Gastroesophageal monitoring is limited to 96 hours by the current technology. This work presents a computational model to investigate symptom association in gastroesophageal reflux disease with larger data samples proving important deficiencies of the current methodology that must be taking into account in clinical evaluation. A computational model based on Monte Carlo analysis was implemented to simulate patients with known statistical characteristics Thus, sets of 2000 10-day-long recordings were simulated and analyzed using the symptom index (SI), the symptom sensitivity index (SSI), and the symptom association probability (SAP). Afterwards, linear regression was applied to define the dependency of these indexes with the number of reflux, the number of symptoms, the duration of the monitoring, and the probability of association. All the indexes were biased estimators of symptom association and therefore they do not consider the effect of chance: when symptom and reflux were completely uncorrelated, the values of the indexes under study were greater than zero. On the other hand, longer recording reduced variability in the estimation of the SI and the SSI while increasing the value of the SAP. Furthermore, if the number of symptoms remains below one-tenth of the number of reflux episodes, it is not possible to achieve a positive value of the SSI. A limitation of this computational model is that it does not consider feeding and sleeping periods, differences between reflux episodes or causation. However, the conclusions are not affected by these limitations. These facts represent important limitations in symptom association analysis, and therefore, invasive treatments must not be considered based on the value of these indexes only until a new methodology provides a more reliable assessment.