Master of Science / Department of Statistics / Christopher Vahl / Non-inferiority trials have been widely used in many medical areas. The goal of a non-inferiority trial is to show that a new test therapy is either better or not too much worse than the active control rather than showing the test therapy is superior to a negative control (i.e. placebo). The appeal of a non-inferiority trial is that it is often unethical to give some patients a treatment with no therapeutic benefit. When designing a non-inferiority trial, the issues of assay sensitivity, sample size, constancy condition, and a suitable non-inferiority margin need to be considered. A poor choice of the non-inferiority margin is a major reason that many non-inferiority trials fail. A numerical example is presented to show how to estimate the non-inferiority margin without historical data.

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Background Nursing presence is an intersubjective connection between the nurse and patient that results in improved patient outcomes. Present day task-oriented healthcare robots possess an evolving capacity to address task-based attributes of nursing care but are far less capable of addressing attributes of nursing presence. The purpose of this study was to explore adults' perception of nurse-expressed empathy, an attribute of nursing presence, as enacted by a semi-autonomous robot nurse compared to a human nurse following a discussion of the adults' health concerns or issues. Methods The design for this study employed a non-inferiority randomized comparison of two groups. The overall hypothesis was that adults' perception of nurse-expressed empathy during human-robot interactions was not inferior to the perception of nurse-expressed empathy during human-human interactions. From a broad geographic community 102 adults, age 21 to 80, were recruited and assigned to an active control or reference treatment group using stratified and blocked randomization. In each group, participants discussed the impact of health issues or concerns on their daily life. Participants in the reference treatment group interacted with a semi-autonomous robot. Participants in the control group interacted with the researcher face-to-face. Participants' perception of nurse-expressed empathy was measured using the Empathic Understanding Scale of the Barrett-Lennard Relationship Inventory. A confidence interval approach using 95%-95% method was used to assess non-inferiority. The first confidence interval was obtained from analysis of seven historical studies that measured empathy using the Empathic Understanding Scale. The second confidence interval was obtained from analyses of the difference in mean perceived empathy between the two study groups. Results Three normalized statistical methods used to evaluate non-inferiority were significant (p<.025) and contained confidence intervals less than the non-inferiority margin (δ= 3.33). This resulted in the rejection of the null hypothesis that empathy communicated by a robot was inferior to empathy communicated by a human nurse. Conclusions This study provided evidence that nurses operating semi-autonomous robots can communicate empathy to adults. Innovation and collaboration among nurses, computer scientists and engineers will ensure that successive generations of robots maintain a nursing perspective while operating at their optimal capacity.

