Just Postmortem Interval Estimation Research
In this special release episode of the 2017 NIJ R&D Symposium, Just Science interviews Dr. Jeffrey Wells and Dr. Lynn LaMotte.
This is the abstract submitted where Dr. LaMotte and Dr. Wells explain their research:
To our knowledge an estimate of time since death is almost never accompanied by the kind of mathematically explicit probability statement that is the standard in most scientific disciplines. This has been a problem both for death investigation casework (and court testimony) and for research, because scientists have not known how to design decomposition experiments to provide adequate statistical power for postmortem interval (PMI) estimation. We have been developing methods for calculating statistical confidence limits about a PMI estimate based on either continuous quantitative or categorical data. The examples we present are from forensic entomology, but the approach is suitable for any postmortem variable. To do this we extended and adapted the time-tested statistical method of inverse prediction (IP, also called calibration) to the PMI estimation setting. Methods to produce valid p-values for this process are known for single, quantitative y and x that follow a linear regression relation and with y having constant variance. Some exist for multivariate y, but only for settings where y has constant variance. Many measurements used for PMI estimation do not fit these criteria. The current project builds on earlier work in which we developed IP methods for non-constant variance of a single, quantitative y (e.g. estimating carrion maggot age using a single size measurement, Wells and LaMotte 1995), and in which we developed the first ever method for IP based on categorical data (e.g. estimating PMI based on carrion insect succession, LaMotte and Wells 2000). One possible barrier to the adoption of these new inverse prediction methods by researchers and death investigators has been that they are not implemented in statistical software packages. In this presentation we will show how IP using categorical data can be done by simply reading a table. Concerning quantitative data we will show how inverse prediction of PMI can be performed using statistical analysis software already widely available for general linear mixed models, where the statistical theory and methodology are well-established. We will show how flexible models using polynomial splines can be fit for both the means and variance-covariance matrices, and how to use dummy variables over a grid of values of x to get the p-values required for confidence sets automatically. Attendees familiar with mixed models and their applications will be able to implement these methods in standard statistical packages.
Statistical Methods for Combining Multivariate and Categorical Data in Postmortem Interval Estimation
Lynn R. LaMotte,1 and Jeffrey D. Wells2 1Biostatistics Program, LSU School of Public Health
Lynn Roy LaMotte, Ph.D., is Professor of Biostatistics at LSU Health – New Orleans. He has collaborated with Jeffrey Wells, Ph.D., for twenty-five years, striving to address the statistical questions and problems that arise in time-since-death investigations. This has entailed adapting extant statistical methodology and developing new theory and methods, resulting in novel contributions for statistical inference in models for both continuous multivariate responses (like length and weight) and discrete multi-category responses, such as development stage.
Jeffrey Wells is an Associate Professor in the Department of Biological Sciences at Florida International University, where he is also affiliated with FIU’s International Forensic Research Institute. He has been active in death investigation casework and research since the late 1980s, and during almost all of that time he collaborated with Lynn LaMotte on the development of statistical methods for estimating time since death. However, he is an expert on carrion insects and forensic molecular biology and not a statistician. Instead his role has been to direct Dr. LaMotte to the important statistical research questions, and then to interpret the results for the broader forensic science community. View Dr. Wells’ Research Gate Site.