910 research outputs found

    Survival Analysis With Uncertain Endpoints Using an Internal Validation Subsample

    Get PDF
    When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when the true endpoint is too invasive or costly to obtain. We develop nonparametric and semiparametric estimated likelihood functions that incorporate both uncertain endpoints available for all participants and true endpoints available for only a subset of participants. We propose maximum estimated likelihood estimators of the discrete survival function of time to the true endpoint and of a hazard ratio representing the effect of a binary or continuous covariate assuming a proportional hazards model. We show that the proposed estimators are consistent and asymptotically normal and develop the analytical forms of the variance estimators. Through extensive simulations, we also show that the proposed estimators have little bias compared to the naïve estimator, which uses only uncertain endpoints, and are more efficient with moderate missingness compared to the complete-case estimator, which uses only available true endpoints. We illustrate the proposed method by estimating the risk of developing Alzheimer\u27s disease using data from the Alzheimer\u27s Disease Neuroimaging Initiative. Using our proposed semiparametric estimator, we develop optimal study design strategies to compare survival across treatment groups for a new trial with these data characteristics. We demonstrate how to calculate the optimal number of true events in the validation set with desired power using simulated data when assuming the baseline distribution of the true event, effect size, correlation between outcomes, and proportion of true outcomes that are missing can be estimated from pilot studies. We also propose a sample size formula that does not depend on baseline distribution of the true event and show that power calculated by the formula matches well with simulation based results. Using results from a Ginkgo Evaluation of Memory study, we calculate the number of true events in the validation set that would need to be observed for new studies comparing development of Alzheimer\u27s disease among those with and without antihypertensive use, as well as the total number of subjects and number in the validation set to be recruited for these new trials

    Statistical Methods in Clinical Trial Design

    Get PDF
    Numerous human medical problems or diseases have been aided by the development of effective treatments such as drugs and medical devices. Clinical trials are an integral part of the development process, determining the safety and efficacy of the new proposed treatment, as required by the Food and Drug Administration of the United States. A reliable, efficient and cost-effective way of conducting the clinical trials is important for advancing useful treatments/devices to market and screening out the useless ones, thus benefiting public health in a timely manner. I developed several statistical methods and applications toward this purpose, ranging from early, small scale Phase I studies to late, large scale Phase III studies in clinical trials. In Phase I studies, I establish a general framework for a multi-stage adaptive design where I jointly model a continuous efficacy outcome and continuous toxicity endpoints from multiple treatment cycles, unlike the traditional method that only considers a binary toxicity endpoint (joint work with Mayo Clinic). Extensive simulations confirmed that the design had a high probability of making the correct dose selection and good overdose control. To our best knowledge, this proposed Phase I dual-endpoint dose-finding design is the first to incorporate multiple cycles of toxicities and a continuous efficacy outcome. I also propose and evaluate a two-stage, adaptive clinical trial design for Phase II studies. Its goal is to determine whether future phase 3 (confirmatory) trials should be conducted, and if so, which population should be enrolled. I compute an approximate Bayes optimal design considering a combination of future health benefits and costs. Turning to Phase III studies, I analyze the performance of adaptive enrichment designs with delayed outcome, leveraging information in baseline variables and short-term outcomes to improve precision by using semiparametric, locally efficient estimators at each interim analysis. I also propose a prediction method for analyzing heterogeneity in treatment response, as a secondary analysis, through the identification of treatment covariate interactions honoring different hierarchical conditions

    Comments on: Missing data methods in longitudinal studies: a review

    Get PDF
    Incomplete data are quite common in biomedical and other types of research, especially in longitudinal studies. During the last three decades, a vast amount of work has been done in the area. This has led, on the one hand, to a rich taxonomy of missing-data concepts, issues, and methods and, on the other hand, to a variety of data-analytic tools. Elements of taxonomy include: missing data patterns, mechanisms, and modeling frameworks; inferential paradigms; and sensitivity analysis frameworks. These are described in detail. A variety of concrete modeling devices is presented. To make matters concrete, two case studies are considered. The first one concerns quality of life among breast cancer patients, while the second one examines data from the Muscatine children’s obesity study

    Targeted Learning for Causality and Statistical Analysis in Medical Research

    Get PDF
    The authors present the use of targeted learning methods for medical research, prepared as a chapter for the upcoming book Statistics: Discovering Your Future Power. The targeted learning framework involves the explicit specification of the data, model, and parameter. The estimators are double robust and efficient, and can incorporate machine learning procedures such as the super learner

    Endpoints In Intensive Care Unit Based Randomized Clinical Trials

    Get PDF
    With few exceptions, intensive care unit (ICU)-based randomized clinical trials (RCTs) have failed to demonstrate hypothesized treatment effects. Undoubtedly, some of these failures are attributable to interventions that truly do not provide hoped-for benefits. However, this dissertation pursues the thesis that many null findings represent “false negatives” that are due not to ineffective therapies but to flawed study designs or analytic approaches. We examine the design and statistical methods traditionally employed in ICU-based RCTs, and their potential impacts on the efficient measurement and interpretation of treatment effects. Paper one presents a systematic review of 146 contemporary ICU-based RCTs in which we find that most trials were underpowered to detect small but potentially important mortality differences between treatment arms. We also find that the majority of RCTs (73%) specified primary outcomes other than mortality, that trials employing nonmortal primary outcomes more frequently identified significant treatment effects, and that both mortal and nonmortal endpoints were heterogeneously defined, measured and analyzed across RCTs. Thus, papers two and three focus on nonmortal endpoints, using ICU length of stay (LOS) as a case study to evaluate how best to measure and analyze duration-based nonmortal endpoints. In paper two, we conduct a statistical simulation study, demonstrating that nonmortal endpoints are interlinked with and confounded by mortality, and that the manner in which investigators choose to account for deaths in LOS analyses may influence their conclusions. In paper three, we examine another potential source of error in LOS analyses, namely the measurement error attributable to the additional ICU time that patients commonly accrue after they are clinically ready for ICU discharge. Using simulated data informed by our own ICU-based RCT, we demonstrate that this “immutable time” (which cannot plausibly be altered by the interventions under study) combines with clinically necessary ICU time to produce overall LOS distributions that may either mask true treatment effects or suggest false treatment effects. Our work provides evidence of the potential benefits and pitfalls when employing nonmortal outcomes in ICU-based RCTs, and also identifies a clear need for standardized methods for defining and analyzing such outcomes

