Mixed effects model in negative exponential curve

Despite many researches on development in education and psychology, not often is the methodology tested with real data. A major barrier to test the growth model is that the design of study includes repeated observations and the nature of the growth is nonlinear. The repeat measurements on a nonlinear model require sophisticated statistical methods. In this study, we present mixed effects model in a negative exponential curve to describe the development of children's reading skills. This model can describe the nature of the growth on children's reading skills and account for intra-individual and inter-individual variation. We also apply simple techniques including cross-validation, regression, and graphical methods to determine the most appropriate curve for data, to find efficient initial values of parameters, and to select potential covariates. We illustrate with an example that motivated this research: a longitudinal study of academic skills from grade 1 to grade 12 in Connecticut public schools. ^

Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development. ^

Is paromomycin an effective and safe treatment against cutaneous leishmaniasis? A meta-analysis of 14 randomized controlled trials.

BACKGROUND: High cost, poor compliance, and systemic toxicity have limited the use of pentavalent antimony compounds (SbV), the treatment of choice for cutaneous leishmaniasis (CL). Paromomycin (PR) has been developed as an alternative to SbV, but existing data are conflicting. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed, Scopus, and Cochrane Central Register of Controlled Trials, without language restriction, through August 2007, to identify randomized controlled trials that compared the efficacy or safety between PR and placebo or SbV. Primary outcome was clinical cure, defined as complete healing, disappearance, or reepithelialization of all lesions. Data were extracted independently by two investigators, and pooled using a random-effects model. Fourteen trials including 1,221 patients were included. In placebo-controlled trials, topical PR appeared to have therapeutic activity against the old world and new world CL, with increased local reactions, when used with methylbenzethonium chloride (MBCL) compared to when used alone (risk ratio [RR] for clinical cure, 2.58 versus 1.01: RR for local reactions, 1.60 versus 1.07). In SbV-controlled trials, the efficacy of topical PR was not significantly different from that of intralesional SbV in the old world CL (RR, 0.70; 95% confidence interval, 0.26-1.89), whereas topical PR was inferior to parenteral SbV in treating the new world CL (0.67; 0.54-0.82). No significant difference in efficacy was found between parenteral PR and parenteral SbV in the new world CL (0.88; 0.56-1.38). Systemic side effects were fewer with topical or parenteral PR than parenteral SbV. CONCLUSIONS/SIGNIFICANCE: Topical PR with MBCL could be a therapeutic alternative to SbV in selected cases of the old world CL. Development of new formulations with better efficacy and tolerability remains to be an area of future research.

Change Detection Memory in Rhesus Monkeys and Humans

Visual short-term memory (VSTM) is the storage of visual information over a brief time period (usually a few seconds or less). Over the past decade, the most popular task for studying VSTM in humans has been the change detection task. In this task, subjects must remember several visual items per trial in order to identify a change following a brief delay interval. Results from change detection tasks have shown that VSTM is limited; humans are only able to accurately hold a few visual items in mind over a brief delay. However, there has been much debate in regard to the structure or cause of these limitations. The two most popular conceptualizations of VSTM limitations in recent years have been the fixed-capacity model and the continuous-resource model. The fixed-capacity model proposes a discrete limit on the total number of visual items that can be stored in VSTM. The continuous-resource model proposes a continuous-resource that can be allocated among many visual items in VSTM, with noise in item memory increasing as the number of items to be remembered increases. While VSTM is far from being completely understood in humans, even less is known about VSTM in non-human animals, including the rhesus monkey (Macaca mulatta). Given that rhesus monkeys are the premier medical model for humans, it is important to understand their VSTM if they are to contribute to understanding human memory. The primary goals of this study were to train and test rhesus monkeys and humans in change detection in order to directly compare VSTM between the two species and explore the possibility that direct species comparison might shed light on the fixed-capacity vs. continuous-resource models of VSTM. The comparative results suggest qualitatively similar VSTM for the two species through converging evidence supporting the continuous-resource model and thereby establish rhesus monkeys as a good system for exploring neurophysiological correlates of VSTM.

