A comparison of power approximations for satterthwaite's test

When testing equality of means from two independent normal populations, many statisticians prefer heterogeneity tolerant tests. Moser, Stevens, and Watts described the noncentral density and a numerical integration algorithm for computing power. We present simple and accurate approximations for the power of the Satterthwaite test statistic. Two advantages accrue. First, the approximations substantially reduce the computational burden for tasks such as plotting power curves. Second, the approximations substantially simplify the programming and thereby make power calculations more widely available. Four methods of power approximation are evaluated for test sizes of .001, .01, .05, and .10, sample sizes of 6 and 51, variance ratios of 1 and 10, and noncentrality parameters from 0 to 50 by 1. A method based on a ratio of expected values is recommended due to its accuracy and simplicity

Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD).

OBJECTIVES: The optimal method of identifying people with chronic obstructive pulmonary disease (COPD) from electronic primary care records is not known. We assessed the accuracy of different approaches using the Clinical Practice Research Datalink, a UK electronic health record database. SETTING: 951 participants registered with a CPRD practice in the UK between 1 January 2004 and 31 December 2012. Individuals were selected for ≥1 of 8 algorithms to identify people with COPD. General practitioners were sent a brief questionnaire and additional evidence to support a COPD diagnosis was requested. All information received was reviewed independently by two respiratory physicians whose opinion was taken as the gold standard. PRIMARY OUTCOME MEASURE: The primary measure of accuracy was the positive predictive value (PPV), the proportion of people identified by each algorithm for whom COPD was confirmed. RESULTS: 951 questionnaires were sent and 738 (78%) returned. After quality control, 696 (73.2%) patients were included in the final analysis. All four algorithms including a specific COPD diagnostic code performed well. Using a diagnostic code alone, the PPV was 86.5% (77.5-92.3%) while requiring a diagnosis plus spirometry plus specific medication; the PPV was slightly higher at 89.4% (80.7-94.5%) but reduced case numbers by 10%. Algorithms without specific diagnostic codes had low PPVs (range 12.2-44.4%). CONCLUSIONS: Patients with COPD can be accurately identified from UK primary care records using specific diagnostic codes. Requiring spirometry or COPD medications only marginally improved accuracy. The high accuracy applies since the introduction of an incentivised disease register for COPD as part of Quality and Outcomes Framework in 2004

Acceptable risks of treatments to prevent rheumatoid arthritis among first-degree relatives:demographic and psychological predictors of risk tolerance.

Objectives:To quantify tolerance to risks of preventive treatments among first-degree relatives (FDRs) of patients with rheumatoid arthritis (RA).Methods:Preventive treatments for RA are under investigation. In a preference survey, adult FDRs assumed a 60% chance of developing RA within 2 years and made choices between no treatment and hypothetical preventive treatment options with a fixed level of benefit (reduction in chance of developing RA from 60% to 20%) and varying levels of risks. Using a probabilistic threshold technique, each risk was increased or decreased until participants switched their choice. Perceived risk of RA, health literacy, numeracy, Brief Illness Perception Questionnaire and Beliefs about Medicines Questionnaire-General were also assessed. Maximum acceptable risk (MAR) was summarised using descriptive statistics. Associations between MARs and participants’ characteristics were assessed using interval regression with effects coding.Results:289 FDRs (80 male) responded. The mean MAR for a 40% reduction in chance of developing RA was 29.08% risk of mild side effects, 9.09% risk of serious infection and 0.85% risk of a serious side effect. Participants aged over 60 years were less tolerant of serious infection risk (mean MAR ±2.06%) than younger participants. Risk of mild side effects was less acceptable to participants who perceived higher likelihood of developing RA (mean MAR ±3.34%) and more acceptable to those believing that if they developed RA it would last for a long time (mean MAR ±4.44%).Conclusions:Age, perceived chance of developing RA and perceived duration of RA were associated with tolerance to some risks of preventive RA therapy

Research Priorities to Increase Confidence in and Acceptance of Health Preference Research:What Questions Should be Prioritized Now?

