Methodology Employed for Annual Report on Hematopoietic Cell Transplant Center-Specific Survival Rates

Similar documents
Joint Modelling Approaches in diabetes research. Francisco Gude Clinical Epidemiology Unit, Hospital Clínico Universitario de Santiago

Parameter Estimates of a Random Regression Test Day Model for First Three Lactation Somatic Cell Scores

The effect of salvage therapy on survival in a longitudinal study with treatment by indication

Modeling the Survival of Retrospective Clinical Data from Prostate Cancer Patients in Komfo Anokye Teaching Hospital, Ghana

THE NATURAL HISTORY AND THE EFFECT OF PIVMECILLINAM IN LOWER URINARY TRACT INFECTION.

NHS Outcomes Framework

Using the Perpendicular Distance to the Nearest Fracture as a Proxy for Conventional Fracture Spacing Measures

Copy Number Variation Methods and Data

Estimation of Relative Survival Based on Cancer Registry Data

Saeed Ghanbari, Seyyed Mohammad Taghi Ayatollahi*, Najaf Zare

WHO S ASSESSMENT OF HEALTH CARE INDUSTRY PERFORMANCE: RATING THE RANKINGS

Impact of Imputation of Missing Data on Estimation of Survival Rates: An Example in Breast Cancer

I I I I I I I I I I I I 60

Using Past Queries for Resource Selection in Distributed Information Retrieval

Economic crisis and follow-up of the conditions that define metabolic syndrome in a cohort of Catalonia,

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS)

Association between cholesterol and cardiac parameters.

Appendix for. Institutions and Behavior: Experimental Evidence on the Effects of Democracy

CONSTRUCTION OF STOCHASTIC MODEL FOR TIME TO DENGUE VIRUS TRANSMISSION WITH EXPONENTIAL DISTRIBUTION

Rainbow trout survival and capture probabilities in the upper Rangitikei River, New Zealand

Analysis of Correlated Recurrent and Terminal Events Data in SAS Li Lu 1, Chenwei Liu 2

Alma Mater Studiorum Università di Bologna DOTTORATO DI RICERCA IN METODOLOGIA STATISTICA PER LA RICERCA SCIENTIFICA

A GEOGRAPHICAL AND STATISTICAL ANALYSIS OF LEUKEMIA DEATHS RELATING TO NUCLEAR POWER PLANTS. Whitney Thompson, Sarah McGinnis, Darius McDaniel,

RENAL FUNCTION AND ACE INHIBITORS IN RENAL ARTERY STENOSISA/adbon et al. 651

NATIONAL QUALITY FORUM

A MIXTURE OF EXPERTS FOR CATARACT DIAGNOSIS IN HOSPITAL SCREENING DATA

HIV/AIDS-related Expectations and Risky Sexual Behavior in Malawi

Modeling Multi Layer Feed-forward Neural. Network Model on the Influence of Hypertension. and Diabetes Mellitus on Family History of

Estimating the distribution of the window period for recent HIV infections: A comparison of statistical methods

HIV/AIDS-related Expectations and Risky Sexual Behavior in Malawi

310 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'16

4 Department of Statistic and Economical applied

Arithmetic Average: Sum of all precipitation values divided by the number of stations 1 n

Non-parametric Survival Analysis for Breast Cancer Using nonmedical

INITIAL ANALYSIS OF AWS-OBSERVED TEMPERATURE

UNIVERISTY OF KWAZULU-NATAL, PIETERMARITZBURG SCHOOL OF MATHEMATICS, STATISTICS AND COMPUTER SCIENCE

A-UNIFAC Modeling of Binary and Multicomponent Phase Equilibria of Fatty Esters+Water+Methanol+Glycerol

A comparison of statistical methods in interrupted time series analysis to estimate an intervention effect

Price linkages in value chains: methodology

Appendix F: The Grant Impact for SBIR Mills

Statistical Analysis on Infectious Diseases in Dubai, UAE

National Polyp Study data: evidence for regression of adenomas

Prediction of Total Pressure Drop in Stenotic Coronary Arteries with Their Geometric Parameters

