Clinical effectiveness of cognitive behavioral therapy for depression in routine care: A propensity score based comparison between randomized controlled trials and clinical practice

Background: The efficacy of cognitive behavioral therapy (CBT) for the treatment of depressive disorders has been demonstrated in many randomized controlled trials (RCTs). This study investigated whether for CBT similar effects can be expected under routine care conditions when the patients are comparable to those examined in RCTs. Method: N=574 CBT patients from an outpatient clinic were stepwise matched to the patients undergoing CBT in the National Institute of Mental Health Treatment of Depression Collaborative Research Program (TDCRP). First, the exclusion criteria of the RCT were applied to the naturalistic sample of the outpatient clinic. Second, propensity score matching (PSM) was used to adjust the remaining naturalistic sample on the basis of baseline covariate distributions. Matched samples were then compared regarding treatment effects using effect sizes, average treatment effect on the treated (ATT) and recovery rates. Results: CBT in the adjusted naturalistic subsample was as effective as in the RCT. However, treatments lasted significantly longer under routine care conditions. Limitations: The samples included only a limited amount of common predictor variables and stemmed from different countries. There might be additional covariates, which could potentially further improve the matching between the samples. Conclusions: CBT for depression in clinical practice might be equally effective as manual-based treatments in RCTs when they are applied to comparable patients. The fact that similar effects under routine conditions were reached with more sessions, however, points to the potential to optimize treatments in clinical practice with respect to their efficiency.

Effects, but no interactions, of ubiquitous pesticide and parasite stressors on honey bee (Apis mellifera) lifespan and behaviour in a colony environment

Interactions between pesticides and parasites are believed to be responsible for increased mortality of honey bee (Apis mellifera) colonies in the northern hemisphere. Previous efforts have employed experimental approaches using small groups under laboratory conditions to investigate influence of these stressors on honey bee physiology and behaviour, although both the colony level and field conditions play a key role for eusocial honey bees. Here, we challenged honey bee workers under in vivo colony conditions with sublethal doses of the neonicotinoid thiacloprid, the miticide tau-fluvalinate and the endoparasite Nosema ceranae, to investigate potential effects on longevity and behaviour using observation hives. In contrast to previous laboratory studies, our results do not suggest interactions among stressors, but rather lone effects of pesticides and the parasite on mortality and behaviour, respectively. These effects appear to be weak due to different outcomes at the two study sites, thereby suggesting that the role of thiacloprid, tau-fluvalinate and N. ceranae and interactions among them may have been overemphasized. In the future, investigations into the effects of honey bee stressors should prioritize the use of colonies maintained under a variety of environmental conditions in order to obtain more biologically relevant data.

Total disc arthroplasty versus anterior cervical interbody fusion: use of the Spine Tango registry to supplement the evidence from randomized control trials.

BACKGROUND CONTEXT Several randomized controlled trials (RCTs) have compared patient outcomes of anterior (cervical) interbody fusion (AIF) with those of total disc arthroplasty (TDA). Because RCTs have known limitations with regard to their external validity, the comparative effectiveness of the two therapies in daily practice remains unknown. PURPOSE This study aimed to compare patient-reported outcomes after TDA versus AIF based on data from an international spine registry. STUDY DESIGN AND SETTING A retrospective analysis of registry data was carried out. PATIENT SAMPLE Inclusion criteria were degenerative disc or disc herniation of the cervical spine treated by single-level TDA or AIF, no previous surgery, and a Core Outcome Measures Index (COMI) completed at baseline and at least 3 months' follow-up. Overall, 987 patients were identified. OUTCOME MEASURES Neck and arm pain relief and COMI score improvement were the outcome measures. METHODS Three separate analyses were performed to compare TDA and AIF surgical outcomes: (1) mimicking an RCT setting, with admission criteria typical of those in published RCTs, a 1:1 matched analysis was carried out in 739 patients; (2) an analysis was performed on 248 patients outside the classic RCT spectrum, that is, with one or more typical RCT exclusion criteria; (3) a subgroup analysis of all patients with additional follow-up longer than 2 years (n=149). RESULTS Matching resulted in 190 pairs with an average follow-up of 17 months that had no residual significant differences for any patient characteristics. Small but statistically significant differences in outcome were observed in favor of TDA, which are potentially clinically relevant. Subgroup analyses of atypical patients and of patients with longer-term follow-up showed no significant differences in outcome between the treatments. CONCLUSIONS The results of this observational study were in accordance with those of the published RCTs, suggesting substantial pain reduction both after AIF and TDA, with slightly greater benefit after arthroplasty. The analysis of atypical patients suggested that, in patients outside the spectrum of clinical trials, both surgical interventions appeared to work to a similar extent to that shown for the cohort in the matched study. Also, in the longer-term perspective, both therapies resulted in similar benefits to the patients.

