Data in Brief 31 (2020) 105904 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article Data of a meta-analysis on pharmacological treatment strategies for lowering prolactin in people with a psychotic disorder and hyperprolactinaemia Javier Labad a,b,c,∗, Itziar Montalvo a,c, Alexandre González-Rodríguez a, Clemente García-Rizo c,d, Benedicto Crespo-Facorro c,e, José Antonio Monreal a,b,c, Diego Palao a,b,c a Department of Mental Health, Hospital Universitari Parc Taulí, I3PT. Sabadell, Barcelona, Spain b Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona. Cerdanyola, Spain c Centro de Investigación Biomédica en Red (CIBERSAM), Spain d Barcelona Clinic Schizophrenia Unit, Hospital Clínic, IDIBAPS. University of Barcelona, Spain e University Hospital Virgen del Rocío, IBiS, Departament of Psychiatry, University of Sevilla, Sevilla, Spain a r t i c l e i n f o a b s t r a c t Article history: The data presented in this paper describe supplementary Received 27 April 2020 material to the article entitled “Pharmacological treatment Revised 13 June 2020 strategies for lowering prolactin in people with a psychotic Accepted 15 June 2020 disorder and hyperprolactinaemia: a systematic review and Available online 23 June 2020 meta-analysis” [1]. Although raw data was published on the original article, additional raw data has been included in the Keywords: current paper (new tables with socio-demographic and clin- Hyperprolactinaemia Schizophrenia ical characteristics of the samples of the studies included in Antipsychotics the systematic review). Supplementary data also include the Aripiprazole PICO scheme of the systematic review, PRISMA checklist, flow Dopamine agonists diagram, an explanation of the method for obtaining pro- Switching lactin concentrations from published figures when data was only available in figures, list of the selected studies, risk of bias summary of all five randomized clinical trials evaluating the addition of aripiprazole for lowering prolactin (included DOI of original article: 10.1016/j.schres.2020.04.031 ∗ Corresponding author. E-mail address: jlabad@tauli.cat (J. Labad). https://doi.org/10.1016/j.dib.2020.105904 2352-3409/© 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) 2 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 in the meta-analysis in the original article). Extra analy- ses, figures and R code of the meta-analysis have been also included. Meta-analysis of randomized clinical trials (RCTs) considering aripiprazole addition for lowering prolactin in people with a psychotic disorder and hyperprolactinaemia were conducted with two softwares: 1) R and the metaphor package (for the meta-analysis of the primary outcome [pro- lactin reduction]); 2) MedCalc version 18.11 (for the meta- analysis of the secondary outcome [withdrawal rates]). Data from a sensitivity analysis (repeating the meta-analysis with only placebo-controlled RCTs) has been also included in the current article. © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) Specifications Table Subject Psychiatry and Mental Health; Endocrinology, Diabetes and Metabolism Specific subject area Management of antipsychotic-induced hyperprolactinaemia Type of data Table Figure Box Code List How data were acquired Four electronic bibliographic databases were searched: PubMed, Scopus, PsycINFO and ClinicalTrials.gov. The following search strategy was used: prolactin AND (switch∗ OR aripiprazole OR bromocriptine OR cabergoline OR "dopamine agonist" OR metformin) AND (schizophrenia OR schizoaffective OR psychosis OR psychotic OR bipolar∗ ). Language was restricted to those articles written in English, Spanish, German or French. Studies published between January 1980 and March 2020 were considered for inclusion. The systematic review contained trials including patients with psychotic disorders (schizophrenia, schizoaffective disorder, bipolar disorder, brief psychotic disorder, delusional disorder, or psychotic disorder not otherwise specified) who had hyperprolactinaemia. Clinical trials and observational studies were included if they assessed the efficacy of any of the following four therapeutic options for lowering prolactin (switching antipsychotic treatment, adding aripiprazole, adding other dopamine agonists [e.g., cabergoline, bromocriptine], or adding metformin) and had information on prolactin levels at baseline and after follow-up that would allow the calculation of the effect size for the reduction in prolactin concentrations. Exclusion criteria were case reports or studies of less than 5 cases, and studies assessing prolactin changes in psychotic patients for whom the main reason for the therapeutic strategy was not hyperprolactinaemia (e.g., switching in treatment-resistant patients). Although the systematic review included clinical trials and observational studies, only randomized clinical trials (RCTs) were considered for conducting a meta-analysis. The primary outcome was defined as the reduction in prolactin concentrations. The software R and the package metafor were used for conducting the meta-analysis on the primary outcome (reduction in prolactin levels). Hedges g’ was used as the effect size for prolactin reduction. An additional meta-analysis for withdrawal rates in placebo-controlled RCTs was performed with MedCalc (version 18.11). Data format Raw data of prolactin concentrations and withdrawal rates are included in the original article Additional raw data on the current article Analyzed Filtered Methodological explanations J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 3 (continued on next page) Parameters for data We aimed to identify clinical trials and observational studies considering four collection therapeutic options for lowering prolactin in people with a psychotic disorder and hyperprolactinaemia: switching antipsychotic treatment, adding aripiprazole, adding other dopamine agonists (e.g., cabergoline, bromocriptine), or adding metformin. Description of data The PRISMA group guidelines were followed. A standardised, pre-piloted form was collection used to extract data from the included studies for assessment of the study quality and evidence synthesis. Two review authors extracted data independently, and discrepancies were identified and resolved through discussion (with two additional authors when necessary). Missing data were requested from study authors. In a few cases in which data were only available in figures (either as individual concentrations or aggregated with mean and standard deviation), we extracted this information from figures using the procedure explained in Box 1. Data source location Institution: Parc Taulí Hospital Universitari City: Sabadell Country: Spain Data accessibility With the article Related research article Javier Labad, Itziar Montalvo, Alexandre González-Rodríguez, Clemente García-Rizo, Benedicto Crespo-Facorro, José Antonio Monreal, Diego Palao. Pharmacological treatment strategies for lowering prolactin in people with a psychotic disorder and hyperprolactinaemia: a systematic review and meta-analysis. Schizophrenia Research. 