Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

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We study the combined impact that all-or-none compliance and subsequent missing outcomes can have on the estimation of the intention-to-treat effect of assignment in randomised studies. In this setting, a standard analysis, which drops subjects with missing outcomes and ignores compliance information, can be biased for the intention-to-treat effect. To address all-or-none compliance that is followed by missing outcomes, we construct a new estimation procedure for the intention-to-treat effect that maintains good randomisation-based properties under more plausible, nonignorable noncompliance and nonignorable missing-outcome conditions: the 'compound exclusion restriction' on the effect of assignment and the 'latent ignorability' of the missing data mechanism. We present both theoretical results and a simulation study. Moreover, we show how the two key concepts of compound exclusion and latent ignorability are relevant in more complicated settings, such as right censoring of a time-to-event outcome.

Importance Agitation is common, persistent, and associated with adverse consequences for patients with Alzheimer's disease (AD). Pharmacological treatment options, including antipsychotics are not satisfactory. Objective The primary objective was to evaluate the efficacy of citalopram for agitation in patients with AD. Key secondary objectives examined effects of citalopram on function, caregiver distress, safety, cognitive safety, and tolerability. Design, Setting and Participants The Citalopram for Agitation in Alzheimer's Disease Study (CitAD) was a multicenter, randomized, placebo-controlled, double-blind, parallel group trial that enrolled 186 patients with probable AD and clinically significant agitation from eight academic centers in the US and Canada from August 2009 to January 2013. Interventions Participants (n=186) were randomized to receive a psychosocial intervention plus either citalopram (n=94) or placebo (n=92) for 9 weeks. Dose began at 10 mg/d with planned titration to 30 mg/d over 3 weeks based on response and tolerability. Main Outcomes and Measures Primary outcome measures were the Neurobehavioral Rating Scale, agitation subscale (NBRS-A) and the modified Alzheimer Disease Cooperative Study-Clinical Global Impression of Change (mADCS-CGIC) Other outcomes were the Cohen-Mansfield Agitation Inventory (CMAI), Neuropsychiatric Inventory (NPI), activities of daily living (ADLs), caregiver distress, cognitive safety (MMSE), and adverse events. Results Participants on citalopram showed significant improvement compared to placebo on both primary outcome measures. NBRS-A estimated treatment difference at week 9 (citalopram minus placebo) was −0.93 [95% CI: −1.80 to −0.06], p = 0.036. mADCS-CGIC results showed 40% of citalopram participants having moderate or marked improvement from baseline compared to 26% on placebo, with estimated treatment effect (odds ratio of being at or better than a given CGIC category) of 2.13 [95% CI 1.23 to 3.69], p = 0.007. Participants on citalopram showed significant improvement on the CMAI, total NPI and caregiver distress scores but not on the NPI agitation subscale, ADLs, or in less use of rescue lorazepam. Worsening of cognition (−1.05 points [95% CI: −1.97 to −0.13], p = 0.026) and QT interval prolongation (18.1 ms [95% CI: 6.1, 30.1], p = 0.004) were seen in the citalopram group. Conclusions and Relevance Among patients with probable Alzheimer's disease and agitation receiving psychosocial intervention, the addition of citalopram compared with placebo significantly reduced agitation and caregiver distress, but cognitive and cardiac adverse effects of citalopram may limit its practical application at the 30 mg/d dose studied in this trial.

Fatigue is the most prevalent and debilitating symptom experienced by breast cancer patients receiving adjuvant chemotherapy or radiation therapy and few evidence-based treatments are available to manage this distressing side-effect. The purpose of this multi-institutional randomized controlled trial was to determine the effects of exercise on fatigue levels during treatment for breast cancer. Sedentary women (N=119) with Stage 0-III breast cancer receiving outpatient adjuvant chemotherapy or radiation therapy were randomized to a home-based moderate-intensity walking exercise program or to usual care for the duration of their cancer treatment. Of participants randomized to exercise, 72% adhered to the exercise prescription; 61% of the usual care group adhered. The intention-to-treat analysis revealed no group differences in part because of a dilution of treatment effect as 39% of the usual care group exercised and 28% of the exercise group did not. When exercise participation was considered using the data analysis method of instrumental variables with principal stratification, a clinically important and statistically significant (p=0.03) effect of exercise on pretest-to-posttest change in fatigue levels was demonstrated. Adherence to a home-based moderate-intensity walking exercise program may effectively mitigate the high levels of fatigue prevalent during cancer treatment.

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