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Statistics in Medicine

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https://www.readbyqxmd.com/read/30411379/practical-issues-in-using-generalized-estimating-equations-for-inference-on-transitions-in-longitudinal-data-what-is-being-estimated
#1
Joe Bible, Paul S Albert, Bruce G Simons-Morton, Danping Liu
Generalized estimating equations (GEEs) are commonly used to estimate transition models. When the Markov assumption does not hold but first-order transition probabilities are still of interest, the transition inference is sensitive to the choice of working correlation. In this paper, we consider a random process transition model as the true underlying data generating mechanism, which characterizes subject heterogeneity and complex dependence structure of the outcome process in a very flexible way. We formally define two types of transition probabilities at the population level: "naive transition probabilities" that average across all the transitions and "population-average transition probabilities" that average the subject-specific transition probabilities...
November 8, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30411376/penalized-variable-selection-for-accelerated-failure-time-models-with-random-effects
#2
Eunyoung Park, Il Do Ha
Accelerated failure time (AFT) models allowing for random effects are linear mixed models under the log-transformation of survival time with censoring and describe dependence in correlated survival data. It is well known that the AFT models are useful alternatives to frailty models. To the best of our knowledge, however, there is no literature on variable selection methods for such AFT models. In this paper, we propose a simple but unified variable-selection procedure of fixed effects in the AFT random-effect models using penalized h-likelihood (HL)...
November 8, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30411375/statistical-modeling-and-prediction-of-clinical-trial-recruitment
#3
Yu Lan, Gong Tang, Daniel F Heitjan
Real-time prediction of clinical trial accrual can support logistical planning, ensuring that studies meet but do not exceed sample size targets. We describe a novel, simulation-based prediction method that is founded on a realistic model for the underlying processes of recruitment. The model reflects key features of enrollment such as the staggered initiation of new centers, heterogeneity in enrollment capacity, and declining accrual within centers. The model's first stage assumes that centers join the trial (ie, initiate accrual) according to an inhomogeneous Poisson process in discrete time...
November 8, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30402914/admissible-multiarm-stepped-wedge-cluster-randomized-trial-designs
#4
Michael J Grayling, Adrian P Mander, James M S Wason
Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multiarm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multiarm trials can be ascertained when data are to be analyzed using a linear mixed model...
November 6, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30397907/sample-size-estimation-for-case-crossover-studies
#5
Sai Dharmarajan, Joo-Yeon Lee, Rima Izem
Case-crossover study designs are observational studies used to assess postmarket safety of medical products (eg, vaccines or drugs). As a case-crossover study is self-controlled, its advantages include better control for confounding because the design controls for any time-invariant measured and unmeasured confounding and potentially greater feasibility as only data from those experiencing an event (or cases) are required. However, self-matching also introduces correlation between case and control periods within a subject or matched unit...
November 5, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30376693/shared-random-parameter-models-a-legacy-of-the-biostatistics-program-at-the-national-heart-lung-and-blood-institute
#6
Paul S Albert
Shared random parameter models (SRPMs) were first introduced by researchers at the National Heart Lung and Blood Institute (NHLBI) Biostatistics Branch for analyzing longitudinal data with informative dropout (Wu and Carroll, 1987; Wu and Bailey, 1988; Follmann and Wu, 1995; Albert and Follmann, 2000; Albert et al, 2002). This work was all focused on characterizing the longitudinal data process in the presence of an informative missing data mechanism that is treated as a nuisance. Shared random parameter modeling approaches have also been developed from the perspective of characterizing the relationship between longitudinal data and a subsequent outcome that may be an event time, a dichotomous measurement, or another longitudinal outcome...
October 30, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30375022/a-bayesian-regularized-mediation-analysis-with-multiple-exposures
#7
Yu-Bo Wang, Zhen Chen, Jill M Goldstein, Germaine M Buck Louis, Stephen E Gilman
Mediation analysis assesses the effect of study exposures on an outcome both through and around specific mediators. While mediation analysis involving multiple mediators has been addressed in recent literature, the case of multiple exposures has received little attention. With the presence of multiple exposures, we consider regularizations that allow simultaneous effect selection and estimation while stabilizing model fit and accounting for model selection uncertainty. In the framework of linear structural-equation models, we analytically show that a two-stage approach regularizing regression coefficients does not guarantee a unimodal posterior distribution and that a product-of-coefficient approach regularizing direct and indirect effects tends to penalize excessively...
October 29, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30368844/assessing-the-influence-of-treatment-nonadherence-on-noninferiority-trials-using-the-tipping-point-approach
#8
Mimi Kim, Cuiling Wang, Xiaonan Xue
In noninferiority (NI) trials, an ongoing methodological challenge is how to handle in the analysis the subjects who are nonadherent to their assigned treatment. Some investigators perform the intent-to-treat (ITT) as the primary analysis and the per-protocol (PP) analysis as sensitivity analysis, whereas others do the reverse since ITT results may be anticonservative in the NI setting. But even when there is agreement between the ITT and PP approaches, NI of the experimental therapy to the comparator is not guaranteed...