Honours Year Project Report Detection of Fractures in Digital X-Ray Images

Physical Model for the Evolution of the Genetic Code

We analyze the effect of tumor repopulation on optimal dose delivery in radiation therapy. We are primarily

Length of Hospital Stay After Acute Myocardial Infarction in the Myocardial Infarction Triage and Intervention (MITI) Project Registry

Subject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field

PDF hosted at the Radboud Repository of the Radboud University Nijmegen

Gurprit Grover and Dulumoni Das* Department of Statistics, Faculty of Mathematical Sciences, University of Delhi, Delhi, India.

Optimal Planning of Charging Station for Phased Electric Vehicle *

Richard Williams Notre Dame Sociology Meetings of the European Survey Research Association Ljubljana,

Insights in Genetics and Genomics

Cancer morbidity in ulcerative colitis

Concentration of teicoplanin in the serum of adults with end stage chronic renal failure undergoing treatment for infection

Leukemia in Polycythemia Vera. Relationship to Splenic Myeloid Metaplasia and Therapeutic Radiation Dose

FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION

THIS IS AN OFFICIAL NH DHHS HEALTH ALERT

THE NORMAL DISTRIBUTION AND Z-SCORES COMMON CORE ALGEBRA II

The Effect of Fish Farmers Association on Technical Efficiency: An Application of Propensity Score Matching Analysis

Fitsum Zewdu, Junior Research Fellow. Working Paper No 3/ 2010

Statistical models for predicting number of involved nodes in breast cancer patients

Causal inference in nonexperimental studies typically

DS May 31,2012 Commissioner, Development. Services Department SPA June 7,2012

AUTOMATED CHARACTERIZATION OF ESOPHAGEAL AND SEVERELY INJURED VOICES BY MEANS OF ACOUSTIC PARAMETERS

Estimation for Pavement Performance Curve based on Kyoto Model : A Case Study for Highway in the State of Sao Paulo

Are Drinkers Prone to Engage in Risky Sexual Behaviors?

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

AUTOMATED DETECTION OF HARD EXUDATES IN FUNDUS IMAGES USING IMPROVED OTSU THRESHOLDING AND SVM

EXAMINATION OF THE DENSITY OF SEMEN AND ANALYSIS OF SPERM CELL MOVEMENT. 1. INTRODUCTION

4.2 Scheduling to Minimize Maximum Lateness

Latent Class Analysis for Marketing Scales Development

Normal variation in the length of the luteal phase of the menstrual cycle: identification of the short luteal phase

PSI Tuberculosis Health Impact Estimation Model. Warren Stevens and David Jeffries Research & Metrics, Population Services International

NUMERICAL COMPARISONS OF BIOASSAY METHODS IN ESTIMATING LC50 TIANHONG ZHOU

ALMALAUREA WORKING PAPERS no. 9

ARTICLE IN PRESS Neuropsychologia xxx (2010) xxx xxx

Encoding processes, in memory scanning tasks

Natural Image Denoising: Optimality and Inherent Bounds

Unobserved Heterogeneity and the Statistical Analysis of Highway Accident Data

Lateral Transfer Data Report. Principal Investigator: Andrea Baptiste, MA, OT, CIE Co-Investigator: Kay Steadman, MA, OTR, CHSP. Executive Summary:

Working Paper Asymmetric Price Responses of Gasoline Stations: Evidence for Heterogeneity of Retailers

STAGE-STRUCTURED POPULATION DYNAMICS OF AEDES AEGYPTI

Evaluation of two release operations at Bonneville Dam on the smolt-to-adult survival of Spring Creek National Fish Hatchery fall Chinook salmon

Research Article Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities

Jurnal Teknologi USING ASSOCIATION RULES TO STUDY PATTERNS OF MEDICINE USE IN THAI ADULT DEPRESSED PATIENTS. Full Paper

A psychophysical theory of Shannon entropy.