    Book of Abstracts XVIII Congreso de Biometría CEBMADRID

    Get PDF
    Abstracts of the XVIII Congreso de Biometría CEBMADRID held from 25 to 27 May in MadridInteractive modelling and prediction of patient evolution via multistate models / Leire Garmendia Bergés, Jordi Cortés Martínez and Guadalupe Gómez Melis : This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019104830RBI00]; and the Generalitat de Catalunya (Spain) [2017SGR622 and 2020PANDE00148].Operating characteristics of a model-based approach to incorporate non-concurrent controls in platform trials / Pavla Krotka, Martin Posch, Marta Bofill Roig : EU-PEARL (EU Patient-cEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development non-profit organisation, Spring works Therapeutics Inc.Modeling COPD hospitalizations using variable domain functional regression / Pavel Hernández Amaro, María Durbán Reguera, María del Carmen Aguilera Morillo, Cristobal Esteban Gonzalez, Inma Arostegui : This work is supported by the grant ID2019-104901RB-I00 from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033.Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain / Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián / Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain : This work was partially supported by the Ministerio de Ciencia e Innovación under Grant PID2020-116873GB-I00; Gobierno de Aragón under Research Group E46_20R: Modelos Estocásticos; and JC-M was supported by Gobierno de Aragón under Doctoral Scholarship ORDEN CUS/581/2020.Estimation of the area under the ROC curve with complex survey data / Amaia Iparragirre, Irantzu Barrio, Inmaculada Arostegui : This work was financially supported in part by IT1294-19, PID2020-115882RB-I00, KK-2020/00049. The work of AI was supported by PIF18/213.INLAMSM: Adjusting multivariate lattice models with R and INLA / Francisco Palmí Perales, Virgilio Gómez Rubio and Miguel Ángel Martínez Beneito : This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, Spain) and FEDER, grant MTM2016-77501-P, funded by Ministerio de Economía y Competitividad (Spain), grant PID2019-106341GB-I00 from Ministerio de Ciencia e Innovación (Spain) and a grant to support research groups by the University of Castilla-La Mancha (Spain). F. Palmí-Perales has been supported by a Ph.D. scholarship awarded by the University of Castilla-La Mancha (Spain)

    The application of Bayesian adaptive design and Markov model in clinical trials

    Get PDF
    In this research, two new designs in clinical trials are proposed. The first problem is a new Bayesian adaptive dose-finding design and its application in an oncology clinical trial. This design is used for phase IB studies with the biomarker as the endpoint and with the fewer patients. The second problem is another new Bayesian adaptive dose-finding design with longitudinal analysis and its application in phase II depression clinical trial. This design is best fit for phase II dosing-finding clinical trials with clinical endpoints. MTD information has been obtained before the trials. In adaptive dose-finding clinical trials, the strategy is to reduce the allocation of patients to non-informative doses and also save the trial cost. Bayesian adaptive dose finding design has the ability to utilize accumulating data obtained in real time to alter the course of the trial, thereby enabling dynamic allocation to different dosing groups and dropping of ineffective dosing group earlier. In this research, Bayesian adaptive method is used as a new and useful approach that applies to phase IB and phase II dose-finding clinical trials to evaluate safety and efficacy of the study treatment. Response model and Normal Dynamic Linear Models (NDLMs) are applied in stages 1-4. Conditional probability for each parameter in the model is derived using appropriate prior distributions. Markov Chain Monte Carlo (MCMC) method is used to do the simulation. Model parameters with meaningful prior distributions and the posterior quantities are obtained to evaluate the trial results and they help to determine the optimal dose level which can be used in later studies. Simulations are done for different scenarios in the two designs and used to validate the model. Five-thousand simulation trials are conducted to verify the repeatability of the results. The posterior probability of success for the trial is greater than 90% based on the simulation results. The results give clearer idea if one needs to go further to test new dose levels based on the thorough evaluation of the existing partial data. Compared with the other adaptive dose finding strategy, the proposed Bayesian adaptive designs are sensitive and robust to help the investigators draw conclusion as early as possible. The designs can also reduce sample size substantially which in turn leads to savings in cost and time. Continuous-time Markov model has the advantage over the traditional survival model and can be used to describe disease as a series of probable transitions between health states. This is an attractive feature since it provides the ability to describe the course of disease over time. It can also describe and estimate expected survival in clinical cohort. In this research, continuous-time Markov model is used in the time-to-event analysis in a phase II oncology trial. Six states are defined in the Markov chain which is used in time to progression analysis for 36 patients with neuroendocrine carcinoma. The transition probability matrix P is defined and used to iterate future transition and survival probabilities. The estimate from matrix analysis shows that the results are reliable and comparable with the Kaplan-Meier results

    Pharmacological modeling and biostatistical analysis of a new drug

    Get PDF
    corecore