ObjectiveTo explore the clinical characteristics of different types of prolactinoma and the therapeutic effect of bromocriptine.MethodsThe medical records of patients with prolactinoma treated by bromocriptine from January 2010 to December 2016 were retrospectively analyzed, and the patients were followed up.ResultsA total of 106 cases of prolactinoma were included, in whom 67 were microprolactinomas, 31 were macroprolactinomas, and 8 were giant prolactinoma. There were differences in the distributions of gender and age, prolactin level, clinical manifestations and the effective dose of bromocriptine among the three groups (P<0.05). After the treatment of bromocriptine, the level of serum prolactin was restored to normal in 61 cases in microprolactinoma group, 26 cases in macroprolactinoma group and 6 cases in giant prolactioma group. For improvement of the main symptoms, there were 63 patients in microprolactinoma group, 27 in macroprolactinoma group and 6 in giant prolactioma group. Furthermore, the shrink or disappearance was achieved in 28 patients in microprolactinoma group, 23 in macroprolactinoma group, and 8 in giant prolactioma group. The statistical results showed no significant difference in normal prolactin level and improvement of symptoms among the three groups (P>0.05), but the reduction of tumor volume were statistically different (P<0.05).ConclusionsMicroprolactinomas and macroprolactinomas are mostly seen in childbearing-aged women with main manifestations of menstrual disorders and lactation, while giant prolactinomas are mostly seen in middle-aged men, with main manifestations of headaches and visual field disorders. Bromocriptine has a good effect on prolactin adenomas with various sizes. Therefore bromocriptine should be the first choice for different types of prolactinomas.
Causal inference is one of the main goals of medical research. However, due to the lack of an in-depth understanding of the theory of causal inference, researchers tend to blindly use multiple statistical methods to analyse the same question to enhance the credibility of the results, which leads to problems in interpretation of the analysis results. Based on the three basic concepts of potential outcomes, causal effects, and distributive mechanisms of the causal inference counterfactual framework, this paper introduced six main target effects in causal inference and discussed their comparability to help researchers understand the principle of causal inference and correctly interpret and compare research results to avoid misleading conclusions.
Randomized controlled trials are the gold standard for evaluating the effects of medical interventions, primarily providing estimates of the average effect of an intervention in the overall study population. However, there may be significant differences in the effect of the same intervention across sub-populations with different characteristics, that is, treatment heterogeneity. Traditional subgroup analysis and interaction analysis tend to have low power to examine treatment heterogeneity or identify the sources of heterogeneity. With the recent development of machine learning techniques, causal forest has been proposed as a novel method to evaluate treatment heterogeneity, which can help overcome the limitations of the traditional methods. However, the application of causal forest in the evaluation of treatment heterogeneity in medicine is still in the beginning stage. In order to promote proper use of causal forest, this paper introduces its purposes, principles and implementation, interprets the examples and R codes, and highlights some attentions needed for practice.
When there is a lack of head-to-head randomized controlled trials between two interventions of interest, indirect comparison methods can be employed to estimate their relative treatment effects. Matching-adjusted indirect comparison (MAIC) is a population-adjusted indirect comparison method that utilizes a weighting approach. Unanchored MAIC is particularly applicable in scenarios where a common control group between the two interventions is not available. This article introduces the background and mathematical theory of unanchored MAIC, along with a demonstration of the operational steps and interpretation of results through an application example.
Rolling enrollment is a common method for participant recruitment in medical practice. In the longitudinal data, where researchers are often interested in outcomes occurring after a certain period of treatment, the definition of causal effects differs from that in the cross-sectional data. It poses new challenges for the application of matching methods in the longitudinal studies. Longitudinal matching is an extension of matching methods in longitudinal studies involving static interventions such as rolling enrollment. Currently, longitudinal matching methods are widely applied in the comparative effectiveness research. This article elucidates the fundamental principles, applicable conditions, code implementation, and application instances of four longitudinal matching methods through theoretical discussions and empirical illustrations. It provides methodological references for estimating causal effects in longitudinal data analysis.