A Bayesian design for phase I cancer therapeutic vaccine trials
Phase I clinical trials are the first step in drug development to test a new drug or drug combination on humans. Typical designs of Phase I trials use toxicity as the primary endpoint and aim to find the maximum tolerable dosage. However, these designs are poorly applicable for the development of cancer therapeutic vaccines because the expected safety concerns for these vaccines are not as much as cytotoxic agents. The primary objectives of a cancer therapeutic vaccine phase I trial thus often include determining whether the vaccine shows biologic activity and the minimum dose necessary to achieve a full immune or even cli...
Source: Statistics in Medicine - October 25, 2018 Category: Statistics Authors: Chenguang Wang,
Gary L. Rosner,
Richard B.S. Roden Tags: RESEARCH ARTICLE Source Type: research
Modified power prior with multiple historical trials for binary endpoints
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 25, 2018 Category: Statistics Authors: Akalu Banbeta,
Joost Rosmalen,
David Dejardin,
Emmanuel Lesaffre Source Type: research
A Bayesian design for phase I cancer therapeutic vaccine trials
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 25, 2018 Category: Statistics Authors: Chenguang Wang,
Gary L. Rosner,
Richard B.S. Roden Source Type: research
A method for determining groups in multiple survival curves
Survival analysis includes a wide variety of methods for analyzing time ‐to‐event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for censored data. When the null hypothesis of equality of curves is rejected, leading to the clear conclusion that at least one curve is different, it can be interesting to ascertain whether curves can be grouped or if all these curves are different from each other. A method is proposed that allows determining groups with...
Source: Statistics in Medicine - October 24, 2018 Category: Statistics Authors: Nora M. Villanueva,
Marta Sestelo,
Lu ís Meira‐Machado Tags: RESEARCH ARTICLE Source Type: research
Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
When designing a study to develop a new prediction model with binary or time ‐to‐event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values ofn andE (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of≥0.9, (ii) small absolute difference of≤ 0.05 in the mo...
Source: Statistics in Medicine - October 24, 2018 Category: Statistics Authors: Richard D Riley,
Kym IE Snell,
Joie Ensor,
Danielle L Burke,
Frank E Harrell Jr,
Karel GM Moons,
Gary S Collins Tags: RESEARCH ARTICLE Source Type: research
A method for determining groups in multiple survival curves
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 24, 2018 Category: Statistics Authors: Nora M. Villanueva,
Marta Sestelo,
Lu ís Meira‐Machado Source Type: research
Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 24, 2018 Category: Statistics Authors: Richard D Riley,
Kym IE Snell,
Joie Ensor,
Danielle L Burke,
Frank E Harrell Jr,
Karel GM Moons,
Gary S Collins Source Type: research
Moving beyond the conventional stratified analysis to estimate an overall treatment efficacy with the data from a comparative randomized clinical study
For a two ‐group comparative study, a stratified inference procedure is routinely used to estimate an overall group contrast to increase the precision of the simple two‐sample estimator. Unfortunately, most commonly used methods including the Cochran‐Mantel‐Haenszel statistic for a binary outcome and the stratified Cox procedure for the event time endpoint do not serve this purpose well. In fact, these procedures may be worse than their two‐sample counterparts even when the observed treatment allocations are imbalanced across strata. Various procedures beyond the conventional stratified method s have been propose...
Source: Statistics in Medicine - October 23, 2018 Category: Statistics Authors: L. Tian,
F. Jiang,
T. Hasegawa,
H. Uno,
M. Pfeffer,
LJ. Wei Tags: RESEARCH ARTICLE Source Type: research
Design and other methodological considerations for the construction of human fetal and neonatal size and growth charts
This paper discusses the features of study design and methodological considerations for constructing reference centile charts for attained size, growth, and velocity charts with a focus on human growth charts used during pregnancy. Recent systematic reviews of pregnancy dating, fetal size, and newborn size charts showed that many studies aimed at constructing charts are still conducted poorly. Important design features such as inclusion and exclusion criteria, ultrasound quality control measures, sample size determination, anthropometric evaluation, gestational age estimation, assessment of outliers, and chart presentation...