2020. Value of the data These data describe the scientific evidence published between 1980 and 2020 on four phar- macologic strategies (switching antipsychotic treatment, adding aripiprazole, adding other dopamine agonists [e.g., cabergoline, bromocriptine], or adding metformin) for reducing prolactin concentrations in people with psychotic disorders and hyperprolactinaemia. The data will be useful for psychiatrists, endocrinologists and other health professionals treat- ing patients with psychotic disorders who may develop hyperprolactinaemia. The data is useful for being considered in future guidelines for treating hyperprolactinaemia in patients with psychotic disorders who usually require long-term antipsychotic treat- ment that might increase prolactin levels. Data description The data presented in this paper describe supplementary material to the original article [1]. Data will be described in the same order of appearance in the text of the article [1]. Table 1 represents the PICO scheme of the systematic review. Table 2 comprises the PRISMA Checklist and references all items and pages in the original article. Fig. 1 represents the flow diagram of included studies. Box 1 explains the procedure for obtaining prolactin concentrations from figures when infor- mation on the text or tables was not available. Code 1 indicates the R code used for calculating the foster plot and funnel plot of the meta- analysis dealing with the primary outcome (prolactin reduction). Code 2 indicates the R code used for calculating the foster plot and funnel plot of the sen- sitivity meta-analysis including only placebo-controlled RCTs dealing with the primary outcome (prolactin reduction). List 1 describes all the studies included in the systematic review. Tables 3, 4 and 5 show raw data on selected articles dealing with socio-demographic and clinical characteristics (Table 3: Switching studies; Table 4: Aripiprazole addition studies; Table 5: Studies focused on the addition of other dopamine agonists). 4 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 Table 1 PICO scheme of the systematic review. Patient, population or Intervention Comparison Outcome Problem What are the What interventions are we What is the alternative to What are the relevant characteristics of the considering? the intervention? outcomes? patients or population? Psychotic disorders, Four strategies for lowering Placebo or maintaining Reduction in prolactin including schizophrenia, prolactin: antipsychotic treatment plasma concentrations. bipolar disorder and Switching antipsychotics (randomized clinical schizophrenia-spectrum Adding aripiprazole trials with comparison psychotic disorders with Adding other dopamine arms will be included in hyperprolactinaemia. agonists (e.g., the meta-analysis). cabergoline, Uncontrolled studies bromocriptine) (with comparator) will Adding metformin be included in the systematic review if there is information regarding changes in prolactin with the strategy. Fig. 1. Flow diagram of included studies. J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 5 Table 2 PRISMA Checklist of the systematic review. Section/topic # Checklist item Reported on page # Title Title 1 Identify the report as a systematic review, meta-analysis, or both. 1 Abstract Structured 2 Provide a structured summary including, as applicable: background; objectives; 1 summary data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number. Introduction Rationale 3 Describe the rationale for the review in the context of what is already known. 1–2 Objectives 4 Provide an explicit statement of questions being addressed with reference to 2 participants, interventions, comparisons, outcomes, and study design (PICOS). Table 1 DIB Methods Protocol and 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., Web 2 registration address), and, if available, provide registration information including registration number. Eligibility 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report 2 criteria characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. Information 7 Describe all information sources (e.g., databases with dates of coverage, contact 2 sources with study authors to identify additional studies) in the search and date last searched. Search 8 Present full electronic search strategy for at least one database, including any 2 limits used, such that it could be repeated. Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in 2 systematic review, and, if applicable, included in the meta-analysis). Fig. 1 DIB Data collection 10 Describe method of data extraction from reports (e.g., piloted forms, 2 process independently, in duplicate) and any processes for obtaining and confirming data from investigators. Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding 2 sources) and any assumptions and simplifications made. Risk of bias in 12 Describe methods used for assessing risk of bias of individual studies (including 3 individual specification of whether this was done at the study or outcome level), and how studies this information is to be used in any data synthesis. Summary 13 State the principal summary measures (e.g., risk ratio, difference in means). 