scholarly journals Power estimations for non-primary outcomes in randomised clinical trials

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e027092 ◽  
Author(s):  
Janus Christian Jakobsen ◽  
Christian Ovesen ◽  
Per Winkel ◽  
Jørgen Hilden ◽  
Christian Gluud ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Present Article ◽  
Clinical Relevance ◽  
Randomised Clinical Trial ◽  
Randomised Clinical Trials ◽  
Type I ◽  
Type Ii ◽  
Limit Problems ◽  
Type Ii Errors

Objective and methods: It is rare that trialists report power estimations of non-primary outcomes. In the present article, we will describe how to define a valid hierarchy of outcomes in a randomised clinical trial, to limit problems with Type I and Type II errors, using considerations on the clinical relevance of the outcomes and power estimations. Conclusion: Power estimations of non-primary outcomes may guide trialists in classifying non-primary outcomes as secondary or exploratory. The power estimations are simple and if they are used systematically, more appropriate outcome hierarchies can be defined, and trial results will become more interpretable.

scholarly journals Assessing gaps in cholesterol treatment guidelines for primary prevention of cardiovascular disease based on available randomised clinical trial evidence: The Rotterdam Study

2017 ◽  
Vol 25 (4) ◽  
pp. 420-431 ◽  
Author(s):  
Jelena Pavlović ◽  
Philip Greenland ◽  
Jaap W Deckers ◽  
Maryam Kavousi ◽  
Albert Hofman ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Primary Prevention ◽  
Randomised Clinical Trial ◽  
Lipid Lowering ◽  
Randomised Clinical Trials ◽  
Atherosclerotic Cardiovascular Disease ◽  
Intermediate Risk ◽  
Clinical Trial Evidence

Background The purpose of this study was to determine how American College of Cardiology/American Heart Association (ACC/AHA) 2013 and European Society of Cardiology 2016 guidelines for the primary prevention of atherosclerotic cardiovascular disease (CVD) compare in reflecting the totality of accrued randomised clinical trial evidence for statin treatment at population level. Methods From 1997–2008, 7279 participants aged 45–75 years, free of atherosclerotic cardiovascular disease, from the population-based Rotterdam Study were included. For each participant, we compared eligibility for each one of 11 randomised clinical trials on statin use in primary prevention of CVD, with recommendations on lipid-lowering therapy from the ACC/AHA and European Society of Cardiology (ESC) guidelines. Atherosclerotic cardiovascular disease incidence and cardiovascular disease mortality rates were calculated. Results The proportion of participants eligible for each trial ranged from 0.4% for ALLHAT-LLT to 30.8% for MEGA. The likelihood of being recommended for lipid-lowering treatment was lowest for those eligible for low-to-intermediate risk RCTs (HOPE-3, MEGA, and JUPITER), and highest for high-risk individuals with diabetes (MRC/BHF HPS, CARDS, and ASPEN) or elderly PROSPER. Eligibility for an increasing number of randomised clinical trials correlated with a greater likelihood of being recommended lipid-lowering treatment by either guideline ( p < 0.001 for both guidelines). Conclusion Compared to RCTs done in high risk populations, randomised clinical trials targeting low-to-intermediate risk populations are less well-reflected in the ACC/AHA, and even less so in the ESC guideline recommendations. Importantly, the low-to-intermediate risk population targeted by HOPE-3, the most recent randomised clinical trial in this field, is not well-captured by the current European prevention guidelines and should be specifically considered in future iterations of the guidelines.

scholarly journals Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

2020 ◽  
Author(s):  
Ravinder Claire ◽  
Christian Gluud ◽  
Ivan Berlin ◽  
Tim Coleman ◽  
Jo Leonardi-Bee
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Sequential Analysis ◽  
Randomised Clinical Trial ◽  
Randomised Clinical Trials ◽  
Trial Sequential Analysis ◽  
Analysis Software ◽  
Cessation Intervention ◽  
Pilot Trials

Abstract Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms.MethodsWe describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention’s effects.ResultsWe provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. ConclusionsTrial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

scholarly journals Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ravinder Claire ◽  
Christian Gluud ◽  
Ivan Berlin ◽  
Tim Coleman ◽  
Jo Leonardi-Bee
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Sequential Analysis ◽  
Randomised Clinical Trial ◽  
Randomised Clinical Trials ◽  
Trial Sequential Analysis ◽  
Analysis Software ◽  
Cessation Intervention ◽  
Pilot Trials

Abstract Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. Methods We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention’s effects. Results We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. Conclusions Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

scholarly journals Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

2020 ◽  
Author(s):  
Ravinder Claire ◽  
Christian Gluud ◽  
Ivan Berlin ◽  
Tim Coleman ◽  
Jo Leonardi-Bee
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Sequential Analysis ◽  
Randomised Clinical Trial ◽  
Randomised Clinical Trials ◽  
Trial Sequential Analysis ◽  
Analysis Software ◽  
Cessation Intervention ◽  
Pilot Trials

