RESUMO
OBJECTIVE: This study aimed to evaluate misinformation surrounding infertility and the COVID-19 vaccine on X (formerly known as Twitter) by analyzing the prevalence and content of this misinformation across a sample of posts on X. METHODS: This study is a retrospective review of posts on X (formerly known as tweets) from the COVID-19-TweetIDs dataset from July 2021 and November 2021. Included posts were from crucial time points in the COVID-19 vaccine discourse and contained at least one word related to COVID-19 vaccination and fertility. Posts were analyzed and categorized based on factuality, common words, and hashtags. Descriptive statistics on total followers, account verification status, and engagement were obtained. Differences between posts on X classified as factual and misinformation were examined using analysis of variance or χ2 tests. Sentiment analysis determined if post content was generally positive, neutral, or negative. RESULTS: A total of 17,418 relevant posts on X were reviewed: 11,436 from timeframe 1 (July 2021) and 5982 from timeframe 2 (December 2021). Misinformation posts rose from 29.9% in July 2021 to 45.1% in November 2021. In both timeframes, accounts sharing factual information had more followers (p < 0.001), and verified users were more likely to share accurate posts (p ≤ 0.001). Factual and misinformation posts had similar engagement. Sentiment analysis identified that real posts were more positive and misinformation posts were more negative (p < 0.001). CONCLUSIONS AND RELEVANCE: Misinformation about the COVID-19 vaccine and fertility is highly prevalent on X and threatens vaccine uptake in patients desiring future fertility. Accounts sharing factual information were likely to have more followers and be verified; therefore, verifying more physicians sharing accurate information is critical.
RESUMO
PURPOSE: Missing scores complicate analysis of the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) because patients with and without missing scores may systematically differ. We focus on optimal analysis methods for incomplete PRO-CTCAE items, with application to two randomized, double-blind, placebo-controlled, phase III trials. METHODS: In Alliance A091105 and COMET-2, patients completed PRO-CTCAE items before randomization and several times post-randomization (N = 64 and 107, respectively). For each trial, we conducted between-arm comparisons on the PRO-CTCAE via complete-case two-sample t-tests, mixed modeling with contrast, and multiple imputation followed by two-sample t-tests. Because interest lies in whether CTCAE grades can inform missing PRO-CTCAE scores, we performed multiple imputation with and without CTCAE grades as auxiliary variables to assess the added benefit of including them in the imputation model relative to only including PRO-CTCAE scores across all cycles. RESULTS: PRO-CTCAE completion rates ranged from 100.0 to 71.4% and 100.0 to 77.1% across time in A091105 and COMET-2, respectively. In both trials, mixed modeling and multiple imputation provided the most similar estimates of the average treatment effects. Including CTCAE grades in the imputation model did not consistently narrow confidence intervals of the average treatment effects because correlations for the same PRO-CTCAE item between different cycles were generally stronger than correlations between each PRO-CTCAE item and its corresponding CTCAE grade at the same cycle. CONCLUSION: For between-arm comparisons, mixed modeling and multiple imputation are informative techniques for handling missing PRO-CTCAE scores. CTCAE grades do not provide added benefit for informing missing PRO-CTCAE scores. CLINICALTRIALS: gov Identifiers: NCT02066181 (Alliance A091105); NCT01522443 (COMET-2).
