scholarly journals Informing power and sample size calculations when using inverse probability of treatment weighting using the propensity score

2021 ◽  
Author(s):  
Peter C. Austin
Keyword(s):  
Propensity Score ◽  
Sample Size ◽  
Inverse Probability ◽  
Sample Size Calculations

scholarly journals Virtual reality-based relaxation for enhancement of perioperative well-being and quality of life: protocol for a randomised pilot trial

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e044193
Author(s):  
Matthias Christian Schrempf ◽  
Julian Quirin Petzold ◽  
Hugo Vachon ◽  
Morten Aagaard Petersen ◽  
Johanna Gutschon ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Virtual Reality ◽  
Sample Size ◽  
Stress Reduction ◽  
Pilot Trial ◽  
Well Being ◽  
Coping Resources ◽  
Patients With Cancer ◽  
Sample Size Calculations

IntroductionPatients with cancer undergoing surgery often suffer from reduced quality of life and various forms of distress. Untreated distress can negatively affect coping resources as well as surgical and oncological outcomes. A virtual reality-based stress reduction intervention may increase quality of life and well-being and reduce distress in the perioperative phase for patients with cancer. This pilot trial aims to explore the feasibility of the proposed intervention, assess patient acceptability and obtain estimates of effect to provide data for sample size calculations.Methods and analysisPatients with colorectal cancer and liver metastasis undergoing elective surgery will be recruited for this single-centre, randomised pilot trial with a three-arm design. A total of 54 participants will be randomised at 1:1:1 ratio to one of two intervention groups or a control receiving standard treatment. Those randomised to an intervention group will either receive perioperative virtual reality-based stress reduction exercises twice daily or listen to classical music twice daily. Primary feasibility outcomes are number and proportions of participants recruited, screened, consented and randomised. Furthermore, adherence to the intervention, compliance with the completion of the quality of life questionnaires and feasibility of implementing the trial procedures will be assessed. Secondary clinical outcomes are measurements of the effectiveness of the interventions to inform sample size calculations.Ethics and disseminationThe study protocol, the patient information and the informed consent form have been approved by the ethics committee of the Ludwigs-Maximilians-University, Munich, Germany (Reference Number: 19–915). Study findings will be submitted for publication in peer-reviewed journals.Trial registration numberDRKS00020909.

Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: A plasmode simulation

2021 ◽  
Vol 40 (9) ◽  
pp. 2101-2112
Author(s):  
Mitchell M. Conover ◽  
Kenneth J. Rothman ◽  
Til Stürmer ◽  
Alan R. Ellis ◽  
Charles Poole ◽  
...  
Keyword(s):  
Measurement Error ◽  
Propensity Score ◽  
Inverse Probability ◽  
Covariate Measurement Error

scholarly journals Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials

2021 ◽  
pp. 174077452110208
Author(s):  
Elizabeth Korevaar ◽  
Jessica Kasza ◽  
Monica Taljaard ◽  
Karla Hemming ◽  
Terry Haines ◽  
...  
Keyword(s):  
Sample Size ◽  
Discrete Time ◽  
Correlation Coefficients ◽  
Time Decay ◽  
Randomised Trials ◽  
Cluster Randomised Trials ◽  
Cluster Randomised ◽  
Sample Size Calculations ◽  
Correlation Structures ◽  
Cluster Correlation

Background: Sample size calculations for longitudinal cluster randomised trials, such as crossover and stepped-wedge trials, require estimates of the assumed correlation structure. This includes both within-period intra-cluster correlations, which importantly differ from conventional intra-cluster correlations by their dependence on period, and also cluster autocorrelation coefficients to model correlation decay. There are limited resources to inform these estimates. In this article, we provide a repository of correlation estimates from a bank of real-world clustered datasets. These are provided under several assumed correlation structures, namely exchangeable, block-exchangeable and discrete-time decay correlation structures. Methods: Longitudinal studies with clustered outcomes were collected to form the CLustered OUtcome Dataset bank. Forty-four available continuous outcomes from 29 datasets were obtained and analysed using each correlation structure. Patterns of within-period intra-cluster correlation coefficient and cluster autocorrelation coefficients were explored by study characteristics. Results: The median within-period intra-cluster correlation coefficient for the discrete-time decay model was 0.05 (interquartile range: 0.02–0.09) with a median cluster autocorrelation of 0.73 (interquartile range: 0.19–0.91). The within-period intra-cluster correlation coefficients were similar for the exchangeable, block-exchangeable and discrete-time decay correlation structures. Within-period intra-cluster correlation coefficients and cluster autocorrelations were found to vary with the number of participants per cluster-period, the period-length, type of cluster (primary care, secondary care, community or school) and country income status (high-income country or low- and middle-income country). The within-period intra-cluster correlation coefficients tended to decrease with increasing period-length and slightly decrease with increasing cluster-period sizes, while the cluster autocorrelations tended to move closer to 1 with increasing cluster-period size. Using the CLustered OUtcome Dataset bank, an RShiny app has been developed for determining plausible values of correlation coefficients for use in sample size calculations. Discussion: This study provides a repository of intra-cluster correlations and cluster autocorrelations for longitudinal cluster trials. This can help inform sample size calculations for future longitudinal cluster randomised trials.

Sample size calculations for ROC studies: parametric robustness and Bayesian nonparametrics

2011 ◽  
Vol 31 (2) ◽  
pp. 131-142 ◽  
Author(s):  
Dunlei Cheng ◽  
Adam J. Branscum ◽  
Wesley O. Johnson
Keyword(s):  
Sample Size ◽  
Bayesian Nonparametrics ◽  
Sample Size Calculations ◽  
Parametric Robustness

Testing bioequivalence for multiple formulations with power and sample size calculations

2012 ◽  
Vol 11 (4) ◽  
pp. 334-341 ◽  
Author(s):  
Cheng Zheng ◽  
Jixian Wang ◽  
Lihui Zhao
Keyword(s):  
Sample Size ◽  
Sample Size Calculations

scholarly journals Power and sample size for multivariate logistic modeling of unmatched case-control studies

2017 ◽  
Vol 28 (3) ◽  
pp. 822-834
Author(s):  
Mitchell H Gail ◽  
Sebastien Haneuse
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Case Control ◽  
Simulation Methods ◽  
Case Control Studies ◽  
Control Data ◽  
Logistic Analysis ◽  
Sample Size Calculations ◽  
Control Designs ◽  
Univariate Analyses

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

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