Bayesian doubly optimal group sequential designs for randomized clinical trials

When conducting a randomized comparative clinical trial, ethical, scientific or economic considerations often motivate the use of interim decision rules after successive groups of patients have been treated. These decisions may pertain to the comparative efficacy or safety of the treatments under study, cost considerations, the desire to accelerate the drug evaluation process, or the likelihood of therapeutic benefit for future patients. At the time of each interim decision, an important question is whether patient enrollment should continue or be terminated; either due to a high probability that one treatment is superior to the other, or a low probability that the experimental treatment will ultimately prove to be superior. The use of frequentist group sequential decision rules has become routine in the conduct of phase III clinical trials. In this dissertation, we will present a new Bayesian decision-theoretic approach to the problem of designing a randomized group sequential clinical trial, focusing on two-arm trials with time-to-failure outcomes. Forward simulation is used to obtain optimal decision boundaries for each of a set of possible models. At each interim analysis, we use Bayesian model selection to adaptively choose the model having the largest posterior probability of being correct, and we then make the interim decision based on the boundaries that are optimal under the chosen model. We provide a simulation study to compare this method, which we call Bayesian Doubly Optimal Group Sequential (BDOGS), to corresponding frequentist designs using either O'Brien-Fleming (OF) or Pocock boundaries, as obtained from EaSt 2000. Our simulation results show that, over a wide variety of different cases, BDOGS either performs at least as well as both OF and Pocock, or on average provides a much smaller trial. ^

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

Continuous safety screens for randomized controlled clinical trials with blinded treatment information

Standard methods for testing safety data are needed to ensure the safe conduct of clinical trials. In particular, objective rules for reliably identifying unsafe treatments need to be put into place to help protect patients from unnecessary harm. DMCs are uniquely qualified to evaluate accumulating unblinded data and make recommendations about the continuing safe conduct of a trial. However, it is the trial leadership who must make the tough ethical decision about stopping a trial, and they could benefit from objective statistical rules that help them judge the strength of evidence contained in the blinded data. We design early stopping rules for harm that act as continuous safety screens for randomized controlled clinical trials with blinded treatment information, which could be used by anyone, including trial investigators (and trial leadership). A Bayesian framework, with emphasis on the likelihood function, is used to allow for continuous monitoring without adjusting for multiple comparisons. Close collaboration between the statistician and the clinical investigators will be needed in order to design safety screens with good operating characteristics. Though the math underlying this procedure may be computationally intensive, implementation of the statistical rules will be easy and the continuous screening provided will give suitably early warning when real problems were to emerge. Trial investigators and trial leadership need these safety screens to help them to effectively monitor the ongoing safe conduct of clinical trials with blinded data.^

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^

A systematic review of community-based colorectal cancer screening randomized controlled trials with multi-ethnic groups

