decision models
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Author(s):  
Zhanao Xue ◽  
Bingxin Sun ◽  
Haodong Hou ◽  
Wenli Pang ◽  
Yanna Zhang

2021 ◽  
Author(s):  
Talitha Feenstra ◽  
Isaac Corro-Ramos ◽  
Dominique Hamerlijnck ◽  
George van Voorn ◽  
Salah Ghabri

2021 ◽  
pp. 147612702110679
Author(s):  
Owen Nelson Parker ◽  
Ke Gong ◽  
Rachel Mui

Organizational reputation is compelling to layman audiences, it is critical for firm performance and myriad organizational phenomena, and recent theory articulates how it shapes the very managerial discretion underpinning strategic decisions. Yet, reputation is still excluded from much of mainstream strategic organization research. We make the case for reputation’s wider inclusion in studies of managerial discretion or strategic decision-making. We first demonstrate reputation’s potential theoretical importance in explaining nuances or non-findings in such studies, detail ways to measure reputation accurately, provide five sources of data for readers to facilitate the inclusion of reputation in their studies, and illustrate how scholars can use freelancers to collect their own archival data for their own, context-specific purposes. By shedding light on reputation’s unique role in shaping managerial discretion and, thereby, strategic decisions, we hope this essay helps scholars better account for decision-making patterns that might otherwise defy the predictions of other organizational theories.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jaclyn M. Beca ◽  
Kelvin K. W. Chan ◽  
David M. J. Naimark ◽  
Petros Pechlivanoglou

Abstract Introduction Extrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up. Methods Simulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30–500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error). Results The true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples. Conclusions Limited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model’s extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence.


2021 ◽  
Author(s):  
Joseph Kwon ◽  
Hazel Squires ◽  
Matt Franklin ◽  
Yujin Lee ◽  
Tracey Young

Abstract Background: Falls impose significant health and economic burdens on older people, making their prevention a priority for care decision-makers. The volume of falls prevention economic evaluations has increased, the findings from which have been synthesised by systematic reviews (SRs) with pre-specified criteria (e.g., objectives, eligibility, data extraction). Such SRs can inform commissioning and design of future evaluations, particularly decision models; however, their findings can be biased and partial dependent on their pre-specified criteria. This study aims to conduct a systematic overview (SO) to: (1) systematically identify SRs of community-based falls prevention economic evaluations; (2) describe the methodology and findings of SRs; (3) critically appraise the methodology of SRs; and (4) suggest commissioning recommendations based on SO findings. Methods: The SO followed the PRISMA guideline and the Cochrane guideline on SO, covering the period 2003-2020. Identified SRs’ aims, search strategies and results, extracted data fields, quality assessment methods and results, and commissioning and research recommendations were synthesised. The comprehensiveness of previous SRs’ data synthesis was judged against criteria drawn from expert guideline and academic literature on falls prevention/public health economic evaluation. Outcomes of general population, lifetime decision models were re-analysed to inform commissioning recommendations. The SO protocol is registered in the Prospective Register of Systematic Reviews (CRD42021234379).Results: Seven SRs were identified, which extracted 8 to 33 data fields from 44 relevant economic evaluations. Four economic evaluation methodological/reporting quality checklists were used; three SRs narratively synthesised methodological features to varying extent and focus. SRs generally did not appraise decision modelling features, including methods for characterising dynamic complexity of falls risk and intervention need. Their commissioning recommendations were based mainly on cost-per-unit ratios (e.g., incremental cost-effectiveness ratios) and neglected aggregate impact. There is model-based evidence of multifactorial and environmental interventions, home assessment and modification and Tai Chi being cost-effective but also the risk that they exacerbate social inequities of health. Conclusions: Current SRs of falls prevention economic evaluations do not holistically inform commissioning and evaluation design. Accounting for broader decisional factors including intervention reach and capacity constraints and a broader grasp of methodological nuances of economic evaluations, particularly decision models, are needed.


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