• Radiation Oncology Journal
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• v.23 no.3
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• pp.143-156
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• 2005

Background: The best dose-fractionation regimen of the definitive radiotherapy for cervix cancer remains to be clearly determined. It seems to be partially attributed to the complexity of the affecting factors and the lack of detailed information on external and intra-cavitary fractionation. To find optimal practice guidelines, our experiences of the combination of external beam radiotherapy (EBRT) and high-dose-rate intracavitary brachytherapy (HDR-ICBT) were reviewed with detailed information of the various treatment parameters obtained from a large cohort of women treated homogeneously at a single institute. Materials and Methods: The subjects were 743 cervical cancer patients (Stage IB 198, IIA 77, IIB 364, IIIA 7, IIIB 89 and IVA 8) treated by radiotherapy alone, between 1990 and 1996. A total external beam radiotherapy (EBRT) dose of $23.4\~59.4$ Gy (Median 45.0) was delivered to the whole pelvis. High-dose-rate intracavitary brachytherapy (HDR-IBT) was also peformed using various fractionation schemes. A Midline block (MLB) was initiated after the delivery of $14.4\~43.2$ Gy (Median 36.0) of EBRT in 495 patients, while In the other 248 patients EBRT could not be used due to slow tumor regression or the huge initial bulk of tumor. The point A, actual bladder & rectal doses were individually assessed in all patients. The biologically effective dose (BED) to the tumor ($\alpha/\beta$=10) and late-responding tissues ($\alpha/\beta$=3) for both EBRT and HDR-ICBT were calculated. The total BED values to point A, the actual bladder and rectal reference points were the summation of the EBRT and HDR-ICBT. In addition to all the details on dose-fractionation, the other factors (i.e. the overall treatment time, physicians preference) that can affect the schedule of the definitive radiotherapy were also thoroughly analyzed. The association between MD-BED $Gy_3$ and the risk of complication was assessed using serial multiple logistic regression models. The associations between R-BED $Gy_3$ and rectal complications and between V-BED $Gy_3$ and bladder complications were assessed using multiple logistic regression models after adjustment for age, stage, tumor size and treatment duration. Serial Coxs proportional hazard regression models were used to estimate the relative risks of recurrence due to MD-BED $Gy_{10}$, and the treatment duration. Results: The overall complication rate for RTOG Grades $1\~4$ toxicities was $33.1\%$. The 5-year actuarial pelvic control rate for ail 743 patients was $83\%$. The midline cumulative BED dose, which is the sum of external midline BED and HDR-ICBT point A BED, ranged from 62.0 to 121.9 $Gy_{10}$ (median 93.0) for tumors and from 93.6 to 187.3 $Gy_3$ (median 137.6) for late responding tissues. The median cumulative values of actual rectal (R-BED $Gy_3$) and bladder Point BED (V-BED $Gy_3$) were 118.7 $Gy_3$ (range $48.8\~265.2$) and 126.1 $Gy_3$ (range: $54.9\~267.5$), respectively. MD-BED $Gy_3$ showed a good correlation with rectal (p=0.003), but not with bladder complications (p=0.095). R-BED $Gy_3$ had a very strong association (p=<0.0001), and was more predictive of rectal complications than A-BED $Gy_3$. B-BED $Gy_3$ also showed significance in the prediction of bladder complications in a trend test (p=0.0298). No statistically significant dose-response relationship for pelvic control was observed. The Sandwich and Continuous techniques, which differ according to when the ICR was inserted during the EBRT and due to the physicians preference, showed no differences in the local control and complication rates; there were also no differences in the 3 vs. 5 Gy fraction size of HDR-ICBT. Conclusion: The main reasons optimal dose-fractionation guidelines are not easily established is due to the absence of a dose-response relationship for tumor control as a result of the high-dose gradient of HDR-ICBT, individual differences In tumor responses to radiation therapy and the complexity of affecting factors. Therefore, in our opinion, there is a necessity for individualized tailored therapy, along with general guidelines, in the definitive radiation treatment for cervix cancer. This study also demonstrated the strong predictive value of actual rectal and bladder reference dosing therefore, vaginal gauze packing might be very Important. To maintain the BED dose to less than the threshold resulting in complication, early midline shielding, the HDR-ICBT total dose and fractional dose reduction should be considered.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.