3 measures Synthesis of 14 Describe the methods of handling data and combining results of studies, if done, 3 results including measures of consistency (e.g., I2 ) for each meta-analysis. Section/topic # Checklist item Reported on page # Risk of bias 15 Specify any assessment of risk of bias that may affect the cumulative evidence 3 across studies (e.g., publication bias, selective reporting within studies). Additional 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, 3 analyses meta-regression), if done, indicating which were pre-specified. RESULTS Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the Fig. 1 DIB review, with reasons for exclusions at each stage, ideally with a flow diagram. Study 18 For each study, present characteristics for which data were extracted (e.g., study 3, List 1 characteristics size, PICOS, follow-up period) and provide the citations. DIB, Tables OA Risk of bias 19 Present data on risk of bias of each study and, if available, any outcome level 4 within studies assessment (see item 12). Table 9 DIB Results of 20 For all outcomes considered (benefits or harms), present, for each study: (a) 4, Tables & individual simple summary data for each intervention group (b) effect estimates and Figure OA studies confidence intervals, ideally with a forest plot. Synthesis of 21 Present results of each meta-analysis done, including confidence intervals and 4 results measures of consistency. Risk of bias 22 Present results of any assessment of risk of bias across studies (see Item 15). Table 9 DIB across studies (continued on next page) 6 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 Table 2 (continued) Section/topic # Checklist item Reported on page # Additional 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, 4 analysis meta-regression [see Item 16]). Discussion Summary of 24 Summarize the main findings including the strength of evidence for each main 6 evidence outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers). Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at 7–8 review-level (e.g., incomplete retrieval of identified research, reporting bias). Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and 8 implications for future research. Funding Funding 27 Describe sources of funding for the systematic review and other support (e.g., Role supply of data); role of funders for the systematic review. funding source OA Pages refer to the original article [1]; Abbreviations: OA= Original article; DIB= Data in Brief.Fig. 1. Flow diagram of included studies. Fig. 2. Funnel plot (meta-analysis of prolactin reduction). Tables 6, 7 and 8 show raw data on diagnostic criteria for psychotic disorders and exclusion criteria related to reproductive or medical conditions that can alter prolactin levels (Table 6: Switching studies; Table 7: Aripiprazole addition studies; Table 8: Studies focused on the addi- tion of other dopamine agonists). Table 9 report the risk of bias summary of all five randomized clinical trials evaluating the addition of aripiprazole for lowering prolactin. Fig. 2 represents the funnel plot of the meta-analysis exploring changes in prolactin concen- trations with the addition of aripiprazole. This figure has been generated with R. J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 7 Table 3 Socio-demographic and clinical characteristics of the samples of studies dealing with switching antipsychotic treatment to lower prolactin. Reference Country Ethnicity Age (years) Duration of Duration of Substance Symptoms Mean (SD) illness treatment use (baseline visit) (years) with PRL- Mean (SD) elevating antipsy- chotic Switch to aripipra- zole Yoon et al., Korea NR 34.7 (8.0) 8.4 (7.4) >1 month substance stable over 2 2016 abuse or months (no dependence changes in were CGI-S) excluded Chen et al. China NR 48.3 (8.2) 17.9 (9.1) NR NR CGI-S: 5.1 (0.9) 2011 Lu et al., China NR 31.7 (9.3) 5.5 (5.4) NR NR CGI-S: 3.4 (0.5) 2008 (Taiwan) PANSS total: 53.4 (7.7) Lee et al., Korea NR 35.0 (7.4) 2.6 (2.3) >2 months NR PANSS total: 2006 6.1 (1.8) 45.0 (9.0) Switch to olanzapine Kinon USA 48.1% 40.0 (10.7) 14.3 (7.9) NR† Alcohol or CGI-S: 3.4 (0.7) et al., 2006 caucasian substance PANSS total: 40.7% abuse were 57.5 (16.0) african excluded 7.4% asian 3.7% hispanic Switch to quetiapine Nakajima Japan NR 55.0 (12.0) 26.6 (11.6) >1 month NR PANSS positive: et al., 2005 12.8 (4.7) PANSS negative: 18.7 (7.0) PANSS general psychopathol- ogy: 41.2 (9.9) Takahashi Japan NR 25.7 (5.1) NR 14.6 (5.3) NR BPRS: 42.4 (7.6) et al., 2003 Switch to paliperi- done Montalvo Spain NR 26.6 (5.9) <5 years >6 months NR PANSS positive: et al., 2013 (early 10.2 (3.5) psychosis) PANSS negative: 14.2 (4.4) PANSS general psychopathol- ogy: 25.5 (4.5) (continued on next page) 8 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 Table 3 (continued) Reference Country Ethnicity Age (years) Duration of Duration of Substance Symptoms Mean (SD) illness treatment use (baseline visit) (years) with PRL- Mean (SD) elevating antipsy- chotic Switch to blo- nanserin Kawabe Japan and NR 53.9 (8.4) 31.7 (9.4) NR NR BPRS: 47.8 (8.4) et al., 2013 South Korea Abbreviations: SD= Standard deviation; NR= Not reported; CGI-S= Clinical Global Impression-Severity scale; PANSS= Positive and Negative Syndrome Scale; BPRS= Brief Psychiatric Rating Scale. † In the study by Kinon et al. (2006), patients who were switched to olanzapine had a mean (SD) duration of previous drug therapy of 11.9 (8.4) years. However, it was not specified the duration of the last antipsychotic drug treatment. Fig. 3. Forest plot of the sensitivity meta-analysis of placebo-controlled RCTs studying adjunctive aripiprazole. Fig. 3 represents the forest plot of the sensitivity meta-analysis including only placebo- controlled RCTs that studied the effects of adjunctive aripiprazole on prolactin reduction. This figure has been generated with R. Fig. 4 represents the funnel plot of the sensitivity meta-analysis including only placebo- controlled RCTs for prolactin reduction. This figure has been generated with R. Fig. 5 represents the forest plot of the meta-analysis of withdrawal rates in placebo-controlled RCTs studying adjunctive aripiprazole. This figure has been generated with MedCalc. Table 10 describes the data regarding the meta-analysis of withdrawal rates in placebo- controlled RCTs studying adjunctive aripiprazole. This table has been generated with MedCalc. Fig. 6 represents the funnel plot (meta-analysis of withdrawal rates in placebo-controlled RCTs studying adjunctive aripiprazole). This figure has been generated with MedCalc. Experimental design, materials, and methods We conducted a systematic review of 4 electronic bibliographic databases: PubMed, Sco- pus, PsycINFO and ClinicalTrials.gov. We aimed to identify clinical trials and observational stud- J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 9 Table 4 Socio-demographic and clinical characteristics of the samples of studies dealing with the addition of aripiprazole to lower prolactin. Reference Country Ethnicity Age (years) Duration of Duration of Substance Symptoms Mean (SD) illness treatment use (baseline visit) (years) with PRL- Mean (SD) elevating antipsy- chotic Kelly et al., USA 66% african 37.8 (8.9) NR† >2 months Substance Clinical stability 2018 american abuse was (psychiatrist 33% white an consideration) exclusion was