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1 / (CAPD) 938 : (CAPD) : :. : :. :. mhossein@yahoo.com 39/8/7 : 39/5/3 : : FBS HDL LDL.. CUREREGR STATA (.).. :.(P=/65).(P</).(P=/88) LDL.(P</) HDL, FBS :.. :..(3). (3/5 3/5 ) :.... CAPD (Continuous Ambulatory Peritoneal Dialysis)

2 /3 ( )..(78). : : (4) CAPD CAPD CUREREGR STATA. HDL LDL FBS. (56).

3 3 / (CAPD). (B) 4 B µ )..(. AIC %55 56 % ( 6 ) %95 5/9. (4/7, 6/23) %47. %5 %4 %95 i.i.d 433. % %7 (4) 4 2 ( ). /. /5.(4)... ( ) H: π= H. ١ i.i.d.. i.i.d..(4). i.i.d) ( :. (wn) ( ) ( (tn) (2 (3 (4 (5 qn (6 µ B. (4) (µ) ١ Independent and Identically Distributed

4 /32 P ro b a b ility PCM overall Probability. KaplanMeier survival estimate Analysis time(years) KM overall 95% CI Survivor function Probab ility Weibull Lognormal Gamma Logistic : % ( )

5 33 / (CAPD) AIC 796/ /56 66/ /387 58/ / / /37 686/27 656/6 686/644 69/429 %95 (/52, /26) (/64, /24) (/37 /62) (/2 /47) ( /39 /77) ( /4 /4) ( /39 /59) ( /3 /63) ( ) (/36 /6) (/34 /66) (/35 /79) (/3 /55) (/38 /66) (/33 /54) (/44 /7) (/6 /77) (/23 /44) (/54 /76) (/23 /54) (/39 /69) 787/45 73/48 658/56 552/ / / /797 68/27 675/ / /56 667/ /5 726/87 655/63 55/ /55 653/ / / / / /4 674/73 78/42 725/ /7 548/ /4 652/88 586/ / /24 65/87 67/34 67/54 LDL HDL FBS %95 /39 /45 /5 /34 /6 /27 /5 /48 / /49 /5 /6 /43 /53 /44 /59 /43 /33 /66 /43 /55 55< 55> 2< 32 3< < 4 >4 4> 4< 2> 2 5 5< <3/5 >3/5 < LDL HDL 2 (/2 /5) /35 <7 (/43 /64) /54 >7 (/47 /67) (/7 /43) /58 /3 <26 >26 FBS (/4 /5) /3 < 8/2 (/35 /6) /48 8/2 /2 (/36 /7) /54 > /2 (/6 /43) /2 < 3/5 (/42 /62) /52 /55 3/5 5/5 > 5/5 (/34 /72)

6 /34 Probability Probability Weibull overall survival upper band(95% CI) lower band(95% CI) logistic overall survival logistic upper band(95%ci) OR/HR %95 (/99 /975) (/997 /23) (/934 /4) (/993 /) (2/8 /743) (/993 /) (/688 /895) (/999 /8) (/973 /994) (/ /) (/998 /) (/78 2/69) (/32 2/686) logistic lower band(95%ci) Probability Probability gamma overall survival upper band(95% Ci) lower band(95% CI) lognormal overall Survival lognormal upper band(95% CI) lognormal lower band(95% CI) %95 P /483 / /46 /9 /65 / /22 /657 /3 /88 /2 /5 /2 /6 / / OR*/HR /947 / /973 /996 4/656 /997 /25 /4 /984 / /999 /494 /87 LDL HDL FBS LDL HDL FBS 8/2 /2 3 3 * OR:Odds Ratio HR: Hazard Ratio

7 35 / (CAPD).(84). 5. ( ).(5). ( )..(6)..(722) %95 3/5 %33 %66 (%95CI: /23/44 ).(%95CI:/54/76) 3/5. / Pvalue 3. FBS HDL. %95. LDL.... CAPD.

