Developing the follow-up schedules for women with breast cancer

Background: The follow-up care for women with breast cancer requires an understanding of disease recurrence patterns and the follow-up visit schedule should be determined according to the times when the recurrence are most likely to occur, so that preventive measure can be taken to avoid or minimize the recurrence. Objective: To model breast cancer recurrence through stochastic process with an aim to generate a hazard function for determining a follow-up schedule. Methods: We modeled the process of disease progression as the time transformed Weiner process and the first-hitting-time was used as an approximation of the true failure time. The women's "recurrence-free survival time" or a "not having the recurrence event" is modeled by the time it takes Weiner process to cross a threshold value which represents a woman experiences breast cancer recurrence event. We explored threshold regression model which takes account of covariates that contributed to the prognosis of breast cancer following development of the first-hitting time model. Using real data from SEER-Medicare, we proposed models of follow-up visits schedule on the basis of constant probability of disease recurrence between consecutive visits. Results: We demonstrated that the threshold regression based on first-hitting-time modeling approach can provide useful predictive information about breast cancer recurrence. Our results suggest the surveillance and follow-up schedule can be determined for women based on their prognostic factors such as tumor stage and others. Women with early stage of disease may be seen less frequently for follow-up visits than those women with locally advanced stages. Our results from SEER-Medicare data support the idea of risk-controlled follow-up strategies for groups of women. Conclusion: The methodology we proposed in this study allows one to determine individual follow-up scheduling based on a parametric hazard function that incorporates known prognostic factors.^

Predicting childhood sexual or physical abuse: a logistic regression geo-mapping approach to prevention.

This study investigates the degree to which gender, ethnicity, relationship to perpetrator, and geomapped socio-economic factors significantly predict the incidence of childhood sexual abuse, physical abuse and non- abuse. These variables are then linked to geographic identifiers using geographic information system (GIS) technology to develop a geo-mapping framework for child sexual and physical abuse prevention.

Progressive regression of left ventricular hypertrophy two years after bariatric surgery.

BACKGROUND: Obesity is a systemic disorder associated with an increase in left ventricular mass and premature death and disability from cardiovascular disease. Although bariatric surgery reverses many of the hormonal and hemodynamic derangements, the long-term collective effects on body composition and left ventricular mass have not been considered before. We hypothesized that the decrease in fat mass and lean mass after weight loss surgery is associated with a decrease in left ventricular mass. METHODS: Fifteen severely obese women (mean body mass index [BMI]: 46.7+/-1.7 kg/m(2)) with medically controlled hypertension underwent bariatric surgery. Left ventricular mass and plasma markers of systemic metabolism, together with body mass index (BMI), waist and hip circumferences, body composition (fat mass and lean mass), and resting energy expenditure were measured at 0, 3, 9, 12, and 24 months. RESULTS: Left ventricular mass continued to decrease linearly over the entire period of observation, while rates of weight loss, loss of lean mass, loss of fat mass, and resting energy expenditure all plateaued at 9 [corrected] months (P <.001 for all). Parameters of systemic metabolism normalized by 9 months, and showed no further change at 24 months after surgery. CONCLUSIONS: Even though parameters of obesity, including BMI and body composition, plateau, the benefits of bariatric surgery on systemic metabolism and left ventricular mass are sustained. We propose that the progressive decrease of left ventricular mass after weight loss surgery is regulated by neurohumoral factors, and may contribute to improved long-term survival.

Relationship between threshold and suprathreshold perception of position and stereoscopic depth.

We seek to determine the relationship between threshold and suprathreshold perception for position offset and stereoscopic depth perception under conditions that elevate their respective thresholds. Two threshold-elevating conditions were used: (1) increasing the interline gap and (2) dioptric blur. Although increasing the interline gap increases position (Vernier) offset and stereoscopic disparity thresholds substantially, the perception of suprathreshold position offset and stereoscopic depth remains unchanged. Perception of suprathreshold position offset also remains unchanged when the Vernier threshold is elevated by dioptric blur. We show that such normalization of suprathreshold position offset can be attributed to the topographical-map-based encoding of position. On the other hand, dioptric blur increases the stereoscopic disparity thresholds and reduces the perceived suprathreshold stereoscopic depth, which can be accounted for by a disparity-computation model in which the activities of absolute disparity encoders are multiplied by a Gaussian weighting function that is centered on the horopter. Overall, the statement "equal suprathreshold perception occurs in threshold-elevated and unelevated conditions when the stimuli are equally above their corresponding thresholds" describes the results better than the statement "suprathreshold stimuli are perceived as equal when they are equal multiples of their respective threshold values."

