MODELING OF INDONESIA CONSUMER PRICE INDEX USING MULTI INPUT INTERVENTION MODEL

There are some events which are expected effecting CPI»s fluctuation, i.e. financial crisis 1997/ 1998, fuel price risings, base year changing»s, independence of Timor-Timur (October 1999), and Tsunami disaster in Aceh (December 2004). During re-search period, there were eight fuel price risings and four base year changing’s. The objective of this research is to obtain multi input intervention model which can describe magnitude and duration of each event effected to CPI. Most of intervention re-searches that have been done are only contain of an intervention with single input, ei-ther step or pulse function. Multi input intervention was used in Indonesia CPI case because there are some events which are expected effecting CPI. Based on the result, those events were affecting CPI. Additionally, other events, such as Ied on January 1999, events on April 2002, July 2003, December 2005, and September 2008, were affecting CPI too. In general, those events gave positive effect to CPI, except events on April 2002 and July 2003 which gave negative effects.JEL Classification: C22, C43, E31, I38Keywords: CPI, Multi Input Intervention, and Fuel Price Rising

PERMODELAN INDEKS HARGA KONSUMEN INDONESIA DENGAN MENGGUNAKAN MODEL INTERVENSI MULTI INPUT

There are some events which are expected effecting CPI’s fluctuation, i.e. financial crisis 1997/1998, fuel price risings, base year changing’s, independence of Timor-Timur (October 1999), and Tsunami disaster in Aceh (December 2004). During re-search period, there were eight fuel price risings and four base year changing’s. The objective of this research is to obtain multi input intervention model which can des-cribe magnitude and duration of each event effected to CPI. Most of intervention re-searches that have been done are only contain of an intervention with single input, ei-ther step or pulse function. Multi input intervention was used in Indonesia CPI case because there are some events which are expected effecting CPI. Based on the result, those events were affecting CPI. Additionally, other events, such as Ied on January 1999, events on April 2002, July 2003, December 2005, and September 2008, were affecting CPI too. In general, those events gave positive effect to CPI, except events on April 2002 and July 2003 which gave negative effects. Keywords: CPI, Multi Input Intervention, and Fuel Price Rising. JEL Classification: C22, C43, E31, I3

Meta-analysis approach as a gene selection method in class prediction: Does it improve model performance? A case study in acute myeloid leukemia

Background: Aggregating gene expression data across experiments via meta-analysis is expected to increase the precision of the effect estimates and to increase the statistical power to detect a certain fold change. This study evaluates the potential benefit of using a meta-analysis approach as a gene selection method prior to predictive modeling in gene expression data. Results: Six raw datasets from different gene expression experiments in acute myeloid leukemia (AML) and 11 different classification methods were used to build classification models to classify samples as either AML or healthy control. First, the classification models were trained on gene expression data from single experiments using conventional supervised variable selection and externally validated with the other five gene expression datasets (referred to as the individual-classification approach). Next, gene selection was performed through meta-analysis on four datasets, and predictive models were trained with the selected genes on the fifth dataset and validated on the sixth dataset. For some datasets, gene selection through meta-analysis helped classification models to achieve higher performance as compared to predictive modeling based on a single dataset; but for others, there was no major improvement. Synthetic datasets were generated from nine simulation scenarios. The effect of sample size, fold change and pairwise correlation between differentially expressed (DE) genes on the difference between MA- and individual-classification model was evaluated. The fold change and pairwise correlation significantly contributed to the difference in performance between the two methods. The gene selection via meta-analysis approach was more effective when it was conducted using a set of data with low fold change and high pairwise correlation on the DE genes. Conclusion: Gene selection through meta-analysis on previously published studies potentially improves the performance of a predictive model on a given gene expression data

Factors affecting the accuracy of a class prediction model in gene expression data

Background: Class prediction models have been shown to have varying performances in clinical gene expression datasets. Previous evaluation studies, mostly done in the field of cancer, showed that the accuracy of class prediction models differs from dataset to dataset and depends on the type of classification function. While a substantial amount of information is known about the characteristics of classification functions, little has been done to determine which characteristics of gene expression data have impact on the performance of a classifier. This study aims to empirically identify data characteristics that affect the predictive accuracy of classification models, outside of the field of cancer. Results: Datasets from twenty five studies meeting predefined inclusion and exclusion criteria were downloaded. Nine classification functions were chosen, falling within the categories: discriminant analyses or Bayes classifiers, tree based, regularization and shrinkage and nearest neighbors methods. Consequently, nine class prediction models were built for each dataset using the same procedure and their performances were evaluated by calculating their accuracies. The characteristics of each experiment were recorded, (i.e., observed disease, medical question, tissue/cell types and sample size) together with characteristics of the gene expression data, namely the number of differentially expressed genes, the fold changes and the within-class correlations. Their effects on the accuracy of a class prediction model were statistically assessed by random effects logistic regression. The number of differentially expressed genes and the average fold change had significant impact on the accuracy of a classification model and gave individual explained-variation in prediction accuracy of up to 72% and 57%, respectively. Multivariable random effects logistic regression with forward selection yielded the two aforementioned study factors and the within class correlation as factors affecting the accuracy of classification functions, explaining 91.5% of the between study variation. Conclusions: We evaluated study- and data-related factors that might explain the varying performances of classification functions in non-cancerous datasets. Our results showed that the number of differentially expressed genes, the fold change, and the correlation in gene expression data significantly affect the accuracy of class prediction models

