Measuring Individual Benefits of Medical Treatments Using Longitudinal Hospital Data with Non-Ignorable Missing Responses Caused by Patient Discharge: Application to the Study of Benefits of Pain Management Post Spinal Fusion

Electronic health records (EHR) provide valuable resources for longitudinal studies and understanding risk factors associated with poor clinical outcomes. However, they may not contain complete follow-ups, and the missing data may not be at random since hospital discharge may depend in part on expected but unrecorded clinical outcomes that occur after patient discharge. These non-ignorable missing data requires appropriate analysis methods. Here, we are interested in measuring and analyzing individual treatment benefits of medical treatments in patients recorded in EHR databases. We present a method for predicting individual benefits that handles non-ignorable missingness due to hospital discharge. The longitudinal clinical outcome of interest is modeled simultaneously with the hospital length of stay using a joint mixed-effects model, and individual benefits are predicted through a frequentist approach: the empirical Bayesian approach. We illustrate our approach by assessing individual pain management benefits to patients who underwent spinal fusion surgery. By calculating sample percentiles of empirical Bayes predictors of individual benefits, we examine the evolution of individual benefits over time. We additionally compare these percentiles with percentiles calculated with a Monte Carlo approach. We showed that empirical Bayes predictors of individual benefits do not only allow examining benefits in specific patients but also reflect overall population trends reliably.

Using population crossover trials to improve the decision process regarding treatment individualization in N-of-1 trials

Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call "partial empirical Bayes." We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual disease severities. We show the estimators' consistency under the most common N-of-1 designs and examine their prediction errors and performance with small numbers of patient's responses. We demonstrate by simulating new patients that our approach is equivalent or superior to both the common clinical practice of recommending the on-average best treatment for all patients and the common individualization method that simply compares average responses to the tested treatments. We conclude that some situations exist in which individualization with N-of-1 trials is highly beneficial while other situations exist in which individualization may be unfruitful.

A parsimonious personalized dose-finding model via dimension reduction

Learning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when the decision function is estimated nonparametrically. To tackle this problem, we propose a dimension reduction framework that effectively reduces the estimation to a lower-dimensional subspace of the covariates. We exploit that the individualized dose rule can be defined in a subspace spanned by a few linear combinations of the covariates, leading to a more parsimonious model. Also, our framework does not require the inverse probability of the propensity score under observational studies due to a direct maximization of the value function. This distinguishes us from the outcome weighted learning framework, which also solves decision rules directly. Under the same framework, we further propose a pseudo-direct learning approach focuses more on estimating the dimensionality-reduced subspace of the treatment outcome. Parameters in both approaches can be estimated efficiently using an orthogonality constrained optimization algorithm on the Stiefel manifold. Under mild regularity assumptions, the asymptotic normality results of the proposed estimators can are established, respectively. We also derive the consistency and convergence rate for the value function under the estimated optimal dose rule. We evaluate the performance of the proposed approaches through extensive simulation studies and a warfarin pharmacogenetic dataset.

Subgroup identification via homogeneity pursuit for dense longitudinal/spatial data

In the clinical trial community, it is usually not easy to find a treatment that benefits all patients since the reaction to treatment may differ substantially across different patient subgroups. The heterogeneity of treatment effect plays an essential role in personalized medicine. To facilitate the development of tailored therapies and improve the treatment efficacy, it is important to identify subgroups that exhibit different treatment effects. We consider a very general framework for subgroup identification via the homogeneity pursuit methods usually employed in econometric time series analysis. The change point detection algorithm in our procedure is most suitable for analyzing dense longitudinal or spatial data which are quite common for biomedical studies these days. We demonstrate that our proposed method is fast and accurate through extensive numerical studies. In particular, our method is illustrated by analyzing a diffusion tensor imaging data set.

Impact of Chemotherapy Regimens on Normal Tissue Complication Probability Models of Acute Hematologic Toxicity in Rectal Cancer Patients Receiving Intensity Modulated Radiation Therapy With Concurrent Chemotherapy From a Prospective Phase III Clinical Trial

Purpose: To determine whether there are differences in bone marrow tolerance to chemoradiotherapy (CRT) between two chemotherapy regimens according to FOWARC protocol and how chemotherapy regimens affect radiation dose parameters and normal tissue complication probability (NTCP) modelings that correlate with acute hematologic toxicity (HT) in rectal cancer patients treated with intensity modulated radiation therapy (IMRT) and concurrent chemotherapy.

Materials and Methods: One hundred and twenty-eight rectal cancer patients who received IMRT from a single institution were recruited from Chinese FOWARC multicenter, open-label, randomized phase III trial. We assessed HT in these patients who were separated into two groups: Oxaliplatin (L-OHP) + 5- fluorouracil (5FU) (FOLFOX, 70 of 128) and 5FU (58 of 128). The pelvic bone marrow (PBM) was divided into three subsites: lumbosacral spine (LSS), ilium (I), and lower pelvic (LP). The endpoint for HT was grade ≥3 (HT3+) and grade ≥2 (HT2+) leukopenia, neutropenia, anemia and thrombocytopenia. Logistic regression was used to analyze the association between HT2+/HT3+ and dosimetric parameters. Lyman-Kutcher-Burman (LKB) model was used to calculate NTCP.

Results: Sixty-eight patients experienced HT2+: 22 of 58 (37.9%) 5FU and 46 of 70 (65.7%) FOLFOX (p = 0.008), while twenty-six patients experienced HT3+: 4 of 58 (6.9%) 5FU and 22 of 70 (31.4%) FOLFOX (p = 0.016). PBM and LP dosimetric parameters were correlated with HT2+ in the 5FU group but not in the FOLFOX group. No PBM dosimetric parameters were correlated with HT3+ in both groups. For PBM, NTCP at HT3+ was 0.32 in FOLFOX group relative to 0.10 in 5FU subset (p < 0.05).

Conclusion: Patients receiving FOLFOX have lower BM tolerance to CRT than those receiving 5FU. Low-dose radiation to the PBM is predictive for HT2+ in patients who received 5FU. NTCP modeling in FOLFOX group predicts much higher risk of HT3+ than 5FU group.

Estimating individual benefits of medical or behavioral treatments in severely ill patients

There is a need for statistical methods appropriate for the analysis of clinical trials from a personalized-medicine viewpoint as opposed to the common statistical practice that simply examines average treatment effects. This article proposes an approach to quantifying, reporting and analyzing individual benefits of medical or behavioral treatments to severely ill patients with chronic conditions, using data from clinical trials. The approach is a new development of a published framework for measuring the severity of a chronic disease and the benefits treatments provide to individuals, which utilizes regression models with random coefficients. Here, a patient is considered to be severely ill if the patient’s basal severity is close to one. This allows the derivation of a very flexible family of probability distributions of individual benefits that depend on treatment duration and the covariates included in the regression model. Our approach may enrich the statistical analysis of clinical trials of severely ill patients because it allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it, and we can also measure the benefits achieved in specific patients including new patients. We illustrate our approach using data from a clinical trial of the anti-depressant imipramine.