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In clinical trials that are subject to noncompliance, the commonly used intention-to-treat estimand is valid as a causal effect of treatment assignment but is sensitive to the level of compliance. An alternative estimand, the complier average causal effect (CACE), measures the average effect of treatment received in the latent subset of subjects who would comply with either assigned treatment. Because the principal stratum of compliers can vary with the circumstances of the trial, CACE too depends on the compliance fraction. We propose a model in which an underlying latent proto-compliance interacts with trial characteristics to determine a subject's compliance behavior. When the latent compliance is independent of the individual treatment effect, the average causal effect is constant across compliance classes, and CACE is robust across trials and equal to the population average causal effect. We demonstrate the potential degree of sensitivity of CACE in a simulation study, an analysis of data from a trial of vitamin A supplementation in children, and a meta-analysis of trials of epidural analgesia in labor. [ABSTRACT FROM AUTHOR]

BackgroundSubstantial variability exists in therapeutic response and adverse effects with pharmacotherapies for tobacco dependence. Biomarkers to optimise treatment choice for individual smokers might improve treatment outcomes. We tested whether a genetically informed biomarker of nicotine clearance, the nicotine metabolite ratio (NMR; 3'-hydroxycotinine:cotinine), predicts response to nicotine patch or varenicline for smoking cessation.MethodsWe undertook NMR-stratified multicentre, randomised, placebo-controlled clinical trial from Nov 16, 2010, to Sept 12, 2014, at four sites. Smokers seeking treatment were randomly assigned by baseline NMR status and study site, in blocks of 12 patients (1:1:1 ratio), to 11 weeks of placebo (placebo pill plus placebo patch), nicotine patch (active patch plus placebo pill), or varenicline (active pill plus placebo patch), plus behavioural counselling. Participants and investigators were masked to group allocation and NMR status. An intention-to-treat analysis was done. Participants were followed up for 12 months after the target quit date. The primary endpoint was biochemically verified 7 day point prevalence abstinence at the end of treatment to estimate the pharmacological effect of treatment by NMR. The trial is registered at ClinicalTrials.gov, number NCT01314001.Findings1246 participants (662 slow metabolisers of nicotine, 584 normal metabolisers of nicotine) were enrolled and randomly assigned to the three interventions (408 placebo, 418 nicotine patch, 420 varenicline). At end of treatment, varenicline was more efficacious than nicotine patch in normal metabolisers (OR 2·17, 95% CI 1·38-3·42; p=0·001), but not in slow metabolisers (OR 1·13, 0·74-1·71; p=0·56). In the longitudinal model including all timepoints, the NMR-by-treatment interaction was significant (ratio of odds ratios [ORR] 1·96, 95% CI 1·11-3·46; p=0·02). An NMR-by-treatment interaction showed that slow (vs normal) metabolisers reported greater overall side-effect severity with varenicline versus placebo (β=-1·06, 95% CI -2·08 to -0·03; p=0·044).InterpretationTreating normal metabolisers with varenicline and slow metabolisers with nicotine patch could optimise quit rates while minimising side-effects.FundingNational Institutes of Health, Canadian Institutes of Health Research, Abramson Cancer Center, Centre for Addiction and Mental Health Foundation, and Pennsylvania Department of Health.

The external validity of the estimated treatment effect from a clinical trial is in doubt when there are effect modifiers whose distribution in the target population differs from that in the trial. Adjusting an estimated treatment effect from a trial to predict its likely value for the target population is a process known as generalization. We review classical and contemporary approaches to this problem. The traditional method is post-stratification, or the reweighting of stratum-specific treatment effect estimates by population distribution proportions. Contemporary methods employ stratification or weighting techniques based on estimates of the probability that an individual is included in the trial, akin to the propensity score. These methods are more flexible in that they readily accommodate continuous effect modifiers. Estimating the probabilities, however, requires in principle that one have individual-level population data, which are seldom available for pharmaceutical trials. When the effect modifiers are all discrete, the post-stratification and probability-weighting approaches give essentially the same estimates. Naïvely computed standard errors with the contemporary methods may be inflated. We illustrate and compare generalization methods in a simulation and using data from the Lipids Research Clinics Coronary Primary Prevention Trial and the New York School Choice Experiment. [ABSTRACT FROM AUTHOR]

Background: Persons newly diagnosed with pancreas cancer and who have survived a previous cancer are often excluded from clinical trials, despite limited evidence about their prognosis. We examined the association between previous cancer and overall survival. Methods: This US population‐based cohort study included older adults (aged ≥66 years) diagnosed with pancreas cancer between 2005 and 2015 in the linked Surveillance, Epidemiology, and End Results‐Medicare data. We used Cox proportional hazards models to estimate stage‐specific effects of previous cancer on overall survival, adjusting for sociodemographic, treatment, and tumor characteristics. Results: Of 32,783 patients, 18.7% were previously diagnosed with another cancer. The most common previous cancers included prostate (29.0%), breast (18.9%), or colorectal (9.7%) cancer. More than half of previous cancers (53.9%) were diagnosed 5 or more years prior to pancreas cancer diagnosis or at an in situ or localized stage (47.8%). The proportions of patients surviving 1, 3, and 5 years after pancreas cancer were nearly identical for those with and without previous cancer. Median survival in months was as follows for those with and without previous cancer respectively: 7 versus 8 (Stage 0/I), 10 versus 10 (Stage II), 7 versus 7 (Stage III), and 3 versus 2 (Stage IV). Cox models indicated that patients with previous cancer had very similar or statistically equivalent survival to those with no previous cancer. Conclusions: Given nearly equivalent survival compared to those without previous cancer, cancer survivors newly diagnosed with pancreas cancer should be considered for inclusion in pancreas cancer clinical trials. [ABSTRACT FROM AUTHOR]

The ISNI (index of sensitivity to local nonignorability) method quantifies local sensitivity of parametric inferences to nonignorable missingness in an outcome variable. Here we extend ISNI to the situations where both outcomes and predictors can be missing and where the missingness mechanism can be either parametric or semi‐parametric. We define the quantity MinNI (minimum nonignorability) to be an approximation to the norm of the smallest value of the transformed nonignorability that gives a nonnegligible displacement of the estimate of the parameter of interest. We illustrate our method in a complete data set from which we synthetically delete observations according to various patterns. We then apply the method to real‐data examples involving the normal linear model and conditional logistic regression. [ABSTRACT FROM AUTHOR]