 417 Abdelaziz, Nourhan 345 Abdelwahab, Ghada M 260 Abdulah, Rizky 314 Abdulkareem, Aishah 437 Abe, Flavia Casale 141 Aben, Katja 195 Abenhaim, Lucien 90 Aberle, Jens 299 Abimbola, Seye 422 Abner, Erin 138, 303 Abraham, Danielle 267 Abrahami, Devin 13 Abrahamowicz, Michal 88, 278 Absalon, Judith 123 AbuAlhommos, Amal Khaleel 361 Abughosh, Susan 247 Abuthawabeh, Rasha 260 Abutheraa, Nouf 426 Acharya, Sahana 324 Acurcio, Francisco A 170, 175 Adams, Val 200, 320 Adedapo, Aduragbenro 292 Adeoye, Ikeola 426 Adgent, Margaret 57, 124, 171, 350 Adigwe, Obi 427 Adimadhyam, Sruthi 101 Adje, David 437 Adkins, Lauren E 148, 220, 285, 378 Adusei-Mensah, Frank 431 Afchain, Pauline 14 Afriyie, Daniel K 260 Agiro, Abiy 106 Aguado, Jaume 84 Aguilar, Kathleen M 233, 287 Agyepong, Irene 430 Ahmed, WaheedRahman 78 Ahmidi, Narges 368 Ajao, Adebola 279 Akar, Joseph 184 Akgun, Kathleen 115 Akhabue, Ehimare 395 Akici, Ahmet 180, 221, 417 Akici, Narin 180 Akinkungbe, Alesha 423 Akinola, Samuel 421 Akumanue, Cynthia 423 Akunne, Onyinye 292 Alabaster, Amy A 18 Alaeddin, Nersi 394 Alalbila, Thelma Mpoku 433 Alalwan, Abdullah A 364 Alanazi, Afnan M. 270 Alanazi, Entisar J. 270 Alanazi, Tahani S 186 Alanazi, Turkiah 135 Alarcon, Graciela 231, 325 Alaseeri, Afnan 153, 189 Albalawi, Omar 289, 362 Albano, Jessica 229, 323 Albeniz, Xabier Garcia de 8, 84 Albert, Lisa 26, 178, 187, 399 Albertson-Junkans, Ladia 93 Albo, Daniel 198 Albogami, Yasser 274, 307, 336, 364, 416 Albuquerque, Fl avia 414 Alcorn, Charles 80 Alcusky, Matthew 51, 291, 309 Aldave, Juan 105 AlDawsari, Asma 426 Aldayel, Atheer 302 Alegiani, Stefania Spila 402 Alenzi, Khalidah A 135, 186 Alenzi, Najah S 135, 186 Alerany, Carmen 410 Alexander, G Caleb 13, 24, 27, 29, 44, 392, 402 Alexander, Kimberly 113 AL-Fadel, Nouf 204, 283, 296, 336 Alfawaz, Maram 153, 189 Alfian, Sofa 314 Algabbani, Aljoharah 289 alghamdi, Maha 302 Alghamdi, Moshari A 135 AlGhannam, Wed A 144 Alghoul, Heba 78 AL-Habardi, Saja 283, 336 Al-Hallaq, Ghaydaa Ramzi 186 AL-Harbi, Fawaz F 144, 160, 204, 283, 296, 302, 336 AlHarbi, Muhanad 129, 144, 358 Alhartani, Yosra J. 260, 261 Alhossan, Abdulaziz 144 Alhusayni, Lama 204 Ali, Ayad K 157, 210, 213, 216, 254, 337 Ali, Karrar Ehsan 361 Ali, M Sanni 70, 273 Ali, Robert 357 Aliabadi, Negar 193 Alibrandi, Angela 413 Aljallal, Mohammed 362 Aljasser, Nasser 153, 189 Aljohani, Hadir 296 Aljrarri, Waad 364 Alkabbani, Wajd 43, 401 Alkhaibri, Abdullah 336 Alkherb, Zakiyah 364 Allen, Janis 234 Allen, Jeff 47 Allignol, Arthur 82 Almaghrabi, Mona 160 Almalki, Mohammed 135 Almansour, Hadi 419 Almansour, Hadi A 144, 342 Almas, Mariana F 126 Almeida-Brasil, Celline 185, 231, 278, 325 Almonysir, Abrar 153, 189 Almotiry, Amal Alshatri 153, 189 Almutairi, Abdulaali 160, 302 Almutairi, Lulu 153, 189 Alnakhibe, Monirah A 135 Alnuaim, Abdulrahman 144 Alolayet, Rayan 144, 358 Alomran, Maha 360 Alomrani, Hamod 135 Alotaibi, Badraa 153, 189 Alotaibi, Basil 305, 311 Alowedi, Nada 289 Alper, Jeffrey 198 AlQadheeb, Eman K 129, 358 DOI: 10.1002/pds.5308

