Adaptation of multiple logistic regression to a multiple inverse sampling design: application to the Isfahan healthy heart program

In observational and experimental studies in the health sciences involving human populations, it is sometimes considered desirable to recruit subjects according to designs that specify a predetermined number of subjects in each of several mutually exclusive classes (generally but not necessarily dem...

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Published in	Statistics in medicine Vol. 25; no. 1; pp. 71 - 85
Main Authors	Salehi, M. Mohammad, Levy, Paul S., Jamalzadeh, Mohammad A., Chang, K.-C.
Format	Journal Article
Language	English
Published	Chichester, UK John Wiley & Sons, Ltd 15.01.2006 Wiley Subscription Services, Inc
Subjects	Age Factors Cardiovascular disease Cardiovascular Diseases - diagnosis Cardiovascular Diseases - epidemiology Cardiovascular Physiological Phenomena Computer Simulation Epidemiologic Research Design Epidemiologic Studies Female Heart Humans Intervention Iran - epidemiology Logistic Models logistic regression Male multiple inverse sampling Murthy estimator Parameter estimation Platelet Aggregation - physiology pseudo maximum likelihood estimation Public health Regression analysis Sampling techniques Sex Factors Triglycerides - blood Iran Isfahan Iran
Online Access	Get full text
ISSN	0277-6715 1097-0258
DOI	10.1002/sim.2288

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Abstract	In observational and experimental studies in the health sciences involving human populations, it is sometimes considered desirable to recruit subjects according to designs that specify a predetermined number of subjects in each of several mutually exclusive classes (generally but not necessarily demographic in nature). This type of adaptive sampling design, now generally referred to as multiple inverse sampling (MIS), has received recent attention, and estimation methods are now available for several sequential MIS sampling designs. In this class of designs, subjects are sampled randomly and sequentially, usually one at a time, until all classes have the pre‐specified number of subjects. In this paper, we extend MIS for finite population sampling to estimation of the parameters in multiple logistic regression under MIS. Using estimated logistic regression parameters and cost components obtained from the Isfahan Healthy Heart Program (IHHP), we report findings from a simulation experiment in which it appears that, at fixed cost, MIS at the last stage of sampling compares favourably to simple random sampling. The IHHP is a large community intervention study for prevention of cardiovascular disease being conducted in Isfahan, Iran and two other cities in Iran. The IHHP identified subjects through a multistage sample survey in which MIS was used at the final stage of sampling. MIS is one of several methods of adaptive sampling that are generating considerable interest and show promise of being useful in a wide variety of applications. Copyright © 2005 John Wiley & Sons, Ltd.
AbstractList	In observational and experimental studies in the health sciences involving human populations, it is sometimes considered desirable to recruit subjects according to designs that specify a predetermined number of subjects in each of several mutually exclusive classes (generally but not necessarily demographic in nature). This type of adaptive sampling design, now generally referred to as multiple inverse sampling (MIS), has received recent attention, and estimation methods are now available for several sequential MIS sampling designs. In this class of designs, subjects are sampled randomly and sequentially, usually one at a time, until all classes have the pre‐specified number of subjects. In this paper, we extend MIS for finite population sampling to estimation of the parameters in multiple logistic regression under MIS. Using estimated logistic regression parameters and cost components obtained from the Isfahan Healthy Heart Program (IHHP), we report findings from a simulation experiment in which it appears that, at fixed cost, MIS at the last stage of sampling compares favourably to simple random sampling. The IHHP is a large community intervention study for prevention of cardiovascular disease being conducted in Isfahan, Iran and two other cities in Iran. The IHHP identified subjects through a multistage sample survey in which MIS was used at the final stage of sampling. MIS is one of several methods of adaptive sampling that are generating considerable interest and show promise of being useful in a wide variety of applications. Copyright © 2005 John Wiley & Sons, Ltd. In observational and experimental studies in the health sciences involving human populations, it is sometimes considered desirable to recruit subjects according to designs that specify a predetermined number of subjects in each of several mutually exclusive classes (generally but not necessarily demographic in nature). This type of adaptive sampling design, now generally referred to as multiple inverse sampling (MIS), has received recent attention, and estimation methods are now available for several sequential MIS sampling designs. In this class of designs, subjects are sampled randomly and sequentially, usually one at a time, until all classes have the pre-specified number of subjects. In this paper, we extend MIS for finite population sampling to estimation of the parameters in multiple logistic regression