ALTERATIONS IN HYPOTHALAMIC CONTENT OF LUTEINIZING HORMONE RELEASING HORMONE AND PITUITARY RESPONSIVENESS ASSOCIATED WITH TESTICULAR REGRESSION INDUCED BY PINEAL RELATED TREATMENTS IN THE GOLDEN HAMSTER

The adult male golden hamster, when exposed to blinding (BL), short photoperiod (SP), or daily melatonin injections (MEL) demonstrates dramatic reproductive collapse. This collapse can be blocked by removal of the pineal gland prior to treatment. Reproductive collapse is characterized by a dramatic decrease in both testicular weight and serum gonadotropin titers. The present study was designed to examine the interactions of the hypothalamus and pituitary gland during testicular regression, and to specifically compare and contrast changes caused by the three commonly employed methods of inducing testicular regression (BL,SP,MEL). Hypothalamic LHRH content was altered by all three treatments. There was an initial increase in content of LHRH that occurred concomitantly with the decreased serum gonadotropin titers, followed by a precipitous decline in LHRH content which reflected the rapid increases in both serum LH and FSH which occur during spontaneous testicular recrudescence. In vitro pituitary responsiveness was altered by all three treatments: there was a decline in basal and maximally stimulatable release of both LH and FSH which paralleled the fall of serum gonadotropins. During recrudescence both basal and maximal release dramatically increased in a manner comparable to serum hormone levels. While all three treatments were equally effective in their ability to induce changes at all levels of the endocrine system, there were important temporal differences in the effects of the various treatments. Melatonin injections induced the most rapid changes in endocrine parameters, followed by exposure to short photoperiod. Blinding required the most time to induce the same changes. This study has demonstrated that pineal-mediated testicular regression is a process which involves dynamic changes in multiply-dependent endocrine relationships, and proper evaluation of these changes must be performed with specific temporal events in mind. ^

Bayesian dual threshold design with Dirichlet distribution: An alternative way to frequentist multi-stage phase II designs in oncology

Many phase II clinical studies in oncology use two-stage frequentist design such as Simon's optimal design. However, they have a common logistical problem regarding the patient accrual at the interim. Strictly speaking, patient accrual at the end of the first stage may have to be suspended until all patients have events, success or failure. For example, when the study endpoint is six-month progression free survival, patient accrual has to be stopped until all outcomes from stage I is observed. However, study investigators may have concern when accrual is suspended after the first stage due to the loss of accrual momentum during this hiatus. We propose a two-stage phase II design that resolves the patient accrual problem due to an interim analysis, and it can be used as an alternative way to frequentist two-stage phase II studies in oncology. ^

Evaluation of the city of Houston HIV/STD prevention program in Houston Independent School District (HISD) high schools

This cross-sectional study was undertaken to evaluate the impact in terms of HIV/STD knowledge and sexual behavior that the City of Houston HIV/STD prevention program in HISD high schools has had on students who have participated in it by comparing them with their peers who have not, based on self reports. The study further evaluated the program cost-effectiveness for averting future HIV infections by computing Cost-Utility Ratios based on reported sexual behavior. ^ Mixed results were obtained, indicating a statistically significant difference in knowledge with the intervention group having scored higher (p-value 0.001) but not for any of the behaviors assessed. The knowledge score outcome's overall p-value after adjusting for each stratifying variable (age, grade, gender and ethnicity) was statistically significant. The Odds Ratio of intervention group participants aged 15 years or more scoring 70% or higher was 1.86 times; that of intervention group female participants was 2.29 times; and that of intervention group Black/African American participants was 2.47 times relative to their comparison group counterparts. The knowledge score results remained statistically significant in the logistic regression model, which controlled for age, grade level, gender and ethnicity. The Odds Ratio in this case was 1.74. ^ Three scenarios based on the difference in the risk of HIV infection between the intervention and comparison group were used for computation of Cost-Utility Ratios: Base, worst and best-case scenario. The best-case scenario yielded cost-effective results for male participants and cost-saving results for female participants when using ethnicity-adjusted HIV prevalence. The scenario remained cost-effective for female participants when using the unadjusted HIV prevalence. ^ The challenge to the program is to devise approaches that can enhance benefits for male participants. If it is a threshold problem implying that male participants require more intensive programs for behavioral change, then programs should first be piloted among boys before being implemented across the board. If it is a reflection of gender differences, then we might have to go back to the drawing board and engage boys in focus group discussions that will help formulate more effective programs. Gender-blind approaches currently in vogue do not seem to be working. ^

