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Strategies for monitoring and evaluation of resource-limited national antiretroviral therapy programs: the two-phase design.

Haneuse S, Hedt-Gauthier B, Chimbwandira F, Makombe S, Tenthani L, Jahn A - BMC Med Res Methodol (2015)

Bottom Line: In contrast, a two-phase design that stratifies on clinic and year of registration achieves greater than 85% power with as few as 1,000 patient samples.Two-phase designs have the potential to augment current M&E efforts in resource-limited settings by providing a framework for the collection and analysis of patient data.The design is cost-efficient in the sense that it often requires far fewer patients to be sampled when compared to standard designs.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. shaneuse@hsph.harvard.edu.

ABSTRACT

Background: In resource-limited settings, monitoring and evaluation (M&E) of antiretroviral treatment (ART) programs often relies on aggregated facility-level data. Such data are limited, however, because of the potential for ecological bias, although collecting detailed patient-level data is often prohibitively expensive. To resolve this dilemma, we propose the use of the two-phase design. Specifically, when the outcome of interest is binary, the two-phase design provides a framework within which researchers can resolve ecological bias through the collection of patient-level data on a sub-sample of individuals while making use of the routinely collected aggregated data to obtain potentially substantial efficiency gains.

Methods: Between 2005-2007, the Malawian Ministry of Health conducted a one-time cross-sectional survey of 82,887 patients registered at 189 ART clinics. Using these patient data, an aggregated dataset is constructed to mimic the type of data that it routinely available. A hypothetical study of risk factors for patient outcomes at 6 months post-registration is considered. Analyses are conducted based on: (i) complete patient-level data; (ii) aggregated data; (iii) a hypothetical case-control study; (iv) a hypothetical two-phase study stratified on clinic type; and, (v) a hypothetical two-phase study stratified on clinic type and registration year. A simulation study is conducted to compare statistical power to detect an interaction between clinic type and year of registration across the designs.

Results: Analyses and conclusions based solely on aggregated data may suffer from ecological bias. Collecting and analyzing patient data using either a case-control or two-phase design resolves ecological bias to provide valid conclusions. To detect the interaction between clinic type and year of registration, the case-control design would require a prohibitively large sample size. In contrast, a two-phase design that stratifies on clinic and year of registration achieves greater than 85% power with as few as 1,000 patient samples.

Conclusions: Two-phase designs have the potential to augment current M&E efforts in resource-limited settings by providing a framework for the collection and analysis of patient data. The design is cost-efficient in the sense that it often requires far fewer patients to be sampled when compared to standard designs.

No MeSH data available.


Related in: MedlinePlus

Estimated post-hoc power to detect an interaction between clinic type and year of registration under (i) a gold-standard complete data design with patient-level data on all N = 82,877 patients; (ii) a case–control design; (iii) a two-phase design, stratifying on clinic type; and, a two-phase design, stratifying on clinic type and year of registration.
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Fig2: Estimated post-hoc power to detect an interaction between clinic type and year of registration under (i) a gold-standard complete data design with patient-level data on all N = 82,877 patients; (ii) a case–control design; (iii) a two-phase design, stratifying on clinic type; and, a two-phase design, stratifying on clinic type and year of registration.

Mentions: Figure 2 provides the results from the simulation study. The grey line indicates that analyses based on the complete data (i.e. N = 82,877) had approximately 90% power to detect the clinic/year interaction. From the Figure we see that a case–control design with n = 10,000 patients would only have approximately 23% power. Increasing the case–control sample size to n = 20,000 only increases power to 53%; at n = 40,000, power is approximately 80%. In comparison, one would only need n = 5,000 phase II samples under two-phase Design #1 to have approximately 80% power. Under Design #2, a phase II sample size as low as n = 500 would provide more than 85% power to detect the clinic/year interaction. Furthermore, when the phase II sample size is n = 2,000, Design #2 has equivalent statistical power to a study in which patient-level data was collected on all N = 82,877 patients.Figure 2


Strategies for monitoring and evaluation of resource-limited national antiretroviral therapy programs: the two-phase design.