Leberco*Celsis Testing

Optimal probability weights for estimating causal effects of time-varying treatments with marginal structural Cox models

Survival Comparisons for Breast Conserving Surgery and Mastectomy Revisited: Community Experience and the Role of Radiation Therapy

Are National School Lunch Program Participants More Likely to be Obese? Dealing with Identification

Computing and Using Reputations for Internet Ratings

Desperation or Desire? The Role of Risk Aversion in Marriage. Christy Spivey, Ph.D. * forthcoming, Economic Inquiry. Abstract

The impact of asthma self-management education programs on the health outcomes: A meta-analysis (systemic review) of randomized controlled trials

Larynx Preservation in Pyriform Sinus Cancer: Preliminary Results of a European Organization for Research and Treatment of Cancer Phase III Trial

VALIDATION TOOL THE SETTING OF THE COMMUNITY PHARMACY

An Introduction to Modern Measurement Theory

Survival Rate of Patients of Ovarian Cancer: Rough Set Approach

Reconstruction of gene regulatory network of colon cancer using information theoretic approach

IMPROVING THE EFFICIENCY OF BIOMARKER IDENTIFICATION USING BIOLOGICAL KNOWLEDGE

Transcription:

Methodology Emloyed for Annual Reort on Hematooetc Cell Translant Center-Secfc Survval Rates Introducton The urose of the annual reort on translant center-secfc survval rates s to rovde otental hematooetc cell translant (HCT recents, ther famles, and the general ublc wth a comarson of survval rates among the centers n the C.W. Bll Young Cell Translantaton Program (the Program network. Translant centers may use these reorts for qualty mrovement ntatves. Reortng center-secfc survval rates s a requrement of the Stem Cell Theraeutc and Research Act of 2005 (re-authorzed n 2010, and ror to that, the 1990 Translant Amendments Act. Because centers vary consderably n the rsk level of cases treated, a statstcal model was develoed to adust for several rsk factors known or susected to nfluence outcome. The outcome reorted s one-year overall survval, for recents of allogenec HCT n the Unted States only. No attemts are made to ncororate other outcomes, such as relase or dsease-free survval. The frst center-secfc rsk-adusted comarsons were ublshed n 1994 1 and yearly snce then. The current teraton of the reort reared by the Center for Internatonal Blood and Marrow Translant Research (CIBMTR ncludes recents of both unrelated and related donor translants facltated by the Program for a three-year tme wndow. The methodology for ths analyss has undergone varous transformatons over the years. The methodology n current use has been emloyed snce 2005, thus allowng drect comarsons over the most recent eght reorts. Ths method adusts for rsk usng a censored data logstc regresson model 2,3,4 that allows ncluson of recents wth ncomlete one-year follow-u. Note that although the method has remaned the same, the tyes of atents studed changed wth the ncluson of related-donor translants n the 2010 reort, whch may affect comarsons over tme. A rskadusted one-year survval rate s calculated for each center, based on results of the censored data logstc regresson. Results are ublshed on the Program webste (htt://bloodcell.translant.hrsa.gov, and a verson of ths reort, as aroved by HRSA, s dstrbuted to HCT centers. Prevously, ths reort was ublshed n a rnted verson of the Natonal Marrow Donor Program (NMDP Translant Center Drectory. The rnt verson of ths Drectory was elmnated n 2009, and ths nformaton now s avalable onlne only at www.bethematch.org/access. Raw numbers of translants and survvng recents are ublshed for each center, stratfed by dagnoss and age. Each center ncluded n the reort erformed at least one unrelated or related donor translant over the three-year wndow of tme for analyss.

Methods Recents and data The current analyss ncludes frst unrelated or related donor translants erformed n a threeyear tme nterval, wth follow-u through one year after the last recent was translanted. The rollng three-year wndow of translants for ncluson was adoted wth the 2011 reort, relacng a rollng fve-year wndow used revously. Ths change was based on the recommendaton of the 2010 Center-Secfc Outcomes Analyss Forum 5, n order to reresent more current translant center outcomes. A mnmum of one-year follow-u s requred for all elgble cases. All U.S. translant centers that erformed at least one HCT n the tme nterval are consdered for ncluson n the reort, rovded they had suffcent data wth at least one year of follow-u avalable. Tycally, about 170 U.S. translant centers are ncluded n the analyss, wth about 19,000 frst allogenec translants erformed by domestc translant centers n the Program network durng ths tme. Demograhcs of the ncluded cases are rovded n tables for unrelated donor translants and related donor translants, broken down by condtonng regmen accordng to myeloablatve vs. reduced-ntensty or non-myeloablatve. Baselne and follow-u data used for the analyss are rovded to the CIBMTR by the translant centers at the tme of translant (baselne, and at 100 days, sx months and annually ost-translant, usng standardzed forms. Race was self-reorted by recents or by the staff at the center. Occasonally, data s not avalable for sgnfcant characterstcs for subects as reorted by the centers. If there were suffcent numbers of such subects, they were ncluded n the multvarate modelng as a dstnct category of the covarate. However, when a category of a sgnfcant varable had too few subects (generally less than 20 subects to ft the multvarate model, those subects were muted to the relevant hghest frequency category wthn the varable. Statstcal Analyss Ratonale for a fxed effects censored data logstc regresson model One of the CIBMTR s goals for the translant center-secfc outcomes analyss s to calculate a far and accurate redcted survval rate gven a center s recent case mx. To do ths, a fxed effects censored regresson model s used. The fxed-effect logstc regresson model rovdes nformaton about how the recents actually treated n a artcular center would have fared had they been translanted at a generc translant center wthn the Program. Ths model assumes no center effect. In other words, t assumes that recents are dyng at the same unform rate across all Program translant centers, after adustng for covarates. The model also adequately accounts for recents wth ncomlete follow-u at one year. Every effort s made to udate follow-u nformaton on each recent. Some recents are ndeed lost to follow-u, and ther fnal survval status at one year s unknown. To address ths roblem, the analyss only ncludes centers that demonstrated 90% comleteness of follow-u, meanng that the one-year status was known for at least 90% of ther translanted recents. However, there are stll some recents for whom survval status at one year s ncomlete, although many recents had follow-u done ust ror to one year. If these recents are excluded from the center-secfc analyss, t may bas the survval estmates. A censored data verson of logstc regresson based on seudo-values roosed by Andersen et al., 2 Klen and Andersen, 3 and Klen et al. 4 addresses ths ssue. Ths method s a generalzaton of logstc HCT Center Survval Reort Methodology Last udated: 9/4/2013 Page 2 of 7

regresson that smlfes to logstc regresson (on the one-year survval robabltes when there s no censorng resent. Ths regresson technque s used to estmate the fxed effects and redct the recents survval robabltes based on ther atent characterstcs alone. These redcted survval robabltes are then used to construct confdence lmts for a center s survval robablty accordng to the characterstcs of the atents translanted at that center. The actual survval observed at that center can be comared to these ntervals to assess the erformance of the center. Ths method s descrbed n more detal below. Detals of fxed effects censored data logstc regresson and confdence lmts Modelng for the center secfc outcomes analyss can be broken down nto 5 stes, as outlned below. I. Defnton of seudo-values To comute the seudo-value for recent, frst comute the ooled samle Kalan-Meer estmate of survval at one year based on the entre samle, S ˆ (1. Next comute the Kalan- Meer estmate of survval at one year based on the entre dataset wth observaton removed S ˆ ( (1. The th seudo-value s defned by ˆ ˆ (1 ( 1 ˆ ( ns n S (1. If there s no censorng, then the th seudo-value s smly the ndcator that the th recent was alve at one year. These seudo-values wll then be used n a regresson model usng a logt lnk, smlar to a standard logstc regresson model, as descrbed n the next secton. The arameters of the regresson model can be estmated usng generalzed estmatng equatons (GEE, whch are mlemented n PROC GENMOD n SAS. II. Model buldng Let ( Z 1,..., Z denote the set of covarates n the fnal model for recent. Frst ft a fxed effects censored data logstc regresson model wth no center effect, ln 0 lzl. 1 l1 III. Center case mx score assgnments Usng the regresson model to defne recents wth the lowest lkelhood of death n the frst year, u to those wth the hghest lkelhood of death, a rsk score for each recent s set of characterstcs can be calculated. A case mx score for each center can then be comuted by averagng the rsk scores (or log-odds for all recents translanted at that center. 1 RS ˆ, n C where n s the number of recents at that center and center. C reresents the set of recents at Centers are then characterzed n fve equal-szed grous, each contanng 20% of the centers. Assgnment to each case mx score s based uon the ercentles of the average rsk scores across recents n each center. HCT Center Survval Reort Methodology Last udated: 9/4/2013 Page 3 of 7

In general, centers n case mx score Grou 1 treated recents wth lower average rsk scores than centers assgned to the hgher case mx score grous. Thus, the case mx scores for each center generally reflect the adusted hazards faced by recents treated at each center. The case mx score s rovded as a gude for the reader, as the score s already accounted for n the calculaton of the redcted survval and confdence ntervals for recents treated at each center. IV. Predcted and observed survval From the ftted logstc regresson model, each recent has an estmated survval rate ex( ˆ ˆ 1 ex( ˆ based on hs or her rsk characterstcs. The redcted survval rate at center based on recent characterstcs E ( S s the average of the estmated survval rates for all recents at center, 1 E( S ˆ *. C n The observed one-year survval rate at center can be comuted usng the Kalan-Meer estmate of survval usng the recents at center. Ths smlfes to the samle roorton of recents alve when there s no censorng ror to one year resent. V. Confdence Lmts Confdence lmts are generated usng a bootstrang methodology. However, the bootstra technque was modfed slghtly from revous years reorts to mrove the coverage robabltes of the ntervals, as descrbed n Logan et al. 6 Prevously, bnary outcomes were generated for each ndvdual to smulate the confdence lmts; however, a more accurate redcton nterval that controls the tye I error rate can be obtaned by re-samlng the resduals from the general lnear model nstead. Defne the scaled Pearson resdual for atent by ˆ ˆ r, ˆ (1 ˆ then the bootstra re-samlng algorthm to generate a redcton nterval for center s as follows. For b=1 to 10,000: 1. Generate r for atent by samlng wth relacement from the set of resduals *b { r, 1,..., n} 2. Comute the bootstra redcted value for atent as * b * b Y ˆ r ˆ (1 ˆ 3. Comute the redcted center outcome for center as * b 1 * b S Y. n C Then the 95% redcted confdence bounds for survval at center are obtaned by takng the 2.5 th and 97.5 th ercentle of S across the 10,000 bootstra samles. *b HCT Center Survval Reort Methodology Last udated: 9/4/2013 Page 4 of 7