Background and Objective: There has been an increase in the study and use of stated-preference methods to inform medicine development decisions. The objective of this study was to identify prioritized topics and questions relating to health preferences based on the perspective of members of the preference research community. Methods: Preference research stakeholders from industry, academia, consultancy, health technology assessment/regulatory, and patient organizations were recruited using professional networks and preference-targeted e-mail listservs and surveyed about their perspectives on 19 topics and questions for future studies that would increase acceptance of preference methods and their results by decision makers. The online survey consisted of an initial importance prioritization task, a best-worst scaling case 1 instrument, and open-ended questions. Rating counts were used for analysis. The best-worst scaling used a balanced incomplete block design. Results: One hundred and one participants responded to the survey invitation with 66 completing the best-worst scaling. The most important research topics related to the synthesis of preferences across studies, transferability across populations or related diseases, and method topics including comparison of methods and non-discrete choice experiment methods. Prioritization differences were found between respondents whose primary affiliation was academia versus other stakeholders. Academic researchers prioritized methodological/less studied topics; other stakeholders prioritized applied research topics relating to consistency of practice. Conclusions: As the field of health preference research grows, there is a need to revisit and communicate previous work on preference selection and study design to ensure that new stakeholders are aware of this work and to update these works where necessary. These findings might encourage discussion and alignment among different stakeholders who might hold different research priorities. Research on the application of previous preference research to new contexts will also help increase the acceptance of health preference information by decision makers.</p

Research Priorities to Increase Confidence in and Acceptance of Health Preference Research: What Questions Should be Prioritized Now?

BACKGROUND AND OBJECTIVE: There has been an increase in the study and use of stated-preference methods to inform medicine development decisions. The objective of this study was to identify prioritized topics and questions relating to health preferences based on the perspective of members of the preference research community. METHODS: Preference research stakeholders from industry, academia, consultancy, health technology assessment/regulatory, and patient organizations were recruited using professional networks and preference-targeted e-mail listservs and surveyed about their perspectives on 19 topics and questions for future studies that would increase acceptance of preference methods and their results by decision makers. The online survey consisted of an initial importance prioritization task, a best-worst scaling case 1 instrument, and open-ended questions. Rating counts were used for analysis. The best-worst scaling used a balanced incomplete block design. RESULTS: One hundred and one participants responded to the survey invitation with 66 completing the best-worst scaling. The most important research topics related to the synthesis of preferences across studies, transferability across populations or related diseases, and method topics including comparison of methods and non-discrete choice experiment methods. Prioritization differences were found between respondents whose primary affiliation was academia versus other stakeholders. Academic researchers prioritized methodological/less studied topics; other stakeholders prioritized applied research topics relating to consistency of practice. CONCLUSIONS: As the field of health preference research grows, there is a need to revisit and communicate previous work on preference selection and study design to ensure that new stakeholders are aware of this work and to update these works where necessary. These findings might encourage discussion and alignment among different stakeholders who might hold different research priorities. Research on the application of previous preference research to new contexts will also help increase the acceptance of health preference information by decision makers

Quantitative Benefit–Risk Assessment: State of the Practice Within Industry

Background: Benefit–risk assessments for medicinal products and devices have advanced significantly over the past decade. The purpose of this study was to characterize the extent to which the life sciences industry is utilizing quantitative benefit–risk assessment (qBRA) methods. Methods: Semi-structured interviews were conducted with a sample of industry professionals working in drug and/or medical device benefit–risk assessments (n = 20). Questions focused on the use, timing, and impact of qBRA; implementation challenges; and future plans. Interviews were recorded, transcribed, and coded for thematic analysis. Results: While most surveyed companies had applied qBRA, application was limited to a small number of assets—primarily to support internal decision-making and regulatory submissions. Positive impacts associated with use included improved team decision-making and communication. Multi-criteria decision analysis and discrete choice experiment were the most frequently utilized qBRA methods. A key challenge of qBRA use was the lack of clarity regarding its value proposition. Championing by senior company leadership and receptivity of regulators to such analyses were cited as important catalysts for successful adoption of qBRA. Investment in qBRA methods, via capability building and pilot studies, was also under way in some instances. Conclusion: qBRA application within this sample of life sciences companies was widespread, but concentrated in a small fraction of assets. Its use was primarily for internal decision-making or regulatory submissions. While some companies had plans to build further capacity in this area, others were waiting for further regulatory guidance before doing so