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On the impact of nonresponse in logistic regression: application to the 45 and Up study

機譯：無響應對邏輯回歸的影響：在45及以上研究中的應用

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Background In longitudinal studies, nonresponse to follow-up surveys poses a major threat to validity, interpretability and generalisation of results. The problem of nonresponse is further complicated by the possibility that nonresponse may depend on the outcome of interest. We identified sociodemographic, general health and wellbeing characteristics associated with nonresponse to the follow-up questionnaire and assessed the extent and effect of nonresponse on statistical inference in a large-scale population cohort study. Methods We obtained the data from the baseline and first wave of the follow-up survey of the 45 and Up Study. Of those who were invited to participate in the follow-up survey, 65.2% responded. Logistic regression model was used to identify baseline characteristics associated with follow-up response. A Bayesian selection model approach with sensitivity analysis was implemented to model nonignorable nonresponse. Results Characteristics associated with a higher likelihood of responding to the follow-up survey include female gender, age categories 55–74, high educational qualification, married/de facto, worked part or partially or fully retired and higher household income. Parameter estimates and conclusions are generally consistent across different assumptions on the missing data mechanism. However, we observed some sensitivity for variables that are strong predictors for both the outcome and nonresponse. Conclusions Results indicated in the context of the binary outcome under study, nonresponse did not result in substantial bias and did not alter the interpretation of results in general. Conclusions were still largely robust under nonignorable missing data mechanism. Use of a Bayesian selection model is recommended as a useful strategy for assessing potential sensitivity of results to missing data.
機譯：背景技術在縱向研究中，對后續調查的不回應構成對有效性，可解釋性和結果概括性的重大威脅。無響應的問題由于無響應可能取決于感興趣的結果而變得更加復雜。我們在后續的問卷調查中確定了與無應答相關的社會人口統計學，總體健康和福祉特征，并在大規模人群隊列研究中評估了無應答對統計推斷的影響和程度。方法我們從45歲及以上研究的隨訪調查的基線和第一波獲得數據。在被邀請參加隨訪調查的受訪者中，有65.2％回答了。 Logistic回歸模型用于識別與隨訪反應相關的基線特征。貝葉斯選擇模型方法與敏感性分析被實現來模擬不可忽略的無響應。結果與跟蹤調查的可能性更高相關的特征包括女性，年齡在55-74歲之間的年齡，受教育程度高，已婚/事實上，部分或部分或完全退休的工作以及較高的家庭收入。在關于缺失數據機制的不同假設之間，參數估計和結論通常是一致的。但是，我們觀察到了一些變量，這些變量對于結果和無響應都是很強的預測指標。結論在所研究的二元結果的背景下顯示的結果是，無應答不會導致實質性偏倚，并且通常不會改變結果的解釋。在不可忽略的缺失數據機制下，結論仍然具有很大的魯棒性。建議使用貝葉斯選擇模型作為評估結果對丟失數據的潛在敏感性的有用策略。

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