Ths confdence nterval refers to the survval rate that mght be observed at that center f there were no center effect and those recents had been translanted at any center n the network. The observed survval rate can be comared wth ths confdence nterval to see f there s evdence of the center over-erformng or under-erformng the overall network. Results Rsk factors Based on the recommendaton of the 2010 Center-Secfc Outcomes Analyss Forum 5, varables recognzed as clncally mortant are forced nto the model regardless of whether they are statstcally sgnfcant. After careful dscusson wth clncal and statstcal translant exerts, the followng essental rsk factors are ncluded n the model: Dagnoss (and dsease stage Donor tye: matched sblng donor vs. other related vs. unrelated donor Coexstng dsease (HCT-secfc comorbdty ndex (HCT-CI, Sorror et al. 7 HLA matchng a Recent age Donor age (unrelated donor marrow or erheral blood stem cells (PBSC only Recent and donor gender Recent cytomegalovrus (CMV serology Recent race (self-reorted Recent Karnofsky/Lansky Performance Status score at translant Pror autologous translant Resstant dsease (non-hodgkn lymhoma (NHL and Hodgkn lymhoma (HL only Stem cell source (bone marrow vs. PBSC vs. cord blood Condtonng ntensty of the rearatve regmen (myeloablatve vs. reduced-ntensty / non-myeloablatve b, searately by dsease Tme from dagnoss to translant (acute leukema not n frst comlete remsson or Prmary Inducton falure (CR1/PIF Number of revous remssons (acute leukema In addton, the followng varables are felt to be of uncertan clncal relevance, and so they are ncluded n the model only f statstcally sgnfcant (<0.05. a For PBSC and marrow translants, hgh-resoluton tyng at HLA-A, -B, -C, and DRB1 s used for the cases where t s avalable. For the remanng atents wth PBSC and bone marrow graft sources, the best avalable matchng nformaton at HLA-A, -B, -C, and -DRB1 s used. 8 For cord blood translants, low/ntermedateresoluton tyng at HLA-A and -B, and hgh-resoluton tyng at -DRB1 only are consdered. b Condtonng regmens are categorzed as myeloablatve, reduced-ntensty, or non-myeloablatve, usng consensus crtera roosed by the Regmen-Related Toxcty Workng Commttee of the CIBMTR. Brefly, all regmens usng total body rradaton (TBI at a dose of > 500 cgy (f gven n a sngle fracton or > 800 cgy (fractonated, and all regmens usng busulfan at a dose > 9 mg/kg (oral or > 7.2 mg/kg (IV, melhalan at a dose > 150 mg/m 2, treosulfan at a dose > 30000 mg/ m 2, or thotea at a dose > 10 mg/kg are consdered myeloablatve. Regmens emloyng TBI between 200 and 500 cgy or between 500 and 800 cgy (fractonated, busulfan doses 9 mg/kg (oral or 7.2 mg/kg (IV, melhalan doses 150 mg/m 2, treosulfan doses 30000 mg/ m 2, thotea doses 10 mg/kg, BEAM, CBV, or VP16+cyclohoshamde are categorzed as reduced ntensty. Fnally, regmens usng fludarabne wthout busulfan and/or melhalan and regmens usng TBI doses of 200 cgy (wth or wthout fludarabne are categorzed as non-myeloablatve. Regmens that do not readly ft these crtera are revewed and assgned by CIBMTR staff. 9 HCT Center Survval Reort Methodology Last udated: 9/4/2013 Page 5 of 7

T-cell lneage n acute lymhoblastc leukema, Phladelha chromosome n acute lymhoblastc leukema Year of translant Donor arty Donor race Donor CMV serology The results of the multvarate model are resented n a set of tables where each varable and ts assocated odds rato are descrbed, along wth 95% confdence lmts. Center-secfc results Fnal center-secfc results are resented, along wth centers hstorcal erformance n tables, and on the ublc webste. Numbers of translanted recents at each center, case mx score, actual (observed survval at one year, redcted survval at one year, 95% confdence ntervals for redcted survval, and erformance status are dslayed for each center. Centers whose actual survval s outsde the 95% confdence lmts for redcted survval have a 1 n the erformance status column f under-erformng, and a 1 n the erformance status column f over-erformng. Centers wth a 0 n the erformance status column are erformng as redcted. Snce the censored data logstc regresson model assumes no center effect, centers wth smaller numbers of translants (e.g. N = 1 or 2 wll not have ther redcted survval roorton regress toward the network average. Rather, the confdence lmts around the redcted survval at that center wll smly be much wder than those of larger centers. Results are also dslayed for centers va a vsual box-lot grahc. Centers are arranged by center number, whle readng from left to rght across these fgures. The actual survval at each center s suermosed wth each box lot (usng the symbol to gve the reader an nstantaneous cture of how close the center s to under- or over-erformng. A dashed lne s ncluded to denote the overall network survval average, usng the Kalan-Meer estmate of oneyear survval from the entre cohort of atents who underwent frst allogenec HCT n the tme nterval ncluded n the analyss. Patents can fnd nformaton about all U.S. translant centers erformng allogenec translants n the onlne U.S. Translant Center Drectory on htt://bethematch.org. Lstngs are organzed by state and can be found at bethematch.org/access. Along wth center outcomes, each lstng ncludes a descrton of that center s rogram, contact nformaton, the number of translants erformed over a secfed tme erod and survval statstcs by atent s age, dsease tye and stage for both related and unrelated donor translants. A lnk to the Translant Center Drectory can also be found on the Health Resources and Servces Admnstraton (HRSA htt://bloodcell.translant.hrsa.gov webste. Because the outcome of nterest s one-year survval, at least one year of follow-u tme s requred to be ncluded n the analyss. Data are refreshed once a year. After the reort on translant center-secfc survval rates s aroved by HRSA, the Translant Center Drectory s reoulated wth the new data. HCT Center Survval Reort Methodology Last udated: 9/4/2013 Page 6 of 7