Background. The CDC estimates that 40% of adults 50 years of age or older do not receive time-appropriate colorectal cancer screening. Sixty percent of colorectal cancer deaths could be prevented by regular screening of adults 50 years of age and older. Yet, in 2000 only 42.5% of adults age 50 or older in the U.S. had received recommended screening. Disparities by health care, nativity status, socioeconomic status, and race/ethnicity are evident. Disparities in minority, underserved populations prevent us from attaining Goal 2 of Healthy People 2010 to “eliminate health disparities.” This review focuses on community-based screening research among underserved populations that includes multiple ethnic groups for appropriate disparities analysis. There is a gap in the colorectal cancer screening literature describing the effectiveness of community-based randomized controlled trials. ^ Objective. To critically review the literature describing community-based colorectal cancer screening strategies that are randomized controlled trials, and that include multiple racial/ethnic groups. ^ Methods. The review includes a preliminary disparities analysis to assess whether interventions were appropriately targeted in communities to those groups experiencing the greatest health disparities. Review articles are from an original search using Ovid Medline and a cross-matching search in Pubmed, both from January 2001 to June 2009. The Ovid Medline literature review is divided into eight exclusionary stages, seven electronic, and the last stage consisting of final manual review. ^ Results. The final studies (n=15) are categorized into four categories: Patient mailings (n=3), Telephone outreach (n=3), Electronic/multimedia (n=4), and Counseling/community education (n=5). Of 15 studies, 11 (73%) demonstrated that screening rates increased for the intervention group compared to controls, including all studies (100%) from the Patient mailings and Telephone outreach groups, 4 of 5 (80%) Counseling/community education studies, and 1 of 4 (25%) Electronic/multimedia interventions. ^ Conclusions. Patient choice and tailoring education and/or messages to individuals have proven to be two important factors in improving colorectal cancer screening adherence rates. Technological strategies have not been overly successful with underserved populations in community-based trials. Based on limited findings to date, future community-based colorectal cancer screening trials should include diverse populations who are experiencing incidence, survival, mortality and screening disparities. ^

A new combined endpoint using U-statistic in analysis in clinical trials

Common endpoints can be divided into two categories. One is dichotomous endpoints which take only fixed values (most of the time two values). The other is continuous endpoints which can be any real number between two specified values. Choices of primary endpoints are critical in clinical trials. If we only use dichotomous endpoints, the power could be underestimated. If only continuous endpoints are chosen, we may not obtain expected sample size due to occurrence of some significant clinical events. Combined endpoints are used in clinical trials to give additional power. However, current combined endpoints or composite endpoints in cardiovascular disease clinical trials or most clinical trials are endpoints that combine either dichotomous endpoints (total mortality + total hospitalization), or continuous endpoints (risk score). Our present work applied U-statistic to combine one dichotomous endpoint and one continuous endpoint, which has three different assessments and to calculate the sample size and test the hypothesis to see if there is any treatment effect. It is especially useful when some patients cannot provide the most precise measurement due to medical contraindication or some personal reasons. Results show that this method has greater power then the analysis using continuous endpoints alone. ^

Ascertainment, meta-analysis and reporting of adverse reactions in clinical trials of antibiotics

The ascertainment and analysis of adverse reactions to investigational agents presents a significant challenge because of the infrequency of these events, their subjective nature and the low priority of safety evaluations in many clinical trials. A one year review of antibiotic trials published in medical journals demonstrates the lack of standards in identifying and reporting these potentially fatal conditions. This review also illustrates the low probability of observing and detecting rare events in typical clinical trials which include fewer than 300 subjects. Uniform standards for ascertainment and reporting are suggested which include operational definitions of study subjects. Meta-analysis of selected antibiotic trials using multivariate regression analysis indicates that meaningful conclusions may be drawn from data from multiple studies which are pooled in a scientifically rigorous manner. ^