an criterion inclusion criterion BPRS: 35.4 (10.0) CGI-S: 3.8 (0.9) Fujioi et al., Japan NR 41.3 (8.0) 15.2 (8.3) >2 months NR BPRS: 44.1 2017 (12.5) CGI-S: 4.0 (0.9) Yoon et al., Korea NR 35.8 (7.1) 11.1 (7.3) >1 month Substance Clinically stable 2016 abuse or over 2 months dependence (no changes in was an CGI-S) exclusion criterion Qiaio et al., China NR 34.1 (7.0) 14.3 (20.9) 1 month Substance PANSS total 2016 dependence between 60 was an and 120 exclusion (inclusion criterion criterion) PANSS total: 66.8 (14.2) Chen et al., China NR 34.4 (8.8) 10.38 (7.0) >6 weeks Substance Clinical stability 2015 9.2 (3.3) use was an (PANSS<70) weeks exclusion was an criterion inclusion criterion Zhao et al., China NR 28.9 (7.8) 4.2 (4.2) >2 months Substance Stable 2015 months use psychiatric (including condition was alcohol an inclusion consump- criterion tion) was PANSS total: an 57.5 (13.5) exclusion criterion Ziadi Trives Spain 100% white 42.0 (11.4) NR >6 months Substance Clinical stability et al., 2013 use was an (no changes in exclusion treatment or criterion ER visits in the previous 6 months) was an inclusion criterion CGI-S: 3.7 (1.3) (continued on next page) 10 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 Table 4 (continued) Reference Country Ethnicity Age (years) Duration of Duration of Substance Symptoms Mean (SD) illness treatment use (baseline visit) (years) with PRL- Mean (SD) elevating antipsy- chotic Van Kooten 83.3% white 47.6 (13.6) 21.3 (12.2) >12 NR NR et al., 2011 Netherlands 16.6% asian months 43.2 (36) months Yasui- Japan 44.5 (9.5) 18.9 (13.4) >3 months NR PANSS total: Furukori 72.9 (25.0) et al., 2010 PANSS positive: 11.9 (3.5) PANSS negative: 19.6 (5.2) Chen et al., China NR 37.4 (9.0) 12.6 (9.1) >1 month NR PANSS total: 2010 (Tai- 9.3 (10.4) 90.2 (57.2) wan) Chen et al., China NR NR NR NR NR NR 2009 Shim et al., Korea NR 38.2 (5.3) 15.3 (6.1) >3 months Substance Clinical stability 2007 use was an was an exclusion inclusion criterion criterion BPRS: 45–5 (12.3) CGI-S: 4.2 (0.9) Abbreviations: SD= Standard deviation; NR= Not reported; CGI-S= Clinical Global Impression-Severity scale; PANSS= Positive and Negative Syndrome Scale; BPRS= Brief Psychiatric Rating Scale; ER= Emergency room. Fig. 4. Funnel plot of the sensitivity meta-analysis of placebo-controlled RCTs for prolactin reduction. J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 11 Table 5 Socio-demographic and clinical characteristics of the samples of studies dealing with the addition of dopamine agonists to lower prolactin. Reference Country Ethnicity Age (years) Duration of Duration of Substance Symptoms Mean (SD) illness treatment use (baseline (years) with PRL- visit) Mean (SD) elevating antipsy- chotic Cabergoline addition Kalkavoura Greece NR 43.6 (9.8) 21.8 (11.9) NR† Alcohol PANSS et al., 2013 abuse was total: 62.9 an (2.5) exclusion criterion. Coronas Spain NR 31.2 (5.0) NR NR NR Clinical et al., 2012 stable before starting cabergoline BPRS: 21.7 (4.9) Cavallaro Italy NR 33.7 (5.6) NR >6 months NR NR et al., 2004 Bromocriptine addition Yuan et al., China NR 31.1 (7.9) 3.2 (3.5) >6 months NR NR 2008 Bliesener Germany NR 20–45 NR >4 months NR NR et al., 2004 years Terguride addition Hashimoto Japan NR 42.9 (10.6) 15.0 (5.8) 26.7 (11.9) NR NR et al., 2014 months Abbreviations: SD= Standard deviation; NR= Not reported; CGI-S= Clinical Global Impression-Severity scale; PANSS= Positive and Negative Syndrome Scale; BPRS= Brief Psychiatric Rating Scale. † In the study Kalkavoura et al. (2013), patients were under antipsychotic treatment for at least 5 years before the inclusion in the study. However, it was not specified the duration of the last antipsychotic drug treatment. Fig. 5. Meta-analysis of withdrawal rates in placebo-controlled RCTs studying adjunctive aripiprazole. 12 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 Table 6 Diagnostic criteria for psychotic disorders and exclusion criteria for conditions that can alter prolactin levels of studies dealing with switching antipsychotics to lower prolactin. Reference Diagnostic criteria for Exclusion criteria (reproductive or medical conditions that psychotic disorders can alter prolactin levels)† Switch to aripiprazole Yoon et al., 2016 DSM-IV pregnant or lactating, other diseases that can elevate the prolactin level such as Cushing disease, primary hypothyroidism, liver cirrhosis, renal failure, or prolactinoma Chen et al. 2011 NR NR Lu et al., 2008 DSM-IV endocrine disease, gynecological problems, or other major medical illnesses . Lee et al., 2006 DSM-IV medical maladies (e.g., thyroid or gynecological diseases) Switch to olanzapine Kinon et al., 2006 DSM-IV suspicious MRI scan, pregnant or nursing, bilateral oophorectomy or hysterectomy during or preceding their peri–menopause, treatment with reproductive hormone therapy, serious unstable illnesses (hepatic, renal, gastroenterologic, respiratory, cardiovascular, endocrinologic, neurologic, immunologic, or hematologic disease), seizures, current agranulocytosis, any other medication with primarily central nervous system activity or that would elevate prolactin Switch to quetiapine Nakajima et al., 2005 DSM-IV NR Takahashi et al., 2003 DSM-IV NR Switch to paliperidone Montalvo et al., 2013 NR NR Switch to blonanserin Kawabe et al., 2013 DSM-IV NR Abbreviations: DSM-IV= Diagnostic and Statistical Manual of Mental Disorders – IV edition; NR= Not reported. † Information regarding substance use and exclusion criteria due to substance use disorders has been included in Table 3. Fig. 6. Funnel plot of the meta-analysis of withdrawal rates in placebo-controlled RCTs studying adjunctive aripiprazole. J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 13 Table 7 Diagnostic criteria for psychotic disorders and exclusion criteria for conditions that can alter prolactin levels of all studies dealing with the addition of aripiprazole to lower prolactin. Reference Diagnostic Exclusion criteria (reproductive or medical conditions that can alter prolactin criteria for levels)† psychotic disorders Aripiprazole addition Kelly et al., DSM-IV postmenopause, pregnancy or current post-pregnancy lactation, history of a 2018 pituitary tumor (microadenoma, macroadenoma, neoplasm) or Cushing disease, medications that may affect prolactin or cause sexual dysfunction through dopaminergic effects (eg, metoclopramide, methyldopa, reserpine, amoxapine, droperidol, prochlorperazine, promethazine, bromocriptine, and cabergoline) Fujioi et al., DSM-IV menopause, pregnancy, or breast-feeding 