Indiana University-Purdue University Indianapolis (IUPUI) / The late-onset outcome issue is common in early phase dose- nding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and e cacy responses are subject to the late-onset outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity{e cacy distribution. We propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient's actual follow-up time. We further extend the proposed method to handle more complex situations where the late-onset outcomes are competing risks or semicompeting risks outcomes. We treat the late-onset competing risks/semi-competing risks outcomes as missing data and develop a series of Bayesian data-augmentation methods to e ciently impute the missing data and draw the posterior samples of the parameters of interest. We also propose adaptive dose- nding algorithms to allocate patients and identify the optimal biological dose during the trial. Simulation studies show that the proposed methods yield desirable operating characteristics and outperform the existing methods.

First-in-man (FIM) Phase I clinical trials are part of the critical path in the development of a new compound entity (NCE). Since FIM clinical trials are the first time that an NCE is dosed in human subjects, the designs used in these trials are unique and geared toward patient safety. We develop a method for obtaining the desired response using an adaptive non-linear approach. This method is applicable for studies in which MTD, NOEL,NOAEL, PK, PD effects or other such endpoints are evaluated to determine the desired dose. The method has application whenever a measurable PD marker is an indicator of potential efficacy and could be particularly useful for dose finding studies. The advantages in the adaptive non-linear methodology is that the actual range of dose response and lowest non-effective dose levels are more quickly and accurately determined using fewer subjects than typically needed for a conventional early phase clinical trial. Using the nonlinear logistic model, we demonstrate, with simulations, that the DOSEFIND approach has better asymptotic relative efficiency than a fixed-dose approach. Further, we demonstrate that, on average, this method is consistent in reproducing .the target dose, that it has very little bias. This is an indicator of reproducibility of the method, showing that the long-run average error is quite small. Additionally, DOSEFIND is more cost effective because the sample size needed to obtain the desired target dose is much smaller than that needed in the fixed dose approach.

Phase I clinical trials in oncology aim to evaluate the toxicity risk of new therapies and identify a safe but also effective dose for future studies. Traditional Phase I trials of chemotherapies focus on estimating the maximum tolerated dose (MTD). The rationale for finding the MTD is that better therapeutic effects are expected at higher dose levels as long as the risk of severe toxicity is acceptable. With the advent of a new generation of cancer treatments such as the molecularly targeted agents (MTAs) and immunotherapies, higher dose levels no longer guarantee increased therapeutic effects, and the focus has shifted to estimating the optimal biological dose (OBD). The OBD is a dose level with the highest biologic activity with acceptable toxicity. The search for OBD requires joint evaluation of toxicity and efficacy. Although several seamleass phase I/II designs have been published in recent years, there is not a consensus regarding an optimal design and further improvement is needed for some designs to be widely used in practice. In this dissertation, we propose a modification to an existing seamless phase I/II design by Wages and Tait (2015) for locating the OBD based on binary outcomes, and extend it to time to event (TITE) endpoints. While the original design showed promising results, we hypothesized that performance could be improved by replacing the original adaptive randomization stage with a different randomization strategy. We proposed to calculate dose assigning probabilities by averaging all candidate models that fit the observed data reasonably well, as opposed to the original design that based all calculations on one best-fit model. We proposed three different strategies to select and average among candidate models, and simulations are used to compare the proposed strategies to the original design. Under most scenarios, one of the proposed strategies allocates more patients to the optimal dose while improving accuracy in selecting the final optimal dose without increasing the overall risk of toxicity. We further extend this design to TITE endpoints to address a potential issue of delayed outcomes. The original design is most appropriate when both toxicity and efficacy outcomes can be observed shortly after the treatment, but delayed outcomes are common, especially for efficacy endpoints. The motivating example for this TITE extension is a Phase I/II study evaluating optimal dosing of all-trans retinoic acid (ATRA) in combination with a fixed dose of daratumumab in the treatment of relapsed or refractory multiple myeloma. The toxicity endpoint is observed in one cycle of therapy (i.e., 4 weeks) while the efficacy endpoint is assessed after 8 weeks of treatment. The difference in endpoint observation windows causes logistical challenges in conducting the trial, since it is not acceptable in practice to wait until both outcomes for each participant have been observed before sequentially assigning the dose of a newly eligible participant. The result would be a delay in treatment for patients and undesirably long trial duration. To address this issue, we generalize the time-to-event continual reassessment method (TITE-CRM) to bivariate outcomes with potentially non-monotonic dose-efficacy relationship. Simulation studies show that the proposed TITE design maintains similar probability in selecting the correct OBD comparing to the binary original design, but the number of patients treated at the OBD decreases as the rate of enrollment increases. We also develop an R package for the proposed methods and document the R functions used in this research. The functions in this R package assist implementation of the proposed randomization strategy and design. The input and output format of these functions follow similar formatting of existing R packages such as "dfcrm" or "pocrm" to allow direct comparison of results. Input parameters include efficacy skeletons, prior distribution of any model parameters, escalation restrictions, design method, and observed data. Output includes recommended dose level for the next patient, MTD, estimated model parameters, and estimated probabilities of each set of skeletons. Simulation functions are included in this R package so that the proposed methods can be used to design a trial based on certain parameters and assess performance. Parameters of these scenarios include total sample size, true dose-toxicity relationship, true dose-efficacy relationship, patient recruit rate, delay in toxicity and efficacy responses.