Background and Objectives We investigate three statistical and methodological issues within the context of non-inferiority randomized controlled trials (RCTs), specifically those of radiotherapy regimens for the prevention of local recurrence in patients with early stage breast cancer who have undergone breast conserving surgery. These issues are: (1) the analysis of multiple time-to-event outcomes in non-inferiority RCTs; (2) the interim analysis of a binary outcome that is repeatedly assessed at pre-specified times; and (3) determining the optimal analysis population for dealing with crossovers in non-inferiority RCTs. Methods Issue 1: We investigated and compared the properties of four statistical models (proportional hazards model, competing risk model, marginal model and frailty model) for analyzing radiotherapy non-inferiority RCTs of patients with early stage breast cancer who are at risk for and may experience multiple failure types. We applied the four methods to data from an existing trial in which subjects with breast cancer could experience local recurrence (the primary outcome), distant recurrence, death, or a combination of these events. In addition, we compared these models using simulated examples of similar non-inferiority trials with varying hazards of each failure type. Issue 2: We investigated and compared the properties of three methods for estimating the event proportions for an interim analysis in RCTs with a binary outcome that is repeatedly assessed at pre-specified times. Generally, interim analyses are performed after half or more of the subjects have completed full follow-up. However, depending on the duration of accrual relative to the length of follow-up, this may be inefficient, since there is a possibility that the trial will have completed accrual prior to the interim analysis. We focussed our simulations on situations where delaying the interim analysis until half or more of subjects have completed full follow-up is an inefficient approach. The methods include: 1) estimation of the event proportion based on subjects who have been followed for a pre-specified time (less than the full follow-up duration) or who experienced the outcome; 2) estimation of the event proportion based on all available data from subjects randomized by the time of the interim analysis; and 3) the Kaplan-Meier approach to estimate the event proportion. We varied the risk of the outcome, the treatment effect and the probability of an event occurring at each pre-specified time. We compared the three methods in terms of overall type I and II errors, as well as the probability of stopping early for benefit. Issue 3: We explored the effect of subject crossover from the experimental to the standard radiotherapy arm prior to treatment initiation on the intention-to-treat, per-protocol, as-treated and combined intention-to-treat and per-protocol analysis in non-inferiority RCTs of radiotherapy for the prevention of local recurrence in patients with early stage breast cancer. We varied the non-inferiority margin, the percent of subjects who cross over and evaluated random and non-random crossover. The main comparison of the methods was done using overall type I error. In addition, we compared the methods based on estimate bias and standard error of the estimate. Results and Conclusions Issue 1: All four models produced similar results for the existing trial (i.e. non-inferiority was observed regardless of the method used). Simulations showed that the event-specific methods yielded contrasting results when the distribution of distant recurrence or death differed between treatment groups. We conclude that multiple models should be used as part of a comprehensive analysis. Issue 2: We showed that conducting an interim analysis when a considerable number of subjects have completed a portion of their full follow-up duration is an efficient approach under certain scenarios where event distribution probabilities are similar between treatment groups. Under these specific scenarios, all three methods preserved the type I and II errors. In these cases, we recommend using the Kaplan-Meier method because it incorporates all the available data and has greater probability of early stopping. Issue 3: The as-treated analysis had the best performance in terms of type I error rate. However, it can be recommended only in scenarios where crossover is random. It performed poorly in scenarios with greater than 2% non-random crossover. The intention-to-treat and per-protocol analysis performed poorly under both random and non-random crossover scenarios. / Thesis / Doctor of Philosophy (PhD)

This work deals with children with a sense of a complex of inferiority, especially in school age. The theoretical part explains the problem of feeling, oran inferiority komplex important role and as the Administrative raise a child to avoid this problem, or at least this problem soften. The practical part is devoted to rosearch. Seeks to analyze and komplex sense or inferiority in the context of different circumstances, and aspects (family, school, welfare and oxen time).

Obstacles sometimes limit enrollment in randomized clinical trials of an exper- imental product versus an active control, making it desirable to augment the ran- domized control group with historical control groups. However, bias between control groups with respect to the mean outcome could lead to spurious conclusions. Meth- ods are necessary that allow for the combination of control groups while controlling for bias. Pocock (1976) developed a Bayesian test to address this need, but it requires sub- jective specification of the variance of the bias between the randomized and historical control groups and is designed to include only a single historical control group. In the context of an actively controlled non-inferiority trial, we extend his method on three fronts. First, we replace subjective specification of the variance of the bias with empirically driven estimates. Second, we develop an adaptive design that re-powers a trial based on an interim estimate of the variance of the bias using observed data. Third, we modify the test to include multiple historical control groups. When including a single historical control group, simulations show that the true bias, if known, can be used in place of the variance of the bias, and that this estimate ivmaintains Type I Error with no loss in power as compared to using the true variance of the bias. Further, we show that using an empirical estimate of the bias to estimate the variance of bias may result in moderately inflated Type I Error, but that using a conservative estimate of the bias (the upper bound of a 90% confidence interval) maintains Type I Error. Simulations also demonstrate that using an estimate of the bias at the interim and conclusion provides designed power but may result in moder- ately inflated Type I Error. Therefore, a conservative estimate of the bias should be used at trial end when using this approach. Lastly, it is shown that if an adequate number of multiple historical control groups are available, the modified test maintains Type I Error when using bias estimates. These methods provide objective guidance on parameter estimation, but further research is necessary in order to improve power.

