Research in Social & Administrative Pharmacy

Includes bibliographical references.

Methodological standards for conducting and reporting meta-analyses: Ensuring the replicability of meta-analyses of pharmacist-led medication review -- Methods for evaluating the benefit and harms of deprescribing in observational research using routinely collected data -- Considerations when conducting moderation analysis with a binary outcome: Applications to clinical and social pharmacy research -- A narrative review of using prescription drug databases for comorbidity adjustment: A less effective remedy or a prescription for improved model fit? -- Assessing the reporting quality of simulated patient studies in pharmacy research using a novel checklist (CRiSP) -- Missing data in surveys: Key concepts, approaches, and applications -- National surveys to evaluate prescribing practices: Methodological considerations -- The use of medication adherence guidelines in medication taking behaviour research -- Multiple comparisons: To compare or not to compare, that is the question -- Towards a reconsideration of the use of agree-disagree questions in measuring subjective evaluations -- Considerations for systematic reviews of quantitative surveys: Learnings from a systematic review of the Patients' Attitudes Towards Deprescribing questionnaire.

[Article Title: Methodological standards for conducting and reporting meta-analyses: Ensuring the replicability of meta-analyses of pharmacist-led medication review/ Aline F Bonetti, Fernanda S Tonin, Rosa C Lucchetta, Roberto Pontarolo and Fernando Fernandez-Llimos, p. 2259-2268]

Abstract:

Background: Meta-analyses of clinical pharmacy services are frequently criticized for restricted data transparency and reproducibility.

Objectives: To describe the methodological characteristics of meta-analyses of pharmacist-led medication reviews, to identify the elements that limit their replicability and robustness, and to propose recommendations for an appropriate conduction and reporting.

Methods: A meta-research study was conducted. Systematic searches of the PubMed, Scopus, and Web of Science databases were performed to identify meta-analyses of pharmacist services. Meta-analyses assessing the effect of pharmacist-led medication reviews were selected for data extraction, analysis and replication. Two replication exercises were performed for the two most common outcomes: (i) considering the data provided by authors to construct the meta-analysis and (ii) considering the raw data available in the primary studies included. Prediction intervals (PI), fragility index (FI), and number needed to treat (NNT) were also calculated for each replicated meta-analysis.

Results: Nine studies reporting meta-analyses about pharmacist-led medication review were found comprising 30 different outcomes. Eleven meta-analyses, including six for hospital admission and five for mortality, were replicated. In five meta-analyses, the pooled effect sizes of the replicated meta-analyses differed from the original ones. Only four meta-analyses mentioned the statistical method used. Other meta-analytic parameters (e.g., q-value, tau2) were omitted in all studies. In nine meta-analyses, the data from primary studies had been incorrectly extracted for at least one variable. The PI demonstrated that the uncertainty intervals of the effect sizes were always underestimated by the authors. NNTs showed wide intervals, ranging from benefit to harm, in almost all meta-analyses. Nine recommendations to facilitate the replication of a meta-analysis were proposed: providing all original data needed to build the analysis; informing about the imputed data or data obtained from different sources; performing sensitivity analyses for imputed or unpublished data; inform about all the statistical methods used; providing all statistical results; and reporting the PI, FI and NNT.

Conclusion: Errors in data extraction and poor reporting of meta-analytic parameters are common in the pharmacy literature. We proposed nine recommendations to enhance data reproducibility and interpretability. Journal editors and peer reviewers should ensure that authors strictly comply with minimum standards for conduction and reporting of meta-analyses.