2017 Yoon et al., DSM-IV pregnant or lactating; other diseases that can elevate the prolactin level such 2016 as Cushing disease, primary hypothyroidism, liver cirrhosis, renal failure, or prolactinoma Qiaio et al., DSM-IV neurologic disorder, severe head trauma, or any unstable medical condition 2016 Chen et al., DSM-IV significant medical illnesses, such as liver or renal dysfunction, cardiovascular 2015 disease, organic brain disorder; pregnant or lactating; other medications than risperidone, anticholinergics or benzodiazepines, such as other antipsychotics, antidepressants, or mood stabilizers etc., which may alter prolactin levels Zhao et al., DSM-IV significant illnesses including severe cardiovascular, hepatic, or renal disease; 2015 history of immunosuppression; current or recent radiation or chemotherapy treatment for cancer; pregnancy or breastfeeding; other conditions (e.g., thyroid or gynecological diseases) that could affect serum prolactin levels Ziadi Trives NR intercurrent illness(es) that affect sexual function; other antipsychotics than et al., 2013 risperidone; drugs that increase prolactin levels during the 6 months before their inclusion in the study (such as selective serotonin reuptake inhibitors), or any other treatment able to interfere with the adenohypophyseal system (oral contraceptives, tricyclic antidepressants, venlafaxine, mood stabilizers, antihypertensives, or H2 receptors’ antagonists) Van Kooten DSM-IV tuberous sclerosis‡ et al., 2011 Yasui-Furukori DSM-IV oral contraceptives or estrogen supplemental therapy et al., 2010 Chen et al., DSM-IV NR 2010 Chen et al., DSM-IV NR 2009 Shim et al., DSM-IV medical and/or neurological illness 2007 Abbreviations: DSM-IV= Diagnostic and Statistical Manual of Mental Disorders – IV edition; NR= Not reported. † Information regarding substance use and exclusion criteria due to substance use disorders has been included in Table 4. ‡ Although no exclusion criteria for medical conditions were reported in the study, the authors specify that one pa- tient with tuberous sclerosis was removed from the study because it could not be excluded that the tuberous sclerosis produced the prolactin elevation. ies considering four therapeutic options for lowering prolactin: switching antipsychotic treat- ment, adding aripiprazole, adding other dopamine agonists (e.g., cabergoline, bromocriptine), or adding metformin. The following search strategy was used: prolactin AND (switch∗ OR aripipra- zole OR bromocriptine OR cabergoline OR "dopamine agonist" OR metformin) AND (schizophre- nia OR schizoaffective OR psychosis OR psychotic OR bipolar∗ ). Language was restricted to those articles written in English, Spanish, German or French. Studies published between Jan- uary 1980 and March 2020 were considered for inclusion. The protocol was registered in PROS- PERO (CRD42018103466). Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [2] were followed. 14 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 Table 8 Diagnostic criteria for psychotic disorders and exclusion criteria for conditions that can alter prolactin levels of all studies dealing with the addition of dopamine agonists to lower prolactin. Diagnostic criteria for psychotic Exclusion criteria (reproductive or medical conditions that can alter Reference disorders prolactin levels)† Cabergoline addition Kalkavoura DSM-IV medication implicated in the increase of PRL levels (tricyclic et al., 2013 antidepressants, selective serotonin reuptake inhibitor, monoamine oxidase, a-methyldopa, estrogens, oral contraceptives), patients receiving medication implicated in sexual dysfunction (antihypertensives, diuretics, hormones, antifungoral therapy, Parkinson’s disease), patients with hyperprolactinaemia due to pregnancy or breastfeeding, hypothyroidism, Cushing’s syndrome, cirrhosis, renal failure, meningioma, craniopharyngioma, sarcoidosis, autoimmune disease, tumors of the hypothalamus, acromegalic dysplasia, prolactinoma, diabetes mellitus Coronas et al., DSM-IV NR 2012 Cavallaro et al., DSM-IV NR 2004 Bromocriptine addition Yuan et al., ICD-10 NR 2008 Bliesener et al., NR NR§ 2004 Terguride addition Hashimoto DSM-IV NR et al., 2014 Abbreviations: DSM-IV= Diagnostic and Statistical Manual of Mental Disorders – IV edition; ICD-10= International Clas- sification of Diseases – 10th revision; NR= Not reported. † Information regarding substance use and exclusion criteria due to substance use disorders has been included in Table 5. § Although no exclusion criteria for medical condition were reported in the study, the authors specify that all patients had normal hepatic, renal and thyroid function. Table 9 Risk of bias summary of all five randomized clinical trials evaluating the addition of aripiprazole for lowering prolactin. Shim et al., Chen et al., Zhao et al., Qiao et al., Kelly et al., Bias 2007 2015 2015 2016 2018 Random sequence generation unclear low unclear unclear low Allocation concealment unclear low unclear unclear low Blinding of participants and low low low low low researchers† Blinding of outcome assessment† low low low low low Incomplete outcome data low low low low low Selective reporting low low low low low Other bias low low low low low Quality of the clinical trial fair good fair fair good † As primary outcome was change in plasma concentrations of prolactin, all randomized clinical trials were consid- ered to have a low risk of bias with respect to the outcome, as this is an objective measure unlikely to be biased even in unblinded situations. Studies were only included if they met the following hierarchical inclusion criteria: (a) con- tained trials including patients with psychotic disorders (schizophrenia, schizoaffective disorder, bipolar disorder, brief psychotic disorder, delusional disorder, or psychotic disorder not other- wise specified) who had hyperprolactinaemia, (b) clinical trials and observational studies (ran- domized or non-randomized, controlled or uncontrolled, blinded or open-label [the latter de- J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 15 Table 10 Data regarding the meta-analysis of withdrawal rates in placebo-controlled RCTs studying adjunctive aripiprazole. Study Intervention Controls Odds ratio 95% CI z P Weight (%) Fixed Random Kelly et al., 2018 4/24 5/20 0,600 0,137 to 2624 39,07 39,07 Chen et al., 2015 8/89 4/30 0,642 0,179 to 2307 51,99 51,99 Shim et al., 2007 2/26 0/28 5816 0,266 to 127,069 8,94 8,94 Total (fixed effects) 14/139 9/78 0,841 0,346 to 2045 −0,382 0,702 10 0,0 0 10 0,0 0 Total (random effects) 14/139 9/78 0,761 0,303 to 1915 −0,579 0,562 10 0,0 0 10 0,0 0 Test for heterogeneity Q 1,8826 DF 2 Significance level P = 0,3901 I2 (inconsistency) 0,00% 95% CI for I2 0,00 to 96,44 fined as a study where both the researchers and participants know which treatment is being administered]) assessing the efficacy of four therapeutic options for lowering prolactin: switch- ing antipsychotic treatment, adding aripiprazole, adding other dopamine agonists (e.g., cabergo- line, bromocriptine), or adding metformin, (c) had information on prolactin levels at baseline and after follow-up that would allow the calculation of the effect size for the reduction in pro- lactin concentrations, (d) were published in peer-reviewed journals, (e) were written in English, French, German or Spanish, and (f) published between January 1980 and March 2020. Exclusion criteria were as follows: (a) case reports or studies of less than 5 cases, (b) studies assessing prolactin changes in psychotic patients for whom the main reason for the therapeutic strategy was not hyperprolactinaemia (e.g., switching in treatment-resistant patients). The main outcome for all studies included in the current systematic review was a change in prolactin levels from baseline to the last available follow-up. As a secondary outcome, we also reviewed withdrawal rates related to safety issues for each treatment strategy. These included adverse effects (physical symptoms or worsening in psychopathological symptoms including psy- chotic relapses) that led to stopping the treatment strategy. Withdrawal was defined as stopping the study or the assigned treatment once the study had begun; withdrawals prior to medication start were not considered. Titles and/or abstracts of studies retrieved using the search strategy and those from addi- tional sources were screened independently by two review authors to identify studies that met the inclusion criteria outlined above. The full text of these potentially eligible studies was re- trieved and independently assessed for eligibility by two review team members. Any disagree- ment between them over the eligibility of particular studies was resolved through discussion with two additional reviewers. A standardised, pre-piloted form was used to extract data from the included studies for as- sessment of the study quality and evidence synthesis. Two review authors extracted data inde- pendently, and discrepancies were identified and resolved through discussion (with two addi- tional authors when necessary). Missing data were requested from study authors. For studies in which the primary condition (hyperprolactinaemia) was included along with other conditions but the outcomes were not specified for the subgroup of patients with hyperprolactinaemia, data were requested from study authors before the study was excluded. For studies that re- ported prolactin measures at an individual level in tables, we computed the mean and SD. If data were available as the median (interquartile range), the mean and SD were estimated as described previously [3]. In a few cases in which data were only available in figures (either as individual concentrations or aggregated with mean and standard deviation), we extracted this information from figures using the procedure explained in Box 1. As not all studies included in our systematic review were RCTs, we estimated the effect sizes for the change in prolactin concentrations of all types of studies but only conducted a meta- analysis in RCTs that used placebo or maintaining antipsychotic treatment as comparator arms. 16 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 Box 1 Procedure for obtaining prolactin concentrations from figures. 1. The PDF of the figure in the article was copied from Adobe PDF zooming by 600%. To determine the prolactin concentration of a particular value (yn ), three positions (in mm) were determined using rules in Microsoft Publisher (zoom view set at 600%): ymax : the maximum concentration of the prolactin scale. We annotated the y position of the Publisher y axis rule (in mm). y0 : the minimal concentration of the prolactin scale (usually zero). We annotated the y position of the Publisher y axis rule (in mm). yn : the concentration of the prolactin value to be extracted (unknown). We annotated the y position of the Publisher y axis rule (in mm). 2. Calculation of d1 and d2. d1= y0 -ymax (in mm). d2= y0 -yn (in mm). 3. We aimed to calculate a ratio that would reflect the increase in prolactin concentrations for each mm of y axis rule increase. Ratio PRL/d1= Range of prolactin concentrations (from y0 to ymax )/d1 4. Calculation of prolactin concentration with the following formula: PRL (yn )= d2 x Ratio PRL/d1 In order to determine the effect size in pre-post designs (all types of studies), the Hedges’ g was used. This effect size measure for changes in prolactin after the therapeutic strategy was determined using an on-line calculator that allows the calculation of the effect size for paired samples tests (https://effect-size-calculator.herokuapp.com/#paired-samples-t-test). Another ef- fect size measure considered in the systematic review was confidence intervals (lower and up- per limits) using Cohen’s d. These measures were calculated using pre-post prolactin and stan- dard deviation measures of all studies. Because the correlation between pre- and post-scores is required to impute the standard deviation within groups from the standard deviation of the difference, this correlation needs to be known. However, because these correlations are not al- ways reported by studies, a common practice is to estimate the correlation from related studies. Therefore, if the correlation between pre- and post-treatment prolactin concentrations was avail- able in the article, we used the known value. For other cases, we estimated the value by using 0.18, a correlation coefficient obtained from the Lu et al. study [4]. The meta-analysis for changes in prolactin concentrations (primary outcome) was performed in R with the metafor package using the Hedge’s g estimator (Code 1). Pre-test and post-test pooled standard deviations were considered as explained by Morris [5]. A random effects sta- tistical model was used. We evaluated the risk of bias using the Cochrane risk of bias tool and recoded RCTs into three categories regarding quality: good, fair and poor. Heterogeneity among J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 17 the included studies was assessed with the I² statistic. Publication bias was explored with