Automatic tuning of a graph-based image segmentation method for digital mammography applications

Los Alamitos, C

Generalizability of randomized controlled trials of substance use disorder treatments

Randomized controlled trials (RCTs) are widely considered the gold standard to assess the effectiveness of new treatments. Decisions for clinical guidelines and health policies are often made based on findings of RCTs. While study designs of RCTs can mitigate threats to internal validity of the estimated treatment effectiveness, they do not assure external validity, which is how well findings from one particular sample can be applied to the target population of individuals for whom a treatment is intended. There is growing concern in the recent literature that the findings from RCTs may not be directly applicable to real world settings. Particularly in the context of RCTs of treatments for substance use disorders (SUD), there is a growing body of literature showing that strict eligibility criteria commonly used in RCTs of SUD treatment would exclude substantial proportions of individuals from the target population, which may adversely impact generalizability of the findings from SUD RCTs. However, very few past studies have assessed generalizability of findings of actual SUD RCTs to the intended target populations. The purpose of this dissertation was to assess generalizability of findings of SUD RCTs that were implemented in various settings, as compared with differently defined target populations. In Chapter 1, we provided an overview of the existing literature and described the data source and methodology used in this dissertation. In Chapter 2, we assessed generalizability of the findings from ten multi-site SUD RCTs to each target population of patients seeking SUD treatment in usual treatment settings in the United States. We weighted the RCT sample treatment effects on three outcomes, on retention, urine toxicology, and abstinence to make the RCT samples resemble the target populations, by using propensity scores representing the conditional probability of participating in RCTs. We found that weighting the samples changed the significance of estimated sample treatment effects. Most commonly, positive treatment effects of RCTs became statistically insignificant after weighting. In Chapter 3, we assessed generalizability of the treatment effects on retention and abstinence from a multi-site web-based SUD intervention to two types of target populations: SUD treatment-seeking individuals and community-dwelling individuals with recent substance use, whether or not they sought treatment. The population effect on abstinence became insignificant after weighting the data by the generalizability weights of both target populations. In Chapter 4, we conducted a meta-analysis of generalized treatment effects on retention and abstinence from four RCTs of cocaine dependence treatments to the same two types of target population used in the previous chapter. We also conducted a network meta-analysis to examine comparative treatment efficacies across these four treatments while taking into account the generalizability of the findings. We found that the overall generalized treatment effect on retention was significantly larger than the unweighted effect. We also found that weighting changed the ranking of the effectiveness across treatments. Lastly, in Chapter 5, we provided a summary of the findings and discussed public health implications in light of strengths and limitations of this dissertation

日本における精神保健と援助要請行動の研究

政策研究大学院大学 / National Graduate Institute for Policy Studies博士 (公共経済学) / Ph.D. in Public Economics論文審査委員: 園部 哲史（主査）, 池田 真介, Robert Leon-Gonzalez, Alistair Munro, 澤田 康幸（東京大学）政策分析プログラム / Policy Analysis Programdoctoral thesi

Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Adjustment for Additional Baseline Variables

We focus on estimating the average treatment effect in clinical trials that involve stratified randomization, which is commonly used. It is important to understand the large sample properties of estimators that adjust for stratum variables (those used in the randomization procedure) and additional baseline variables, since this can lead to substantial gains in precision and power. Surprisingly, to the best of our knowledge, this is an open problem. It was only recently that a simpler problem was solved by Bugni et al. (2018) for the case with no additional baseline variables, continuous outcomes, the analysis of covariance (ANCOVA) estimator, and no missing data. We generalize their results in three directions. First, in addition to continuous outcomes, we handle binary and time-to-event outcomes; this broadens the applicability of the results. Second, we allow adjustment for an additional, preplanned set of baseline variables, which can improve precision. Third, we handle missing outcomes under the missing at random assumption. We prove that a wide class of estimators is asymptotically normally distributed under stratified randomization and has equal or smaller asymptotic variance than under simple randomization. For each estimator in this class, we give a consistent variance estimator. This is important in order to fully capitalize on the combined precision gains from stratified randomization and adjustment for additional baseline variables. The above results also hold for the biased-coin covariate-adaptive design. We demonstrate our results using completed trial data sets of treatments for substance use disorder, where adjustment for additional baseline variables brings substantial variance reduction

