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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.08.003 Byline: James C. Felli, Wesley H. Anderson, James P. Kremidas, Stephen J. Ruberg Keywords: Simulation; Health system -- case study Abstract: Clinical trials constitute large, resource-intensive endeavors for pharmaceutical companies, academic institutions and government agencies. Accounts have estimated the number of patients involved in clinical trials in the US as high as 725,000 patients with costs in excess of $19 billion. Despite the scope of these activities, little has been done by way of a general model to highlight the progression of patients through the various stages of a clinical trial. We present a semi-Markov model that can be used to explore the patient population profile of a clinical trial for arbitrarily many patient classes, trial sites and start-times. Author Affiliation: Eli Lilly & Company, Drop Code 2120, Lilly Corporate Center, Indianapolis, IN 46285, USA Article History: Received 13 December 2004; Accepted 3 August 2005

Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonization (ICH)‐E9 addendum (R1), intercurrent events (ICEs) need to be considered when defining an estimand, and principal stratum is one of the five strategies to handle ICEs. Qu et al. (2020, Statistics in Biopharmaceutical Research 12:1–18) proposed estimators for the adherer average causal effect (AdACE) for estimating the treatment difference for those who adhere to one or both treatments based on the causal‐inference framework, and demonstrated the consistency of those estimators; however, this method requires complex custom programming related to high‐dimensional numeric integrations. In this article, we implemented the AdACE estimators using multiple imputation (MI) and constructed confidence intervals (CIs) through bootstrapping. A simulation study showed that the MI‐based estimators provided consistent estimators with the nominal coverage probabilities of CIs for the treatment difference for the adherent populations of interest. As an illustrative example, the new method was applied to data from a real clinical trial comparing two types of basal insulin for patients with type 1 diabetes. [ABSTRACT FROM AUTHOR]

Abstract Background To identify a simple decision tree using early symptom change to predict response to atypical antipsychotic therapy in patients with (Diagnostic and Statistical Manual, Fourth Edition, Text Revised) chronic schizophrenia. Methods Data were pooled from moderately to severely ill patients (n = 1494) from 6 randomized, double-blind trials (N = 2543). Response was defined as a ≥30% reduction in Positive and Negative Syndrome Scale (PANSS) Total score by Week 8 of treatment. Analyzed predictors were change in individual PANSS items at Weeks 1 and 2. A decision tree was constructed using classification and regression tree (CART) analysis to identify predictors that most effectively differentiated responders from non-responders. Results A 2-branch, 6-item decision tree was created, producing 3 distinct groups. First branch criterion was a 2-point score decrease in at least 2 of 5 PANSS positive items (Week 2). Second branch criterion was a 2-point score decrease in the PANSS excitement item (Week 2). "Likely responders" met the first branch criteria; "likely non-responders" did not meet first or second criterion; "not predictable" patients did not meet the first but did meet the second criterion. Using this approach, response to treatment could be predicted in most patients (92%) with high positive predictive value (79%) and high negative predictive value (75%). Predictive findings were confirmed through analysis of data from 2 independent trials. Conclusions Using a data-driven approach, we identified decision rules using early change in the scores of selected PANSS items to accurately predict longer-term treatment response or non-response to atypical antipsychotic therapy. This could lead to development of a simple quantitative evaluation tool to help guide early treatment decisions. Trial Registration This is a retrospective, non-intervention study in which pooled results from 6 previously published reports were analyzed; thus, clinical trial registration is not required.

Null hypothesis significance testing (NHST) with its benchmark P value < 0.05 has long been a stalwart of scientific reporting and such statistically significant findings have been used to imply scientifically or clinically significant findings. Challenges to this approach have arisen over the past 6 decades, but they have largely been unheeded. There is a growing movement for using Bayesian statistical inference to quantify the probability that a scientific finding is credible. There have been differences of opinion between the frequentist (i.e., NHST) and Bayesian schools of inference, and warnings about the use or misuse of P values have come from both schools of thought spanning many decades. Controversies in this arena have been heightened by the American Statistical Association statement on P values and the further denouncement of the term "statistical significance" by others. My experience has been that many scientists, including many statisticians, do not have a sound conceptual grasp of the fundamental differences in these approaches, thereby creating even greater confusion and acrimony. If we let A represent the observed data, and B represent the hypothesis of interest, then the fundamental distinction between these two approaches can be described as the frequentist approach using the conditional probability pr(A | B) (i.e., the P value), and the Bayesian approach using pr(B | A) (the posterior probability). This paper will further explain the fundamental differences in NHST and Bayesian approaches and demonstrate how they can co‐exist harmoniously to guide clinical trial design and inference. [ABSTRACT FROM AUTHOR]

Keywords: reproducibility; posterior probability; Bayes Factor Bound; positive predictive value; prior probability EN reproducibility posterior probability Bayes Factor Bound positive predictive value prior probability 49 54 6 02/24/21 20210101 NES 210101 1 Introduction Gibson ([7]) seeks to tackle a difficult and persistent problem in clinical research and indeed the scientific community as a whole - what is the strength of evidence for a scientific hypothesis based on observed data. Let I p i SB 0 sb be the prior probability that I H i SB 0 sb is false and let I p i = I p i -value from the test of I H i SB 0 sb from the current experiment. (Even those who say that a I p i -value should be reported as a continuous measure of evidence and not dichotomized must recall that the I p i -value is I not i a probability measure about I H i SB 0 sb .). [Extracted from the article]

Summary: Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data. The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes. [ABSTRACT FROM AUTHOR]