Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity
Abstract Background Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming missing completely at random or missing...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-023-01887-8 |
| _version_ | 1797806669232603136 |
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| author | Jiaqi Tong Fan Li Michael O. Harhay Guangyu Tong |
| author_facet | Jiaqi Tong Fan Li Michael O. Harhay Guangyu Tong |
| author_sort | Jiaqi Tong |
| collection | DOAJ |
| description | Abstract Background Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming missing completely at random or missing at random have been previously developed, the impact of attrition on the power for detecting heterogeneous treatment effects in cluster randomized trials remains unknown. Methods We provide a sample size formula for testing for a heterogeneous treatment effect assuming the outcome is missing completely at random. We also propose an efficient Monte Carlo sample size procedure for assessing heterogeneous treatment effect assuming covariate-dependent outcome missingness (missing at random). We compare our sample size methods with the direct inflation method that divides the estimated sample size by the mean follow-up rate. We also evaluate our methods through simulation studies and illustrate them with a real-world example. Results Simulation results show that our proposed sample size methods under both missing completely at random and missing at random provide sufficient power for assessing heterogeneous treatment effect. The proposed sample size methods lead to more accurate sample size estimates than the direct inflation method when the missingness rate is high (e.g., ≥ 30%). Moreover, sample size estimation under both missing completely at random and missing at random is sensitive to the missingness rate, but not sensitive to the intracluster correlation coefficient among the missingness indicators. Conclusion Our new sample size methods can assist in planning cluster randomized trials that plan to assess a heterogeneous treatment effect and participant attrition is expected to occur. |
| first_indexed | 2024-03-13T06:10:47Z |
| format | Article |
| id | doaj.art-c01eadda0e0d4b92a0559137c14cc10b |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-13T06:10:47Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-c01eadda0e0d4b92a0559137c14cc10b2023-06-11T11:17:29ZengBMCBMC Medical Research Methodology1471-22882023-04-0123111410.1186/s12874-023-01887-8Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneityJiaqi Tong0Fan Li1Michael O. Harhay2Guangyu Tong3Department of Biostatistics, Yale School of Public HealthDepartment of Biostatistics, Yale School of Public HealthDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of PennsylvaniaDepartment of Biostatistics, Yale School of Public HealthAbstract Background Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming missing completely at random or missing at random have been previously developed, the impact of attrition on the power for detecting heterogeneous treatment effects in cluster randomized trials remains unknown. Methods We provide a sample size formula for testing for a heterogeneous treatment effect assuming the outcome is missing completely at random. We also propose an efficient Monte Carlo sample size procedure for assessing heterogeneous treatment effect assuming covariate-dependent outcome missingness (missing at random). We compare our sample size methods with the direct inflation method that divides the estimated sample size by the mean follow-up rate. We also evaluate our methods through simulation studies and illustrate them with a real-world example. Results Simulation results show that our proposed sample size methods under both missing completely at random and missing at random provide sufficient power for assessing heterogeneous treatment effect. The proposed sample size methods lead to more accurate sample size estimates than the direct inflation method when the missingness rate is high (e.g., ≥ 30%). Moreover, sample size estimation under both missing completely at random and missing at random is sensitive to the missingness rate, but not sensitive to the intracluster correlation coefficient among the missingness indicators. Conclusion Our new sample size methods can assist in planning cluster randomized trials that plan to assess a heterogeneous treatment effect and participant attrition is expected to occur.https://doi.org/10.1186/s12874-023-01887-8Heterogeneity of treatment effectMissing dataMissing at randomMissing completely at randomPower calculationIntracluster correlation coefficient |
| spellingShingle | Jiaqi Tong Fan Li Michael O. Harhay Guangyu Tong Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity BMC Medical Research Methodology Heterogeneity of treatment effect Missing data Missing at random Missing completely at random Power calculation Intracluster correlation coefficient |
| title | Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity |
| title_full | Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity |
| title_fullStr | Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity |
| title_full_unstemmed | Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity |
| title_short | Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity |
| title_sort | accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity |
| topic | Heterogeneity of treatment effect Missing data Missing at random Missing completely at random Power calculation Intracluster correlation coefficient |
| url | https://doi.org/10.1186/s12874-023-01887-8 |
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