Background The epidemiologic impact of hereditary angioedema (HAE) is difficult to quantify, due to misclassification in retrospective studies resulting from non-specific diagnostic coding. The aim of this study was to identify cohorts of patients with HAE-1/2 by evaluating structured and unstructured data in a US ambulatory electronic medical record (EMR) database. Methods A retrospective feasibility study was performed using the GE Centricity EMR Database (2006–2017). Patients with ≥ 1 diagnosis code for HAE-1/2 (International Classification of Diseases, Ninth Revision, Clinical Modification 277.6 or International Classification of Diseases, Tenth Revision, Clinical Modification D84.1) and/or ≥ 1 physician note regarding HAE-1/2 and ≥ 6 months’ data before and after the earliest code or note (index date) were included. Two mutually exclusive cohorts were created: probable HAE (≥ 2 codes or ≥ 2 notes on separate days) and suspected HAE (only 1 code or note). The impact of manually reviewing physician notes on cohort formation was assessed, and demographic and clinical characteristics of the 2 final cohorts were described. Results Initially, 1691 patients were identified: 190 and 1501 in the probable and suspected HAE cohorts, respectively. After physician note review, the confirmed HAE cohort comprised 254 patients and the suspected HAE cohort decreased to 1299 patients; 138 patients were determined not to have HAE and were excluded. The overall false-positive rate for the initial algorithms was 8.2%. Across final cohorts, the median age was 50 years and > 60% of patients were female. HAE-specific prescriptions were identified for 31% and 2% of the confirmed and suspected HAE cohorts, respectively. Conclusions Unstructured EMR data can provide valuable information for identifying patients with HAE-1/2. Further research is needed to develop algorithms for more representative HAE cohorts in retrospective studies.

The factors associated with chronic opioid therapy (COT) in patients with HIV is understudied. Using Medicaid data (2002-2009), this retrospective cohort study examines COT in beneficiaries with HIV who initiated standard combination anti-retroviral therapy (cART). We used generalized estimating equations on logistic regression models with backward selection to identify significant predictors of COT initiation. COT was initiated among 1014 out of 9615 beneficiaries with HIV (male: 10.4%; female: 10.7%). Those with older age, any malignancy, Hepatitis C infection, back pain, arthritis, neuropathy pain, substance use disorder, polypharmacy, (use of) benzodiazepines, gabapentinoids, antidepressants, and prior opioid therapies were positively associated with COT. In sex-stratified analyses, multiple predictors were shared between male and female beneficiaries; however, chronic obstructive pulmonary disease, liver disease, any malignancy, and antipsychotic therapy were unique to female beneficiaries. Comorbidities and polypharmacy were important predictors of COT in Medicaid beneficiaries with HIV who initiated cART.

INTRODUCTION:Psoriasis Longitudinal Assessment and Registry (PSOLAR) was designed in 2007 as the first disease-based registry for patients with psoriasis.OBJECTIVE:The aim of this study was to discuss methodological limitations and post hoc analyses in long-term safety registries using learnings from analyses of a potential safety risk for major adverse cardiovascular events (MACE) in PSOLAR.METHODS:PSOLAR is an international observational study of over 12,000 psoriasis patients that was conducted to meet postmarketing safety commitments for infliximab and ustekinumab. A recent annual review of registry data indicated a potential MACE risk for ustekinumab vs. non-biologics based on prespecified COX model regression analyses, which yielded an adjusted hazard ratio (HR) of 1.533 (95% confidence interval [CI] 1.103-2.131). Therefore, we conducted a comprehensive review of key statistical methodology and implemented post hoc analytical methods to address specific limitations.RESULTS:The following limiting factors were identified: (1) inclusion of both prevalent and incident (new) users of biologics; (2) unanticipated imbalances in patient characteristics between treatment cohorts at baseline; (3) limited availability of relevant clinical data after enrollment; and (4) divergence of characteristics associated with outcomes among comparator groups over time. The analysis was modified to include only incident users, propensity scores were used to weight HRs, and adalimumab was deemed a more clinically appropriate comparator. The revised HR was 0.820 (95% CI 0.532-1.265), indicating no meaningful increase in MACE risk for ustekinumab.CONCLUSION:Our results, which do not support a causal association between ustekinumab exposure and MACE risk, underscore the need for ongoing assessment of analytical methods in long-term observational studies.

