scholarly journals Power formulas for mixed effects models with random slope and intercept comparing rate of change across groups

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yu Zhao ◽  
Steven D. Edland
Keyword(s):  
Clinical Trials ◽  
Cohort Studies ◽  
Future Study ◽  
Mixed Effects ◽  
Rate Of Change ◽  
Placebo Control ◽  
Unequal Variance ◽  
Power Calculations ◽  
Variance Parameters ◽  
Random Slopes

Abstract We have previously derived power calculation formulas for cohort studies and clinical trials using the longitudinal mixed effects model with random slopes and intercepts to compare rate of change across groups [Ard & Edland, Power calculations for clinical trials in Alzheimer’s disease. J Alzheim Dis 2011;21:369–77]. We here generalize these power formulas to accommodate 1) missing data due to study subject attrition common to longitudinal studies, 2) unequal sample size across groups, and 3) unequal variance parameters across groups. We demonstrate how these formulas can be used to power a future study even when the design of available pilot study data (i.e., number and interval between longitudinal observations) does not match the design of the planned future study. We demonstrate how differences in variance parameters across groups, typically overlooked in power calculations, can have a dramatic effect on statistical power. This is especially relevant to clinical trials, where changes over time in the treatment arm reflect background variability in progression observed in the placebo control arm plus variability in response to treatment, meaning that power calculations based only on the placebo arm covariance structure may be anticonservative. These more general power formulas are a useful resource for understanding the relative influence of these multiple factors on the efficiency of cohort studies and clinical trials, and for designing future trials under the random slopes and intercepts model.

scholarly journals Mitochondrial Reprogramming—What Is the Benefit of Hypothermic Oxygenated Perfusion in Liver Transplantation?

2021 ◽  
Vol 2 (2) ◽  
pp. 149-161
Author(s):  
Rebecca Panconesi ◽  
Mauricio Flores Carvalho ◽  
Matteo Mueller ◽  
Philipp Dutkowski ◽  
Paolo Muiesan ◽  
...  
Keyword(s):  
Liver Transplantation ◽  
Clinical Trials ◽  
Randomized Controlled Trial ◽  
Cohort Studies ◽  
Controlled Trial ◽  
Machine Perfusion ◽  
Randomized Controlled ◽  
Hypothermic Perfusion ◽  
Underlying Mechanisms ◽  
Circulatory Death

Although machine perfusion is a hot topic today, we are just at the beginning of understanding the underlying mechanisms of protection. Recently, the first randomized controlled trial reported a significant reduction of ischemic cholangiopathies after transplantation of livers donated after circulatory death, provided the grafts were treated with an endischemic hypothermic oxygenated perfusion (HOPE). This approach has been known for more than fifty years, and was initially mainly used to preserve kidneys before implantation. Today there is an increasing interest in this and other dynamic preservation technologies and various centers have tested different approaches in clinical trials and cohort studies. Based on this, there is a need for uniform perfusion settings (perfusion route and duration), and the development of general guidelines regarding the duration of cold storage in context of the overall donor risk is also required to better compare various trial results. This article will highlight how cold perfusion protects organs mechanistically, and target such technical challenges with the perfusion setting. Finally, the options for viability testing during hypothermic perfusion will be discussed.

S40 Estimation of rate of change in longitudinal studies with attrition with application in hiv clinical trials

1997 ◽  
Vol 18 (3) ◽  
pp. S65
Author(s):  
Giota Toulomi ◽  
Stuart Pocock ◽  
Abdel Babiker ◽  
Janet Derbyshire
Keyword(s):  
Clinical Trials ◽  
Longitudinal Studies ◽  
Rate Of Change ◽  
Hiv Clinical Trials

Conditional power calculations for clinical trials with historical controls

2006 ◽  
Vol 25 (17) ◽  
pp. 2922-2931 ◽  
Author(s):  
Edward L. Korn ◽  
Boris Freidlin
Keyword(s):  
Clinical Trials ◽  
Conditional Power ◽  
Power Calculations ◽  
Historical Controls

Use of a placebo in clinical trials

1998 ◽  
Vol 13 (5) ◽  
pp. 254-263 ◽  
Author(s):  
G Emilien ◽  
JM Maloteaux ◽  
A Seghers ◽  
G Charles
Keyword(s):  
Clinical Trials ◽  
Placebo Response ◽  
Drug Efficacy ◽  
Placebo Treatment ◽  
High Response Rate ◽  
Experimental Methodology ◽  
Necessary Condition ◽  
Control Group ◽  
Placebo Control ◽  
Experimental Drug

