Statistical issues on studies of mixed longitudinal design

Mixed longitudinal designs are important study designs for many areas of medical research. Mixed longitudinal studies have several advantages over cross-sectional or pure longitudinal studies, including shorter study completion time and ability to separate time and age effects, thus are an attractive choice. Statistical methodology used in general longitudinal studies has been rapidly developing within the last few decades. Common approaches for statistical modeling in studies with mixed longitudinal designs have been the linear mixed-effects model incorporating an age or time effect. The general linear mixed-effects model is considered an appropriate choice to analyze repeated measurements data in longitudinal studies. However, common use of linear mixed-effects model on mixed longitudinal studies often incorporates age as the only random-effect but fails to take into consideration the cohort effect in conducting statistical inferences on age-related trajectories of outcome measurements. We believe special attention should be paid to cohort effects when analyzing data in mixed longitudinal designs with multiple overlapping cohorts. Thus, this has become an important statistical issue to address. ^ This research aims to address statistical issues related to mixed longitudinal studies. The proposed study examined the existing statistical analysis methods for the mixed longitudinal designs and developed an alternative analytic method to incorporate effects from multiple overlapping cohorts as well as from different aged subjects. The proposed study used simulation to evaluate the performance of the proposed analytic method by comparing it with the commonly-used model. Finally, the study applied the proposed analytic method to the data collected by an existing study Project HeartBeat!, which had been evaluated using traditional analytic techniques. Project HeartBeat! is a longitudinal study of cardiovascular disease (CVD) risk factors in childhood and adolescence using a mixed longitudinal design. The proposed model was used to evaluate four blood lipids adjusting for age, gender, race/ethnicity, and endocrine hormones. The result of this dissertation suggest the proposed analytic model could be a more flexible and reliable choice than the traditional model in terms of fitting data to provide more accurate estimates in mixed longitudinal studies. Conceptually, the proposed model described in this study has useful features, including consideration of effects from multiple overlapping cohorts, and is an attractive approach for analyzing data in mixed longitudinal design studies.^

A simulation study of the standard design, the rolling six design, the CRM, and the modified CRM in Phase I clinical trials

The Phase I clinical trial is considered the "first in human" study in medical research to examine the toxicity of a new agent. It determines the maximum tolerable dose (MTD) of a new agent, i.e., the highest dose in which toxicity is still acceptable. Several phase I clinical trial designs have been proposed in the past 30 years. The well known standard method, so called the 3+3 design, is widely accepted by clinicians since it is the easiest to implement and it does not need a statistical calculation. Continual reassessment method (CRM), a design uses Bayesian method, has been rising in popularity in the last two decades. Several variants of the CRM design have also been suggested in numerous statistical literatures. Rolling six is a new method introduced in pediatric oncology in 2008, which claims to shorten the trial duration as compared to the 3+3 design. The goal of the present research was to simulate clinical trials and compare these phase I clinical trial designs. Patient population was created by discrete event simulation (DES) method. The characteristics of the patients were generated by several distributions with the parameters derived from a historical phase I clinical trial data review. Patients were then selected and enrolled in clinical trials, each of which uses the 3+3 design, the rolling six, or the CRM design. Five scenarios of dose-toxicity relationship were used to compare the performance of the phase I clinical trial designs. One thousand trials were simulated per phase I clinical trial design per dose-toxicity scenario. The results showed the rolling six design was not superior to the 3+3 design in terms of trial duration. The time to trial completion was comparable between the rolling six and the 3+3 design. However, they both shorten the duration as compared to the two CRM designs. Both CRMs were superior to the 3+3 design and the rolling six in accuracy of MTD estimation. The 3+3 design and rolling six tended to assign more patients to undesired lower dose levels. The toxicities were slightly greater in the CRMs.^

Design and analysis for three level hierarchical structures with count response

In numerous intervention studies and education field trials, random assignment to treatment occurs in clusters rather than at the level of observation. This departure of random assignment of units may be due to logistics, political feasibility, or ecological validity. Data within the same cluster or grouping are often correlated. Application of traditional regression techniques, which assume independence between observations, to clustered data produce consistent parameter estimates. However such estimators are often inefficient as compared to methods which incorporate the clustered nature of the data into the estimation procedure (Neuhaus 1993).1 Multilevel models, also known as random effects or random components models, can be used to account for the clustering of data by estimating higher level, or group, as well as lower level, or individual variation. Designing a study, in which the unit of observation is nested within higher level groupings, requires the determination of sample sizes at each level. This study investigates the design and analysis of various sampling strategies for a 3-level repeated measures design on the parameter estimates when the outcome variable of interest follows a Poisson distribution. ^ Results study suggest that second order PQL estimation produces the least biased estimates in the 3-level multilevel Poisson model followed by first order PQL and then second and first order MQL. The MQL estimates of both fixed and random parameters are generally satisfactory when the level 2 and level 3 variation is less than 0.10. However, as the higher level error variance increases, the MQL estimates become increasingly biased. If convergence of the estimation algorithm is not obtained by PQL procedure and higher level error variance is large, the estimates may be significantly biased. In this case bias correction techniques such as bootstrapping should be considered as an alternative procedure. For larger sample sizes, those structures with 20 or more units sampled at levels with normally distributed random errors produced more stable estimates with less sampling variance than structures with an increased number of level 1 units. For small sample sizes, sampling fewer units at the level with Poisson variation produces less sampling variation, however this criterion is no longer important when sample sizes are large. ^ 1Neuhaus J (1993). “Estimation efficiency and Tests of Covariate Effects with Clustered Binary Data”. Biometrics , 49, 989–996^

Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^

A qualitative and quantitative assessment of cancer patients' perceptions of barriers to treatment

This study examined barriers that cancer patients experience in obtaining treatment. The principal aim of the study was to conduct a comprehensive quantitative and qualitative assessment of barriers to cancer treatment for Texas cancer patients. The three specific aims of the study were to: (1) conduct a review and critique of published and unpublished research on barriers to cancer treatment; (2) conduct focus groups for the qualitative assessment of cancer patients' perceived barriers to cancer treatment; and (3) survey a representative sample of cancer patients regarding perceived barriers to treatment. The study was guided by the Aday and Andersen access framework of predisposing, enabling, and need determinants of care-seeking.^ To address the first specific aim, a total of 732 abstracts were examined, from which 154 articles were selected for review. Of these 154 articles, 57 that related directly to research on barriers to cancer treatment were chosen for subsequent analysis. Criteria were applied to each article to evaluate the strength of the study design, sampling and measurement procedures. The major barriers that were consistently documented to influence whether or not cancer patients sought or continued required treatment included problems with communication between the patient and provider, lack of information on side effects, the cost of treatment and associated difficulties in obtaining and maintaining insurance coverage, and the absence of formal and informal networks of social support. Access barriers were generally greater for older, minority women, and patients of lower socioeconomic status.^ To address the second specific aim, a total of eight focus groups (n = 44) were conducted across the State of Texas with cancer patients identified by the Texas Community Oncology Network, American Cancer Society, and community health centers. One important finding was that cost is the greatest hurdle that patients face. Another finding was that with the health care/insurance crisis, an increasing number of physicians are working with their patients to develop individually-tailored payment plans. For people in rural areas, travel to treatment sites is a major barrier due to the travel costs as well as work time forfeited by patients and their family members. A third major finding was the patients' family and church play important roles in providing social and emotional support for cancer patients.^ To address the third aim, a total of 910 cancer patients were surveyed during October and November, 1993. Approximately 65% of the cancer patients responded to the survey. The findings showed that the major barriers to treatment included costs of medications and diagnostic tests, transportation, lack of social support, problems understanding the written information regarding their disease as well as losing coverage or having higher premiums or copayments once they were diagnosed (particularly among blacks).^ Significant differences in reported barriers were found between racial groups. The minority respondents (i.e., blacks and Hispanics) tended to experience more barriers to treatment compared to the white respondents. More specifically, Hispanics were more likely to report transportation as a barrier to treatment than both white and blacks. Future research is needed to better understand the problems that minority cancer patients experience in receiving treatment. (Abstract shortened by UMI.) ^

AN ANALYSIS OF THE GENERAL WELL-BEING OF BLACKS AND WHITES

The importance of race as a factor in mental health status has been a topic of controversy. This study reviews the history of research in this area and examines racial variances in the relationship between selected socio-demographic variables and general well-being. The study also examines the appropriateness of an additive versus an interactive statistical model for this investigation.^ The sample consists of 6,913 persons who completed the General Well-Being Schedules as administered in the detailed component of the first National Health and Nutrition Examination Survey (NHANES I) conducted by the National Center for Health Statistics between April, 1971 and October, 1975. The sampling design is a multistage, probability sample of clusters of persons in area based segments. Of the 6,913 persons, 873 are Black.^ Unlike other recent community based mental health studies, this study revealed significant differences between the general well-being of Blacks and Whites. Blacks continued to exhibit significantly lower levels of well-being even after adjustments were made for income, education, marital status, sex, age and place of residence. Statistical interaction was found between race and sex with Black females reporting lower levels of well-being than either Black or White males or their White female counterparts.^ The study includes a detailed review of the NHANES I sample design. It is shown that selected aspects of the design make it difficult to render appropriate national comparisons of Black-White differences. As a result conclusions pertaining to these differences based on NHANES I may be of questionable validity. ^