under MIS. Using estimated logistic regression parameters and cost components obtained from the Isfahan Healthy Heart Program (IHHP), we report findings from a simulation experiment in which it appears that, at fixed cost, MIS at the last stage of sampling compares favourably to simple random sampling. The IHHP is a large community intervention study for prevention of cardiovascular disease being conducted in Isfahan, Iran and two other cities in Iran. The IHHP identified subjects through a multistage sample survey in which MIS was used at the final stage of sampling. MIS is one of several methods of adaptive sampling that are generating considerable interest and show promise of being useful in a wide variety of applications. [PUBLICATION ABSTRACT] In observational and experimental studies in the health sciences involving human populations, it is sometimes considered desirable to recruit subjects according to designs that specify a predetermined number of subjects in each of several mutually exclusive classes (generally but not necessarily demographic in nature). This type of adaptive sampling design, now generally referred to as multiple inverse sampling (MIS), has received recent attention, and estimation methods are now available for several sequential MIS sampling designs. In this class of designs, subjects are sampled randomly and sequentially, usually one at a time, until all classes have the pre-specified number of subjects. In this paper, we extend MIS for finite population sampling to estimation of the parameters in multiple logistic regression under MIS. Using estimated logistic regression parameters and cost components obtained from the Isfahan Healthy Heart Program (IHHP), we report findings from a simulation experiment in which it appears that, at fixed cost, MIS at the last stage of sampling compares favourably to simple random sampling. The IHHP is a large community intervention study for prevention of cardiovascular disease being conducted in Isfahan, Iran and two other cities in Iran. The IHHP identified subjects through a multistage sample survey in which MIS was used at the final stage of sampling. MIS is one of several methods of adaptive sampling that are generating considerable interest and show promise of being useful in a wide variety of applications. In observational and experimental studies in the health sciences involving human populations, it is sometimes considered desirable to recruit subjects according to designs that specify a predetermined number of subjects in each of several mutually exclusive classes (generally but not necessarily demographic in nature). This type of adaptive sampling design, now generally referred to as multiple inverse sampling (MIS), has received recent attention, and estimation methods are now available for several sequential MIS sampling designs. In this class of designs, subjects are sampled randomly and sequentially, usually one at a time, until all classes have the pre-specified number of subjects. In this paper, we extend MIS for finite population sampling to estimation of the parameters in multiple logistic regression under MIS. Using estimated logistic regression parameters and cost components obtained from the Isfahan Healthy Heart Program (IHHP), we report findings from a simulation experiment in which it appears that, at fixed cost, MIS at the last stage of sampling compares favourably to simple random sampling. The IHHP is a large community intervention study for prevention of cardiovascular disease being conducted in Isfahan, Iran and two other cities in Iran. The IHHP identified subjects through a multistage sample survey in which MIS was used at the final stage of sampling. MIS is one of several methods of adaptive sampling that are generating considerable interest and show promise of being useful in a wide variety of applications.In observational and experimental studies in the health sciences involving human populations, it is sometimes considered desirable to recruit subjects according to designs that specify a predetermined number of subjects in each of several mutually exclusive classes (generally but not necessarily demographic in nature). This type of adaptive sampling design, now generally referred to as multiple inverse sampling (MIS), has received recent attention, and estimation methods are now available for several sequential MIS sampling designs. In this class of designs, subjects are sampled randomly and sequentially, usually one at a time, until all classes have the pre-specified number of subjects. In this paper, we extend MIS for finite population sampling to estimation of the parameters in multiple logistic regression under MIS. Using estimated logistic regression parameters and cost components obtained from the Isfahan Healthy Heart Program (IHHP), we report findings from a simulation experiment in which it appears that, at fixed cost, MIS at the last stage of sampling compares favourably to simple random sampling. The IHHP is a large community intervention study for prevention of cardiovascular disease being conducted in Isfahan, Iran and two other cities in Iran. The IHHP identified subjects through a multistage sample survey in which MIS was used at the final stage of sampling. MIS is one of several methods of adaptive sampling that are generating considerable interest and show promise of being useful in a wide variety of applications.
Author	Salehi, M. Mohammad Jamalzadeh, Mohammad A. Levy, Paul S. Chang, K.-C.