A meta-analysis: Obesity and colorectal cancer screening

Of cancer death, colorectal cancer death ranks second in the United States. Obesity is an important risk factor for colorectal cancer (1). Early detection of colorectal cancer when it is localized can effectively reduce mortality of colorectal cancer and increase survival time of patients if they are treated. Also, previous studies showed that obese women were more likely to delay breast cancer screening and cervical cancer screening than normal weight women (2-5). However, results from prior studies demonstrating the relationship between obesity and colorectal cancer screening are not consistent. This research was done to conduct a meta-analysis of previous cross-sectional studies selected from the Medline database and to evaluate the association between obesity and colorectal cancer screening. While the odds ratio was not statistically different from one, the results from this meta-analysis under the random effects model showed that obese people are slightly less likely to have colorectal cancer screening compared to normal weight individuals (OR,0.93;95% CI 0.75-1.15). This meta-analysis was particularly sensitive to one individual study (6) and the effect of obesity on colorectal cancer screening was statistically significant (OR, 0.87; 95% CI, 0.81-0.92) after removing Heo's study. Further systematic studies focused on whether the effect of obesity on colorectal cancer screening is limited to women only are suggested. ^

A systematic review of clostridium difficile infection in patients with iatrogenic immunesuppression

Background: Incidence of C. difficile infection (CDI) has increased dramatically in the past decade and is the most frequent cause of nosocomial infectious diarrhea. The outcome of infection may range from mild diarrhea to life-threatening pseudomembranous colitis depending on the immunological response of the host, which is highly compromised in this special population that includes bone marrow transplant (BMT), solid organ transplant (SOT) and cancer patients on cytotoxic chemotherapy. ^ Objectives: We conducted a meta-analysis to assess the incidence rates of CDI and the time to onset of infection in patients with iatrogenic immune suppression. ^ Methods: Original studies were identified through an extensive search of electronic databases including PubMed, Ovid Medline (R), RefWorks and Biological Abstracts and their references. The overall incidence rate of CDI in the immune suppressed population was calculated using random effects model and their 95% confidence interval was derived. Differences in the incidence of CDI and time to onset of infection were calculated between the groups and within the groups. Publication bias was assessed using a funnel plot. Results: Twenty nine published articles involving 7,424 patients met the eligibility requirements. The overall incidence of CDI in the immune suppressed population is 11.1% (95% Confidence Interval (CI): 9.2–13.4%). The incidence of CDI was higher in SOT patients (14.2%, 95% CI: 6.8–21.5%); (p-value-0.022) and in cancer patients on cytotoxic chemotherapy (11.4%, 95% CI: 8.4–15.4%); (p = 0.042) than in BMT patients (10.5%, 95% CI: 7.9–13.1%). In a subgroup analysis of BMT population, the incidence of CDI is significantly higher in patients who received allogeneic BMT (15.1%, 95% CI: 11.2–20.0%; p value <0.0001). Similarly, in the SOT population, the incidence of CDI was higher in patients who underwent liver transplantation (11.0%, 95% CI: 5.6–20.3%); (p= 0.0672). The median time to onset of infection was shorter in BMT patients (p=0.0025). ^ Conclusions: It is evident from the combined analysis of these 29 published studies that the incidence of CDI in the immune suppressed population is higher. However, early diagnosis and treatment of CDI will help reduce the morbidity and mortality due to CDI in this special population.^

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 systematic review of personalized therapy in patients with advanced cancer

Background: An increased understanding of the pathogenesis of cancer at the molecular level has led to the development of personalized cancer therapy based on the mutation status of the tumor. Tailoring treatments to genetic signatures has improved treatment outcomes in patients with advanced cancer. We conducted a meta-analysis to provide a quantitative summary of the response to treatment on a phase I clinical trial matched to molecular aberration in patients with advanced solid tumors. ^ Methods: Original studies that reported the results of phase I clinical trials in patients with advanced cancer treated with matched anti-cancer therapies between January 2006 and November 2011 were identified through an extensive search of Medline, Embase, Web of Science and Cochrane Library databases. Odds Ratio (OR) with 95% confidence interval (CI) was estimated for each study to assess the strength of an association between objective response rate (ORR) and mutation status. Random effects model was used to estimate the pooled OR and their 95% CI was derived. Funnel plot was used to assess publication bias. ^ Results: Thirteen studies published between January 2006 and November 2011that reported on responses to matched phase I clinical trials in patients with advanced cancer were included in the meta-analysis. Nine studies reported on the responses seen in 538 of the 835 patients with driver mutations responsive to therapy and seven studies on the responses observed in 234 of the 306 patients with mutation predictive for negative response. Random effects model was used to estimate pooled OR, which was 7.767(95% CI = 4.199 − 14.366; p-value=0.000) in patients with activating mutations that were responsive to therapy and 0.287 (95% CI = 0.119 − 0.694; p-value=0.009) in patients with mutation predictive of negative response. ^ Conclusion: It is evident from the meta-analysis that somatic mutations present in tumor tissue of patients are predictive of responses to therapy in patients with advanced cancer in phase I setting. Plethora of research and growing evidence base indicate that selection of patients based on mutation analysis of the tumor and personalizing therapy is a step forward in the war against cancer.^