Summary A censored data logstc regresson model s ftted to survval data for frst unrelated and related donor hematooetc cell translants at U.S. centers. The model s adusted for dsease/status; recent age; donor age (unrelated donor bone marrow or PBSC only; donor tye/graft tye/hla matchng; recent CMV status; recent race; coexstng dsease (Sorror HCT-CI; Karnofsky/Lansky score; year of translant (unrelated donor only; condtonng regmen ntensty (NHL, HL, PCD and OM only; resstant dsease (NHL and HL only; nterval from dagnoss to translant (ALL and AML only; donor/recent gender match; year of translant, and ror autologous translant. The reort on translant center-secfc survval rates hels dentfy centers that may have under-erformed or over-erformed comared to the overall network of translant centers durng ths secfed tme erod. References 1. Natonal Marrow Donor Program Center-Secfc Reort. Internal NMDP Reort, 1998. 2. Andersen PK, Klen JP and Rostho S. (2003. Generalzed lnear models or correlated seudo-observatons wth alcatons to mult-state models. Bometrka, 90: 15-27. 3. Klen JP and Andersen PK. (2005 Regresson modelng of cometng rsks data based on seudo-values of the cumulatve ncdence functon. Bometrcs, 61: 223-229. 4. Klen JP, Logan BR, Harhoff M and Andersen PK. (2007 Analyzng survval curves at a fxed ont n tme. Statstcs n Medcne, 26: 4505-4519. 5. Recommendatons from the 2010 Center-Secfc Outcomes Analyss Forum can be found at htt://www.cbmtr.org/meetngs/materals/csoaforum/documents/center_forum_summar y2010.df. 6. Logan BR, Nelson G and Klen JP. (2008. Analyzng center-secfc outcomes n hematooetc cell translantaton. Lfetme Data Analyss, 14: 389-404. 7. Sorror ML, Mars MB, Storb R, Baron F, Sandmaer BM, Maloney DG, Storer B. (2005. Hematooetc Cell Translantaton (HCT-secfc comorbdty ndex: a new tool for rsk assessment before allogenec HCT. Blood, 106(8:2912-2919. 8. Wesdorf D, Sellman S, Haagenson M, et al. (2008. Classfcaton of HLA-matchng for retrosectve analyss of unrelated donor translantaton: revsed defntons to redct survval. Bol Blood Marrow Translant, 14:748-758. 9. Bacgaluo A, Ballen K, Rzzo D, Gralt S, Lazarus H, Ho V, Aerley J, Slavn S, Pasqun M, Sandmaer BM, Barrett J, Blase D, Lowsk R and Horowtz M. (2009. Defnng the ntensty of condtonng regmens: workng defntons. Bol Blood Marrow Translant, 15:1628-1633. HCT Center Survval Reort Methodology Last udated: 9/4/2013 Page 7 of 7