10.1016/j.sapharm.2021.06.002

[Article Title: Methods for evaluating the benefit and harms of deprescribing in observational research using routinely collected data/ Frank Moriarty, Wade Thompson and Fiona Boland, p. 2269-2275]

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

10.1016/j.sapharm.2021.05.007

[Article Title: Considerations when conducting moderation analysis with a binary outcome: Applications to clinical and social pharmacy research/ John P Bentley, Sujith Ramachandran and Teresa M Salgado, p. 2276-2282]

Abstract: Clinical and social pharmacy researchers often have questions regarding contingencies of effects (i.e., moderation) that are tested by including interactions in statistical models. Much of the available literature for estimating and testing effects that emanate from moderation models is based on extensions of the linear model with continuous outcomes. Binary (or dichotomous) outcome variables, such as prescription-medication misuse versus no misuse, are commonly encountered by clinical and social pharmacy researchers. In moderation analysis, binary outcomes have led to an increased focus on the fact that measures of interaction are scale-dependent; thus, researchers may need to consider both additive interaction and multiplicative interaction. Further complicating interpretation is that the statistical model chosen for an interaction can provide different answers to questions of moderation. This manuscript will: 1) identify research questions in clinical and social pharmacy that necessitate the use of these statistical methods, 2) review statistical models that can be used to estimate effects when the outcome of interest is binary, 3) review basic concepts of moderation, 4) describe the challenges inherent in conducting moderation analysis when modeling binary outcomes, and 5) demonstrate how to conduct such analyses and interpret relevant statistical output (including interpretations of interactions on additive and multiplicative scales with a focus on identifying which statistical models for binary outcomes lead to which measure of interaction). Although much of the basis for this paper comes from research in epidemiology, recognition of these issues has occurred in other disciplines.

10.1016/j.sapharm.2021.04.020

[Article Title: A narrative review of using prescription drug databases for comorbidity adjustment: A less effective remedy or a prescription for improved model fit?/ Mitchell J Barnett, Vista Khosraviani, Shadi Doroudgar and Eric J Ip, p. 2283-2300]

Abstract:

Background: The use of claims data for identifying comorbid conditions in patients for research purposes has been widely explored. Traditional measures of comorbid adjustment included diagnostic data (e.g., ICD-9-CM or ICD-10-CM codes), with the Charlson and Elixhauser methodology being the two most common approaches. Prescription data has also been explored for use in comorbidity adjustment, however early methodologies were disappointing when compared to diagnostic measures.

Objective: The objective of this methodological review is to compare results from newer studies using prescription-based data with more traditional diagnostic measures.

Methods: A review of studies found on PubMed, Medline, Embase or CINAHL published between January 1990 and December 2020 using prescription data for comorbidity adjustment. A total of 50 studies using prescription drug measures for comorbidity adjustment were found.

Conclusions: Newer prescription-based measures show promise fitting models, as measured by predictive ability, for research, especially when the primary outcomes are utilization or drug expenditure rather than diagnostic measures. More traditional diagnostic-based measures still appear most appropriate if the primary outcome is mortality or inpatient readmissions.

10.1016/j.sapharm.2021.06.016

[Article Title: Assessing the reporting quality of simulated patient studies in pharmacy research using a novel checklist (CRiSP)/ Suvini Amaratunge, Morgan Harrison, Danae Perry, Christine Bond, Michael Ceulemans, Veerle Foulon, Rhonda Clifford and Liza Seubert, p. 2301-2307]

Abstract:

Background: Use of simulated patients (SP) to assess the quality of pharmacy services and impact of interventions is increasing. The CRiSP (Checklist for Reporting research using Simulated Patient methodology) checklist was recently developed, assisting researchers to report items necessary to meet a minimum agreed standard.

Objective(s): To identify which CRiSP items were reported in SP studies for community pharmacy research, identify any gaps in reporting and describe the overall quality of reporting for the SP studies identified.

Methods: Papers published during 2018-2020 using SP methodology in community pharmacy settings were identified from MEDLINE and Embase. The 50 most recent ones were selected. Data were extracted independently and in duplicate. Each paper received a coded numerical value denoting compliance with each item of CRiSP (1 = yes, 2 = no, 3 = unclear, 4 = not applicable, 5 = partially complete). Data were analysed using Microsoft Excel and reported as frequencies and percentages of each code for the checklist items, across the 50 papers.