SummaryThe use of a placebo control group in the evaluation of a new product is today considered by most as a necessary condition of experimental drug research. Placebo response is an essential consideration in all clinical trials. If not properly controlled, incorrect and dangerous conclusion may be inferred for a product efficacy and safety profile. However, the inclusion of a placebo group in clinical trials in neuropsychiatric research raises several ethical and scientific questions. Whereas in certain indications, such as suicidal patients and severe and psychotic depression, the use of a placebo is generally not accepted, it is difficult to assess drug efficacy. This article discusses the concept of placebo in clinical trials, the occurrence of adverse events after placebo treatment and the high response rate of placebo in neuropsychiatric clinical research. The experimental methodology to adequately control all the factors involved is also analysed and discussed.

Is Medicaid expansion associated with increases in palliative treatments for metastatic cancer?

2021 ◽  
Author(s):  
Alexander P Cole ◽  
Stuart R Lipsitz ◽  
Adam S Kibel ◽  
Brandon A Mahal ◽  
Nelya Melnitchouk ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Affordable Care Act ◽  
Odds Ratio ◽  
Metastatic Cancer ◽  
Mixed Effects ◽  
Rate Of Change ◽  
Medicaid Expansion ◽  
Demographic Differences ◽  
Men And Women ◽  
Lower Baseline

Background: Medicaid expansion following the 2010 Affordable Care Act has an unknown impact on palliative treatments. Materials & methods: This registry-based study of individuals with metastatic cancer from 2010 to 2016 identified men and women with metastatic cancer in expansion and non-expansion states who received palliative treatments. A mixed effects logistic regression compared trends in expansion and non-expansion states and generated risk-adjusted probabilities or receiving palliative treatments each year. Results: Despite lower baseline use of palliative treatments, the rate of change was more rapid in expansion states (odds ratio [OR]: 1.02; 95% CI: 1.01–1.03; p < 0.001). The adjusted probability of receiving palliative treatments rose from 21.3 to 26.0% in non-expansion states, and from 19.7 to 26.9% in expansion states. Conclusion: Use of palliative treatments among metastatic cancer patients increased from 2010 to 2016 with a significantly greater increase in Medicaid expansion states, even when adjusting for demographic differences between states.

scholarly journals POWER CALCULATIONS IN CLINICAL TRIALS WITH COMPLEX CLINICAL OBJECTIVES

2015 ◽  
Vol 28 (1) ◽  
pp. 15-50 ◽  
Author(s):  
Alex Dmitrienko ◽  
Gautier Paux ◽  
Thomas Brechenmacher
Keyword(s):  
Clinical Trials ◽  
Power Calculations

scholarly journals Estimating Bayes Factors for Linear Models with Random Slopes on Continuous Predictors

2017 ◽  
Author(s):  
Mirko Thalmann ◽  
Marcel Niklaus ◽  
Klaus Oberauer
Keyword(s):  
Bayesian Statistics ◽  
Random Effects ◽  
Mixed Models ◽  
Mixed Effects ◽  
Bayes Factors ◽  
Mixed Effects Models ◽  
Type I ◽  
Frequentist Statistics ◽  
Random Slopes ◽  
Continuous Predictors

Using mixed-effects models and Bayesian statistics has been advocated by statisticians in recent years. Mixed-effects models allow researchers to adequately account for the structure in the data. Bayesian statistics – in contrast to frequentist statistics – can state the evidence in favor of or against an effect of interest. For frequentist statistical methods, it is known that mixed models can lead to serious over-estimation of evidence in favor of an effect (i.e., inflated Type-I error rate) when models fail to include individual differences in the effect sizes of predictors ("random slopes") that are actually present in the data. Here, we show through simulation that the same problem exists for Bayesian mixed models. Yet, at present there is no easy-to-use application that allows for the estimation of Bayes Factors for mixed models with random slopes on continuous predictors. Here, we close this gap by introducing a new R package called BayesRS. We tested its functionality in four simulation studies. They show that BayesRS offers a reliable and valid tool to compute Bayes Factors. BayesRS also allows users to account for correlations between random effects. In a fifth simulation study we show, however, that doing so leads to slight underestimation of the evidence in favor of an actually present effect. We only recommend modeling correlations between random effects when they are of primary interest and when sample size is large enough. BayesRS is available under https://cran.r-project.org/web/packages/BayesRS/, R code for all simulations is available under https://osf.io/nse5x/?view_only=b9a7caccd26a4764a084de3b8d459388

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