Complete case logistic regression with a dichotomised continuous outcome: a simulation study

Background A complete case logistic regression will give a biased estimate of the exposure odds ratio only if there is a multiplicative interaction between the exposure and outcome with respect to the probability of missingness – whereas linear regression with a continuous outcome is biased in more scenarios, including when only the outcome causes missingness. It is not clear whether a complete case logistic regression will give a biased estimate of the odds ratio if missingness depends on a continuous outcome but this outcome is dichotomised for the analysis – a common situation in epidemiology. Methods We investigated this using a simulation study and data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK birth cohort. We also examined whether any bias could be reduced by including a proxy for the binary outcome as an auxiliary variable in multiple imputation. Results There was negligible bias in the exposure odds ratio when the probability of being a complete case was independently associated with the exposure and (continuous) outcome but important bias in the presence of an interaction, particularly at high levels of missing data. Inclusion of the proxy led to significant bias reductions when this had high sensitivity and specificity in relation to the study outcome. Conclusions The robustness of logistic regression to missing data is maintained even when the outcome is a binary version of a continuous outcome. Bias due to an interaction between the exposure and outcome in their effect on selection could be reduced by including proxies for the missing outcome as auxiliary variables in MI. If such proxies are available, we would recommend using MI over a complete case analysis because, in practice, it would be difficult to rule out an interaction.

Revisiting Union: A New Perspective on an Old Outcome

Background: To present the inherent and unique challenges associated with utilizing fracture healing as an outcome measure in foot and ankle orthopedics, specifically the statistical methods used in assessing time to union. Methods: In a previously published manuscript assessing the effect of delayed weightbearing on time to union following intramedullary (IM) screw fixation of Jones (Zone 2 fifth metatarsal base) fractures, patients were divided into early weightbearing (EWB, n=20) and delayed weightbearing (DWB, n=21) cohorts (within or beyond 2 weeks, respectively). Time to union was determined and compared between the 2 cohorts using cumulative link model analysis, with delayed union (12.5 weeks) defined from established literature. Results: Cumulative link model analysis demonstrated no significant differences in time to union (EWB: 25% by 6th week, 55% by 12th week; DWB: 33% by 6th week, 43% by 12th week; P = .819) or delayed unions (EWB, 20% vs DWB, 24%; P > .999). Conclusion: Our analysis using cumulative link models, or ordinal regression, in the statistical analysis of time to union, determined that that early weightbearing following IM screw fixation in Jones fractures appeared to be safe without delaying fracture healing. This statistical approach can be considered when describing a continuous outcome captured by infrequent observations.

Estimating and evaluating personalized treatment recommendations from randomized trials with ptr

One of the targets of personalized medicine is to provide treatment recommendations using patient characteristics. We present the command ptr, which both predicts a personalized treatment recommendation algorithm and evaluates its effectiveness versus an alternative regime, using randomized trial data. The command allows for multiple (continuous or categorical) biomarkers and a binary or continuous outcome. Confidence intervals for the evaluation parameter are provided using bootstrap resampling.

Dissatisfaction with Local Medical Services for Middle-Aged and Elderly in China: What Is Relevant?

As violent clashes between doctors and patients in China intensify, patient dissatisfaction has been identified as a major concern in the current healthcare reform in China. This study aims to investigate the main determinants of dissatisfaction with local medical services attributable to middle-aged and elderly characteristics and identify areas for improvement. A total of 14,263 rural participants and 4898 urban participants were drawn from the China Health and Retirement Longitudinal Study in 2018. Dissatisfaction was measured by two methods: binary outcome (1 = Dissatisfaction; 0 = No) demonstrated the risk of occurring dissatisfaction among various characteristics, and continuous outcome (ranges from score 1 to 5) showed the degree. The mean score of dissatisfaction was 2.73 ± 1.08. Sixteen percent of rural participants and 19% of urban participants reported dissatisfaction with local medical services, respectively. The multilevel analyses demonstrated that participants’ utilization of paid family doctor services decreased the risk of occurring dissatisfaction; dissatisfaction was less focused on females; having chronic diseases increased the risk of dissatisfaction. This study suggests promotion of family doctor services can effectively reduce middle-aged and elderly dissatisfaction with the local medical services. In addition, more attention should be focused on males and middle-aged and elderly with chronic diseases in order to decrease dissatisfaction.

Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach

Appropriate handling of aggregate missing outcome data is necessary to minimise bias in the conclusions of systematic reviews. The two-stage pattern-mixture model has been already proposed to address aggregate missing continuous outcome data. While this approach is more proper compared with the exclusion of missing continuous outcome data and simple imputation methods, it does not offer flexible modelling of missing continuous outcome data to investigate their implications on the conclusions thoroughly. Therefore, we propose a one-stage pattern-mixture model approach under the Bayesian framework to address missing continuous outcome data in a network of interventions and gain knowledge about the missingness process in different trials and interventions. We extend the hierarchical network meta-analysis model for one aggregate continuous outcome to incorporate a missingness parameter that measures the departure from the missing at random assumption. We consider various effect size estimates for continuous data, and two informative missingness parameters, the informative missingness difference of means and the informative missingness ratio of means. We incorporate our prior belief about the missingness parameters while allowing for several possibilities of prior structures to account for the fact that the missingness process may differ in the network. The method is exemplified in two networks from published reviews comprising a different amount of missing continuous outcome data.