Receptor occupancy assessed by Positron Emission Tomography (PET) can provide important translational information to help bridge information from one drug to another or from animal to man. The aim of this thesis was to develop nonlinear mixed effects methods for estimation of the relationship between drug exposure and receptor occupancy for the two mGluR5 antagonists AZD9272 and AZD2066 and for the 5HT1B receptor antagonist AZD3783. Also the optimal design for improved estimation of the relationship between drug exposure and receptor occupancy as well as for improved dose finding in neuropathic pain treatment, was investigated. Different modeling approaches were applied. For AZD9272, the radioligand kinetics and receptor occupancy was simultaneously estimated using arterial concentrations as input function and including two brain regions of interest. For AZD2066, a model was developed where brain/plasma partition coefficients from ten different brain regions were included simultaneously as observations. For AZD3783, the simplified reference tissue model was extended to allow different non-specific binding in the reference region and brain regions of interest and the possibility of using white matter as reference was also evaluated. The optimal dose-selection for improved precision of receptor occupancy as well as for improved precision of the minimum effective dose of a neuropathic pain treatment was assessed, using the D-optimal as well as the Ds-optimal criteria. Simultaneous modelling of radioligand and occupancy provided a means to avoid simplifications or approximations and provided the possibility to tests or to relax assumptions. Inclusion of several brain regions of different receptor density simultaneously in the analysis, markedly improved the precision of the affinity parameter. Higher precision was achieved in relevant parameters with designs based on the Ds compared to the D-optimal criterion. The optimal design for improved precision of the relationship between dose and receptor occupancy depended on the number of brain regions and the receptor density of these regions. In conclusion, this thesis presents novel non-linear mixed effects models estimating the relationship between drug exposure and receptor occupancy, providing useful translational information, allowing for a better informed drug-development.

