Assessing the commonly used assumptions in estimating the principal causal effect in clinical trials

In addition to the average treatment effect (ATE) for all randomized patients, sometimes it is important to understand the ATE for a principal stratum, a subset of patients defined by one or more post-baseline variables. For example, what is the ATE for those patients who could be compliant with the experimental treatment? Commonly used assumptions include monotonicity, principal ignorability, and cross-world assumptions of principal ignorability and principal strata independence. Most of these assumptions cannot be evaluated in clinical trials with parallel treatment arms. In this article, we evaluate these assumptions through a 2x2 cross-over study in which the potential outcomes under both treatments can be observed, provided there are no carry-over and study period effects. From this example, it seemed the monotonicity assumption and the within-treatment principal ignorability assumptions did not hold well. On the other hand, the assumptions of cross-world principal ignorability and cross-world principal stratum independence conditional on baseline covaraites seemed to hold well. With the latter assumptions, we estimated the ATE for principal strata, defined by whether the blood glucose standard deviation increased in each treatment period, without relying on the cross-over feature. These estimates were very close to the ATE estimate when exploiting the cross-over feature of the trial. To the best of our knowledge, this article is the first attempt to evaluate the plausibility of commonly used assumptions for estimating ATE for principal strata using the setting of a cross-over trial.Comment: 25 pages, 4 table

Accurate collection of reasons for treatment discontinuation to better define estimands in clinical trials

Background: Reasons for treatment discontinuation are important not only to understand the benefit and risk profile of experimental treatments, but also to help choose appropriate strategies to handle intercurrent events in defining estimands. The current case report form (CRF) commonly in use mixes the underlying reasons for treatment discontinuation and who makes the decision for treatment discontinuation, often resulting in an inaccurate collection of reasons for treatment discontinuation. Methods and results: We systematically reviewed and analyzed treatment discontinuation data from nine phase 2 and phase 3 studies for insulin peglispro. A total of 857 participants with treatment discontinuation were included in the analysis. Our review suggested that, due to the vague multiple-choice options for treatment discontinuation present in the CRF, different reasons were sometimes recorded for the same underlying reason for treatment discontinuation. Based on our review and analysis, we suggest an intermediate solution and a more systematic way to improve the current CRF for treatment discontinuations. Conclusion: This research provides insight and directions on how to optimize the CRF for recording treatment discontinuation. Further work needs to be done to build the learning into Clinical Data Interchange Standards Consortium standards.Comment: 13 pages, 3 figures, 1 tabl

Efficiency of Two Sample Tests via the t-Mean Survival Time for Analyzing Event Time Observations

In comparing two treatments with the event time observations, the hazard ratio (HR) estimate is routinely used to quantify the treatment difference. However, this model dependent estimate may be difficult to interpret clinically especially when the proportional hazards (PH) assumption is violated. An alternative estimation procedure for treatment efficacy based on the restricted means survival time or t-year mean survival time (t-MST) has been discussed extensively in the statistical and clinical literature. On the other hand, a statistical test 1 via the HR or its asymptotically equivalent counterpart, the logrank test, is asymptotically distribution-free. In this paper, we assess the relative efficiency of the hazard ratio and t-MST tests with respect to the statistical power using various PH and non-PH models under theoretical and practical settings. When the PH assumption is valid, the t-MST test performs almost as well as the HR test. For non-PH models, the t-MST test can substantially outperform its HR counter- part. On the other hand, the HR test can be powerful when the true difference of two survival functions is quite large at end of the study. Unfortunately, for this case, the HR estimate may not have a simple clinical interpretation for the treatment effect due to the violation of the PH assumption

Lake Erie hypoxia prompts Canada‐U.S. study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95631/1/eost15589.pd

Chronicles of hypoxia: Time-series buoy observations reveal annually recurring seasonal basin-wide hypoxia in Muskegon Lake – A Great Lakes estuary

We chronicled the seasonally recurring hypolimnetic hypoxia in Muskegon Lake – a Great Lakes estuary over 3 years, and examined its causes and consequences. Muskegon Lake is a mesotrophic drowned river mouth that drains Michigan\u27s 2nd largest watershed into Lake Michigan. A buoy observatory tracked ecosystem changes in the Muskegon Lake Area of Concern (AOC), gathering vital time-series data on the lake\u27s water quality from early summer through late fall from 2011 to 2013 (www.gvsu.edu/buoy). Observatory-based measurements of dissolved oxygen (DO) tracked the gradual development, intensification and breakdown of hypoxia (mild hypoxia b4 mg DO/L, and severe hypoxia b2 mg DO/L) below the ~6 m thermocline in the lake, occurring in synchrony with changes in temperature and phytoplankton biomass in the water column during July–October. Time-series data suggest that proximal causes of the observed seasonal hypolimnetic DO dynamics are stratified summer water-column, reduced wind-driven mixing, longer summer residence time, episodic intrusions of cold DO-rich nearshore Lake Michigan water, nutrient run off from watershed, and phytoplankton blooms. Additional basin-wide water-column profiling (2011–2012) and ship-based seasonal surveys (2003–2013) confirmed that bottom water hypoxia is an annually recurring lake-wide condition. Volumetric hypolimnetic oxygen demand was high (0.07–0.15 mg DO/Liter/day) and comparable to other temperate eutrophic lakes. Over 3 years of intense monitoring, ~9–24% of Muskegon Lake\u27s volume experienced hypoxia for ~29–85 days/year – with the potential for hypolimnetic habitat degradation and sediment phosphorus release leading to further eutrophication. Thus, time-series observatories can provide penetrating insights into the inner workings of ecosystems and their external drivers

Identification of early changes in specific symptoms that predict longer-term response to atypical antipsychotics in the treatment of patients with schizophrenia

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p> <p>Trial Registration</p> <p>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.</p