Behavioral facets of cancer chemoprevention trials

Few studies have explored factors related to participation in cancer chemoprevention trials. The purpose of this dissertation was to conduct investigations in this emerging field by studying aspects of participation at three phases of cancer chemoprevention trials: at enrollment, during a placebo run-in period, and post-trial. In all three studies, subjects had a history of cancer and were at high risk of recurrence or second primary tumors.^ The first study explored correlates of enrollment in a head and neck cancer chemoprevention trial by comparing participants and eligible nonparticipants. Of 148 subjects who met the trial's preliminary eligibility criteria, 40% enrolled. In multivariate analysis, enrollment was positively associated with being male (OR 2.36) and being employed (OR 2.73). The most commonly cited reason for declining participation among nonparticipants was transportation.^ The second study examined outcomes of an eight-week placebo run-in period in a head and neck cancer chemoprevention trial. Of 391 subjects, 91.3% were randomized after the run-in. Adherence to drug capsules ranged from 0% to 120.3% (mean $\pm$ SD, 95.8% $\pm$ 15.1). In multivariate analysis, the main variable predicting run-in outcome was race; white subjects were 3.45 times more likely to be randomized than non-white subjects. Subjects with Karnofsky scores of 100 were 2.13 times more likely to be randomized than were subjects with lower scores.^ The third study used post-trial questionnaires to assess subjects' (n = 64) perceptions of participation in a cancer chemoprevention trial. The most highly rated trial benefit was the perception of potential colon cancer prevention, and the most troublesome barrier was erroneous billing for study visits. Perceived benefits were positively associated with interest in participating in future trials of the same (p = 0.05) and longer (p = 0.02) duration, and difficulty with trial pills and procedures was inversely related to interest in future placebo-controlled trials (p = 0.01).^ These are among the first behavioral studies to be completed in the rapidly growing field of cancer chemoprevention. Much work has yet to be done, however, to advance our understanding of the complex issues relating to chemoprevention trial participation. ^

Prevalence of symptoms of neurotoxicity and potential occupational pesticide and solvent exposures among high school students in Starr County, Texas: A pilot study

Numerous harmful occupational exposures affect working teens in the United States. Teens working in agriculture and other heavy-labor industries may be at risk for occupational exposures to pesticides and solvents. The neurotoxicity of pesticides and solvents at high doses is well-known; however, the long term effects of these substances at low doses on occupationally exposed adolescents have not been well-studied. To address this research gap, a secondary analysis of cross-sectional data was completed in order to estimate the prevalence of self-reported symptoms of neurotoxicity among a cohort of high school students from Starr County, Texas, a rural area along the Texas-Mexico border. Multivariable linear regression was used to estimate the association between work status (i.e., no work, farm work, and non-farm work) and symptoms of neurotoxicity, while controlling for age, gender, Spanish speaking preference, inhalant use, tobacco use, and alcohol use. The sample included 1,208 students. Of these, the majority (85.84%) did not report having worked during the prior nine months compared to 4.80% who did only farm work, 6.21% who did only non-farm work, and 3.15% who did both types of work. On average, students reported 3.26 symptoms with a range from 0-16. The most commonly endorsed items across work status were those related to memory impairment. Adolescents employed in non-farm work jobs reported more neurotoxicity symptoms than those who reported that they did not work (Mean 4.31; SD 3.97). In the adjusted multivariable regression model, adolescents reporting non-farm work status reported an average of 0.77 more neurotoxicity symptoms on the Q16 than those who did not work (P = 0.031). The confounding variables included in the final model were all found to be factors significantly associated with report of neurotoxicity symptoms. Future research should examine the relationship between these variables and self-report of symptoms of neurotoxicity.^

Bayesian adaptive designs for drug combinations in early phase clinical trials

Treating patients with combined agents is a growing trend in cancer clinical trials. Evaluating the synergism of multiple drugs is often the primary motivation for such drug-combination studies. Focusing on the drug combination study in the early phase clinical trials, our research is composed of three parts: (1) We conduct a comprehensive comparison of four dose-finding designs in the two-dimensional toxicity probability space and propose using the Bayesian model averaging method to overcome the arbitrariness of the model specification and enhance the robustness of the design; (2) Motivated by a recent drug-combination trial at MD Anderson Cancer Center with a continuous-dose standard of care agent and a discrete-dose investigational agent, we propose a two-stage Bayesian adaptive dose-finding design based on an extended continual reassessment method; (3) By combining phase I and phase II clinical trials, we propose an extension of a single agent dose-finding design. We model the time-to-event toxicity and efficacy to direct dose finding in two-dimensional drug-combination studies. We conduct extensive simulation studies to examine the operating characteristics of the aforementioned designs and demonstrate the designs' good performances in various practical scenarios.^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.