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References	Lui K-J. A note on the use of inverse sampling: point estimation between successive infections. Journal of Official Statistics 2000; 16:31-37. Salehi MM, Chang K-C. Multiple inverse sampling with subpopulation size unknown. Journal of Statistical Planning and Inference 2005; 131(2):379-392. Hosmer DW, Lemeshow S. Applied Logistic Regression (2nd edn). Wiley: New York, 1999. Salehi MM, Seber GAF. A new proof of Murthy estimator which applies to sequential sampling. Australian and New Zealand Journal of Statistics 2001; 43:281-286. Skinner CJ, Holt D, Smith TMF. Analysis of Complex Surveys. Wiley: New York, 1989; 23-59. Cox DR. The Analysis of Binary Data. Chapman & Hall: London, 1970. Thompson SK, Seber GAF. Adaptive Sampling. Wiley: New York, 1996. Everitt BS. The Cambridge Dictionary of Statistics. Cambridge University Press: Cambridge, 1998. Murthy MN. Ordered and unordered estimators in sampling without replacement. Sankhya 1957; 18:379-390. Haldane JBS. On a method of estimating frequencies. Biometrika 1945; 33:222-225. Sarraf-Zadegan N, Sadri Gh, Malek Afzali H, Baghaei M, Mohammadi Fard N, Shahrokhi S, Tolooie H, Poormoghaddas M, Sadeghi M, Tavassoli A, Rafiei M, Kelishadi R, Rabiei K, Bashardoost N, Boshtam M, Asgary S, Naderi G, Changiz T, Yousefie A. Isfahan Healthy Heart Programme: a comprehensive integrated community-based programme for cardiovascular disease prevention and control. Design, methods and initial experience. Acta Cardiologica 2003; 58(4):309-320. Binder DA. On the variance of asymptotically normal estimators from complex surveys. International Statistical Review 1983; 51:279-292. Levy PS, Stolte K. Statistical methods in public health and epidemiology: a look at the recent past and projections for the next decade. Invited paper, Statistical Methods in Medical Research 2000 9:41-55. Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association 1952; 47:663-685. Chang K-C, Liu J-F, Han C-P. Multiple inverse sampling in post-stratification. Journal of Statistical Planning and Inference 1998; 69:209-227. Scott AJ, Wild CJ. Case-control studies with complex sampling. Journal of the Royal Statistical Society, Series C 2001; 50:389-401. Korn EL, Graubard BI. Analysis of Health Surveys. Wiley: New York, 1999. 2001; 50 1952; 47 2000; 16 2005; 131 2000; 9 1945; 33 1998 2003; 58 1996 1983; 51 1970 1957; 18 1998; 69 1989 2001; 43 1999 Everitt BS (e_1_2_1_10_2) 1998 Murthy MN (e_1_2_1_8_2) 1957; 18 Hosmer DW (e_1_2_1_13_2) 1999 Skinner CJ (e_1_2_1_14_2) 1989 Lui K‐J (e_1_2_1_5_2) 2000; 16 e_1_2_1_6_2 e_1_2_1_7_2 e_1_2_1_4_2 e_1_2_1_2_2 e_1_2_1_11_2 e_1_2_1_15_2 e_1_2_1_16_2 Cox DR (e_1_2_1_12_2) 1970 Thompson SK (e_1_2_1_3_2) 1996 e_1_2_1_17_2 e_1_2_1_9_2 e_1_2_1_18_2
References_xml	– reference: Hosmer DW, Lemeshow S. Applied Logistic Regression (2nd edn). Wiley: New York, 1999. – reference: Skinner CJ, Holt D, Smith TMF. Analysis of Complex Surveys. Wiley: New York, 1989; 23-59. – reference: Thompson SK, Seber GAF. Adaptive Sampling. Wiley: New York, 1996. – reference: Chang K-C, Liu J-F, Han C-P. Multiple inverse sampling in post-stratification. Journal of Statistical Planning and Inference 1998; 69:209-227. – reference: Korn EL, Graubard BI. Analysis of Health Surveys. Wiley: New York, 1999. – reference: Salehi MM, Chang K-C. Multiple inverse sampling with subpopulation size unknown. Journal of Statistical Planning and Inference 2005; 131(2):379-392. – reference: Binder DA. On the variance of asymptotically normal estimators from complex surveys. International Statistical Review 1983; 51:279-292. – reference: Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association 1952; 47:663-685. – reference: Everitt BS. The Cambridge Dictionary of Statistics. Cambridge University Press: Cambridge, 1998. – reference: Salehi MM, Seber GAF. A new proof of Murthy estimator which applies to sequential sampling. Australian and New Zealand Journal of Statistics 2001; 43:281-286. – reference: Sarraf-Zadegan N, Sadri Gh, Malek Afzali H, Baghaei M, Mohammadi Fard N, Shahrokhi S, Tolooie H, Poormoghaddas M, Sadeghi M, Tavassoli A, Rafiei M, Kelishadi R, Rabiei K, Bashardoost N, Boshtam M, Asgary S, Naderi G, Changiz T, Yousefie A. Isfahan Healthy Heart Programme: a comprehensive integrated community-based programme for cardiovascular disease prevention and control. Design, methods and initial experience. Acta Cardiologica 2003; 58(4):309-320. – reference: Lui K-J. A note on the use of inverse sampling: point estimation between successive infections. Journal of Official Statistics 2000; 16:31-37. – reference: Cox DR. The Analysis of Binary Data. Chapman & Hall: London, 1970. – reference: Haldane JBS. On a method of estimating frequencies. Biometrika 1945; 33:222-225. – reference: Scott AJ, Wild