Time series regression model applied to fecal hemoglobin concentration attributed to time trend, seasonal variation, and ambient temperature.

• Main Authors: Yun-Chu Ni, 倪韻筑
• Other Authors: Hsiu-Hsi Chen
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/19019091276541088687

碩士 === 國立臺灣大學 === 流行病學與預防醫學研究所 === 103 === Background Due to the widely used fecal immunochemical test (FIT) in population-based cancer screening for colorectal cancer (CRC), elucidating time series data on fecal hemoglobin(f-Hb) is therefore of great interest on the ground of several reasons. As f-Hb has been demonstrated to be a good predictor for incident colorectal neoplasia it is therefore interesting to monitor the evolution of f-Hb so as to predict the time trend of CRC due to biological causes. This consideration together with seasonal variation and temperature both of which are central components of time series analysis model calls for the conduction of different types of time series analysis, which have never been addressed. Aims By using a longitudinal follow-up of time-series data on f-Hb and hemoglobin (H-b) from population-based colorectal cancer screening and community-based integrated screening data, my thesis aimed (1) to elucidate how time-series factors such as time trend, temperature, seasonal variation made contribution to f-Hb concentration using a linear regression model; (2) to evaluate the effect of time trend, temperature, and seasonal variation on fecal hemoglobin (f-Hb) concentration with Bayesian autoregression random-effect model; (3) to evaluate the effect of time trend, temperature, and seasonal variation on hemoglobin level of blood with Bayesian autoregression model; and (4) to use the Fourier analysis in modeling the periodical pattern of f-Hb concentration. Materials and Methods A total of 970,492 participates aged between 50 and 69 years who underwent CRC screening with FIT during the periods between 2004 and 2009 were enrolled and the totals of 97,731 subjects aged over 20 who attend the community-based integrated screening with hemoglobin information between 2000 and 2009 were enrolled in our analysis. The non-Bayesian linear regression models were applied to time-series data above to assess the effect of age, gender, temperature, time trend, and seasonal variation on f-Hb concentration. A Bayesian random-effect model with the incorporation of random effect at area level was further used to tackle the correlated property of data within the same region. Result As the second-order autocorrelation of f-Hb plays an important role in the subsequent outcomes of f-Hb, the results were mainly based on the second order autoregressive model for assessing the effects of time trend, seasonal variation or ambient temperature adjusting for sex, age, and possibly the disease status of CRC. There was a significantly increasing time trend (2.67, 95% CI: 1.02-4.26) on weekly f-Hb concentration regardless of whether seasonal variation or ambient temperature, age, gender, and disease status were adjusted. The inverse relationship of ambient temperature o f-Hb was found and estimated as -10.14 (95% CI: -29.54-9.2). However, the direction was reversed after adjusting for disease status (15.84, 95% CI: 6.17-25.8). Similar findings were noted for seasonal variation with f-Hb concentration mostly elevated in winter compared with spring (3.91, S.D.: 2.01, P=0.05). Such a relationship disappeared when second autoregressive order was incorporated. The results of H-b were similar to those of f-Hb, the second-order autocorrelation of H-b still plays the predominant role. The opposite findings compared with f-Hb were noted for seasonal variation of H-b when age, gender, seasonal variation, temperature, and the disease status were controlled. Considering the association between H-b and f-Hb, the weekly H-b was statistically associated with the corresponding f-Hb (11.45, 95% CI: 0.14-22.73) after controlling for age, gender, second autoregressive order, seasonal variation, and ambient temperature. Conclusion Time series analysis with Bayesian and non-Bayesian approach were applied to analyzing time trend, seasonal variation, ambient temperature, and autoregressive orders of the weekly fecal hemoglobin concentration (f-Hb) and hemoglobin (H-b). The main results included seasonal variation with lower f-Hb or higher H-b in summer or due to high ambient temperature, a significant time trend of f-Hb or H-b, and the positive association between concurrent f-Hb and H-b. From the aspect of methodology, such cycle of seasonal variation was confirmed by Fouier transformation. The role of second autoregressive order has a significant implication for the degradation of fecal sample attributed to seasonal or ambient temperature change when collected.