Partially hidden Markov model for time-varying principal stratification in HIV prevention trials

A Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes

In youth with Type 1 diabetes, adherence to medical treatment regimens requires the involvement of both parent and child. A clinic-integrated behavioral intervention in the Family Management of Diabetes (FMOD) trial was shown to be effective in controlling deterioration in glycemic level; yet the mechanism remains unknown. It is possible that the effectiveness is through improved parent–child relation. To investigate whether the intervention improves parent–child relations, we proposed a novel approach that allows differential perceptions of parent and child toward the unobserved parent–child relationship. Leveraging manifesto data collected from both parent and child in the FMOD trial, the proposed approach extended a standard hidden Markov model by inserting a layer of parent- and child-specific hidden states. We took a Bayesian perspective to estimation and developed an efficient computational algorithm to sample from the joint posterior distribution. Extensive simulations were conducted to demonstrate the performance of the proposed modeling framework. Application to the FMOD trial data reveals that families in the intervention arm are more likely to stay in the Harmonious parent–child relation state and less likely to transition from Harmonious to Indifferent state. Compared to parent, child tends to have a more heterogeneous perception of the parent–child relation.

Using principal stratification in analysis of clinical trials

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in Supplemental Materials.

Statistical Considerations on the Evaluation of Imbalances of Adverse Events in Randomized Clinical Trials

Adverse events (AEs) data compose the main body of safety data in clinical trials. Medically important imbalances of AEs in large double-blind randomized controlled trials (RCTs) are signals of potential adverse drug reactions. They will be further evaluated for causality and shape the initial label that gives users necessary information on the safe use of the drug. However, causality assessment in premarketing RCTs can be challenging. This article highlights key aspects that need attention and statistical analysis approaches that could be helpful for screening and evaluation of signals generated from imbalances of AEs in moderate or large RCTs.

Pharmacological Measures of Treatment Adherence and Risk of HIV Infection in the VOICE Study

BACKGROUND

None of the 3 regimens tested in the VOICE study showed protection against human immunodeficiency virus (HIV) infection in intent-to-treat analyses. Plasma tenofovir concentrations demonstrated poor adherence to the study product among study subjects. Statistical analyses to explore the causal treatment effect on the prevention of HIV infection among adherent individuals are needed.

METHODS

We developed an analytical strategy to evaluate whether conventional covariate adjustment removes confounding and thereby reveals a prevention effect among adherent individuals. We applied this strategy to the VOICE study, using 2 dichotomized proxy measures of product use: detection of tenofovir in plasma at least once during follow-up and detection of tenofovir in plasma at the 3-month follow-up visit.

RESULTS

After adjustment for a set of baseline predictors of the risk of HIV transmission, the confounding associated with comparison of adherent individuals in the tenofovir gel arm to placebo recipients was nearly eliminated. The relative risk for a prevention effect among those ever having tenofovir detected was 0.53 (P = .038); the relative risk among those having tenofovir detected at 3 months was 0.40 (P = .045).

CONCLUSIONS

A novel regression approach was proposed for causal as-treated analyses in the VOICE study. While intent-to-treat analyses yield null results, this exploratory approach presented evidence suggesting a prevention effect among gel users.

CLINICAL TRIALS REGISTRATION

NCT00705679.

Identification and estimation of survivor average causal effects

In longitudinal studies, outcomes ascertained at follow-up are typically undefined for individuals who die prior to the follow-up visit. In such settings, outcomes are said to be truncated by death and inference about the effects of a point treatment or exposure, restricted to individuals alive at the follow-up visit, could be biased even if as in experimental studies, treatment assignment were randomized. To account for truncation by death, the survivor average causal effect (SACE) defines the effect of treatment on the outcome for the subset of individuals who would have survived regardless of exposure status. In this paper, the author nonparametrically identifies SACE by leveraging post-exposure longitudinal correlates of survival and outcome that may also mediate the exposure effects on survival and outcome. Nonparametric identification is achieved by supposing that the longitudinal data arise from a certain nonparametric structural equations model and by making the monotonicity assumption that the effect of exposure on survival agrees in its direction across individuals. A novel weighted analysis involving a consistent estimate of the survival process is shown to produce consistent estimates of SACE. A data illustration is given, and the methods are extended to the context of time-varying exposures. We discuss a sensitivity analysis framework that relaxes assumptions about independent errors in the nonparametric structural equations model and may be used to assess the extent to which inference may be altered by a violation of key identifying assumptions. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.