To assess non-inferiority of an experimental product to an active control in a clinical trial, an ideal design is to include a placebo arm to ensure both the experimental product and the active control is superior to placebo. We aim to identify methodology to control Type I error rate and maintain adequate power in a superiority/non-inferiority seamless clinical trial defined as: 1. selecting the best experimental treatment dose vs. placebo out of multiple treatment doses in Stage I; and 2. assessing non-inferiority of the chosen experimental dose to an active control, after adding subjects to yield adequate power for non-inferiority, in Stage II. The trial design here is an antihypertensive trial with change in systolic blood pressure as the outcome. The trial has three experimental treatment doses arms of experimental, a placebo control arm, and an active control arm. A simulation study of 20,000 such trials was conducted. We apply multiple comparison methodologies in Stage I to detect the most beneficial experimental treatment dose versus placebo, and test non-inferiority of the selected experimental dose to the active control in Stage II. Simulated Type I error rate and power for claiming non-inferiority are calculated for various dose-response trends. The need to adjust alpha to control Type I error either stage is assessed, seeking the optimal approach for doing so. Next, type I error and power for various fixed and variable non-inferiority margins are evaluated, exploring a range of margins informed by the first stage results of the study. A variable non-inferiority margin informed completely by the first stage of the trial approach results in inflated error rate which cannot be controlled by suggested multiplicity adjustments. We assess a synthesis approach between the fixed and variable margins, to both control the family-wise error rates and reach adequate power, depending on a tuning parameter defined in our work. We conclude that well-designed and adequately controlled seamless superiority/non-inferiority trials are possible with appropriate multiple comparisons adjustments and could result in less development time and fewer subjects needed to assess efficacy than separate trials.

Indiana University-Purdue University Indianapolis (IUPUI) / Survival analysis has broad applications in diverse research areas. In this dissertation, we consider an innovative application of survival analysis approach to phase I dose-finding design and the modeling of multivariate survival data. In the first part of the dissertation, we apply time to event analysis in an innovative dose-finding design. To account for the unique feature of a new class of oncology drugs, T-cell engagers, we propose a phase I dose-finding method incorporating systematic intra-subject dose escalation. We utilize survival analysis approach to analyze intra-subject dose-escalation data and to identify the maximum tolerated dose. We evaluate the operating characteristics of the proposed design through simulation studies and compare it to existing methodologies. The second part of the dissertation focuses on multivariate survival data with semi-competing risks. Time-to-event data from the same subject are often correlated. In addition, semi-competing risks are sometimes present with correlated events when a terminal event can censor other non-terminal events but not vice versa. We use a semiparametric frailty model to account for the dependence between correlated survival events and semi-competing risks and adopt penalized partial likelihood (PPL) approach for parameter estimation. In addition, we investigate methods for variable selection in semi-parametric frailty models and propose a double penalized partial likelihood (DPPL) procedure for variable selection of fixed effects in frailty models. We consider two penalty functions, least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) penalty. The proposed methods are evaluated in simulation studies and illustrated using data from Indianapolis-Ibadan Dementia Project.

No description available.

Pharmaceutical drug development is a costly and time consuming process. Reportedly, it takes about 10-15 years and ~900 million dollars of investment to launch a new drug in the world market. Any measure that increases the power and also decreases uncertainty about that power also increases drug net present value. For some time now, it has been argued that judicious utilization of available data might lead to more efficient use of resources during drug development. Conventionally, assessment of effectiveness has been based on comparing change from baseline at some pre-specified time for the control and test treatment (SPA). The last observation carry forward (LOCF) is a widely used technique if the data are missing due to any reason. Although, LOCF is known to introduce bias, the direction and magnitude is debatable.The primary aim of the proposed simulation experiments was to assess the properties of the random effects model (REM) and mixed model repeated measures (MMRM) methods that utilize all the data collected during pivotal trials. A total of 43 scenarios based on disease progression, magnitude of drug effect, between and within subject variability and patient drop-outs were analyzed. Three analysis methods, viz. SPA, REM and MMRM, were investigated. For the SPA method, the missing data were imputed with four different methods, such as LOCF, mean imputation, population and individual regression. The false-positive, false-negative inferences and bias in estimating the effect size for each method was assessed.The most important finding of this report is that the REM and MMRM methods are efficient alternatives to the SPA methods with ~50% savings on sample size. These methods are based on sound scientific principles and provide stronger evidence against the null hypothesis. The choice of the REM versus MMRM method is dependent on the purpose of the analysis and data gathered from the experimental design. The results support the use of likelihood-based MMRM methods for regulatory decision making. The REM methods are useful in understanding the time course of the disease and drug effect, making predictions based on the data and gaining insights into time to steady state effect for rational decision making. The SPA methods are less powerful across all the scenarios. The SPA-LOCF yielded anticonservative results in some cases with type-1 error rate exceeding 15% if data were missing due to toxicity. On the other hand, the drug effect was consistently underestimated (~40%), if data were missing due to lack of effectiveness. The results demonstrate that the SPA-LOCF methods make it practically impossible to establish effectiveness in these areas with a reasonable sample size.