Source: Statistics in Medicine - October 23, 2018 Category: Statistics Authors: Eric O. Ohuma,
Douglas G. Altman Tags: SPECIAL ISSUE PAPER Source Type: research
Design and other methodological considerations for the construction of human fetal and neonatal size and growth charts
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 23, 2018 Category: Statistics Authors: Eric O. Ohuma,
Douglas G. Altman Source Type: research
Moving beyond the conventional stratified analysis to estimate an overall treatment efficacy with the data from a comparative randomized clinical study
Statistics in Medicine, EarlyView. (Source: Statistics in Medicine)
Source: Statistics in Medicine - October 23, 2018 Category: Statistics Authors: L. Tian,
F. Jiang,
T. Hasegawa,
H. Uno,
M. Pfeffer,
LJ. Wei Source Type: research
Allowing for uncertainty due to missing and LOCF imputed outcomes in meta ‐analysis
The use of thelast observation carried forward (LOCF) method for imputing missing outcome data in randomized clinical trials has been much criticized and its shortcomings are well understood. However, only recently have published studies widely started using more appropriate imputation methods. Consequently, meta ‐analyses often include several studies reporting their results according to LOCF. The results from such meta‐analyses are potentially biased and overprecise. We develop methods for estimating summary treatment effects for continuous outcomes in the presence of both missing and LOCF‐imputed ou tcome data. Ou...
Source: Statistics in Medicine - October 22, 2018 Category: Statistics Authors: Dimitris Mavridis,
Georgia Salanti,
Toshi A. Furukawa,
Andrea Cipriani,
Anna Chaimani,
Ian R. White Tags: RESEARCH ARTICLE Source Type: research
Estimating causal effects of treatment in RCTs with provider and subject noncompliance
Subject noncompliance is a common problem in the analysis of randomized clinical trials (RCTs). With cognitive behavioral interventions, the addition of provider noncompliance further complicates making causal inference. As a motivating example, we consider an RCT of a motivational interviewing (MI) ‐based behavioral intervention for treating problem drug use. Treatment receipt depends on compliance of both a therapist (provider) and a patient (subject), where MI isreceived when the therapist adheres to the MI protocol and the patient actively participates in the intervention. However, therapists cannot be forced to foll...
Source: Statistics in Medicine - October 22, 2018 Category: Statistics Authors: Elisa Sheng,
Wei Li,
Xiao ‐Hua Zhou Tags: RESEARCH ARTICLE Source Type: research
Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a linear regression model is developed to predict an individual's outcome value conditional on values of multiple predictors (covariates). To improve model development and reduce the potential for overfitting, a suitable sample size is required in terms of the number of subjects (n) relative to the number of predictor parameters (p) for potential inclusion. We propose that the minimum value ofn should meet the following four key criteria: (i) small optimism in predictor ...
Source: Statistics in Medicine - October 22, 2018 Category: Statistics Authors: Richard D. Riley,
Kym I.E. Snell,
Joie Ensor,
Danielle L. Burke,
Frank E. Harrell,
Karel G.M. Moons,
Gary S. Collins Tags: RESEARCH ARTICLE Source Type: research
Propensity ‐score matching with competing risks in survival analysis
We describe how both relative and absolute measures of treatment effect can be obtained when using propensity‐score matching with competing risks data. Estimates of the relative effect of treatment can be obtained by using cause‐specific hazard models in the matched sample. Estimates of absolute t reatment effects can be obtained by comparing cumulative incidence functions (CIFs) between matched treated and matched control subjects. We conducted a series of Monte Carlo simulations to compare the empirical type I error rate of different statistical methods for testing the equality of CIFs esti mated in the matched sampl...
Source: Statistics in Medicine - October 22, 2018 Category: Statistics Authors: Peter C. Austin,
Jason P. Fine Tags: RESEARCH ARTICLE Source Type: research
