NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

Childhood cancer survivor satisfaction with characteristics of clinical care

This study investigated the characteristics of a clinic that affect how satisfied survivors of childhood cancer are with their medical care. Questionnaire and interview data from the Passport for Care: Texas Implementation project collected between January 2011 to April 2012 were analyzed. Eleven clinics in Texas participated. Questionnaire respondents were childhood cancer survivor patients who had been off therapy for at least 2 years, or their parents. Interview respondents were clinical providers or research staff at the participating clinics. The outcomes evaluated were answers to a single question on satisfaction with care and a composite Percent Satisfaction Score created from seven other questionnaire items that were correlated (Spearman Rho >0.3) with the question on satisfaction. The following characteristics were also evaluated: sex, age, race, education, and type of cancer. The following clinic indicators were evaluated: type of clinic (general vs. dedicated cancer survivor clinics), number of providers, number of survivors, ratio of survivors/providers, distribution of handouts, distribution of treatment summaries, and use of Children's Oncology Group (COG) guidelines. ^ The only demographic characteristic that affected satisfaction was race. A Kruskal-Wallis test showed a statistically significant difference (Chi-square 6.129, 2 d.f., p = 0.0467). To analyze this further, Wilcoxon Rank Sum test of pairings of the three groups were performed. A Bonferroni correction for multiple testing was applied, with p = 0.017 indicating significance at alpha = 0.05. There was no significant difference between the White and Hispanic groups or between the Hispanic and "Other" groups. For the White and "Other" groups there was a significant difference for the satisfaction item (p = 0.0123) but not for the Percent Satisfaction Score (p = 0.0289). These results suggest that race may influence satisfaction and should be evaluated further in future studies. ^ None of the clinic indicators affected the Percent Satisfaction Score. Going to a clinic that distributed patient information handouts (Wilcoxon Rank Sum p = 0.048) and going to a clinic with >=100 survivors (Wilcoxon Rank Sum p = 0.021) were associated with increased satisfaction. The population of childhood cancer survivors is a growing group of individuals with special health needs. In the future survivors will likely seek medical care in a variety of clinical settings, so it is important to investigate features to improve patient satisfaction with clinical care.^