Les Phases I sont un domaine des essais cliniques dans lequel les statisticiens ont encore beaucoup à apporter. Depuis trente ans, ce secteur bénéficie d'un intérêt croissant et de nombreuses méthodes ont été proposées pour gérer l'allocation séquentielle des doses aux patients intégrés à l'étude. Durant cette Phase, il s'agit d'évaluer la toxicité, et s'adressant à des patients gravement atteints, il s'agit de maximiser les effets curatifs du traitement dont les retours toxiques sont une conséquence. Parmi une gamme de doses, on cherche à déterminer celle dont la probabilité de toxicité est la plus proche d'un seuil souhaité et fixé par les praticiens cliniques. Cette dose est appelée la MTD (maximum tolerated dose). La situation canonique dans laquelle sont introduites la plupart des méthodes consiste en une gamme de doses finie et ordonnée par probabilité de toxicité croissante. Dans cette thèse, on introduit une modélisation très générale du problème, la SPM (semi-parametric methods), qui recouvre une large classe de méthodes. Cela permet d'aborder des questions transversales aux Phases I. Quels sont les différents comportements asymptotiques souhaitables? La MTD peut-elle être localisée? Comment et dans quelles circonstances? Différentes paramétrisations de la SPM sont proposées et testées par simulations. Les performances obtenues sont comparables, voir supérieures à celles des méthodes les plus éprouvées. Les résultats théoriques sont étendus au cas spécifique de l'ordre partiel. La modélisation de la SPM repose sur un traitement hiérarchique inférentiel de modèles satisfaisant des contraintes linéaires de paramètres inconnus. Les aspects théoriques de cette structure sont décrits dans le cas de lois à supports discrets. Dans cette circonstance, de vastes ensembles de lois peuvent aisément être considérés, cela permettant d'éviter les cas de mauvaises spécifications. / Phase I clinical trials is an area in which statisticians have much to contribute. For over 30 years, this field has benefited from increasing interest on the part of statisticians and clinicians alike and several methods have been proposed to manage the sequential inclusion of patients to a study. The main purpose is to evaluate the occurrence of dose limiting toxicities for a selected group of patients with, typically, life threatening disease. The goal is to maximize the potential for therapeutic success in a situation where toxic side effects are inevitable and increase with increasing dose. From a range of given doses, we aim to determine the dose with a rate of toxicity as close as possible to some threshold chosen by the investigators. This dose is called the MTD (maximum tolerated dose). The standard situation is where we have a finite range of doses ordered with respect to the probability of toxicity at each dose. In this thesis we introduce a very general approach to modeling the problem - SPM (semi-parametric methods) - and these include a large class of methods. The viewpoint of SPM allows us to see things in, arguably, more relevant terms and to provide answers to questions such as asymptotic behavior. What kind of behavior should we be aiming for? For instance, can we consistently estimate the MTD? How, and under which conditions? Different parametrizations of SPM are considered and studied theoretically and via simulations. The obtained performances are comparable, and often better, to those of currently established methods. We extend the findings to the case of partial ordering in which more than one drug is under study and we do not necessarily know how all drug pairs are ordered. The SPM model structure leans on a hierarchical set-up whereby certain parameters are linearly constrained. The theoretical aspects of this structure are outlined for the case of distributions with discrete support. In this setting the great majority of laws can be easily considered and this enables us to avoid over restrictive specifications than can results in poor behavior.