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In health related studies, non-inferiority tests are used to demonstrate that a new treatment is not worse than a currently existing treatment by more than a pre-specified margin. In this thesis, we discuss three approaches; a Z-score approach, a generalized p-value approach and a Bayesian approach, to test the non-inferiority hypotheses in two-arm trials for ratio of log-normal means. The log-normal distribution is widely used to describe the positive random variables with positive skewness which is appealing for data arising from studies with small sample sizes. We demonstrate the approaches using data arising from an experimental aging study on cognitive penetrability of posture control. We also examine the suitability of three methods under various sample sizes via simulations. The results from the simulation studies indicate that the generalized p-value and the Bayesian approaches reach an agreement approximately and the degree of the agreement increases when the sample sizes increase. However, the Z-score approach can produce unsatisfactory results even under large sample sizes.

As most adaptive clinical trial designs are implemented in stages, well-understood methods of sequential trial monitoring are needed. In the frequentist paradigm, examples of sequential monitoring methodologies include the p-value combination tests, conditional error, conditional power, and alpha spending approaches. Within the Bayesian framework, posterior and predictive probabilities are used as monitoring criteria, with the latter being analogous to the conditional power approach. In a placebo or active-contolled dose response clinical trial, we are interested in achieving two objectives: selecting the best therapeutic dose and confirming this selected dose. Traditional approach uses the parallel group design with Dunnett's adjustment. Recently, some two- stage Seamless II/III designs have been proposed. The drop-the-losers design considers selecting the dose with the highest empirical mean after the first stage, while another design assumes a dose-response model to aid dose selection. These designs however do not consider prioritizing the doses and adaptively inserting new doses. We propose an adaptive staggered dose design for a normal endpoint that makes minimal assumption regarding the dose response and sequentially adds doses to the trial. An alpha spending function is applied in a novel way to monitor the doses across the trial. Through numerical and simulation studies, we confirm that optimistic alpha spending coupled with informative dose ordering jointly produce some desirable operating characteristics when compared to drop-the-losers and model-based Seamless designs. In addition, we show how the design parameters can be flexibly varied to further improve its performance and how it can be extended to binary and survival endpoints. In a biosimilarity trial, we are interested in establishing evidence of comparable efficacy between a follow-on biological product and a reference innovator product. So far, no standard method for biosimilarity has been endorsed by regulatory agency. We propose a Bayesian hierarchical bias model and a non-inferiority hypothesis framework to prove biosimilarity. A two-stage adaptive design using predictive probability as early stopping criterion is pro- posed. Through simulation study, the proposed design controls the type I error better than the frequentist approach and Bayesian power is superior when biosimilarity is plausible. Two-stage design further reduces the expected sample size.

Background: At the onset of the COVID-19 pandemic, there was an increased uptake in telehealth services. However, little research has compared the efficacy of individual cognitive behavioral therapy (CBT) for youth with anxiety administered via (a) telehealth and (b) in-person. The present study examined outcomes for youth with anxiety disorders (diagnosed by an Independent Evaluator; IE) treated via telehealth during the COVID-19 pandemic and youth treated via in-person therapy prior to the COVID-19 pandemic. Methods: Participants (n = 92) were 46 families who completed telehealth treatment and 46 families who completed services in-person, matched on age and principal anxiety diagnosis. One-sided t-tests for non-inferiority tested whether telehealth is non-inferior to in-person therapy, a gold standard treatment. ANOVAs and regression models estimated treatment differences and candidate moderators (e.g., social anxiety disorder, comorbid attention problems). Results: Results support non-inferiority across multiple indices of outcomes (i.e., self- and caregiver-reported anxiety symptoms, IE-rated functional impairment, and IE-rated treatment response). Analyses indicate that both treatments were effective in reducing anxiety symptoms and functional impairment. Caregivers reported higher levels of anxiety for youth treated via telehealth than youth treated in person. No variables moderated the differences in outcomes between treatment modality. Conclusions: Findings support that CBT administered via telehealth is similarly efficacious as CBT administered in-person. Implications regarding the availability and accessibility of evidence-based treatment for youth with anxiety are discussed. / Psychology