Abstract Background: Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms.Methods: We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention's effects. Results: We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. Conclusions: Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

scholarly journals Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

2020 ◽  
Author(s):  
Ravinder Claire ◽  
Christian Gluud ◽  
Ivan Berlin ◽  
Tim Coleman ◽  
Jo Leonardi-Bee
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Smoking Cessation ◽  
Sample Size ◽  
Sequential Analysis ◽  
Randomised Clinical Trial ◽  
Randomised Clinical Trials ◽  
Trial Sequential Analysis ◽  
Cessation Intervention ◽  
Pilot Trials

Abstract Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. Methods We describe using feasibility and pilot trial data in the Trial Sequential Analysis program to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using Trial Sequential Analysis to assess the intervention’s effects. Results We provide a worked example to illustrate how we successfully used Trial Sequential Analysis methods to argue for the research funds needed to undertake a randomised clinical trial. Conclusions Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

An old friend is better than two new ones? Approaches to market research in the context of digital transformation for the antitrust laws enforcement

2020 ◽  
pp. 37-55 ◽  
Author(s):  
A. E. Shastitko ◽  
O. A. Markova
Keyword(s):  
Business Models ◽  
Rapid Development ◽  
Digital Transformation ◽  
Market Definition ◽  
Type I ◽  
Type Ii ◽  
Digital Platforms ◽  
Advantages And Disadvantages ◽  
Pass Through ◽  
Type Ii Errors

Digital transformation has led to changes in business models of traditional players in the existing markets. What is more, new entrants and new markets appeared, in particular platforms and multisided markets. The emergence and rapid development of platforms are caused primarily by the existence of so called indirect network externalities. Regarding to this, a question arises of whether the existing instruments of competition law enforcement and market analysis are still relevant when analyzing markets with digital platforms? This paper aims at discussing advantages and disadvantages of using various tools to define markets with platforms. In particular, we define the features of the SSNIP test when being applyed to markets with platforms. Furthermore, we analyze adjustment in tests for platform market definition in terms of possible type I and type II errors. All in all, it turns out that to reduce the likelihood of type I and type II errors while applying market definition technique to markets with platforms one should consider the type of platform analyzed: transaction platforms without pass-through and non-transaction matching platforms should be tackled as players in a multisided market, whereas non-transaction platforms should be analyzed as players in several interrelated markets. However, if the platform is allowed to adjust prices, there emerges additional challenge that the regulator and companies may manipulate the results of SSNIP test by applying different models of competition.

The Potential of Tax Surprises to Affect Measures of Tax Avoidance and Researchers' Inferences

2018 ◽  
Vol 41 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Chelsea Rae Austin
Keyword(s):  
Stock Options ◽  
Tax Avoidance ◽  
Type I ◽  
Type Ii ◽  
Effective Tax Rate ◽  
Tax Rate ◽  
Tax Savings ◽  
Type Ii Errors

ABSTRACT While not explicitly stated, many tax avoidance studies seek to investigate tax avoidance that is the result of firms' deliberate actions. However, measures of firms' tax avoidance can also be affected by factors outside the firms' control—tax surprises. This study examines potential complications caused by tax surprises when measuring tax avoidance by focusing on one specific type of surprise tax savings—the unanticipated tax benefit from employees' exercise of stock options. Because the cash effective tax rate (ETR) includes the benefits of this tax surprise, the cash ETR mismeasures firms' deliberate tax avoidance. The analyses conducted show this mismeasurement is material and can lead to both Type I and Type II errors in studies of deliberate tax avoidance. Suggestions to aid researchers in mitigating these concerns are also provided.

The Effect of Stage of Development and Financial Health on Auditor Decision Behavior in the Going-Concern Task

1999 ◽  
Vol 18 (1) ◽  
pp. 37-54 ◽  
Author(s):  
Andrew J. Rosman ◽  
Inshik Seol ◽  
Stanley F. Biggs
Keyword(s):  
Information Acquisition ◽  
Type I ◽  
Type Ii ◽  
Financial Health ◽  
Inference Problem ◽  
Going Concern ◽  
Start Up ◽  
Mitigating Factors ◽  
Type Ii Errors ◽  
Problem Representations

The effect of different task settings within an industry on auditor behavior is examined for the going-concern task. Using an interactive computer process-tracing method, experienced auditors from four Big 6 accounting firms examined cases based on real data that differed on two dimensions of task settings: stage of organizational development (start-up and mature) and financial health (bankrupt and nonbankrupt). Auditors made judgments about each entity's ability to continue as a going concern and, if they had substantial doubt about continued existence, they listed evidence they would seek as mitigating factors. There are seven principal results. First, information acquisition and, by inference, problem representations were sensitive to differences in task settings. Second, financial mitigating factors dominated nonfinancial mitigating factors in both start-up and mature settings. Third, auditors' behavior reflected configural processing. Fourth, categorizing information into financial and nonfinancial dimensions was critical to understanding how auditors' information acquisition and, by inference, problem representations differed across settings. Fifth, Type I errors (determining that a healthy company is a going-concern problem) differed from correct judgments in terms of information acquisition, although Type II errors (determining that a problem company is viable) did not. This may indicate that Type II errors are primarily due to deficiencies in other stages of processing, such as evaluation. Sixth, auditors who were more accurate tended to follow flexible strategies for financial information acquisition. Finally, accurate performance in the going-concern task was found to be related to acquiring (1) fewer information cues, (2) proportionately more liquidity information and (3) nonfinancial information earlier in the process.

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