High quality protocols facilitate proper conduct, reporting, and external review
 of clinical trials. However, the completeness of trial protocols is often
 inadequate. To help improve the content and quality of protocols, an
 international group of stakeholders developed the SPIRIT 2013 Statement
 (Standard Protocol Items: Recommendations for Interventional Trials). The SPIRIT
 Statement provides guidance in the form of a checklist of recommended items to
 include in a clinical trial protocol. This SPIRIT 2013 Explanation and Elaboration paper provides important
 information to promote full understanding of the checklist recommendations. For
 each checklist item, we provide a rationale and detailed description; a model
 example from an actual protocol; and relevant references supporting its
 importance. We strongly recommend that this explanatory paper be used in
 conjunction with the SPIRIT Statement. A website of resources is also available
 (www.spirit-statement.org). The SPIRIT 2013 Explanation and Elaboration paper, together with the Statement,
 should help with the drafting of trial protocols. Complete documentation of key
 trial elements can facilitate transparency and protocol review for the benefit
 of all stakeholders.

https://www.bmj.com/content/bmj/346/bmj.e7586.full.pdf

SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials

DNA sequence copy number is the number of copies of DNA at a region of a genome. Cancer progression often involves alterations in DNA copy number. Newly developed microarray technologies enable simultaneous measurement of copy number at thousands of sites in a genome. We have developed a modification of binary segmentation, which we call circular binary segmentation, to translate noisy intensity measurements into regions of equal copy number. The method is evaluated by simulation and is demonstrated on cell line data with known copy number alterations and on a breast cancer cell line data set.

/pdf/circular-binary-segmentation-for-the-analysis-of-array-based-4j1juwtxxm.pdf

Circular binary segmentation for the analysis of array-based DNA copy number data.

In this article, we consider the problem of detecting multiple changepoints in large datasets. Our focus is on applications where the number of changepoints will increase as we collect more data: for example, in genetics as we analyze larger regions of the genome, or in finance as we observe time series over longer periods. We consider the common approach of detecting changepoints through minimizing a cost function over possible numbers and locations of changepoints. This includes several established procedures for detecting changing points, such as penalized likelihood and minimum description length. We introduce a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost, which, under mild conditions, is linear in the number of observations. This compares favorably with existing methods for the same problem whose computational cost can be quadratic or even cubic. In simulation studies, we show that our new method can...

Optimal Detection of Changepoints With a Linear Computational Cost

Bridge regression, a special family of penalized regressions of a penalty function Σ|βj|γ with γ ≤ 1, considered. A general approach to solve for the bridge estimator is developed. A new algorithm for the lasso (γ = 1) is obtained by studying the structure of the bridge estimators. The shrinkage parameter γ and the tuning parameter λ are selected via generalized cross-validation (GCV). Comparison between the bridge model (γ ≤ 1) and several other shrinkage models, namely the ordinary least squares regression (λ = 0), the lasso (γ = 1) and ridge regression (γ = 2), is made through a simulation study. It is shown that the bridge regression performs well compared to the lasso and ridge regression. These methods are demonstrated through an analysis of a prostate cancer data. Some computational advantages and limitations are discussed.

http://gautampendse.com/software/lasso/webpage/Fu1998.pdf

Penalized Regressions: The Bridge versus the Lasso

One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been developed to provide users with a choice of multiple changepoint search methods to use in conjunction with a given changepoint method and in particular provides an implementation of the recently proposed PELT algorithm. This article describes the search methods which are implemented in the package as well as some of the available test statistics whilst highlighting their application with simulated and practical examples. Particular emphasis is placed on the PELT algorithm and how results differ from the binary segmentation approach.

/pdf/changepoint-an-r-package-for-changepoint-analysis-4f9vs6okd3.pdf

changepoint: An R Package for Changepoint Analysis

Research suggestions on spatial interaction models

On tolerable route deviations in van pooling

A method for manually calibrating the gravity model using elementary analysis is described. Requisite for the method is an O-D matrix, which is first transformed to logarithms before column and row averages are subtracted, resulting in a residuals matrix. The same operation is performed in a matrix of distances yielding a distance residuals matrix. These two residuals are graphed to determine the friction term of the gravity model. This friction factor, computed for a sample from a suburban circumferential corridor, was applied to another sample. Using the gravity model, the O-D matrix is estimated in a straightforward, noniterative manner, yet they are remarkably similar to the actual trip matrix. The method is proposed as a useful trip distribution modeling and forecasting technique, whenever the study is relatively small and quick results are desired. Larger problems may use the same method but require computer analysis.

Elementary analysis in trip distribution modeling

This paper presents a procedure for estimating small changes in maximum-likelihood estimates of gravitv model parameters in response to small changes in base period data. The procedure can be used to derive covariance matrices for such parameter estimates from which confidence intervals may be obtained and which may be used for tests of hypotheses. Also the procedure extends a similar procedure for estimating sensitivities, etc., of gravity model forecasts. While this type of problem has been addressed by others, our approach is the only one that is applicable for the large numbers of origin and destination zones one typically encounters.

Ashish Sen

Papers

Research suggestions on spatial interaction models

On tolerable route deviations in van pooling

Elementary analysis in trip distribution modeling

On the Sensitivity of Maximum Likelihood Estimates of Gravity Model Parameters

A simple method for analysis of travel data