Model-based Recursive Partitioning for Subgroup Analyses

The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.Comment: 26 pages, 6 figure

Designing screening protocols for amphibian disease that account for imperfect and variable capture rates of individuals

The amphibian chytrid fungus, Batrachochytrium dendrobatidis, is one of the main factors in global amphibian decline. Accurate knowledge of its presence and prevalence in an area is needed to trigger conservation actions. However, imperfect capture rates determine the number of individuals caught and tested during field surveys, and contribute to the uncertainty surrounding estimates of prevalence. Screening programs should be planned with the objective of minimizing such uncertainty. We show how this can be achieved by using predictive models that incorporate information about population size and capture rates. Using as a case study an existing screening program for three populations of the yellow-bellied toad (Bombina variegata pachypus) in northern Italy, we sought to quantify the effect of seasonal variation in individual capture rates on the uncertainty surrounding estimates of chytrid prevalence. We obtained estimates of population size and capture rates from mark-recapture data, and found wide seasonal variation in the individual recapture rates. We then incorporated this information in a binomial model to predict the estimates of prevalence that would be obtained by sampling at different times in the season, assuming no infected individuals were found. Sampling during the period of maximum capture probability was predicted to decrease upper 95% credible intervals by a maximum of 36%, compared with least suitable periods, with greater gains when using uninformative priors. We evaluated model predictions by comparing them with the results of screening surveys in 2012. The observed results closely matched the predicted figures for all populations, suggesting that this method can be reliably used to maximize the sampling size of surveillance programs, thus improving their efficiency

Improved Methods for Modeling High Dimensional Binary Features Data with Applications for Assessing Disease Burden from Diagnostic History and for Dealing with Missing Covariates in Administrative Health Records

Healthcare outcomes research based on administrative data is frequently hindered by two important challenges: (1) accurate adjustment for disease burden and (2) effective management of missing data in key variables. Standard approaches exist for both problems, but these may contribute to biased results. For example, several well- established summary measures are used to adjust for disease burden, often without consideration for whether other methods could perform this task more accurately. Similarly, observations with missing values are often arbitrarily excluded, or the values are imputed without regard for the involved assumptions. Despite recent substantial gains in computing power, statistical approaches and machine learning methods, no comprehensive effort has been made to develop an improved comorbidity index based on predictive performance comparisons of competing approaches. Similarly, recently developed machine learning approaches have shown promise in addressing missing data problems, but these have not been compared with parametric methods via a rigorous simulation study using large-dimensional data with the complete range of missingness types. This makes it difficult to assess the relative merits of each procedure. This work accomplished three broad aims: (1) Improved models for summarizing disease burden were developed by comparing the predictive performance of a wide variety of statistical and machine learning methods. (2) A new comorbidity summary score for predicting five-year mortality was developed. (3) A comprehensive comparison of machine learning and model-based multiple imputation methods was completed, both in simulations and through an application to real data. Several sensitivity analyses were also examined for variables with missing not at random (MNAR) missingness. This work successfully demonstrated several new approaches for summarizing disease burden. Each of the competing disease burden models in the first aim and the summary score from the second aim had superior predictive performance when compared to the Elixhauser index, a commonly-used summary measure. This research also led to new applications for applying machine learning methods within the multiple imputation with chained equations (MICE) framework. Additionally, several MNAR sensitivity methods were adapted and applied to demonstrate that unbiased inference under MNAR may not be possible in some situations, even when the missingness mechanism is fully understood

A model-based multithreshold method for subgroup identification

Thresholding variable plays a crucial role in subgroup identification for personalizedmedicine. Most existing partitioning methods split the sample basedon one predictor variable. In this paper, we consider setting the splitting rulefrom a combination of multivariate predictors, such as the latent factors, principlecomponents, and weighted sum of predictors. Such a subgrouping methodmay lead to more meaningful partitioning of the population than using a singlevariable. In addition, our method is based on a change point regression modeland thus yields straight forward model-based prediction results. After choosinga particular thresholding variable form, we apply a two-stage multiple changepoint detection method to determine the subgroups and estimate the regressionparameters. We show that our approach can produce two or more subgroupsfrom the multiple change points and identify the true grouping with high probability.In addition, our estimation results enjoy oracle properties. We design asimulation study to compare performances of our proposed and existing methodsand apply them to analyze data sets from a Scleroderma trial and a breastcancer study

Statistical Concepts in Clinical Research

The overall objectives of the reference guide are: To introduce or review concepts to consider when designing a clinical trial To introduce or review the four phases of clinical trials including different types of designs for Phase I and Phase II clinical trials To introduce or review observational studies To introduce or review analysis of categorical, continuous, and time-to event measures as well as Bayesian methodology.https://openworks.mdanderson.org/mozart/1006/thumbnail.jp