Haneuse S, Hedt-Gauthier B, Chimbwandira F, Makombe S, Tenthani L, Jahn A - BMC Med Res Methodol (2015)

Estimated post-hoc power to detect an interaction between clinic type and year of registration under (i) a gold-standard complete data design with patient-level data on all N = 82,877 patients; (ii) a case–control design; (iii) a two-phase design, stratifying on clinic type; and, a two-phase design, stratifying on clinic type and year of registration.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4404107&req=5

Fig2: Estimated post-hoc power to detect an interaction between clinic type and year of registration under (i) a gold-standard complete data design with patient-level data on all N = 82,877 patients; (ii) a case–control design; (iii) a two-phase design, stratifying on clinic type; and, a two-phase design, stratifying on clinic type and year of registration.
Mentions: Figure 2 provides the results from the simulation study. The grey line indicates that analyses based on the complete data (i.e. N = 82,877) had approximately 90% power to detect the clinic/year interaction. From the Figure we see that a case–control design with n = 10,000 patients would only have approximately 23% power. Increasing the case–control sample size to n = 20,000 only increases power to 53%; at n = 40,000, power is approximately 80%. In comparison, one would only need n = 5,000 phase II samples under two-phase Design #1 to have approximately 80% power. Under Design #2, a phase II sample size as low as n = 500 would provide more than 85% power to detect the clinic/year interaction. Furthermore, when the phase II sample size is n = 2,000, Design #2 has equivalent statistical power to a study in which patient-level data was collected on all N = 82,877 patients.Figure 2

Bottom Line: In contrast, a two-phase design that stratifies on clinic and year of registration achieves greater than 85% power with as few as 1,000 patient samples.Two-phase designs have the potential to augment current M&E efforts in resource-limited settings by providing a framework for the collection and analysis of patient data.The design is cost-efficient in the sense that it often requires far fewer patients to be sampled when compared to standard designs.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. shaneuse@hsph.harvard.edu.

ABSTRACT

Background: In resource-limited settings, monitoring and evaluation (M&E) of antiretroviral treatment (ART) programs often relies on aggregated facility-level data. Such data are limited, however, because of the potential for ecological bias, although collecting detailed patient-level data is often prohibitively expensive. To resolve this dilemma, we propose the use of the two-phase design. Specifically, when the outcome of interest is binary, the two-phase design provides a framework within which researchers can resolve ecological bias through the collection of patient-level data on a sub-sample of individuals while making use of the routinely collected aggregated data to obtain potentially substantial efficiency gains.

Methods: Between 2005-2007, the Malawian Ministry of Health conducted a one-time cross-sectional survey of 82,887 patients registered at 189 ART clinics. Using these patient data, an aggregated dataset is constructed to mimic the type of data that it routinely available. A hypothetical study of risk factors for patient outcomes at 6 months post-registration is considered. Analyses are conducted based on: (i) complete patient-level data; (ii) aggregated data; (iii) a hypothetical case-control study; (iv) a hypothetical two-phase study stratified on clinic type; and, (v) a hypothetical two-phase study stratified on clinic type and registration year. A simulation study is conducted to compare statistical power to detect an interaction between clinic type and year of registration across the designs.

Results: Analyses and conclusions based solely on aggregated data may suffer from ecological bias. Collecting and analyzing patient data using either a case-control or two-phase design resolves ecological bias to provide valid conclusions. To detect the interaction between clinic type and year of registration, the case-control design would require a prohibitively large sample size. In contrast, a two-phase design that stratifies on clinic and year of registration achieves greater than 85% power with as few as 1,000 patient samples.

Conclusions: Two-phase designs have the potential to augment current M&E efforts in resource-limited settings by providing a framework for the collection and analysis of patient data. The design is cost-efficient in the sense that it often requires far fewer patients to be sampled when compared to standard designs.

No MeSH data available.


Related in: MedlinePlus