Results: No paper fulfilled all items in the CRiSP checklist. The mode(s) of delivery of SP assessments (item 17) was reported in all papers, while use of the term SP (item 1); number of SPs (4a); scenario details (9a); describing procedures12; data collection procedure (18); and ethics approval (23a) were reported in at least 80% of papers. Items not reported in over 50% of papers were: scenario development (8a), validation (8b) and flexibility (9b); materials used (10a) and copies of materials (10b); and procedures for SP identification (15). Researchers found interpretation of the checklist unclear and utilised working definitions to ensure consistency in coding.

Conclusions: This review identified that pharmacy research involving SP methodology is often inadequately reported by researchers. The CRiSP checklist is a comprehensive tool to assess the quality of SP methodology reporting but may require some refinement to ensure consistency in use.

10.1016/j.sapharm.2021.04.007

[Article Title: Missing data in surveys: Key concepts, approaches, and applications/ Ardalan Mirzaei, Stephen R Carter, Asad E Patanwala and Carl R Schneider, p. 2308-2316]

Abstract: A recent review of missing data in pharmacy literature has highlighted that a low proportion of studies reported how missing data was handled. In this paper we discuss the concept of missing data in survey research, how missing data is classified, common techniques to account for missingness and how to report on missing data. The paper provides guidance to mitigate the occurrence of missing data through planning. Considerations include estimating expected missing data, intended vs unintended missing data, survey length, working with electronic surveys, choosing between standard and filtered form questions, forced responses and straight-lining, as well as responses that can generate missingness like "I don't know" and "Not Applicable". We introduce methods for analysing data with missing values, such as deletion, imputation and likelihood methods. The manuscript provides a framework and flow chart for choosing the appropriate analysis method based on how much missing data is observed and the type of missingness. Special circumstances involving missing data have been discussed, such as in studies with repeated or cohort measures, factor analysis or as part of data integration. Finally, a checklist of questions are provided for researchers to guide the reporting of the missing data when conducting future research.

10.1016/j.sapharm.2021.03.009

[Article: National surveys to evaluate prescribing practices: Methodological considerations/ Rajender R Aparasu and Sanika Rege, p. 2317-2324]

Abstract: With the changing healthcare landscape, evaluating the care provision in ambulatory settings is vital to understand outpatient care. The national surveys such as the National Ambulatory Medical Care Survey (NAMCS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS) are valuable resources to pharmacy researchers because of their availability and generalizability. With the recent focus on real-world data, the national surveys are critical in providing practice and policy evidence by evaluating ambulatory care, especially prescribing practices. The use of these surveys requires an understanding of the survey content, scope, complex sampling scheme, and analytical and research considerations. There are several methodological and practical considerations that make these national surveys useful to both novice and seasoned researchers. Although some generalized approaches are available for analyzing the national surveys, there is limited focus on the NAMCS and the NHAMCS. This paper provides an in-depth understanding of the NAMCS/NHAMCS, including methodological considerations for evaluating prescribing practices in ambulatory settings.

10.1016/j.sapharm.2021.06.020

[Article Title: The use of medication adherence guidelines in medication taking behaviour research/ Charlotte L Bekker, Parisa Aslani and Timothy F Chen, p. 2325-2330]

Abstract: Medication nonadherence continues to be a serious issue in a range of long-term medical conditions and has been studied extensively over the past few decades. However, despite the plethora of research studies on medication adherence, poor methodological rigour in many studies has contributed to limited generalisability of the positive findings, limited impact on patients' medication adherence, and inability to compare between studies. This paper focuses on current guidelines designed specifically for research on medication adherence. It discusses key elements to consider during study design, selection of adherence measurements, and reporting on medication adherence research, to ensure a higher quality of research in medication adherence. Overall, there appears to be variations in adherence terminology reported in the literature despite improvements in defining medication taking behaviour and the availability of taxonomies. In addition, limited guidance exists on how best to measure adherence. Recommendations are provided on appropriate adherence measures for the adherence behaviour being investigated, including careful consideration of adherence concepts, validity of adherence instruments, appropriate instrument selection, definition of nonadherence threshold, and how to report medication adherence. Improving adherence research requires greater clarity and standardisation of descriptions of nonadherence behaviour, increased methodological rigour in study designs, better selection of adherence measurements, and comprehensive reporting.