This chapter presents important concepts such as masking (blinding), randomization, and clinical equipoise as they relate to pragmatic trials (pRCTs). Masking strategies for pRCTs are often selected based on the subjectivity of an outcome's measurement or interpretation; therefore, masking strategies are presented with details on how they relate to outcome categories. In addition, we discuss various randomization techniques such as individual and cluster-based randomization.

Masking (or blinding) of treatment assignment is routinely implemented in classical randomized clinical trials (RCTs) to isolate the effect of the intervention itself and to minimize the potential for bias that could occur with traditional trials. Such biases could be introduced with the conduct, assessment of endpoints, management of conditions, analysis, and reporting when the treatment assignments are known. However, masking of treatments is not only complex but it hinders how generalizable the findings are to the "real world" clinical setting. Pragmatic RCTs (pRCTs) are intended to evaluate the effects of interventions within routine medical care, and as such, do not typically mask treatment groups; moreover, pRCTs assess comparators that are available in routine medical practice, not masked placebos. Whether pRCTs should be masked if intended for regulatory or other purposes has recently been questioned. The literature on pRCTs, while extensive, does not address how much actual benefit is gained from masking outcomes and how masking may affect the "real world" nature of a study. Here, we propose an approach to evaluate sources of bias, describe stakeholders in the conduct of pRCTs who are most likely affected, and offer a framework for considering how masking may be implemented effectively while maintaining generalizability.

On December 8, 2016, the New England Journal of Medicine published a sounding board on Real World Evidence (RWE) 1 by the US Food and Drug Administration (FDA) leadership. While the value of RWE based on nonrandomized observational studies was appreciated, such as for hypothesis generating, safety, and measuring quality in healthcare delivery, the authors expressed concerns on the quality of data sources and the ability of methodologies to control for confounding. In response, we offer a few considerations regarding these concerns.

A key goal for managed care organizations is to improve patient health outcomes and reduce costs. One strategy involves increasing medication adherence among patients with chronic diseases. The Kentucky Department for Medicaid Services contracted with three managed care organizations in November 2011 to transition the state’s traditional fee-for-service Medicaid patients into capitated managed care. The purpose of this study is to determine differences in medication adherence before and after the switch from fee-for-service to managed care for Medicaid patients in Kentucky with essential hypertension between 2010 and 2012. The retrospective cohort study sample will be drawn from a database of Kentucky Medicaid patient (age 18-64) medical and prescription claims between 2010 and 2012. The University of Kentucky Internal Review Board approved the study. The study will include descriptive statistics of the medication possession ratio (MPR) and control variables including patient demographics, type of antihypertensive, and comorbidities. Bivariate analyses will measure the effect of each variable on the change in MPR as a result of the switch. Multivariate analysis will be a difference-in-difference regression model, measuring the pre and post differences in MPR due to the introduction of managed care. Initial data collected indicate that average MPR decreased by about 13 percentage points, regardless of medication class, after Medicaid managed care in Kentucky took effect, with other factors held constant. A 13-percentage point decrease in MPR corresponds to about 45 fewer days of medication possession. Results are preliminary, but indicate a need to address the efficacy of Medicaid managed care on adherence to antihypertensives in Kentucky. Additional studies should be conducted with data from 2013 to ensure confounding due to transitional issues is eliminated. Future studies will examine the effect of Medicaid managed care on adherence in hyperlipidemia, diabetes, asthma, and mental health disorders.

With increasing pressure to conduct research during residency training, and given the availability of administrative claims data, pharmacy residents will likely consider using large administrative databases for their research project. With competing time commitments and the short duration of residencies, residents and their preceptors must consider the 10 factors outlined above in order to produce a thoughtful, clinically relevant research project. While this discussion focused on the completion of a residency research project, these topics are also relevant to a broader pharmacy audience. Colleges of pharmacy are increasingly requiring research projects as part of their curriculum, and pharmacy students and practitioners often consider obtaining additional degrees requiring a research component. Both students and practitioners can use the guidance provided herein when planning research projects and investigations to aid in the successful completion of research using administrative claims data.