Inconclusives, errors, and error rates in forensic firearms analysis:Three statistical perspectives.

Error rates that have been published in recent open black box studies of forensic firearms examiner performance have been very low, typically below one percent. These low error rates have been challenged, however, as not properly taking into account one of the categories, "Inconclusive", that examiners can reach in comparing a pair of bullets or cartridges. These challenges have themselves been challenged; how to consider the inconclusives and their effect on error rates is currently a matter of sharp debate. We review several viewpoints that have been put forth, and then examine the impact of inconclusives on error rates from three fresh statistical perspectives: (a) an ideal perspective using objective measurements combined with statistical algorithms, (b) basic sampling theory and practice, and (c) standards of experimental design in human studies. Our conclusions vary with the perspective: (a) inconclusives can be simple errors (or, on the other hand, simply correct or at least well justified); (b) inconclusives need not be counted as errors to bring into doubt assessments of error rates; (c) inconclusives are potential errors, more explicitly, inconclusives in studies are not necessarily the equivalent of inconclusives in casework and can mask potential errors in casework. From all these perspectives, it is impossible to simply read out trustworthy estimates of error rates from those studies which have been carried out to date. At most, one can put reasonable bounds on the potential error rates. These are much larger than the nominal rates reported in the studies. To get straightforward, sound estimates of error rates requires a challenging but critical improvement to the design of firearms studies. A proper study-one in which inconclusives are not potential errors, and which yields direct, sound estimates of error rates-will require new objective measures or blind proficiency testing embedded in ordinary casework.

Adjusted logistic propensity weighting methods for population inference using nonprobability volunteer-based epidemiologic cohorts.

Many epidemiologic studies forgo probability sampling and turn to nonprobability volunteer-based samples because of cost, response burden, and invasiveness of biological samples. However, finite population (FP) inference is difficult to make from the nonprobability sample due to the lack of population representativeness. Aiming for making inferences at the population level using nonprobability samples, various inverse propensity score weighting methods have been studied with the propensity defined by the participation rate of population units in the nonprobability sample. In this article, we propose an adjusted logistic propensity weighting (ALP) method to estimate the participation rates for nonprobability sample units. The proposed ALP method is easy to implement by ready-to-use software while producing approximately unbiased estimators for population quantities regardless of the nonprobability sample rate. The efficiency of the ALP estimator can be further improved by scaling the survey sample weights in propensity estimation. Taylor linearization variance estimators are proposed for ALP estimators of FP means that account for all sources of variability. The proposed ALP methods are evaluated numerically via simulation studies and empirically using the naïve unweighted National Health and Nutrition Examination Survey III sample, while taking the 1997 National Health Interview Survey as the reference, to estimate the 15-year mortality rates.

Association of organic food consumption with obesity in a nationally representative sample.

The increased prevalence and adverse health consequences of obesity have made it one of the leading public health issues in recent years. Importantly, several epidemiological studies have revealed significant associations between BMI and organic food consumption. However, although these studies have suggested that this factor holds promise to prevent obesity, they all suffer from methodological limitations, including self-reporting methods to assess BMI, not controlling for potential confounding factors or using a non-representative sample. Moreover, all were restricted to an adult sample. We present the results of a cross-sectional epidemiological study assessing the association of organic food consumption with BMI and obesity in a representative lifespan French sample (INCA3 study). Objective methods were used to measure BMI, and several potentially confounding variables were controlled for. In total, 1775 children and adolescents and 2121 adults underwent anthropometric measurements and completed questionnaires concerning their dietary habits and lifestyle. Unadjusted models systematically revealed negative associations between organic food consumption and both BMI and obesity across all age groups. These associations tended to remain statistically significant even after controlling for several confounding variables concerning socio-economic status, quality of the diet and physical activity. The effect sizes were, however, small. These data confirm the association between organic food consumption and obesity during both childhood and adulthood. Evidence from randomised controlled trials is required to investigate causality between organic food consumption and lower BMI or obesity rate.

Model-assisted estimators for time-to-event data from complex surveys.

We develop model-assisted estimators for complex survey data for the proportion of a population that experienced some event by a specified time t. Theory for the new estimators uses time-to-event models as the underlying framework but have both good model-based and design-based properties. The estimators are compared in a simulation to traditional survey estimation methods and are also applied to a study of nurses' health. The new estimators take advantage of covariates predictive of the event and reduce standard errors compared to conventional alternatives.

Efficient Use of Commercial Lists in U.S. Household Sampling.

Sampling households using commercial lists has the potential to reduce costs and to efficiently identify some subgroups for which target sample sizes are desired. However, the information on the lists for demographics like age is usually incomplete and inaccurate. We demonstrate that this inexact information can still be used to improve the efficiency with which some, but not all, demographic subgroups can be located during sampling. The paper also illustrates the use of nonlinear programming as a means for finding sample allocations that are subject to a variety of practical constraints. A commercial address list and data from the National Survey of Family Growth and the Health and Retirement Study are used to illustrate the calculation of allocations to strata of housing units defined by information on the list.

Partial misspecification of survey design features sufficed to severely bias estimates of health-related outcomes.

OBJECTIVE: Surveys frequently deviate from simple random sampling through the use of unequal probability sampling, stratified sampling, and multistage sampling. This work uses a survey of public health to systematically illustrate the effects of incompletely accounting for strata, clustering, and weights. STUDY DESIGN AND SETTING: Data analysis was based on the Study of Health in Pomerania (n=4,308, 20-79 years), a two-stage regional survey with high sampling fractions at the first stage. Effects of survey design features comprising weights, stratification, clustering, and finite population correction on point and variance estimates of lifestyle indicators and clinical parameters were assessed. RESULTS: Misspecifications of the survey design substantially affected both the point estimates of health characteristics and their standard errors (SEs). The strongest bias in SEs concerned the omission of the second sampling stage. Ignoring the sampling design led to minor differences in variance estimates from the complete setup. Weighting predominantly affected point estimates of lifestyle factors. CONCLUSION: A partial misspecification of survey design elements may bias variance estimates severely and is sometimes even more harmful compared with completely neglecting design elements. If subgroups are sampled at different rates, weighting is of particular relevance with regard to prevalence estimates of lifestyle indicators.