A Markov Model for Analysing Cancer Markers and Disease States in Survival Studies

Kay, Richard

doi:10.2307/2530699

Cited by 261 publications

(202 citation statements)

References 10 publications

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“…P 12 (t -Dt) 9 P 23 (Dt), which is called compound probability, is an approximation to dP 13 (t)/dt. The merit of using compound probability has been described in Duffy et al study [14] and Kay [15] as it can accommodate the rapid and slow progression of breast cancer.…”

Section: Incident Screenmentioning

confidence: 99%

Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer

Hakama

Anttila

et al. 2010

Breast Cancer Res Treat

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-Fang Yen, Nea Malila, et al.. Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer. Breast Cancer Research and Treatment, Springer Verlag, 2010, 122 (2), pp.553-566. <10.1007/s10549-009-0701-x>. EPIDEMIOLOGYEstimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer Abstract Estimating the natural history parameters of breast cancer not only elucidates the disease progression but also make contributions to assessing the impact of interscreening interval, sensitivity, and attendance rate on reducing advanced breast cancer. We applied three-state and five-state Markov models to data on a two-yearly routine mammography screening in Finland between 1988 and 2000. The mean sojourn time (MST) was computed from estimated transition parameters. Computer simulation was implemented to examine the effect of inter-screening interval, sensitivity, and attendance rate on reducing advanced breast cancers. In three-state model, the MST was 2.02 years, and the sensitivity for detecting preclinical breast cancer was 84.83%. In five-state model, the MST was 2.21 years for localized tumor and 0.82 year for non-localized tumor. Annual, biennial, and triennial screening programs can reduce 53, 37, and 28% of advanced cancer. The effectiveness of intensive screening with poor attendance is the same as that of infrequent screening with high attendance rate. We demonstrated how to estimate the natural history parameters using a service screening program and applied these parameters to assess the impact of inter-screening interval, sensitivity, and attendance rate on reducing advanced cancer. The proposed method makes contribution to further costeffectiveness analysis. However, these findings had better be validated by using a further long-term follow-up data.

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Section: Incident Screenmentioning

confidence: 99%

Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer

Hakama

Anttila

et al. 2010

Breast Cancer Res Treat

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“…Assessing Markov assumption in these methods is based on the effect of sojourn time of the process in former states on the transition rate to latter states. In this method, of course, the precision and accuracy of results is based on the precision of transition times among states because observing states occurrence of in a multi-state model occur often in optional times (Kay, 1986;Pérez-Ocón et al, 2000). In these models the exact which in turn can affect the results.…”

Section: In This Equation H(t)mentioning

confidence: 99%

“…But as the number of observations per person increases, the application of these methods is limited (Chen et al, 1999). This is why most researchers tend to use the following issues to select these points: i) clinical indications (Sharples et al, 2001); ii) investigating the model of empirical hazard function (Pérez-Ocón et al, 2001); and iii) selecting points so that the number of observations becomes equal (Kay, 1986).…”

Section: In This Equation H(t)mentioning

confidence: 99%

Assessing Markov and Time Homogeneity Assumptions in Multi-state Models: Application in Patients with Gastric Cancer Undergoing Surgery in the Iran Cancer Institute

Zare

Mahmoodi

Mohammad³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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Background: Multi-state models are appropriate for cancer studies such as gastrectomy which have high mortality statistics. These models can be used to better describe the natural disease process. But reaching that goal requires making assumptions like Markov and homogeneity with time. The present study aims to investigate these hypotheses. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. To assess Markov assumption and time homogeneity in modeling transition rates among states of multi-state model, Cox-Snell residuals, Akaikie information criteria and Schoenfeld residuals were used, respectively. Results: The assessment of Markov assumption based on Cox-Snell residuals and Akaikie information criterion showed that Markov assumption was not held just for transition rate of relapse (state 1 state 2) and for other transition rates -death hazard without relapse (state 1 state 3) and death hazard with relapse (state 2 state 3) -this assumption could also be made. Moreover, the assessment of time homogeneity assumption based on Schoenfeld residuals revealed that this assumption -regarding the general test and each of the variables in the model -was held just for relapse (state 1 state 2) and death hazard with a relapse (state 2 state 3). Conclusions: Most researchers take account of assumptions such as Markov and time homogeneity in modeling transition rates. These assumptions can make the multi-state model simpler but

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“…Disregarding these states or intermediate events and the time of their occurrence can influence the results of study and bias the data analysis (Kay, 1986;Andersen, 1988;Andersen and Keiding, 2002). Considering these intermediate states, of course, as time-dependent covariates has been proposed as an alternative approach.…”

Section: Introductionmentioning

confidence: 99%

Survival Analysis of Patients with Gastric Cancer Undergoing Surgery at the Iran Cancer Institute: A Method Based on Multi-State Models

Zare

Mahmoodi²,

Mohammad³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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Background: Gastric cancer is one of the most common causes of cancer deaths all over the world and the most important reason for its high rate of death is its belated diagnosis at advanced stages of the disease. Events occur in patients which are regarded not only as themselves factors affecting patients' survival but also which can be affected by other factors. This study was designed and implemented aiming to identify these events and to investigate factors affecting their occurrence. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at the Iran Cancer Institute from 1995-1999 were analyzed. The survival time of these patients was determined after surgery and the effects of various factors including demographic, diagnostic and clinical as well as medical, and post-surgical varuiables on the occurrence of death hazard without relapse, hazard of relapse, and death hazard with a relapse were assessed. Results: The median survival time for these patients was 16.3 months and the 5-year survival rate was 21.6%. Based on the results of multi-state model, age and distant metastases affected relapse whereas disease stage, type and extent of surgery, lymph nodes metastases, and number of renewed treatments affected death hazard without relapse. Moreover, age, type and extent of surgery, number of renewed treatments, and liver metastases were identified as factors affecting death hazard in patients with relapse. Conclusions: Most cancer studies pay heed to factors which have effect on death occurrence, but some events occur which should be taken into consideration to better describe the natural process of the disease and provide researchers with more accurate data.

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A Markov Model for Analysing Cancer Markers and Disease States in Survival Studies

Cited by 261 publications

References 10 publications

Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer

Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer

Assessing Markov and Time Homogeneity Assumptions in Multi-state Models: Application in Patients with Gastric Cancer Undergoing Surgery in the Iran Cancer Institute

Survival Analysis of Patients with Gastric Cancer Undergoing Surgery at the Iran Cancer Institute: A Method Based on Multi-State Models

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