Adaptive list sequential sampling method for population-based observational studies

Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired...
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Autor*in:	Hof, Michel H [verfasserIn] Ravelli, Anita CJ Zwinderman, Aeilko H

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	List sequential sampling Sample representativeness ps sample Population-based observational studies

Anmerkung:	© Hof et al.; licensee BioMed Central Ltd. 2014

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 14(2014), 1 vom: 25. Juni
Übergeordnetes Werk:	volume:14 ; year:2014 ; number:1 ; day:25 ; month:06

Links:	Volltext

DOI / URN:	10.1186/1471-2288-14-81

Katalog-ID:	SPR027368599

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520			\|a Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation.
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allfields	10.1186/1471-2288-14-81 doi (DE-627)SPR027368599 (SPR)1471-2288-14-81-e DE-627 ger DE-627 rakwb eng Hof, Michel H verfasserin aut Adaptive list sequential sampling method for population-based observational studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hof et al.; licensee BioMed Central Ltd. 2014 Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation. List sequential sampling (dpeaa)DE-He213 Sample representativeness (dpeaa)DE-He213 ps sample (dpeaa)DE-He213 Population-based observational studies (dpeaa)DE-He213 Ravelli, Anita CJ aut Zwinderman, Aeilko H aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 14(2014), 1 vom: 25. Juni (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:14 year:2014 number:1 day:25 month:06 https://dx.doi.org/10.1186/1471-2288-14-81 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2014 1 25 06
spelling	10.1186/1471-2288-14-81 doi (DE-627)SPR027368599 (SPR)1471-2288-14-81-e DE-627 ger DE-627 rakwb eng Hof, Michel H verfasserin aut Adaptive list sequential sampling method for population-based observational studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hof et al.; licensee BioMed Central Ltd. 2014 Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation. List sequential sampling (dpeaa)DE-He213 Sample representativeness (dpeaa)DE-He213 ps sample (dpeaa)DE-He213 Population-based observational studies (dpeaa)DE-He213 Ravelli, Anita CJ aut Zwinderman, Aeilko H aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 14(2014), 1 vom: 25. Juni (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:14 year:2014 number:1 day:25 month:06 https://dx.doi.org/10.1186/1471-2288-14-81 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2014 1 25 06
allfields_unstemmed	10.1186/1471-2288-14-81 doi (DE-627)SPR027368599 (SPR)1471-2288-14-81-e DE-627 ger DE-627 rakwb eng Hof, Michel H verfasserin aut Adaptive list sequential sampling method for population-based observational studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hof et al.; licensee BioMed Central Ltd. 2014 Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation. List sequential sampling (dpeaa)DE-He213 Sample representativeness (dpeaa)DE-He213 ps sample (dpeaa)DE-He213 Population-based observational studies (dpeaa)DE-He213 Ravelli, Anita CJ aut Zwinderman, Aeilko H aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 14(2014), 1 vom: 25. Juni (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:14 year:2014 number:1 day:25 month:06 https://dx.doi.org/10.1186/1471-2288-14-81 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2014 1 25 06
allfieldsGer	10.1186/1471-2288-14-81 doi (DE-627)SPR027368599 (SPR)1471-2288-14-81-e DE-627 ger DE-627 rakwb eng Hof, Michel H verfasserin aut Adaptive list sequential sampling method for population-based observational studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hof et al.; licensee BioMed Central Ltd. 2014 Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation. List sequential sampling (dpeaa)DE-He213 Sample representativeness (dpeaa)DE-He213 ps sample (dpeaa)DE-He213 Population-based observational studies (dpeaa)DE-He213 Ravelli, Anita CJ aut Zwinderman, Aeilko H aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 14(2014), 1 vom: 25. Juni (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:14 year:2014 number:1 day:25 month:06 https://dx.doi.org/10.1186/1471-2288-14-81 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2014 1 25 06
allfieldsSound	10.1186/1471-2288-14-81 doi (DE-627)SPR027368599 (SPR)1471-2288-14-81-e DE-627 ger DE-627 rakwb eng Hof, Michel H verfasserin aut Adaptive list sequential sampling method for population-based observational studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Hof et al.; licensee BioMed Central Ltd. 2014 Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation. List sequential sampling (dpeaa)DE-He213 Sample representativeness (dpeaa)DE-He213 ps sample (dpeaa)DE-He213 Population-based observational studies (dpeaa)DE-He213 Ravelli, Anita CJ aut Zwinderman, Aeilko H aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 14(2014), 1 vom: 25. Juni (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:14 year:2014 number:1 day:25 month:06 https://dx.doi.org/10.1186/1471-2288-14-81 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2014 1 25 06
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When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. 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author	Hof, Michel H
spellingShingle	Hof, Michel H misc List sequential sampling misc Sample representativeness misc ps sample misc Population-based observational studies Adaptive list sequential sampling method for population-based observational studies
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title	Adaptive list sequential sampling method for population-based observational studies
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title_sort	adaptive list sequential sampling method for population-based observational studies
title_auth	Adaptive list sequential sampling method for population-based observational studies
abstract	Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation. © Hof et al.; licensee BioMed Central Ltd. 2014
abstractGer	Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation. © Hof et al.; licensee BioMed Central Ltd. 2014
abstract_unstemmed	Background In population-based observational studies, non-participation and delayed response to the invitation to participate are complications that often arise during the recruitment of a sample. When both are not properly dealt with, the composition of the sample can be different from the desired composition. Inviting too many individuals or too few individuals from a particular subgroup could lead to unnecessary costs or decreased precision. Another problem is that there is frequently no or only partial information available about the willingness to participate. In this situation, we cannot adjust the recruitment procedure for non-participation before the recruitment period starts. Methods We have developed an adaptive list sequential sampling method that can deal with unknown participation probabilities and delayed responses to the invitation to participate in the study. In a sequential way, we evaluate whether we should invite a person from the population or not. During this evaluation, we correct for the fact that this person could decline to participate using an estimated participation probability. We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. Conclusions The adaptive list sequential sampling method can successfully recruit a sample with a specific desired composition when we have partial or no information about the willingness to participate before we start the recruitment period and when individuals may have a delayed response to the invitation. © Hof et al.; licensee BioMed Central Ltd. 2014
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title_short	Adaptive list sequential sampling method for population-based observational studies
url	https://dx.doi.org/10.1186/1471-2288-14-81
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up_date	2024-07-04T01:33:41.766Z
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We use the information from all previously invited persons to estimate the participation probabilities for the non-evaluated individuals. Results The simulations showed that the adaptive list sequential sampling method can be used to estimate the participation probability during the recruitment period, and that it can successfully recruit a sample with a specific composition. 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Adaptive list sequential sampling method for population-based observational studies

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