Les essais cliniques de phase I en cancérologie permettent d’identifier la dose optimale (DO), définie comme la dose maximale tolérée (DMT). Les approches conventionnelles de recherche de dose reposent uniquement sur les événements de toxicité observés au cours du premier cycle de traitement. Le développement des thérapies moléculaires ciblées (TMC), habituellement administrées sur de longues périodes, a remis en question cet objectif. Considérer uniquement le premier cycle de traitement n’est pas suffisant. De plus, comme l'activité n'augmente pas nécessairement de façon monotone avec la dose, la toxicité et l'activité doivent être prises en compte pour identifier la DO. Récemment, les biomarqueurs continus sont de plus en plus utilisés pour mesurer l'activité.L’objectif de cette thèse était de proposer et d'évaluer des designs adaptatifs pour identifier la DO. Nous avons développé deux designs de recherche de dose, basés sur une modélisation conjointe des mesures longitudinales de l'activité des biomarqueurs et de la première toxicité dose-limitante (DLT), avec un effet aléatoire partagé. En utilisant des propriétés de distribution normales asymétriques, l'estimation reposait sur la vraisemblance sans approximation ce qui est une propriété importante dans le cas de petits échantillons qui sont souvent disponibles dans ces essais. La DMT est associée à un certain risque cumulé de DLT sur un nombre prédéfini de cycles de traitement. La DO a été définie comme la dose la moins toxique parmi les doses actives, sous la contrainte de ne pas dépasser la DMT. Le second design étendait cette approche pour les cas d’une relation dose-activité qui pouvait atteindre un plateau. Un modèle à changement de pente a été implémenté. Nous avons évalué les performances des designs avec des études de simulations en étudiant plusieurs scénarios et divers degrés d'erreur de spécification des modèles.Finalement, nous avons effectué une analyse de 27 études des TMCs de phase I, en tant que monothérapie. Les études ont été réalisées par l'Institut National du Cancer. L'objectif principal était d'estimer le risque par cycle et l’incidence cumulative de la toxicité sévère, jusqu’à six cycles. Les analyses ont été effectuées séparément pour différents sous-groupes de doses, ainsi que pour les toxicités hématologiques et non-hématologiques. / Conventional dose-finding approaches in oncology of phase I clinical trials aim to identify the optimal dose (OD) defined as the maximum tolerated dose (MTD), based on the toxicity events observed during the first treatment cycle. The constant development of molecularly targeted agents (MTAs), usually administered in chronic schedules, has challenged this objective. Not only, the outcomes after the first cycle are of importance, but also activity does not necessarily increase monotonically with dose. Therefore, both toxicity and activity should be considered for the identification of the OD. Lately, continuous biomarkers are used more and more to monitor activity. The aim of this thesis was to propose and evaluate adaptive designs for the identification of the OD. We developed two dose-finding designs, based on a joint modeling of longitudinal continuous biomarker activity measurements and time to first dose limiting toxicity (DLT), with a shared random effect, using skewed normal distribution properties. Estimation relied on likelihood that did not require approximation, an important property in the context of small sample sizes, typical of phase I/II trials. We addressed the important case of missing at random data that stem from unacceptable toxicity, lack of activity and rapid deterioration of phase I patients. The MTD was associated to some cumulative risk of DLT over a predefined number of treatment cycles. The OD was defined as the lowest dose within a range of active doses, under the constraint of not exceeding the MTD. The second design extended this approach for cases of a dose-activity relationship that could reach a plateau. A change point model was implemented. The performance of the approaches was evaluated through simulation studies, investigating a wide range of scenarios and various degrees of data misspecification. As a last part, we performed an analysis of 27 phase I studies of MTAs, as monotherapy, conducted by the National Cancer Institut. The primary focus was to estimate the per-cycle risk and the cumulative incidence function of severe toxicity, over up to six cycles. Analyses were performed separately for different dose subgroups, as well as for hematologic and non-hematologic toxicities.