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Inverse Probability (inverse + probability)

Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Maximum entropy inference for mixed continuous-discrete variables

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 4 2010
Hermann Singer
We represent knowledge by probability distributions of mixed continuous and discrete variables. From the joint distribution of all items, one can compute arbitrary conditional distributions, which may be used for prediction. However, in many cases only some marginal distributions, inverse probabilities, or moments are known. Under these conditions, a principle is needed to determine the full joint distribution of all variables. The principle of maximum entropy (Jaynes, Phys Rev 1957;106:620,630 and 1957;108:171,190; Jaynes, Probability Theory,The Logic of Science, Cambridge, UK: Cambridge University Press, 2003; Haken, Synergetics, Berlin: Springer-Verlag, 1977; Guiasu and Shenitzer, Math Intell 1985;117:83,106) ensures an unbiased estimation of the full multivariate relationships by using only known facts. For the case of discrete variables, the expert shell SPIRIT implements this approach (cf. Rödder, Artif Intell 2000;117:83,106; Rödder and Meyer, in Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, 2006; Rödder et al., Logical J IGPL 2006;14(3):483,500). In this paper, the approach is generalized to continuous and mixed continuous-discrete distributions and applied to the problem of credit scoring. © 2010 Wiley Periodicals, Inc. [source]

Estimating ethnic differences in self-reported new use of antidepressant medications: results from the Multi-Ethnic Study of Atherosclerosis

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 7 2009
Joseph A. C. Delaney PhD
Abstract Introduction There is evidence that the utilization of antidepressant medications (ADM) may vary between different ethnic groups in the United States population. Methods The Multi-Ethnic Study of Atherosclerosis (MESA) is a population-based prospective cohort study of 6814 US adults from 4 different ethnic groups. After excluding baseline users of ADM, we examined the relation between baseline depression and new use of ADM for 4 different ethnicities: African,Americans (n,=,1822), Asians (n,=,784) Caucasians (n,=,2300), and Hispanics (n,=,1405). Estimates of the association of ethnicity and ADM use were adjusted for age, study site, gender, Center for Epidemiologic Studies Depression Scale (CES-D), alcohol use, smoking, blood pressure, diabetes, education, and exercise. Non-random loss to follow-up was present and estimates were adjusted using inverse probability of censoring weighting (IPCW). Results Of the four ethnicities, Caucasian participants had the highest rate of ADM use (12%) compared with African,American (4%), Asian (2%), and Hispanic (6%) participants. After adjustment, non-Caucasian ethnicity was associated with reduced ADM use: African,American (HR: 0.42; 95% Confidence Interval (CI): 0.31,0.58), Asian (HR: 0.14; 95%CI: 0.08,0.26), and Hispanic (HR: 0.47; 95%CI: 0.31,0.65). Applying IPCW to correct for non-random loss to follow-up among the study participants weakened but did not eliminate these associations: African,American (HR: 0.48; 95%CI: 0.30,0.57), Asian (HR: 0.23; 95%CI: 0.13,0.37), and Hispanic (HR: 0.58; 95%CI: 0.47,0.67). Conclusion Non-Caucasian ethnicity is associated with lower rates of new ADM use. After IPCW adjustment, the observed ethnicity differences in ADM use are smaller although still statistically significant. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Insights into different results from different causal contrasts in the presence of effect-measure modification,

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 10 2006
Til Stürmer
Abstract Purpose Both propensity score (PS) matching and inverse probability of treatment weighting (IPTW) allow causal contrasts, albeit different ones. In the presence of effect-measure modification, different analytic approaches produce different summary estimates. Methods We present a spreadsheet example that assumes a dichotomous exposure, covariate, and outcome. The covariate can be a confounder or not and a modifier of the relative risk (RR) or not. Based on expected cell counts, we calculate RR estimates using five summary estimators: Mantel-Haenszel (MH), maximum likelihood (ML), the standardized mortality ratio (SMR), PS matching, and a common implementation of IPTW. Results Without effect-measure modification, all approaches produce identical results. In the presence of effect-measure modification and regardless of the presence of confounding, results from the SMR and PS are identical, but IPTW can produce strikingly different results (e.g., RR,=,0.83 vs. RR,=,1.50). In such settings, MH and ML do not estimate a population parameter and results for those measures fall between PS and IPTW. Conclusions Discrepancies between PS and IPTW reflect different weighting of stratum-specific effect estimates. SMR and PS matching assign weights according to the distribution of the effect-measure modifier in the exposed subpopulation, whereas IPTW assigns weights according to the distribution of the entire study population. In pharmacoepidemiology, contraindications to treatment that also modify the effect might be prevalent in the population, but would be rare among the exposed. In such settings, estimating the effect of exposure in the exposed rather than the whole population is preferable. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression

