Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all combinations of possible controls as well as specified functional form issues, variable definitions, standard error calculations, and estimation commands. This allows analysts to present their core, preferred estimate in the context of a distribution of plausible estimates. Second, we develop a model influence analysis showing how each model ingredient affects the coefficient of interest. This shows which model assumptions, if any, are critical to obtaining an empirical result. We demonstrate the architecture and interpretation of multimodel analysis using data on the union wage premium, gender dynamics in mortgage lending, and tax flight migration among U.S. states. These illustrate how initial results can be strongly robust to alternative model specifications or remarkably dependent on a knife-edge specification.

Objective To develop and internally validate a multivariable predictive model for days with new‐onset migraine headaches based on patient self‐prediction and exposure to common trigger factors. Background Accurate real‐time forecasting of one’s daily risk of migraine attack could help episodic migraine patients to target preventive medications for susceptible time periods and help decrease the burden of disease. Little is known about the predictive utility of common migraine trigger factors. Methods We recruited adults with episodic migraine through online forums to participate in a 90‐day prospective daily‐diary cohort study conducted through a custom research application for iPhone. Every evening, participants answered questions about migraine occurrence and potential predictors including stress, sleep, caffeine and alcohol consumption, menstruation, and self‐prediction. We developed and estimated multivariable multilevel logistic regression models for the risk of a new‐onset migraine day vs a healthy day and internally validated the models using repeated cross‐validation. Results We had 178 participants complete the study and qualify for the primary analysis which included 1870 migraine events. We found that a decrease in caffeine consumption, higher self‐predicted probability of headache, a higher level of stress, and times within 2 days of the onset of menstruation were positively associated with next‐day migraine risk. The multivariable model predicted migraine risk only slightly better than chance (within‐person C‐statistic: 0.56, 95% CI: 0.54, 0.58). Conclusions In this study, episodic migraine attacks were not predictable based on self‐prediction or on self‐reported exposure to common trigger factors. Improvements in accuracy and breadth of data collection are needed to build clinically useful migraine prediction models.

The substantial amount of limited duty for lower limb musculoskeletal injuries among soldiers highlights the need for improvement in training-related injury screening, prevention and timely treatment with particular attention to knee injuries. The excessive impact of lower limb injuries on female soldiers' occupational functions should be a surveillance priority in the current environment of expanding gender-integrated training.

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