Cohort Attrition: Multi-Site, Anomaly Detection, Cross-Sectional Analysis
| dc.contributor | Patient-Centered Outcomes Research Institute |
| dc.contributor.author | PEDSnet Data Coordinating Center |
| dc.contributor.other | PEDSnet Data Coordinating Center |
| dc.date.accessioned | 2024-09-09T17:16:45Z |
| dc.date.created | 2024-06-05 |
| dc.description.abstract | This check provides an exploratory analysis of patient eligibility criteria for a study. It summarizes each step of attrition criteria in the cohort construction for a given site. |
| dc.identifier.uri | https://hdl.handle.net/20.500.14642/772 |
| dc.identifier.uri | https://doi.org/10.24373/pdsp-430 |
| dc.publisher | PEDSnet |
| dc.rights | a CC-BY Attribution 4.0 License. |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0 |
| dc.subject | Multi-Site Analysis |
| dc.subject | Data Anomaly Method |
| dc.subject | Cross-Sectional Analysis |
| dc.subject | Person-Level Analysis |
| dc.title | Cohort Attrition: Multi-Site, Anomaly Detection, Cross-Sectional Analysis |
| dspace.entity.type | DQCheck |
| local.code.github | https://github.com/ssdqa/cohortattrition |
| local.code.package | # install.packages("devtools") devtools::install_github('ssdqa/https://github.com/ssdqa/cohortattrition') |
| local.description.raw | The raw data output of this check produces twenty-two columns of data: <br> |Column |Data Type|Definition | |-------------------|---------|-------------------------------------------------------------------------| |`num_pts` |numeric |the number of patients associated with a given attrition step - provided in the attrition table by the user | |`step_number` |numeric |an integer indicating the step associated with the attrition - provided in the attrition table by the user | |`attrition_step` |character|a string describing the attrition step - provided in the attrition table by the user | |`site` |character|the name of the site being targeted | |`...` | |any additional columns that were included in the user-provided attrition table will also appear in the output| |`prop_retained_prior`|numeric |the proportion of patients retained from the previous attrition step | |`ct_diff_prior` |numeric |the difference in patient count between a given attrition step and the previous step | |`prop_diff_prior` |numeric |the proportion difference between a given attrition step and the previous step | |`prop_retained_start`|numeric |the proportion of patients retained from the user-defined "start" attrition step | |`mean_val` |numeric |the mean difference value (based on user selection) for each group across sites | |`median_val` |numeric |the median difference value (based on user selection) for each group across sites | |`sd_val` |numeric |the standard deviation of the difference value (based on user selection) for each group across sites | |`mad_val` |numeric |the median absolute deviation of the difference value (based on user selection) for each group across sites | |`cov_val` |numeric |the coefficient of variance of the difference value (based on user selection) for each group across sites | |`max_val` |numeric |the maximum difference value (based on user selection) for each group across sites | |`min_val` |numeric |the minimum difference value (based on user selection) for each group across sites | |`range_val` |numeric |the range of the difference value (based on user selection) for each group across sites | |`total_ct` |numeric |the total number of group members | |`analysis_eligible` |character|a string indicating whether the group is eligible for anomaly detection analysis | |`lower_tail` |numeric |the lower bound used to identify low anomalies | |`upper_tail` |numeric |the upper bound used to identify high anomalies | |`anomaly_yn` |character|a string indicating whether the value is anomalous or not | {.dqcheck-table} |
| local.description.viz | This check provides the option of a dot plot visualization or a line graph with reference table. The user inputs the attrition step at which the visualization should begin and selects a variable to facet by (num_pts, prop_retained_start, prop_retained_prior or prop_diff_prior). Anomolous data points are represented with a star. Data point size is directly proportional to the mean value per step and data point color corresponds to the proportion of the user-selected variable (ex: prop_diff_prior). In the line graph output option, line color corresponds to the site. Hovering over the graph provides displays a tooltip with a description of the attrition step, the site name, mean, standard deviation, median, MAD, and the value of the user-selected variable. |
| local.dqcheck.category | Plausibility |
| local.dqcheck.clinicalprobe | Targeted Patient Population |
| local.dqcheck.clinicalprobe | Valid Diagnostic Criteria |
| local.dqcheck.measurement | Hotspots Outlier Detection |
| local.dqcheck.probe | Misclassification Detection |
| local.dqcheck.probe | Eligibility Criteria Assessment |
| local.dqcheck.probe | External Benchmarking |
| local.dqcheck.probe | Selection Error or Bias Detection |
| local.dqcheck.probe | Anomalous Values from Internal Distributions |
| local.dqcheck.requirement | attrition_tbl |
| local.dqcheck.requirement | multi_or_single_site |
| local.dqcheck.requirement | anomaly_or_exploratory |
| local.dqcheck.requirement | start_step_num |
| local.dqcheck.requirement | var_col |
| local.dqcheck.requirement | p_value |
| local.dqcheck.type | Cohort Identification |
| local.dqcheck.viz | Dot and Star Plot |
| relation.isCodeOfDQCheck | 929c8dfc-2c8b-4e62-8e1d-0fa06c542832 |
| relation.isCodeOfDQCheck.latestForDiscovery | 929c8dfc-2c8b-4e62-8e1d-0fa06c542832 |
| relation.isDQResultOfDQCheck | c447e602-f494-47a4-90d0-050e5fbbba94 |
| relation.isDQResultOfDQCheck.latestForDiscovery | c447e602-f494-47a4-90d0-050e5fbbba94 |
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