Academic literature on the topic 'Multiple penalized spline of propensity prediction'

Propensity score (PS) based methods, such as matching, stratification, regression adjustment, simple and augmented inverse probability weighting, are popular for controlling for observed confounders in observational studies of causal effects. More recently, we proposed penalized spline of propensity prediction (PENCOMP), which multiply-imputes outcomes for unassigned treatments using a regression model that includes a penalized spline of the estimated selection probability and other covariates. For PS methods to work reliably, there should be sufficient overlap in the propensity score distributions between treatment groups. Limited overlap can result in fewer subjects being matched or in extreme weights causing numerical instability and bias in causal estimation. The problem of limited overlap suggests (a) defining alternative estimands that restrict inferences to subpopulations where all treatments have the potential to be assigned, and (b) excluding or down-weighting sample cases where the propensity to receive one of the compared treatments is close to zero. We compared PENCOMP and other PS methods for estimation of alternative causal estimands when limited overlap occurs. Simulations suggest that, when there are extreme weights, PENCOMP tends to outperform the weighted estimators for ATE and performs similarly to the weighted estimators for alternative estimands. We illustrate PENCOMP in two applications: the effect of antiretroviral treatments on CD4 counts using the Multicenter AIDS cohort study (MACS) and whether right heart catheterization (RHC) is a beneficial treatment in treating critically ill patients.

Without randomization of treatments, valid inference of treatment effects from observational studies requires controlling for all confounders because the treated subjects generally differ systematically from the control subjects. Confounding control is commonly achieved using the propensity score, defined as the conditional probability of assignment to a treatment given the observed covariates. The propensity score collapses all the observed covariates into a single measure and serves as a balancing score such that the treated and control subjects with similar propensity scores can be directly compared. Common propensity score-based methods include regression adjustment and inverse probability of treatment weighting using the propensity score. We recently proposed a robust multiple imputation-based method, penalized spline of propensity for treatment comparisons (PENCOMP), that includes a penalized spline of the assignment propensity as a predictor. Under the Rubin causal model assumptions that there is no interference across units, that each unit has a non-zero probability of being assigned to either treatment group, and there are no unmeasured confounders, PENCOMP has a double robustness property for estimating treatment effects. In this study, we examine the impact of using variable selection techniques that restrict predictors in the propensity score model to true confounders of the treatment-outcome relationship on PENCOMP. We also propose a variant of PENCOMP and compare alternative approaches to standard error estimation for PENCOMP. Compared to the weighted estimators, PENCOMP is less affected by inclusion of non-confounding variables in the propensity score model. We illustrate the use of PENCOMP and competing methods in estimating the impact of antiretroviral treatments on CD4 counts in HIV+ patients.

Abstract. The spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to the required soil depth. The field-based generation of large soil datasets and conventional soil maps remains costly. Meanwhile, legacy soil data and comprehensive sets of spatial environmental data are available for many regions.Digital soil mapping (DSM) approaches relating soil data (responses) to environmental data (covariates) face the challenge of building statistical models from large sets of covariates originating, for example, from airborne imaging spectroscopy or multi-scale terrain analysis. We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping the effective soil depth available to plants (SD), pH, soil organic matter (SOM), effective cation exchange capacity (ECEC), clay, silt, gravel content and fine fraction bulk density for four soil depths (totalling 48 responses). Models were built from 300–500 environmental covariates by selecting linear models through (1) grouped lasso and (2) an ad hoc stepwise procedure for robust external-drift kriging (georob). For (3) geoadditive models we selected penalized smoothing spline terms by component-wise gradient boosting (geoGAM). We further used two tree-based methods: (4) boosted regression trees (BRTs) and (5) random forest (RF). Lastly, we computed (6) weighted model averages (MAs) from the predictions obtained from methods 1–5.Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3–6 % of all covariates). Differences in predictive performance, tested on independent validation data, were mostly small and did not reveal a single best method for 48 responses. Nevertheless, RF was often the best among methods 1–5 (28 of 48 responses), but was outcompeted by MA for 14 of these 28 responses. RF tended to over-fit the data. The performance of BRT was slightly worse than RF. GeoGAM performed poorly on some responses and was the best only for 7 of 48 responses. The prediction accuracy of lasso was intermediate. All models generally had small bias. Only the computationally very efficient lasso had slightly larger bias because it tended to under-fit the data. Summarizing, although differences were small, the frequencies of the best and worst performance clearly favoured RF if a single method is applied and MA if multiple prediction models can be developed.