Meta-analysis methods for class prediction in gene expression data

Genomics profiling based on high dimensional data from high throughput experiments that measure the expression of tens of thousands of genes or biomarkers holds great promises for clinical application. Diagnosis, prognosis and treatment selection for individual patient can become more accurate with strong statistical prediction models based on robust informative gene lists. Numerous studies have been published claiming to have built accurate prediction models. However the initial enthusiasm has been tempered by the uncovering of many false claims. The reason for these false claims lies mainly in the inadequate statistical methodology that is being used to develop the quantitative model underlying prediction or classification. Predictive modeling in gene expression data is challenging and it suffers from a lack of reproducibility as well as instability of the findings, which is strongly associated to the curse of dimensionality in these datasets (a very low number of samples relative to the number of available genes). Literatures showed there is no unique gene signature list resulting from different prediction models that were constructed on the same data. The set of genes involved in predictive models depended heavily on the chosen subset of samples in predictive modeling. We used information from published gene expression studies to serve the two following goals. First, we evaluated potential factors affecting the accuracy of predictive models in binary outcome data. Second, meta-analysis was performed as a method to generate a more accurate list of differentially expressed genes, as well as to evaluate the added value of meta-analysis as a feature selection method in predictive modeling

A protocol for urine collection and storage prior to DNA methylation analysis

Background: Urine poses an attractive non-invasive means for obtaining liquid biopsies for oncological diagnostics. Especially molecular analysis on urinary DNA is a rapid growing field. However, optimal and practical storage conditions that result in preservation of urinary DNA, and in particular hypermethylated DNA (hmDNA), are yet to be determined. Aim: To determine the most optimal and practical conditions for urine storage that result in adequate preservation of DNA for hmDNA analysis. Methods: DNA yield for use in methylation analysis was determined by quantitative methylation specific PCR (qMSP) targeting the ACTB and RASSF1A genes on bisulfite modified DNA. First, DNA yield (ACTB qMSP) was determined in a pilot study on urine samples of healthy volunteers using two preservatives (Ethylenediaminetetraacetic acid (EDTA) and Urine Conditioning Buffer, Zymo Research) at four different temperatures (room temperature (RT), 4°C, -20°C, -80°C) for four time periods (1, 2, 7, 28 days). Next, hmDNA levels (RASSF1A qMSP) in stored urine samples of patients suffering from bladder cancer (n = 10) or non-small cell lung cancer (NSCLC; n = 10) were measured at day 0 and 7 upon storage with and without the addition of 40mM EDTA and/or 20 μl/ml Penicillin Streptomycin (PenStrep) at RT and 4°C. Results: In the pilot study, DNA for methylation analysis was only maintained at RT upon addition of preserving agents. In urine stored at 4°C for a period of 7 days or more, the addition of either preserving agent yielded a slightly better preservation of DNA. When urine was stored at -20 °C or -80 °C for up to 28 days, DNA was retained irrespective of the addition of preserving agents. In bladder cancer and NSCLC samples stored at RT loss of DNA was significantly less if EDTA was added compared to no preserving agents (p0.99). Upon storage at 4°C, no difference in DNA preservation was found after the addition of preserving agents (p = 0.18). The preservation of methylated DNA (RASSF1A) was strongly correlated to that of unmethylated DNA (ACTB) in most cases, except when PCR values became inaccurate. Conclusions: Addition of EDTA offers an inexpensive preserving agent for urine storage at RT up to seven days allowing for reliable hmDNA analysis. To avoid bacterial overgrowth PenStrep can be added without negatively affecting DNA preservation

Host Cell Deoxyribonucleic Acid Methylation Markers for the Detection of High-grade Anal Intraepithelial Neoplasia and Anal Cancer

Contains fulltext : 215634.pdf (publisher's version ) (Open Access)BACKGROUND: High-grade anal intraepithelial neoplasia (AIN2/3; HGAIN) is highly prevalent in human immunodeficiency virus positive (HIV+) men who have sex with men (MSM), but only a minority will eventually progress to cancer. Currently, the cancer risk cannot be established, and therefore all HGAIN is treated, resulting in overtreatment. We assessed host cell deoxyribonucleic acid (DNA) methylation markers for detecting HGAIN and anal cancer. METHODS: Tissue samples of HIV+ men with anal cancer (n = 26), AIN3 (n = 24), AIN2 (n = 42), AIN1 (n = 22) and HIV+ male controls (n = 34) were analyzed for methylation of 9 genes using quantitative methylation-specific polymerase chain reaction. Univariable and least absolute shrinkage and selection operator logistic regression, followed by leave-one-out cross-validation, were used to determine the performance for AIN3 and cancer detection. RESULTS: Methylation of all genes increased significantly with increasing severity of disease (P 0.85). ZNF582 (AUC = 0.89), detected all cancers and 54% of AIN3 at 93% specificity. Slightly better performance (AUC = 0.90) was obtained using a 5-marker panel. CONCLUSIONS: DNA methylation is associated with anal carcinogenesis. A marker panel that includes ZNF582 identifies anal cancer and HGAIN with a cancer-like methylation pattern, warrantingvalidation studies to verify its potential for screening and management of HIV+ MSM at risk for anal cancer