Treatment effects may remain the same even when trial participants differed from the target population

Objective RCTs have been criticised for lacking external validity. We assessed whether a trial in people with type I diabetes mellitus (T1DM) mirrored the wider population, and applied sample-weighting methods to assess the impact of differences on our trial's findings. Study design and setting The REPOSE trial was nested within a large UK cohort capturing demographic, clinical and quality of life (QoL) data for people with T1DM undergoing structured diabetes-specific education. We firstly assessed whether our RCT participants were comparable to this cohort using propensity score modelling. Following this we re-weighted the trial population to better match the wider cohort and re-estimated the treatment effect. Results Trial participants differed from the cohort in regards to sex, weight, HbA1c and also QoL and satisfaction with current treatment. Nevertheless, the treatment effects derived from alternative model weightings were similar to that of the original RCT. Conclusions Our RCT participants differed in composition to the wider population but the original findings were unaffected by sampling adjustments. We encourage investigators take steps to address criticisms of generalisability, but doing so is problematic: external data, even if available, may contain limited information and analyses can be susceptible to model misspecification

Generalizability of randomized controlled trials of substance use disorder treatments

Randomized controlled trials (RCTs) are widely considered the gold standard to assess the effectiveness of new treatments. Decisions for clinical guidelines and health policies are often made based on findings of RCTs. While study designs of RCTs can mitigate threats to internal validity of the estimated treatment effectiveness, they do not assure external validity, which is how well findings from one particular sample can be applied to the target population of individuals for whom a treatment is intended. There is growing concern in the recent literature that the findings from RCTs may not be directly applicable to real world settings. Particularly in the context of RCTs of treatments for substance use disorders (SUD), there is a growing body of literature showing that strict eligibility criteria commonly used in RCTs of SUD treatment would exclude substantial proportions of individuals from the target population, which may adversely impact generalizability of the findings from SUD RCTs. However, very few past studies have assessed generalizability of findings of actual SUD RCTs to the intended target populations. The purpose of this dissertation was to assess generalizability of findings of SUD RCTs that were implemented in various settings, as compared with differently defined target populations. In Chapter 1, we provided an overview of the existing literature and described the data source and methodology used in this dissertation. In Chapter 2, we assessed generalizability of the findings from ten multi-site SUD RCTs to each target population of patients seeking SUD treatment in usual treatment settings in the United States. We weighted the RCT sample treatment effects on three outcomes, on retention, urine toxicology, and abstinence to make the RCT samples resemble the target populations, by using propensity scores representing the conditional probability of participating in RCTs. We found that weighting the samples changed the significance of estimated sample treatment effects. Most commonly, positive treatment effects of RCTs became statistically insignificant after weighting. In Chapter 3, we assessed generalizability of the treatment effects on retention and abstinence from a multi-site web-based SUD intervention to two types of target populations: SUD treatment-seeking individuals and community-dwelling individuals with recent substance use, whether or not they sought treatment. The population effect on abstinence became insignificant after weighting the data by the generalizability weights of both target populations. In Chapter 4, we conducted a meta-analysis of generalized treatment effects on retention and abstinence from four RCTs of cocaine dependence treatments to the same two types of target population used in the previous chapter. We also conducted a network meta-analysis to examine comparative treatment efficacies across these four treatments while taking into account the generalizability of the findings. We found that the overall generalized treatment effect on retention was significantly larger than the unweighted effect. We also found that weighting changed the ranking of the effectiveness across treatments. Lastly, in Chapter 5, we provided a summary of the findings and discussed public health implications in light of strengths and limitations of this dissertation