10.1016/j.sapharm.2021.08.006

[Article Title: Multiple comparisons: To compare or not to compare, that is the question/ Mitchell J Barnett, Shadi Doroudgar, Vista Khosraviani and Eric J Ip, p. 2331-2334]

Abstract: Researchers attempt to minimize Type-I errors (concluding there is a relationship between variables, when there in fact, isn't one) in their experiments by exerting control over the p-value thresholds or alpha level. If a statistical test is conducted only once in a study, it is indeed possible for the researcher to maintain control, so that the likelihood of a Type-I error is equal to or less than the significance (p-value) level. When making multiple comparisons in a study, however, the likelihood of making a Type-I error can dramatically increase. When conducting multiple comparisons, researchers frequently attempt to control for the increased risk of Type-I errors by making adjustments to their alpha level or significance threshold level. The Bonferroni adjustment is the most common of these types of adjustment. However, these, often rigid adjustments, are not without risk and are often applied arbitrarily. The objective of this review is to provide a balanced commentary on the advantages and disadvantages of making adjustments when undertaking multiple comparisons. A summary discussion of familiar- and experiment-wise error is also presented. Lastly, advice on when researchers should consider making adjustments in p-value thresholds and when they should be avoided, is provided.

10.1016/j.sapharm.2021.07.006

[Article Title: Towards a reconsideration of the use of agree-disagree questions in measuring subjective evaluations/ Jennifer Dykema, Nora Cate Schaeffer, Dana Garbarski, Nadia Assad and Steven Blixt, p. 2335-2344]

Abstract: Agree-disagree (AD) or Likert questions (e.g., "I am extremely satisfied: strongly agree … strongly disagree") are among the most frequently used response formats to measure attitudes and opinions in the social and medical sciences. This review and research synthesis focuses on the measurement properties and potential limitations of AD questions. The research leads us to advocate for an alternative questioning strategy in which items are written to directly ask about their underlying response dimensions using response categories tailored to match the response dimension, which we refer to as item-specific (IS) (e.g., "How satisfied are you: not at all … extremely"). In this review we: 1) synthesize past research comparing data quality for AD and IS questions; 2) present conceptual models of and review research supporting respondents' cognitive processing of AD and IS questions; and 3) provide an overview of question characteristics that frequently differ between AD and IS questions and may affect respondents' cognitive processing and data quality. Although experimental studies directly comparing AD and IS questions yield some mixed results, more studies find IS questions are associated with desirable data quality outcomes (e.g., validity and reliability) and AD questions are associated with undesirable outcomes (e.g., acquiescence, response effects, etc.). Based on available research, models of cognitive processing, and a review of question characteristics, we recommended IS questions over AD questions for most purposes. For researchers considering the use of previously administered AD questions and instruments, issues surrounding the challenges of translating questions from AD to IS response formats are discussed.

10.1016/j.sapharm.2021.06.014

[Article Title: Considerations for systematic reviews of quantitative surveys: Learnings from a systematic review of the Patients' Attitudes Towards Deprescribing questionnaire/ Kristie Rebecca Weir, Nagham J Ailabouni, Carl R Schneider, Sarah N Hilmer and Emily Reeve, p. 2345-2349]

Abstract: This commentary looks at the process of conducting a systematic review of surveys and validated questionnaires. Surveys and other questionnaire style tools are often used in the field of social and administrative pharmacy, to capture beliefs, attitudes and experiences of patients and healthcare professionals (including pharmacists). Currently, there is little guidance available on how to conduct a systematic review of these types of studies. Considerations related to the process of a systematic review are highlighted, including identification of articles, data extraction, assessing quality of articles and synthesis and analysis of data.

10.1016/j.sapharm.2021.07.024