CJ. Case-control studies with complex sampling. Journal of the Royal Statistical Society, Series C 2001; 50:389-401. – reference: Levy PS, Stolte K. Statistical methods in public health and epidemiology: a look at the recent past and projections for the next decade. Invited paper, Statistical Methods in Medical Research 2000 9:41-55. – reference: Murthy MN. Ordered and unordered estimators in sampling without replacement. Sankhya 1957; 18:379-390. – volume: 9 start-page: 41 year: 2000 end-page: 55 article-title: Statistical methods in public health and epidemiology: a look at the recent past and projections for the next decade. Invited paper publication-title: Statistical Methods in Medical Research – volume: 18 start-page: 379 year: 1957 end-page: 390 article-title: Ordered and unordered estimators in sampling without replacement publication-title: Sankhya – start-page: 23 year: 1989 end-page: 59 – volume: 131 start-page: 379 issue: 2 year: 2005 end-page: 392 article-title: Multiple inverse sampling with subpopulation size unknown publication-title: Journal of Statistical Planning and Inference – volume: 43 start-page: 281 year: 2001 end-page: 286 article-title: A new proof of Murthy estimator which applies to sequential sampling publication-title: Australian and New Zealand Journal of Statistics – year: 1996 – volume: 58 start-page: 309 issue: 4 year: 2003 end-page: 320 article-title: Isfahan Healthy Heart Programme: a comprehensive integrated community‐based programme for cardiovascular disease prevention and control. Design, methods and initial experience publication-title: Acta Cardiologica – volume: 47 start-page: 663 year: 1952 end-page: 685 article-title: A generalization of sampling without replacement from a finite universe publication-title: Journal of the American Statistical Association – volume: 50 start-page: 389 year: 2001 end-page: 401 article-title: Case‐control studies with complex sampling publication-title: Journal of the Royal Statistical Society, Series C – volume: 33 start-page: 222 year: 1945 end-page: 225 article-title: On a method of estimating frequencies publication-title: Biometrika – year: 1970 – volume: 16 start-page: 31 year: 2000 end-page: 37 article-title: A note on the use of inverse sampling: point estimation between successive infections publication-title: Journal of Official Statistics – volume: 51 start-page: 279 year: 1983 end-page: 292 article-title: On the variance of asymptotically normal estimators from complex surveys publication-title: International Statistical Review – year: 1998 – volume: 69 start-page: 209 year: 1998 end-page: 227 article-title: Multiple inverse sampling in post‐stratification publication-title: Journal of Statistical Planning and Inference – year: 1999 – ident: e_1_2_1_11_2 doi: 10.2143/AC.58.4.2005288 – ident: e_1_2_1_16_2 doi: 10.2307/1402588 – ident: e_1_2_1_17_2 doi: 10.1080/01621459.1952.10483446 – volume-title: The Cambridge Dictionary of Statistics year: 1998 ident: e_1_2_1_10_2 – start-page: 23 volume-title: Analysis of Complex Surveys year: 1989 ident: e_1_2_1_14_2 – ident: e_1_2_1_15_2 doi: 10.1002/9781118032619 – volume-title: Adaptive Sampling year: 1996 ident: e_1_2_1_3_2 – ident: e_1_2_1_18_2 doi: 10.1111/1467-9876.00242 – volume: 18 start-page: 379 year: 1957 ident: e_1_2_1_8_2 article-title: Ordered and unordered estimators in sampling without replacement publication-title: Sankhya – ident: e_1_2_1_2_2 doi: 10.1191/096228000666554731 – volume-title: Applied Logistic Regression year: 1999 ident: e_1_2_1_13_2 – ident: e_1_2_1_4_2 doi: 10.1093/biomet/33.3.222 – ident: e_1_2_1_7_2 doi: 10.1111/1467-842X.00174 – volume-title: The Analysis of Binary Data year: 1970 ident: e_1_2_1_12_2 – volume: 16 start-page: 31 year: 2000 ident: e_1_2_1_5_2 article-title: A note on the use of inverse sampling: point estimation between successive infections publication-title: Journal of Official Statistics – ident: e_1_2_1_9_2 doi: 10.1016/j.jspi.2004.02.002 – ident: e_1_2_1_6_2 doi: 10.1016/S0378-3758(97)00157-2
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SubjectTerms	Age Factors Cardiovascular disease Cardiovascular Diseases - diagnosis Cardiovascular Diseases - epidemiology Cardiovascular Physiological Phenomena Computer Simulation Epidemiologic Research Design Epidemiologic Studies Female Heart Humans Intervention Iran - epidemiology Logistic Models logistic regression Male multiple inverse sampling Murthy estimator Parameter estimation Platelet Aggregation - physiology pseudo maximum likelihood estimation Public health Regression analysis Sampling techniques Sex Factors Triglycerides - blood
Title	Adaptation of multiple logistic regression to a multiple inverse sampling design: application to the Isfahan healthy heart program
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