fun- nel plots. We did not use tests for funnel plot asymmetry because less than 10 studies were in- cluded in the meta-analysis [6]. A sensitivity analysis was also conducted including only placebo- controlled RCTs. We also conducted another meta-analysis exploring withdrawal from the study (secondary outcome) with MedCalc version 18.11 (MedCalc Software bvba), considering odds ra- tios as the summary measures. Code 1. R code used for the meta-analysis of 5 RCTs on the addition of aripiprazole for reducing prolactin concentrations in patients with a psychotic disorder and hyperprolactinaemia. library (metafor) # Creating data frame for the treatment group. Prolactin concentrations are represented in the same units as they have been published. Data from the Kelly et al. 2018 study was remitted from the authors. datT <- data.frame( m_pre = c(89.36,2173.9,90.34,82.13,1406.64), m_post = c(59.97,718.9,32.91,17.66,514.38), sd_pre = c(65.76,807.9,49.79,88.64,682.46), sd_post = c(55.58,291.9,28.93,36.61,427.0), ni = c(24,30,89,24,56), ri = c(0.18,0.18,0.18,0.18,0.18)) # Creating data frame for the control group. Prolactin concentrations are represented in the same units as they have been published. Data from the Kelly et al. 2018 study was remitted from the authors. datC <- data.frame( m_pre = c(76.86,2623.2,91.61,84.63,1493.75), m_post = c(80.96,3419.9,87.72,91.35,20 0 0.69), sd_pre = c(40.94,1541.7,57.88,37.36,576.3), sd_post = c(42.73,2091.8,57.24,45.10,1077.28), ni = c(18,25,89,27,57), ri = c(0.18,0.18,0.18,0.18,0.18)) datT <- escalc(measure="SMCR", m1i=m_post, m2i=m_pre, sd1i=sd_pre, ni=ni, ri=ri, data=datT) datC <- escalc(measure="SMCR", m1i=m_post, m2i=m_pre, sd1i=sd_pre, ni=ni, ri=ri, data=datC) dat <- data.frame(yi = datT$yi - datC$yi, vi = datT$vi + datC$vi) # Pooled (Morris). sd_pool <- sqrt((with(datT, (ni-1)∗ sd_pre^2) + with(datC, (ni-1)∗ sd_pre^2)) / (datT$ni + datC$ni - 2)) dat <- data.frame(yi = metafor:::.cmicalc(datT$ni + datC$ni - 2) ∗ (with(datT, m_post - m_pre) - with(datC, m_post - m_pre)) / sd_pool) dat$vi <- 2∗ (1-datT$ri) ∗ (1/datT$ni + 1/datC$ni) + dat$yi^2 / (2∗ (datT$ni + datC$ni)) round(dat, 2) # Meta-analysis (Random Effects, Hedges g). meta<-rma(yi, vi, data=dat, method="HE", digits=2) 18 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 meta confint(meta) # Forest plot. forest (meta) # Funnel plot. funnel (meta,digits=1) Code 2. R code used for the sensitivity meta-analysis of 3 placebo-controlled RCTs on the addi- tion of aripiprazole for reducing prolactin concentrations in patients with a psychotic disorder and hyperprolactinaemia. library (metafor) # Sensitivity meta-analysis including only 3 placebo-controlled RCTs on aripiprazole addition. # Creating data frame for the treatment group. Prolactin concentrations are represented in the same units as they have been published. Data from the Kelly et al. 2018 study was remitted from the authors. datT <- data.frame( m_pre = c(89.36,90.34,82.13), m_post = c(59.97,32.91,17.66), sd_pre = c(65.76,49.79,88.64), sd_post = c(55.58,28.93,36.61), ni = c(24,89,24), ri = c(0.18,0.18,0.18)) # Creating data frame for the control group. Prolactin concentrations are represented in the same units as they have been published. Data from the Kelly et al. 2018 study was remitted from the authors. datC <- data.frame( m_pre = c(76.86,91.61,84.63), m_post = c(80.96,87.72,91.35), sd_pre = c(40.94,57.88,37.36), sd_post = c(42.73,57.24,45.10), ni = c(18,89,27), ri = c(0.18,0.18,0.18)) datT <- escalc(measure="SMCR", m1i=m_post, m2i=m_pre, sd1i=sd_pre, ni=ni, ri=ri, data=datT) datC <- escalc(measure="SMCR", m1i=m_post, m2i=m_pre, sd1i=sd_pre, ni=ni, ri=ri, data=datC) dat <- data.frame(yi = datT$yi - datC$yi, vi = datT$vi + datC$vi) # Pooled (Morris). sd_pool <- sqrt((with(datT, (ni-1)∗ sd_pre^2) + with(datC, (ni-1)∗ sd_pre^2)) / (datT$ni + datC$ni - 2)) dat <- data.frame(yi = metafor:::.cmicalc(datT$ni + datC$ni - 2) ∗ (with(datT, m_post - m_pre) - with(datC, m_post - m_pre)) / sd_pool) J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 19 dat$vi <- 2∗ (1-datT$ri) ∗ (1/datT$ni + 1/datC$ni) + dat$yi^2 / (2∗ (datT$ni + datC$ni)) round(dat, 2) # Meta-analysis (Random Effects, Hedges g). meta<-rma(yi, vi, data=dat, method="HE", digits=2) meta confint(meta) # Forest plot. forest (meta) # Funnel plot. funnel (meta,digits=1) List 1. Studies included in the systematic review (alphabetical order). Those studies included in the meta-analysis are indicated in bold. 1 Bliesener N, Yokusoglu H, Quednow BB, Klingmüller D, Kühn KU. Usefulness of bromocriptine in the treatment of amisulpride-induced hyperprolactinemia: A case report. Pharmacopsychi- atry. 2004; 37:189–191.. doi:10.1055/s-2004–827,176 2 Cavallaro R, Cocchi F, Angelone SM, Lattuada E, Smeraldi E. Cabergoline treatment of risperidone-induced hyperprolactinemia: a pilot study. J Clin Psychiatry. 2004;65:187–190. doi:10.4088/JCP.v65n0207 3 Chen CK, Huang YS, Ree SC, Hsiao CC. Differential add-on effects of aripipra- zole in resolving hyperprolactinemia induced by risperidone in comparison to benza- mide antipsychotics. Prog Neuro-Psychopharmacology Biol Psychiatry. 2010; 34:1495–1499. doi:10.1016/j.pnpbp.2010.08.012 4 Chen CY, Lin TY, Wang CC, Shuai HA. Improvement of serum prolactin and sexual function after switching to aripiprazole from risperidone in schizophrenia: a case series. Psychiatry Clin Neurosci. 2011; 65:95–97. doi:10.1111/j.1440–1819.2010.02156.x 5 Chen JX, Su YA, Bian QT, et al. Adjunctive aripiprazole in the treatment of risperidone-induced hyperprolactinemia: A randomized, double-blind, placebo- controlled, dose-response study. Psychoneuroendocrinology. 2015;58:130–140. doi:10.1016/j.psyneuen.2015.04.011 6 Chen JX, Su YA, Wang SL, et al. Aripiprazole treatment of risperidone-induced hyperpro- lactinemia. J Clin Psychiatry. 2009; 70:1058–1059.. doi:10.4088/JCP.08l04671 7 Coronas R, Cobo J, Gimenez-Palop O, Ortega E, Marquez M. Safety of Cabergoline in the Man- agement of Pituitary Prolactin-Induced Symptoms with Patients Treated with Atypical Neu- roleptics. Curr Drug Saf. 2012;7:92–98. doi:10.2174/157,488,612,802,715,753 8 Fujioi J, Iwamoto K, Banno M et al. Effect of Adjunctive Aripiprazole on Sexual Dysfunc- tion in Schizophrenia: A Preliminary Open-Label Study. Pharmacopsychiatry. 2017; 50:74–78. doi:10.1055/s-0042–116,323 9 Hashimoto K, Sugawara N, Ishioka M, Nakamura K, Yasui-Furukori N. The effects of additional treatment with terguride, a partial dopamine agonist, on hyperprolactinemia induced by an- tipsychotics in schizophrenia patients: A preliminary study. Neuropsychiatr Dis Treat. 