Logistic regression models for ordinal response: A study of self-efficacy in colorectal cancer screening

The ordinal logistic regression models are used to analyze the dependant variable with multiple outcomes that can be ranked, but have been underutilized. In this study, we describe four logistic regression models for analyzing the ordinal response variable. ^ In this methodological study, the four regression models are proposed. The first model uses the multinomial logistic model. The second is adjacent-category logit model. The third is the proportional odds model and the fourth model is the continuation-ratio model. We illustrate and compare the fit of these models using data from the survey designed by the University of Texas, School of Public Health research project PCCaSO (Promoting Colon Cancer Screening in people 50 and Over), to study the patient’s confidence in the completion colorectal cancer screening (CRCS). ^ The purpose of this study is two fold: first, to provide a synthesized review of models for analyzing data with ordinal response, and second, to evaluate their usefulness in epidemiological research, with particular emphasis on model formulation, interpretation of model coefficients, and their implications. Four ordinal logistic models that are used in this study include (1) Multinomial logistic model, (2) Adjacent-category logistic model [9], (3) Continuation-ratio logistic model [10], (4) Proportional logistic model [11]. We recommend that the analyst performs (1) goodness-of-fit tests, (2) sensitivity analysis by fitting and comparing different models.^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

The role of workplace absence as an organizational mechanism in predicting employee alcohol and drug disorders

Background. EAP programs for airline pilots in companies with a well developed recovery management program are known to reduce pilot absenteeism following treatment. Given the costs and safety consequences to society, it is important to identify pilots who may be experiencing an AOD disorder to get them into treatment. ^ Hypotheses. This study investigated the predictive power of workplace absenteeism in identifying alcohol or drug disorders (AOD). The first hypothesis was that higher absenteeism in a 12-month period is associated with higher risk that an employee is experiencing AOD. The second hypothesis was that AOD treatment would reduce subsequent absence rates and the costs of replacing pilots on missed flights. ^ Methods. A case control design using eight years (time period) of monthly archival absence data (53,000 pay records) was conducted with a sample of (N = 76) employees having an AOD diagnosis (cases) matched 1:4 with (N = 304) non-diagnosed employees (controls) of the same profession and company (male commercial airline pilots). Cases and controls were matched on the variables age, rank and date of hire. Absence rate was defined as sick time hours used over the sum of the minimum guarantee pay hours annualized using the months the pilot worked for the year. Conditional logistic regression was used to determine if absence predicts employees experiencing an AOD disorder, starting 3 years prior to the cases receiving the AOD diagnosis. A repeated measures ANOVA, t tests and rate ratios (with 95% confidence intervals) were conducted to determine differences between cases and controls in absence usage for 3 years pre and 5 years post treatment. Mean replacement costs were calculated for sick leave usage 3 years pre and 5 years post treatment to estimate the cost of sick leave from the perspective of the company. ^ Results. Sick leave, as measured by absence rate, predicted the risk of being diagnosed with an AOD disorder (OR 1.10, 95% CI = 1.06, 1.15) during the 12 months prior to receiving the diagnosis. Mean absence rates for diagnosed employees increased over the three years before treatment, particularly in the year before treatment, whereas the controls’ did not (three years, x = 6.80 vs. 5.52; two years, x = 7.81 vs. 6.30, and one year, x = 11.00cases vs. 5.51controls. In the first year post treatment compared to the year prior to treatment, rate ratios indicated a significant (60%) post treatment reduction in absence rates (OR = 0.40, CI = 0.28, 0.57). Absence rates for cases remained lower than controls for the first three years after completion of treatment. Upon discharge from the FAA and company’s three year AOD monitoring program, case’s absence rates increased slightly during the fourth year (controls, x = 0.09, SD = 0.14, cases, x = 0.12, SD = 0.21). However, the following year, their mean absence rates were again below those of the controls (controls, x = 0.08, SD = 0.12, cases, x¯ = 0.06, SD = 0.07). Significant reductions in costs associated with replacing pilots calling in sick, were found to be 60% less, between the year of diagnosis for the cases and the first year after returning to work. A reduction in replacement costs continued over the next two years for the treated employees. ^ Conclusions. This research demonstrates the potential for workplace absences as an active organizational surveillance mechanism to assist managers and supervisors in identifying employees who may be experiencing or at risk of experiencing an alcohol/drug disorder. Currently, many workplaces use only performance problems and ignore the employee’s absence record. A referral to an EAP or alcohol/drug evaluation based on the employee’s absence/sick leave record as incorporated into company policy can provide another useful indicator that may also carry less stigma, thus reducing barriers to seeking help. This research also confirms two conclusions heretofore based only on cross-sectional studies: (1) higher absence rates are associated with employees experiencing an AOD disorder; (2) treatment is associated with lower costs for replacing absent pilots. Due to the uniqueness of the employee population studied (commercial airline pilots) and the organizational documentation of absence, the generalizability of this study to other professions and occupations should be considered limited. ^ Transition to Practice. The odds ratios for the relationship between absence rates and an AOD diagnosis are precise; the OR for year of diagnosis indicates the likelihood of being diagnosed increases 10% for every hour change in sick leave taken. In practice, however, a pilot uses approximately 20 hours of sick leave for one trip, because the replacement will have to be paid the guaranteed minimum of 20 hour. Thus, the rate based on hourly changes is precise but not practical. ^ To provide the organization with practical recommendations the yearly mean absence rates were used. A pilot flies on average, 90 hours a month, 1080 annually. Cases used almost twice the mean rate of sick time the year prior to diagnosis (T-1) compared to controls (cases, x = .11, controls, x = .06). Cases are expected to use on average 119 hours annually (total annual hours*mean annual absence rate), while controls will use 60 hours. The cases’ 60 hours could translate to 3 trips of 20 hours each. Management could use a standard of 80 hours or more of sick time claimed in a year as the threshold for unacceptable absence, a 25% increase over the controls (a cost to the company of approximately of $4000). At the 80-hour mark, the Chief Pilot would be able to call the pilot in for a routine check as to the nature of the pilot’s excessive absence. This management action would be based on a company standard, rather than a behavioral or performance issue. Using absence data in this fashion would make it an active surveillance mechanism. ^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^