October 28, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30368868/a-bayesian-design-for-phase-i-cancer-therapeutic-vaccine-trials
#9
Chenguang Wang, Gary L Rosner, Richard B S Roden
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 clinical response...
October 25, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30360016/modified-power-prior-with-multiple-historical-trials-for-binary-endpoints
#10
Akalu Banbeta, Joost van Rosmalen, David Dejardin, Emmanuel Lesaffre
Including historical data may increase the power of the analysis of a current clinical trial and reduce the sample size of the study. Recently, several Bayesian methods for incorporating historical data have been proposed. One of the methods consists of specifying a so-called power prior whereby the historical likelihood is downweighted with a weight parameter. When the weight parameter is also estimated from the data, the modified power prior (MPP) is needed. This method has been used primarily when a single historical trial is available...
October 25, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30357878/a-method-for-determining-groups-in-multiple-survival-curves
#11
Nora M Villanueva, Marta Sestelo, Luís Meira-Machado
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...
October 24, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30357870/minimum-sample-size-for-developing-a-multivariable-prediction-model-part-ii-binary-and-time-to-event-outcomes
#12
Richard D Riley, Kym Ie Snell, Joie Ensor, Danielle L Burke, Frank E Harrell, Karel Gm Moons, Gary S Collins
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 of n and E (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...
October 24, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30352489/design-and-other-methodological-considerations-for-the-construction-of-human-fetal-and-neonatal-size-and-growth-charts
#13
Eric O Ohuma, Douglas G Altman
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 are seldom well addressed, considered, or reported...
October 23, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30352486/moving-beyond-the-conventional-stratified-analysis-to-estimate-an-overall-treatment-efficacy-with-the-data-from-a-comparative-randomized-clinical-study
#14
L Tian, F Jiang, T Hasegawa, H Uno, M Pfeffer, L J Wei
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...
October 23, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30347470/minimum-sample-size-for-developing-a-multivariable-prediction-model-part-i-continuous-outcomes
#15
Richard D Riley, Kym I E Snell, Joie Ensor, Danielle L Burke, Frank E Harrell, Karel G M Moons, Gary S Collins
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...
October 22, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30347462/estimating-causal-effects-of-treatment-in-rcts-with-provider-and-subject-noncompliance
#16
Elisa Sheng, Wei Li, Xiao-Hua Zhou
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 is received when the therapist adheres to the MI protocol and the patient actively participates in the intervention...
October 22, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30347461/propensity-score-matching-with-competing-risks-in-survival-analysis
#17
Peter C Austin, Jason P Fine
Propensity-score matching is a popular analytic method to remove the effects of confounding due to measured baseline covariates when using observational data to estimate the effects of treatment. Time-to-event outcomes are common in medical research. Competing risks are outcomes whose occurrence precludes the occurrence of the primary time-to-event outcome of interest. All non-fatal outcomes and all cause-specific mortality outcomes are potentially subject to competing risks. There is a paucity of guidance on the conduct of propensity-score matching in the presence of competing risks...
October 22, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30347460/allowing-for-uncertainty-due-to-missing-and-locf-imputed-outcomes-in-meta-analysis
#18
Dimitris Mavridis, Georgia Salanti, Toshi A Furukawa, Andrea Cipriani, Anna Chaimani, Ian R White
The use of the last 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 outcome data...
October 22, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30368858/the-use-of-weight-adjusted-for-height-rather-than-body-mass-index-to-assess-growth-trajectory-results-from-a-population-based-cohort
#19
Joana Araújo, Elisabete Ramos, Gita D Mishra, Milton Severo
We compared different growth models parameterizations regarding (i) adjustment of weight-for-height, as denoted by body mass index (BMI); (ii) adjustment for different covariates, ie, age or height; and (iii) the use of different smoothing methods, ie, polynomial, fractional polynomial, or linear splines. A total of 11 459 measurements of weight and height from 719 participants were used, obtained from the EPITeen cohort at 13, 17, and 21 years, and extracted from child health books. The individual growth curves were modeled using mixed-effects polynomial, fractional polynomial, and linear splines, and each model parameterization included as covariate age or height...
October 18, 2018: Statistics in Medicine
https://www.readbyqxmd.com/read/30338563/investigating-hospital-heterogeneity-with-a-competing-risks-frailty-model
#20
Anja J Rueten-Budde, Hein Putter, Marta Fiocco
Survival analysis is used in the medical field to identify the effect of predictive variables on time to a specific event. Generally, not all variation of survival time can be explained by observed covariates. The effect of unobserved variables on the risk of a patient is called frailty. In multicenter studies, the unobserved center effect can induce frailty on its patients, which can lead to selection bias over time when ignored. For this reason, it is common practice in multicenter studies to include a random frailty term modeling center effect...
October 18, 2018: Statistics in Medicine
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