8 /36.(3) Identifiability Collett D. Modelling survival data in medical research. CRC press; Kleinbaum D, Klein M. Survival analysis: a selflearning text. Springer Verlag; Klein J, Moeschberger M. Survival analysis: techniques for censored and truncated data.springer Verlag; Maller R, Zhou X. Survival analysis with longterm survivors. Wiley New York; Churchill D, Taylor D, Keshaviah P. the CANUSA Peritoneal Dialysis Study Group. Adequacy of dialysis and nutrition in continuous peritoneal dialysis: association with clinical outcomes. J Am Soc Nephrol 996; 7: Churchill D, Thorpe K, Nolph K, et al. Increased peritoneal membrane transport is associated with decreased patient and technique survival for continuous peritoneal dialysis patients. The CanadaUSA (CANUSA) Peritoneal Dialysis Study Group. Journal of the American Society of Nephrology 998; 9: Berkson J, Gage RP. Survival curve for cancer patients following treatment. Journal of the American Statistical Association 952; 47: Boag WJ. Maximum likelihood estimates of the proportion of patients cured by cancer therapy. J Royal Statist Soc 949; B: Farewell V. The combined effect of breast cancer risk factors. Cancer 977; 4: Sposto R. Cure model analysis in cancer: an application to data from the Children's Cancer Group. Statistics in medicine 22; 2: ). (CAPD CAPD.. ().. relative (23) survival. CAPD Non. Milner P, Watts M. Effect of socioeconomic status on survival from cervical cancer in Sheffield. Journal of epidemiology and community health 987; 4: Haybittle J. The estimation of the proportion of patients cured after treatment for cancer of the breast. British Journal of Radiology 959; 32: BejanAngoulvant T, Bouvier AM, Bossard N, et al. Hazard regression model and cure rate model in colon cancer relative survival trends: are they telling the same story? European Journal of Epidemiology 28; 23: Mould R, Boag J. A test of several parametic statistical models for estimating success rate in the treatment of carcinoma cervix uteri. British Journal of Cancer 975; 32: Atoof F, Mahmoudi M, Zeraati H, et al. Survival analysis of gastric cancer patients refering to EmamKhomeini hospital using Weibull cure model. feyz 2; 56: Rahimzadeh M, Hajizadeh E, Eskandari F, et al. Factors influencing the cure rate in the corneal graft rejection with survival analysis. Qom University of Medical Sciences Journal 29; 3: Pe5Pe2, En. 7. Carvounis CP, Manis T, Coritsidis G, et al. Total lymphocyte count: a promising prognostic index of mortality in patients on CAPD. Peritoneal dialysis international 2; 2: Chung S, Lindholm B, Lee H. Influence of initial nutritional status on continuous ambulatory peritoneal dialysis patient survival. Peritoneal Dialysis International 2; 2: CuetoManzano AM, QuintanaPina E, CorreaRotter R. Longterm CAPD survival and analysis of mortality risk factors: 2year experience of a single Mexican center. Peritoneal dialysis international 2; 2: 48.

9 37 / (CAPD) 2. Maiorca R, Cancarini G, Brunori G, et al. Morbidity and mortality of CAPD and hemodialysis. Kidney international Supplement 993; 4:S4. 2. Sharma AP, Gupta A, Sharma RK, et al. Does Serum Albumin at Start of Continuous Ambulatory Peritoneal Dialysis (CAPD) or Its Drop During CAPD Determine Patient Outcome? ADVANCES IN PERITONEAL DIALYSIS 2;6: Woodrow G, Turney JH, Brownjohn AM. Technique failure in peritoneal dialysis and its impact on patient survival. Peritoneal dialysis international 997; 7: Dickman PW, Sloggett A, Hills M, et al. Regression models for relative survival. Statistics in Medicine 24; 23: 56

10 38 Iranian Journal of Epidemiology 22; 8(2): Original Article A Comparison Between Weibull, Gama, Log Normal and Log Logistic Mixture Cure Models in Survival Analysis of Patients Undergoing (Continuous Ambulatory Peritoneal Dialysis) CAPD Akhlaghi AA, Hosseini M 2, Mahmoodi M 3, Shamsipour M 4, Najafi 5 MSc student of Biostatistic, Department of Epidemiology and Biostatistic, School of Public Health, Tehran University of Medical Science 2 Professor, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 3 Professor, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 4PhD student of Epidemiology,Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan institute, ACECR, Tehran, Iran 5 Nefrologist, Department of Nephrology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran Corresponding author: Hosseini M., mhossein@yahoo.com Background & Objectives: Peritoneal dialysis is one of the most common types of dialysis in patients with renal failure. However multivariate analysis such as log rank test and Cox have usually used to evaluate association of risk factors in survival of this group of patients, the aim of this study was to perform of Weibull, Gamma, Lognormal and Logistic Mixture cure models in survival analysis of these patients. Methods: Data of 433 patients undergoing CAPD who registered in two centers in Tehran, Iran between 997 to 29 were used in this analysis. We investigated center, gender, age, cholesterol, Low Density Lipoprotein (LDL), High density lipoprotein (HDL), triglyceride, albumin, hemoglobin, creatinine, Fasting Blood Sugar (FBS), calcium and phosphorous as variables effect with KaplanMeier and cure model STATA. software and CUREREGR module was used for survival analysis. Results: Comparison of AIC (Akaike Information Criterion) of Weibull, Gama, Lognormal and Logistic Mixture cure models showed that Weibull distribution AIC is lower for almost all variables than other distributions. Weibull distribution has better fitness for data than others. In the multivariate Weibull model, age and albumin variables had significant effect on longterm survival of patients (P<.). Triglycerides effect on longterm survival had borderline (P =.65). Also HDL, FBS and calcium were significant on short term survival (P<.) but significance of LDL was borderline (P=.88). Conclusion: Cure models have the ability to analyze dialysis patients survival data and can differentiate longterm survival from short term survival. The interpretation of survival data with these statistical models could be more accurate and would help to make better prediction for patients by health care professionals. Keywords: Mixture cure models, CAPD, Survival analysis, Cure fraction