Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Impact of Confounder Selection in Propensity Scores for Rare Events Data - with Applications to Birth Defects
98
0
0.0
(
0
)
Added by
Jue Hou
Publication date
2017
fields
Mathematical Statistics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Our work was motivated by a recent study on birth defects of infants born to pregnant women exposed to a certain medication for treating chronic diseases. Outcomes such as birth defects are rare events in the general population, which often translate to very small numbers of events in the unexposed group. As drug safety studies in pregnancy are typically observational in nature, we control for confounding in this rare events setting using propensity scores (PS). Using our empirical data, we noticed that the estimated odds ratio for birth defects due to exposure varied drastically depending on the specific approach used. The commonly used approaches with PS are matching, stratification, inverse probability weighting (IPW) and regression adjustment. The extremely rare events setting renders the matching or stratification infeasible. In addition, the PS itself may be formed via different approaches to select confounders from a relatively long list of potential confounders. We carried out simulation experiments to compare different combinations of approaches: IPW or regression adjustment, with 1) including all potential confounders without selection, 2) selection based on univariate association between the candidate variable and the outcome, 3) selection based on change in effects (CIE). The simulation showed that IPW without selection leads to extremely large variances in the estimated odds ratio, which help to explain the empirical data analysis results that we had observed. The simulation also showed that IPW with selection based on univariate association with the outcome is preferred over IPW with CIE. Regression adjustment has small variances of the estimated odds ratio regardless of the selection methods used.
rate research
Read More
The root-cause diagnostics of product quality defects in multistage manufacturing processes often requires a joint identification of crucial stages and process variables. To meet this requirement, this paper proposes a novel penalized matrix regression methodology for two-dimensional variable selection. The method regresses a scalar response variable against a matrix-based predictor using a generalized linear model. The unknown regression coefficient matrix is decomposed as a product of two factor matrices. The rows of the first factor matrix and the columns of the second factor matrix are simultaneously penalized to inspire sparsity. To estimate the parameters, we develop a block coordinate proximal descent (BCPD) optimization algorithm, which cyclically solves two convex sub-optimization problems. We have proved that the BCPD algorithm always converges to a critical point with any initialization. In addition, we have also proved that each of the sub-optimization problems has a closed-form solution if the response variable follows a distribution whose (negative) log-likelihood function has a Lipschitz continuous gradient. A simulation study and a dataset from a real-world application are used to validate the effectiveness of the proposed method.
Background: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among a wide variety of decision-making models for determining the degree of disease deterioration at the bedside. AutoScore was proposed as a useful clinical score generator based on machine learning and a generalized linear model. Its current framework, however, still leaves room for improvement when addressing unbalanced data of rare events. Methods: Using machine intelligence approaches, we developed AutoScore-Imbalance, which comprises three components: training dataset optimization, sample weight optimization, and adjusted AutoScore. All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i.e., mean value of sensitivity and specificity). By utilizing a publicly accessible dataset from Beth Israel Deaconess Medical Center, we assessed the proposed model and baseline approaches in the prediction of inpatient mortality. Results: AutoScore-Imbalance outperformed baselines in terms of AUC and balanced accuracy. The nine-variable AutoScore-Imbalance sub-model achieved the highest AUC of 0.786 (0.732-0.839) while the eleven-variable original AutoScore obtained an AUC of 0.723 (0.663-0.783), and the logistic regression with 21 variables obtained an AUC of 0.743 (0.685-0.800). The AutoScore-Imbalance sub-model (using down-sampling algorithm) yielded an AUC of 0. 0.771 (0.718-0.823) with only five variables, demonstrating a good balance between performance and variable sparsity. Conclusions: The AutoScore-Imbalance tool has the potential to be applied to highly unbalanced datasets to gain further insight into rare medical events and to facilitate real-world clinical decision-making.
We investigate the causal effects of drug exposure on birth defects, motivated by a recent cohort study of birth outcomes in pregnancies of women treated with a given medication, that revealed a higher rate of major structural birth defects in infants born to exposed versus unexposed women. An outstanding problem in this study was the missing birth defect outcomes among pregnancy losses resulting from spontaneous abortion. This led to missing not at random because, according to the theory of terathanasia, a defected fetus is more likely to be spontaneously aborted. In addition, the previous analysis stratified on live birth against spontaneous abortion, which was itself a post-exposure variable and hence did not lead to causal interpretation of the stratified results. In this paper we aimed to estimate and provide inference for the causal parameters of scientific interest, including the principal effects, making use of the missing data mechanism informed by terathanasia. During this process we also dealt with complications in the data including left truncation, observational nature, and rare events. We report our findings which shed light on how studies on causal effects of medication or other exposures during pregnancy may be analyzed.
This paper studies binary logistic regression for rare events data, or imbalanced data, where the number of events (observations in one class, often called cases) is significantly smaller than the number of nonevents (observations in the other class, often called controls). We first derive the asymptotic distribution of the maximum likelihood estimator (MLE) of the unknown parameter, which shows that the asymptotic variance convergences to zero in a rate of the inverse of the number of the events instead of the inverse of the full data sample size. This indicates that the available information in rare events data is at the scale of the number of events instead of the full data sample size. Furthermore, we prove that under-sampling a small proportion of the nonevents, the resulting under-sampled estimator may have identical asymptotic distribution to the full data MLE. This demonstrates the advantage of under-sampling nonevents for rare events data, because this procedure may significantly reduce the computation and/or data collection costs. Another common practice in analyzing rare events data is to over-sample (replicate) the events, which has a higher computational cost. We show that this procedure may even result in efficiency loss in terms of parameter estimation.
Box score statistics in the National Basketball Association are used to measure and evaluate player performance. Some of these statistics are subjective in nature and since box score statistics are recorded by scorekeepers hired by the home team for each game, there exists potential for inconsistency and bias. These inconsistencies can have far reaching consequences, particularly with the rise in popularity of daily fantasy sports. Using box score data, we estimate models able to quantify both the bias and the generosity of each scorekeeper for two of the most subjective statistics: assists and blocks. We then use optical player tracking data for the 2014-2015 season to improve the assist model by including other contextual spatio-temporal variables such as time of possession, player locations, and distance traveled. From this model, we present results measuring the impact of the scorekeeper and of the other contextual variables on the probability of a pass being recorded as an assist. Results for adjusting season assist totals to remove scorekeeper influence are also presented.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