2014; 10:1571–1576. doi:10.2147/NDT.S68298 10 Kalkavoura C, Michopoulos I, Arvanitakis P, et al. Effects of Cabergoline on Hyperprolactine- mia, Psychopathology, and Sexual Functioning in Schizophrenic Patients. Exp Clin Psychophar- macol. 2013;21:332–341. doi:http://dx.doi.org/10.1037/a0033448 11 Kawabe K, Horiuchi F, Ueno SI. Blonanserin, a novel antipsychotic, is suitable for treating schizophrenia associated with hyperprolactinemia: A case series. Clin Neuropharmacol. 2013; 36:239–241. doi:10.1097/WNF.0 0 0 0 0 0 0 0 0 0 0 0 0 0 06 20 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 12 Kelly DL, Powell MM, Wehring HJ, et al. Adjunct Aripiprazole Reduces Prolactin and Prolactin-Related Adverse Effects in Premenopausal Women with Psychosis: Re- sults from the DAAMSEL Clinical Trial. J Clin Psychopharmacol. 2018; 38:317–326. doi:10.1097/JCP.0 0 0 0 0 0 0 0 0 0 0 0 0898 13 Kinon BJ, Ahl J, Liu-Seifert H, Maguire GA. Improvement in hyperprolactinemia and re- productive comorbidities in patients with schizophrenia switched from conventional an- tipsychotics or risperidone to olanzapine. Psychoneuroendocrinology. 2006; 31:577–588. doi:10.1016/j.psyneuen.20 05.12.0 06 14 Lee BH, Kim YK, Park SH. Using aripiprazole to resolve antipsychotic-induced symptomatic hyperprolactinemia: A pilot study. Prog Neuro-Psychopharmacology Biol Psychiatry. 2006; 30:714–717. doi:10.1016/j.pnpbp.20 06.02.0 01 15 Lu ML, Shen WW, Chen CH. Time course of the changes in antipsychotic-induced hyperpro- lactinemia following the switch to aripiprazole. Prog Neuro-Psychopharmacology Biol Psychi- atry. 2008; 32:1978–1981. doi:10.1016/j.pnpbp.2008.09.016 16 Montalvo I, Ortega L, López X, et al. Changes in prolactin levels and sexual function in young psychotic patients after switching from long-acting injectable risperidone to paliperidone palmitate. Int Clin Psychopharmacol. 2013; 28:46–49. doi:10.1097/YIC.0b013e32835ba832 17 Nakajima M, Terao T, Iwata N, Nakamura J. Switching female schizophrenic patients to que- tiapine from conventional antipsychotic drugs: Effects on hyperprolactinemia. Pharmacopsy- chiatry. 2005; 38:17–9. doi:10.1055/s-2005–837,766 18 Qiao Y, Yang F, Li C, et al. Add-on effects of a low-dose aripiprazole in resolving hyper- prolactinemia induced by risperidone or paliperidone. Psychiatry Res. 2016; 237:83–89. doi:10.1016/j.psychres.2015.12.033 19 Shim JC, Shin JGK, Kelly DL, et al. Adjunctive treatment with a dopamine partial ag- onist, aripiprazole, for antipsychotic-induced hyperprolactinemia: A placebo-controlled trial. Am J Psychiatry. 2007; 164:1404–1410. doi:10.1176/appi.ajp.2007.06071075 20 Takahashi H, Higuchi H, Kamata M, et al. Effectiveness of Switching to Quetiapine for Neuroleptic-Induced Amenorrhea. J Neuropsychiatry Clin Neurosci. 2014; 15:375–377. doi:10.1176/jnp.15.3.375 21 Van Kooten M, Arends J, Cohen D. Preliminary report: A naturalistic study of the effect of aripiprazole addition on risperidone-related hyperprolactinemia in patients treated with risperidone long-acting injection. J Clin Psychopharmacol. 2011; 31:126–128. doi:10.1097/JCP.0b013e318205e1aa 22 Yasui-Furukori N, Furukori H, Sugawara N, Fujii A, Kaneko S. Dose-dependent effects of adjunctive treatment with aripiprazole on hyperprolactinemia induced by risperi- done in female patients with schizophrenia. J Clin Psychopharmacol. 2010; 30:596–599. doi:10.1097/JCP.0b013e3181ee832d 23 Yoon HW, Lee JS, Park SJ, et al. Comparing the effectiveness and safety of the addi- tion of and switching to aripiprazole for resolving antipsychotic-induced hyperprolactine- mia: A multicenter, open-label, prospective study. Clin Neuropharmacol. 2016; 39:288–294. doi:10.1097/WNF.0 0 0 0 0 0 0 0 0 0 0 0 0175 24 Yuan HN, Wang CY, Sze CW, et al. A randomized, crossover comparison of herbal medicine and bromocriptine against risperidone-induced hyperprolactinemia in patients with schizophrenia. J Clin Psychopharmacol. 2008; 28:264–370. doi:10.1097/JCP.0b013e318172473c 25 Zhao J, Song X, Ai X, et al. Adjunctive aripiprazole treatment for risperidone-induced hyperprolactinemia: An 8-week randomized, open-label, comparative clinical trial. PLoS One. 2015; 10:e0139717. doi:10.1371/journal.pone.0139717 26 Ziadi Trives M, Bonete Llácer J-M, García Escudero M-A, Martínez Pastor CJ. Effect of the addi- tion of aripiprazole on hyperprolactinemia associated with risperidone long-acting injection. J Clin Psychopharmacol. 2013; 33:538–541. doi:10.1097/jcp.0b013e3182970431 J. Labad, I. Montalvo and A. González-Rodríguez et al. / Data in Brief 31 (2020) 105904 21 Declaration of Competing Interest Javier Labad, Benedicto Crespo-Facorro and Clemente García-Rizo have received fees for con- sultancy or advice services and lecture fees from Janssen-Cilag, Lundbeck and Otsuka Pharma- ceuticals. Although some of the authors have received honoraria for lectures or advisory boards from Otsuka, Lundbeck and Janssen-Cilag, we want to underscore that no drug company has partici- pated in the design or funding of the study. Acknowledgements Javier Labad and Itziar Montalvo have received an Intensification of the Research Activ- ity Grant by the Health Department from the Generalitat de Catalunya (SLT0 06/17/0 0 012 and SLT0 08/18/0 0 074). Javier Labad has received an Intensification of the Research Activity Grant by the Instituto de Salud Carlos III (INT19/0 0 071). References [1] J. Labad, I. Montalvo, A. González-Rodríguez, C. García-Rizo, B. Crespo-Facorro, J.A. Monreal, D. Palao, Pharmacological treatment strategies for lowering prolactin in people with a psychotic disorder and hyperprolactinaemia: a system- atic review and meta-analysis, Schizophr. Res. (2020) S0920-9964(20)30238-3, doi:10.1016/j.schres.2020.04.031. [2] D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, BMJ 339 (2009) b2535 –b2535 https://doi.org/10.1136/bmj.b2535 . [3] X. Wan, W. Wang, J. Liu, T. Tong, Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range, BMC Med. Res. Methodol. 14 (2014) 135 https://doi.org/10.1186/1471- 2288- 14- 135. [4] M.L. Lu, W.W. Shen, C.H. Chen, Time course of the changes in antipsychotic-induced hyperprolactinemia following the switch to aripiprazole, Prog. Neuro-Psychopharmacology Biol. Psychiatry 32 (2008) 1978–1981 https://doi.org/10. 1016/j.pnpbp.2008.09.016. [5] S.B. Morris, Estimating effect sizes from pretest-posttest-control group designs, Organ. Res. Methods (2008) https: //doi.org/10.1177/1094428106291059. [6] G. S., J.P.T. Higgins (Ed.), Cochrane Collab., 2011.