Randomized clinical trials to identify optimal antibiotic treatment duration
Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials...
Ausführliche Beschreibung
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| Autor*in: |
Horsburgh, C Robert [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Horsburgh et al.; licensee BioMed Central Ltd. 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Trials - London : BioMed Central, 2000, 14(2013), 1 vom: 28. März |
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| Übergeordnetes Werk: |
volume:14 ; year:2013 ; number:1 ; day:28 ; month:03 |
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| DOI / URN: |
10.1186/1745-6215-14-88 |
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SPR03006208X |
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| 520 | |a Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. | ||
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| 700 | 1 | |a Shea, Kimberly M |4 aut | |
| 700 | 1 | |a Phillips, Patrick |4 aut | |
| 700 | 1 | |a LaValley, Michael |4 aut | |
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10.1186/1745-6215-14-88 doi (DE-627)SPR03006208X (SPR)1745-6215-14-88-e DE-627 ger DE-627 rakwb eng Horsburgh, C Robert verfasserin aut Randomized clinical trials to identify optimal antibiotic treatment duration 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Horsburgh et al.; licensee BioMed Central Ltd. 2013 Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. Antibiotic trials (dpeaa)DE-He213 Antibiotic treatment duration (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Shea, Kimberly M aut Phillips, Patrick aut LaValley, Michael aut Enthalten in Trials London : BioMed Central, 2000 14(2013), 1 vom: 28. März (DE-627)326173552 (DE-600)2040523-6 1745-6215 nnns volume:14 year:2013 number:1 day:28 month:03 https://dx.doi.org/10.1186/1745-6215-14-88 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2013 1 28 03 |
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10.1186/1745-6215-14-88 doi (DE-627)SPR03006208X (SPR)1745-6215-14-88-e DE-627 ger DE-627 rakwb eng Horsburgh, C Robert verfasserin aut Randomized clinical trials to identify optimal antibiotic treatment duration 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Horsburgh et al.; licensee BioMed Central Ltd. 2013 Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. Antibiotic trials (dpeaa)DE-He213 Antibiotic treatment duration (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Shea, Kimberly M aut Phillips, Patrick aut LaValley, Michael aut Enthalten in Trials London : BioMed Central, 2000 14(2013), 1 vom: 28. März (DE-627)326173552 (DE-600)2040523-6 1745-6215 nnns volume:14 year:2013 number:1 day:28 month:03 https://dx.doi.org/10.1186/1745-6215-14-88 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2013 1 28 03 |
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10.1186/1745-6215-14-88 doi (DE-627)SPR03006208X (SPR)1745-6215-14-88-e DE-627 ger DE-627 rakwb eng Horsburgh, C Robert verfasserin aut Randomized clinical trials to identify optimal antibiotic treatment duration 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Horsburgh et al.; licensee BioMed Central Ltd. 2013 Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. Antibiotic trials (dpeaa)DE-He213 Antibiotic treatment duration (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Shea, Kimberly M aut Phillips, Patrick aut LaValley, Michael aut Enthalten in Trials London : BioMed Central, 2000 14(2013), 1 vom: 28. März (DE-627)326173552 (DE-600)2040523-6 1745-6215 nnns volume:14 year:2013 number:1 day:28 month:03 https://dx.doi.org/10.1186/1745-6215-14-88 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2013 1 28 03 |
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10.1186/1745-6215-14-88 doi (DE-627)SPR03006208X (SPR)1745-6215-14-88-e DE-627 ger DE-627 rakwb eng Horsburgh, C Robert verfasserin aut Randomized clinical trials to identify optimal antibiotic treatment duration 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Horsburgh et al.; licensee BioMed Central Ltd. 2013 Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. Antibiotic trials (dpeaa)DE-He213 Antibiotic treatment duration (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Shea, Kimberly M aut Phillips, Patrick aut LaValley, Michael aut Enthalten in Trials London : BioMed Central, 2000 14(2013), 1 vom: 28. März (DE-627)326173552 (DE-600)2040523-6 1745-6215 nnns volume:14 year:2013 number:1 day:28 month:03 https://dx.doi.org/10.1186/1745-6215-14-88 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2013 1 28 03 |
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10.1186/1745-6215-14-88 doi (DE-627)SPR03006208X (SPR)1745-6215-14-88-e DE-627 ger DE-627 rakwb eng Horsburgh, C Robert verfasserin aut Randomized clinical trials to identify optimal antibiotic treatment duration 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Horsburgh et al.; licensee BioMed Central Ltd. 2013 Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. Antibiotic trials (dpeaa)DE-He213 Antibiotic treatment duration (dpeaa)DE-He213 Tuberculosis (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Shea, Kimberly M aut Phillips, Patrick aut LaValley, Michael aut Enthalten in Trials London : BioMed Central, 2000 14(2013), 1 vom: 28. März (DE-627)326173552 (DE-600)2040523-6 1745-6215 nnns volume:14 year:2013 number:1 day:28 month:03 https://dx.doi.org/10.1186/1745-6215-14-88 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2013 1 28 03 |
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Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. 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Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. © Horsburgh et al.; licensee BioMed Central Ltd. 2013 |
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Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. © Horsburgh et al.; licensee BioMed Central Ltd. 2013 |
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Background Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen. Methods Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model. Results Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin. Conclusions This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance. © Horsburgh et al.; licensee BioMed Central Ltd. 2013 |
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| score |
7.4003124 |