BIOMETRICS, Issue 1 2010
Donglin Zeng
Summary In this article, we study the estimation of mean response and regression coefficient in semiparametric regression problems when response variable is subject to nonrandom missingness. When the missingness is independent of the response conditional on high-dimensional auxiliary information, the parametric approach may misspecify the relationship between covariates and response while the nonparametric approach is infeasible because of the curse of dimensionality. To overcome this, we study a model-based approach to condense the auxiliary information and estimate the parameters of interest nonparametrically on the condensed covariate space. Our estimators possess the double robustness property, i.e., they are consistent whenever the model for the response given auxiliary covariates or the model for the missingness given auxiliary covariate is correct. We conduct a number of simulations to compare the numerical performance between our estimators and other existing estimators in the current missing data literature, including the propensity score approach and the inverse probability weighted estimating equation. A set of real data is used to illustrate our approach. [source]

Marginal Analysis of Incomplete Longitudinal Binary Data: A Cautionary Note on LOCF Imputation

BIOMETRICS, Issue 3 2004
Richard J. Cook
Summary In recent years there has been considerable research devoted to the development of methods for the analysis of incomplete data in longitudinal studies. Despite these advances, the methods used in practice have changed relatively little, particularly in the reporting of pharmaceutical trials. In this setting, perhaps the most widely adopted strategy for dealing with incomplete longitudinal data is imputation by the "last observation carried forward" (LOCF) approach, in which values for missing responses are imputed using observations from the most recently completed assessment. We examine the asymptotic and empirical bias, the empirical type I error rate, and the empirical coverage probability associated with estimators and tests of treatment effect based on the LOCF imputation strategy. We consider a setting involving longitudinal binary data with longitudinal analyses based on generalized estimating equations, and an analysis based simply on the response at the end of the scheduled follow-up. We find that for both of these approaches, imputation by LOCF can lead to substantial biases in estimators of treatment effects, the type I error rates of associated tests can be greatly inflated, and the coverage probability can be far from the nominal level. Alternative analyses based on all available data lead to estimators with comparatively small bias, and inverse probability weighted analyses yield consistent estimators subject to correct specification of the missing data process. We illustrate the differences between various methods of dealing with drop-outs using data from a study of smoking behavior. [source]

Epidemiology of Lower Extremity Injuries among U.S. High School Athletes

ACADEMIC EMERGENCY MEDICINE, Issue 7 2007
William G. Fernandez MD
Objectives:Despite the health benefits of organized sports, high school athletes are at risk for lower extremity sports-related injuries (LESRIs). The authors documented the epidemiology of LESRIs among U.S. high school athletes. Methods:Via two-stage sampling, 100 U.S. high schools were randomly selected. During the 2005 school year, LESRIs in nine sports were reported: boys' baseball, football, and wrestling; girls' softball and volleyball; and boys' and girls' basketball and soccer. The authors calculated rates as the ratio of LESRIs to the number of athlete exposures. National estimates were generated by assigning injuries a sample weight based on the inverse probability of the school's selection into the study. Results:Among high school athletes in 2005, 2,298 of 4,350 injuries (52.8%) were LESRIs. This represents an estimated 807,222 LESRIs in U.S. high school athletes in nine sports (1.33/1,000 athlete exposures). Football had the highest LESRI rate for boys (2.01/1,000) and soccer the highest for girls (1.59/1,000). Leading diagnoses were sprains (50%), strains (17%), contusions (12%), and fractures (5%). The ankle (40%), knee (25%), and thigh (14%) were most frequently injured. Fractures occurred most often in the ankle (42%), lower leg (29%), or foot (18%). Girls with ligamentous knee injuries required surgery twice as often as boys (67% vs. 35%; p < 0.01). Girls had 1.5 times the proportion of season-ending LESRIs of boys (12.5% vs. 8%; p < 0.01). Conclusions:While LESRIs occur commonly in high school athletes, team- and gender-specific patterns exist. Emergency department staff will likely encounter such injuries. To optimize prevention strategies, ongoing surveillance is needed. [source]