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Aim: To identify psychosocial predictors of medication adherence in young adults with youth‐onset type 2 diabetes in the Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) cohort. Methods: Participants (mean age: 26 years) completed validated psychosocial measures. Unannounced telephone pill counts were completed at T1 (baseline) and T2 (follow‐up, approximately 1 year later) to assess adherence to oral hypoglycaemia agents (OHAs). Adherence to insulin was assessed by self‐report. Logistic and linear regressions identified factors that predicted 'low adherence' (<80% of pills/insulin) and per cent adherence, adjusted for potential confounders. Results: Of 171 participants with OHA adherence scores at T1 and T2 (65% women, 43% Hispanic and 35% non‐Hispanic Black), 65.4% were low adherent. After adjustment (including T1 adherence), concerns about diabetes medicines (adverse effects, dependence) at T1 predicted higher odds of being low adherent (categorical) at T2 (p = 0.019). Housing insecurity (p = 0.045) and reporting ≥2 need insecurities (p = 0.027) at T1 predicted lower per cent adherence (continuous) at T2. Of 157 participants with insulin adherence scores at T1 and T2 (69% women, 38% Hispanic and 38% non‐Hispanic Black), 36.3% were low adherent. After adjustment (including T1 adherence), beliefs that medicines are overused predicted higher odds of insulin low adherence at T2 (p = 0.013), and beliefs that medicines are harmful (p = 0.004) and overused (p = 0.010) predicted lower per cent insulin adherence at T2. Conclusions: Suboptimal medication adherence, common in young adults with youth‐onset type 2 diabetes, is predicted by interfering beliefs about medicines and social factors. We must address these beliefs and unmet needs to develop tailored interventions for this vulnerable group. [ABSTRACT FROM AUTHOR]

For the analysis of clinical trials, the study participants are usually assumed to be representative sample of a target population. This assumption is rarely fulfilled in clinical trials, and particularly not if the sample size is small. In addition, covariate imbalances may affect the trial. Randomization tests provide a nonparametric analysis method of the treatment effect that does not rely on population‐based assumptions. We propose a nonparametric statistical model that yields a formal basis for randomization tests. We adapt the model for the presence of covariate imbalance in the form of selection bias and investigate the effects of bias on the rejection probability of the randomization test using Monte Carlo simulations. Finally, we show that ancillary statistics can be used to control for the influence of bias. We show that covariate imbalance leads to an inflation of the type I error probability. The proposed nonparametric model allows for the use of ancillary statistics that yield an unbiased adjusted randomization test. [ABSTRACT FROM AUTHOR]

We examine the use of randomization-based inference for analyzing multiarmed randomized clinical trials, including the application of conditional randomization tests to multiple comparisons. The view is taken that the linkage of the statistical test to the experimental design (randomization procedure) should be recognized. A selected collection of randomization procedures generalized to multiarmed treatment allocation is summarized, and generalizations for two randomization procedures that heretofore were designed for only two treatments are developed. We explain the process of computing the randomization test and conditional randomization test via Monte Carlo simulation, developing an efficient algorithm that makes multiple comparisons possible that would not be possible using a standard algorithm, demonstrate the preservation of type I error rate, and explore the relationship of statistical power to the randomization procedure in the presence of a time trend and outliers. We distinguish between the interpretation of the p-value in the randomization test and in the population test and verify that the randomization test can be approximated by the population test on some occasions. Data from two multiarmed clinical trials from the literature are reanalyzed to illustrate the methodology. [ABSTRACT FROM AUTHOR]

"…The customary test for an observed difference…is based on an enumeration of the probabilities, on the initial hypothesis that two treatments do not differ in their effects,…of all the various results which would occur if the trial were repeated indefinitely with different random samples of the same size as those actually used." -Peter Armitage ("Sequential tests in prophylactic and therapeutic trials" in Quarterly Journal of Medicine, 1954;23(91):255-274). Randomization has been the hallmark of the clinical trial since Sir Bradford Hill adopted it in the 1946 streptomycin trial. An exploration of the early literature yields three rationales, ie, (i) the incorporation of randomization provides unpredictability in treatment assignments, thereby mitigating selection bias; (ii) randomization tends to ensure similarity in the treatment groups on known and unknown confounders (at least asymptotically); and (iii) the act of randomization itself provides a basis for inference when random sampling is not conducted from a population model. Of these three, rationale (iii) is often forgotten, ignored, or left untaught. Today, randomization is a rote exercise, scarcely considered in protocols or medical journal articles. Yet, the literature of the last century is rich with statistical articles on randomization methods and their consequences, authored by some of the pioneers of the biostatistics and statistics world. In this paper, we review some of this literature and describe very simple methods to rectify some of the oversight. We describe how randomization-based inference can be used for virtually any outcome of interest in a clinical trial. Special mention is made of nonstandard clinical trials situations. [ABSTRACT FROM AUTHOR]