Effects of informative censoring on the hazard function: Application to the proportional hazards regression model

The standard analyses of survival data involve the assumption that survival and censoring are independent. When censoring and survival are related, the phenomenon is known as informative censoring. This paper examines the effects of an informative censoring assumption on the hazard function and the estimated hazard ratio provided by the Cox model.^ The limiting factor in all analyses of informative censoring is the problem of non-identifiability. Non-identifiability implies that it is impossible to distinguish a situation in which censoring and death are independent from one in which there is dependence. However, it is possible that informative censoring occurs. Examination of the literature indicates how others have approached the problem and covers the relevant theoretical background.^ Three models are examined in detail. The first model uses conditionally independent marginal hazards to obtain the unconditional survival function and hazards. The second model is based on the Gumbel Type A method for combining independent marginal distributions into bivariate distributions using a dependency parameter. Finally, a formulation based on a compartmental model is presented and its results described. For the latter two approaches, the resulting hazard is used in the Cox model in a simulation study.^ The unconditional survival distribution formed from the first model involves dependency, but the crude hazard resulting from this unconditional distribution is identical to the marginal hazard, and inferences based on the hazard are valid. The hazard ratios formed from two distributions following the Gumbel Type A model are biased by a factor dependent on the amount of censoring in the two populations and the strength of the dependency of death and censoring in the two populations. The Cox model estimates this biased hazard ratio. In general, the hazard resulting from the compartmental model is not constant, even if the individual marginal hazards are constant, unless censoring is non-informative. The hazard ratio tends to a specific limit.^ Methods of evaluating situations in which informative censoring is present are described, and the relative utility of the three models examined is discussed. ^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

Assessing time-by-covariate interactions in Cox proportional hazards regression models using cubic spline functions

This dissertation develops and explores the methodology for the use of cubic spline functions in assessing time-by-covariate interactions in Cox proportional hazards regression models. These interactions indicate violations of the proportional hazards assumption of the Cox model. Use of cubic spline functions allows for the investigation of the shape of a possible covariate time-dependence without having to specify a particular functional form. Cubic spline functions yield both a graphical method and a formal test for the proportional hazards assumption as well as a test of the nonlinearity of the time-by-covariate interaction. Five existing methods for assessing violations of the proportional hazards assumption are reviewed and applied along with cubic splines to three well known two-sample datasets. An additional dataset with three covariates is used to explore the use of cubic spline functions in a more general setting. ^