Dynamic treatment regimes are a set of time-adaptive decision rules that can be used to personalize treatment across multiple stages of care. Grounded in causal inference methods, dynamic treatment regimes identify variables that differentiate the treatment effect and may be used to tailor treatments across individuals based on the patient’s own characteristics – thereby representing an important step toward personalized medicine. In this manuscript we introduce Penalized Spline-Involved Tree-based Learning, which seeks to improve upon existing tree-based approaches to estimating an optimal dynamic treatment regime. Instead of using weights determined from the estimated propensity scores, which may result in unstable estimates when weights are highly variable, we predict missing counterfactual outcomes using regression models that incorporate a penalized spline of the propensity score and other covariates predictive of the outcome. We further develop a novel purity measure applied within a decision tree framework to produce a flexible yet interpretable method for estimating an optimal multi-stage multi-treatment dynamic treatment regime. In simulation experiments we demonstrate good performance of Penalized Spline-Involved Tree-based Learning relative to competing methods and, in particular, we show that Penalized Spline-Involved Tree-based Learning may be advantageous when the sample size is small and/or when the level of confounding of the outcome is high. We apply Penalized Spline-Involved Tree-based Learning to the retrospectively-collected Medical Information Mart for Intensive Care dataset to identify variables that may be used to tailor early fluid resuscitation strategies in septic patients.

BackgroundMagnetic resonance imaging (MRI) and transrectal ultrasound (TRUS)-guided cognitive or image fusion biopsy is performed to target a prostate imaging reporting and data system (PI-RADS) 3–5 lesion. Biopsy Gleason score (GS) is frequently underestimated compared to prostatectomy GS. However, it is still unclear about how many cores on target are necessary to reduce undergrading and if additional cores around the target may improve grade prediction on surgical specimen.PurposeTo determine the number of target cores and targeting strategy to reduce GS underestimation.Materials and MethodsBetween May 2017 and April 2020, a total of 385 patients undergoing target cognitive or image fusion biopsy of PI-RADS 3–5 index lesions and radical prostatectomies (RP) were 2:1 matched with propensity score using multiple variables and divided into the 1–4 core (n = 242) and 5–6 core (n = 143) groups, which were obtained with multiple logistic regression with restricted cubic spline curve. Target cores of 1–3 and 4–6 were sampled from central and peripheral areas, respectively. Pathologic outcomes and target cores were retrospectively assessed to analyze the GS difference or changes between biopsy and RP with Wilcoxon signed-rank test.ResultsThe median of target cores was 3 and 6 in the 1–4 core and 5–6 core groups, respectively (p < 0.001). Restricted cubic spline curve showed that GS upgrade was significantly reduced from the 5th core and there was no difference between 5th and 6th cores. Among the matched patients, 35.4% (136/385; 95% confidence interval, 0.305–0.403) had a GS upgrade after RP. The GS upgrades in the 1–4 core and 5–6 core groups were observed in 40.6% (98/242, 0.343–0.470) and 26.6% (38/143, 0.195–0.346), respectively (p = 0.023). Although there was no statistical difference between the matched groups in terms of RP GS (p = 0.092), the 5–6 core group had significantly higher biopsy GS (p = 0.006) and lower GS change from biopsy to RP (p = 0.027).ConclusionFive or more target cores sampling from both periphery and center of an index tumor contribute to reduce GS upgrade.