Dataset Information

Virological measures and factors associated with outcomes, and missing outcome data in HIV clinical trials: a methodological study.

ABSTRACT:

Background

To evaluate the definition of HIV virological outcomes in the literature and factors associated with outcomes and missing outcome data.

Methods

We conducted a methodological review of HIV RCTs using a search (2009-2019) of PubMed, Embase and the Cochrane Central Register of Controlled Trials.Only full-text, peer-reviewed, randomised controlled trials (RCTs) that measured virological outcomes in people living with HIV, and published in English were included.We extracted study details and outcomes. We used logistic regression to identify factors associated with a viral threshold ≤50 copies/mL and linear regression to identify factors associated with missing outcome data.

Results

Our search yielded 5847 articles; 180 were included. A virological outcome was the primary outcome in 73.5% of studies. 89 studies (49.4%) used virological success. The remaining used change in viral load (VL) (33 studies, 18.3%); virological failure (59 studies, 32.8%); or virological rebound (9 studies, 5.0%). 96 studies (53.3%) set the threshold at ≤50 copies/mL; and 33.1% used multiple measures.Compared with government and privately funded studies, RCTs with industry funding (adjusted OR 6.39; 95% CI 2.15 to 19.00; p<0.01) were significantly associated with higher odds of using a VL threshold of ≤50 copies/mL. Publication year, intervention type, income level and number of patients were not associated with a threshold of ≤50 copies/mL. Trials with pharmacological interventions had less missing data (β=-11.04; 95% CI -20.02 to -1.87; p=0.02).

Discussion

Country source of funding was associated with VL threshold choice and studies with pharmacological interventions had less missing data, which may in part explain heterogeneous virological outcomes across studies. Multiple measures of VL were not associated with missing data. The development of formal guidelines on virological outcome reporting in RCTs is needed.

SUBMITTER: Youssef M

PROVIDER: S-EPMC8547356 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The aim of this review was to establish the frequency with which trials take into account missingness, and to discover what methods trialists use for adjustment in randomised controlled trials with longitudinal measurements. Failing to address the problems that can arise from missing outcome data can result in misleading conclusions. Missing data should be addressed as a means of a sensitivity analysis of the complete case analysis results. One hundred publications of randomised controlled trials with longitudinal measurements were selected randomly from trial publications from the years 2005 to 2012. Information was extracted from these trials, including whether reasons for dropout were reported, what methods were used for handing the missing data, whether there was any explanation of the methods for missing data handling, and whether a statistician was involved in the analysis. The main focus of the review was on missing data post dropout rather than missing interim data. Of all the papers in the study, 9 (9%) had no missing data. More than half of the papers included in the study failed to make any attempt to explain the reasons for their choice of missing data handling method. Of the papers with clear missing data handling methods, 44 papers (50%) used adequate methods of missing data handling, whereas 30 (34%) of the papers used missing data methods which may not have been appropriate. In the remaining 17 papers (19%), it was difficult to assess the validity of the methods used. An imputation method was used in 18 papers (20%). Multiple imputation methods were introduced in 1987 and are an efficient way of accounting for missing data in general, and yet only 4 papers used these methods. Out of the 18 papers which used imputation, only 7 displayed the results as a sensitivity analysis of the complete case analysis results. 61% of the papers that used an imputation explained the reasons for their chosen method. Just under a third of the papers made no reference to reasons for missing outcome data. There was little consistency in reporting of missing data within longitudinal trials.

Project description:BackgroundMissing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs.MethodsA targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared.ResultsA total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods.ConclusionsControlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters.