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Developing a prognostic index for stomach cancer
Abstracts
427
The performance of a sequence of fixed inclusion/exclusion criteria involving one or more variables is assessed in the presence of seasonal variation and global/local trends in the variables of interest. The impact of regression-to-the mean is considered. Illustrations are drawn from the West of Scotland Coronary Prevention Study, the LRC Coronary Primary Prevention Trial, and the Helsinki Heart Study. The use of more flexible screening regimes is discussed, with emphasis on balancing complexity with ease and speed of use. Making best use of available data at the design stage is considered: for example, calibrating the screening model with data from previous studies. Finally, a hypothetical example is constructed to furnish a simple indication of the potential impact on: (1) statistical power, and (2) cost of a time-dependent
against fixed screening
DEVELOPING
regime.
A PROGNOSTIC
P63 INDEX FOR STOMACH
CANCER
Janet A. Dunn and Michael T. Halllssey for the Brltlsh Stomach Cancer Group University of Birmingham Birmingham, England Survival in gastric cancer has not altered over the last 30 years. Advances in medical care and adjuvant therapy have not had a significant impact on the results of surgery, the mainstay of treatment. The Japanese society for research in stomach cancer (JSRSC) has demonstrated the importance of careful documentation of operative and pathological factors in identifying and targeting therapy. The BSCG conducted two large multi-centre, prospective, randomised trials between 1976 and 1966 looking at the effect of adjuvant therapy in operable gastric cancer. The first trial recruited 411 patients and the following trial 436 patients. Both trials failed to demonstrate any survival advantage to those patients receiving adjuvant therapy. The median survival remains at around 13 months. Survival was the main endpoint in these studies. A multivariate analysis of prognostic factors was carried out on the first BSCG trial when the minimum follow-up reached 5 years. The Cox proportional hazards model demonstrated that stage of disease, nodal and resection margin involvement were significant pathological variables in the influence of survival. The presence of residual disease and weight loss before surgery were significant clinical factors. The second BSCG trial design incorporated the detailed documentation recommended by JSRSC for operative and pathological factors. The data were collected to confirm the results of the first trial multivariate analysis and identify new independent prognostic factors. The minimum follow-up in the second trial is now 5 years. The application of the Cox model applied to these data is compared to the results of the first trial and new prognostic factors determined. The combination of preoperative symptoms and intraoperative findings may be used to select the optimum form of surgical treatment for this disease.
REGRESSION
TO THE MEAN IN CLINICAL
Maryland
TRI::
WITH ENTRY BASED ON COUNTING
EVENTS
Robert P. McMahon Medical Research Institute Baltimore, Maryland
Entry criteria for clinical trials may require that the number of discrete events (e.g., episodes of cardiac ischemia) observed during a baseline screening period exceed some threshold. If the number of events seen on follow up is a study outcome, regression to the mean may create a “placebo effect.” Methods of estimation of this effect proposed previously (e.g., Beath and Dobson, 1991) have assumed a normal distribution for within-patient variation. This assumption will not apply to series of discrete counts from the same patient. A parametric model for estimating effects related to regression to the mean is discussed. Occurrence of events for the i-th patient is assumed distributed Poisson (ri), and a gamma distribution is assumed for the distribution of rates among patients. Under this model the compound distribution of the observed events will be negative binomial. Bivariate negative binomial distributions arising from gamma-Poisson mixtures developed by Edwards and Gurland (1961) are used to estimate the expected reduction in average number of events seen at a second screening and the expected proportion of patients with zero events (“cured”) at a second screening. In addition, design strategies to reduce regression to the mean with count data are discussed.
427
The performance of a sequence of fixed inclusion/exclusion criteria involving one or more variables is assessed in the presence of seasonal variation and global/local trends in the variables of interest. The impact of regression-to-the mean is considered. Illustrations are drawn from the West of Scotland Coronary Prevention Study, the LRC Coronary Primary Prevention Trial, and the Helsinki Heart Study. The use of more flexible screening regimes is discussed, with emphasis on balancing complexity with ease and speed of use. Making best use of available data at the design stage is considered: for example, calibrating the screening model with data from previous studies. Finally, a hypothetical example is constructed to furnish a simple indication of the potential impact on: (1) statistical power, and (2) cost of a time-dependent
against fixed screening
DEVELOPING
regime.
A PROGNOSTIC
P63 INDEX FOR STOMACH
CANCER
Janet A. Dunn and Michael T. Halllssey for the Brltlsh Stomach Cancer Group University of Birmingham Birmingham, England Survival in gastric cancer has not altered over the last 30 years. Advances in medical care and adjuvant therapy have not had a significant impact on the results of surgery, the mainstay of treatment. The Japanese society for research in stomach cancer (JSRSC) has demonstrated the importance of careful documentation of operative and pathological factors in identifying and targeting therapy. The BSCG conducted two large multi-centre, prospective, randomised trials between 1976 and 1966 looking at the effect of adjuvant therapy in operable gastric cancer. The first trial recruited 411 patients and the following trial 436 patients. Both trials failed to demonstrate any survival advantage to those patients receiving adjuvant therapy. The median survival remains at around 13 months. Survival was the main endpoint in these studies. A multivariate analysis of prognostic factors was carried out on the first BSCG trial when the minimum follow-up reached 5 years. The Cox proportional hazards model demonstrated that stage of disease, nodal and resection margin involvement were significant pathological variables in the influence of survival. The presence of residual disease and weight loss before surgery were significant clinical factors. The second BSCG trial design incorporated the detailed documentation recommended by JSRSC for operative and pathological factors. The data were collected to confirm the results of the first trial multivariate analysis and identify new independent prognostic factors. The minimum follow-up in the second trial is now 5 years. The application of the Cox model applied to these data is compared to the results of the first trial and new prognostic factors determined. The combination of preoperative symptoms and intraoperative findings may be used to select the optimum form of surgical treatment for this disease.
REGRESSION
TO THE MEAN IN CLINICAL
Maryland
TRI::
WITH ENTRY BASED ON COUNTING
EVENTS
Robert P. McMahon Medical Research Institute Baltimore, Maryland
Entry criteria for clinical trials may require that the number of discrete events (e.g., episodes of cardiac ischemia) observed during a baseline screening period exceed some threshold. If the number of events seen on follow up is a study outcome, regression to the mean may create a “placebo effect.” Methods of estimation of this effect proposed previously (e.g., Beath and Dobson, 1991) have assumed a normal distribution for within-patient variation. This assumption will not apply to series of discrete counts from the same patient. A parametric model for estimating effects related to regression to the mean is discussed. Occurrence of events for the i-th patient is assumed distributed Poisson (ri), and a gamma distribution is assumed for the distribution of rates among patients. Under this model the compound distribution of the observed events will be negative binomial. Bivariate negative binomial distributions arising from gamma-Poisson mixtures developed by Edwards and Gurland (1961) are used to estimate the expected reduction in average number of events seen at a second screening and the expected proportion of patients with zero events (“cured”) at a second screening. In addition, design strategies to reduce regression to the mean with count data are discussed.