早上也跟曾文毅教授談論此事
尤其他跟指導教授長期合作
他的看法是，因為環境/人事差異，畢業的博士生，難以複製，因此一段時間後自然有所區隔。
也就是說，博班畢業生須要參考所在環境與合作對象，找出最佳的長期研究主題。
if so, 就沒有與指導教授研究方向「區隔」之議題了！
2012年4月30日 星期一
2012年4月29日 星期日
王文中教授的新書
統計學與 Excel: 資料分析之實習應用（第6版）
目錄如網址： http://www.healthup.org.tw/rasch/book_sp20060.pdf
博碩出版
預計5月發行。
屆時請怡靜提醒玉雲去買二本，怡靜跟恭宏可以開始自學基礎統計了。
目錄如網址： http://www.healthup.org.tw/rasch/book_sp20060.pdf
博碩出版
預計5月發行。
屆時請怡靜提醒玉雲去買二本，怡靜跟恭宏可以開始自學基礎統計了。
2012年4月28日 星期六
養成「建構團隊」的能力
以 KAP 解釋之：
1. 團隊作戰才能截長補短，互補不足
2. 團隊/跨領域才能讓研究主題暨深又廣，確實解決盤根錯雜的臨床問題
3. 團隊才能營造持續的動能/相互支援，不因個人的心情、困擾而有所延誤
4. 態度上，自覺有需求，否則自我感覺良好，不假他人，格局小。。。。
5. 態度上，瞭解分享（付出）是營造團隊的關鍵，若吝於付出/分享，則團隊難以形成/持續
具備上述知識與心態之後，
須規劃自己中長期（至少5-8年[或10年]以上）研究方向，瞭解所需之研究專長與資源
除了自己努力培養專長與尋找資源，其它就需要他人協助或支援
那就按圖索驥
持續努力「投入」「付出」與「分享」。。。
個人的經驗，常看到的是：researchers不知或無法體會團隊之好處，或不知臨床研究需要團隊才能做好研究。if so, 一切大概已到盡頭，除非她/他改行做數理研究，自己鑽研即可。
另一種是 researchers 捨不得「付出」與「分享」，常抱怨合作者付出少又佔便宜。。。結局各位可想而知。
還有一種也很棘手，就是不知道自己的中長期的研究方向，騎驢找馬，當然就難以擘劃研究願景尋找高手加入。。。這只能再培養基本研究能力/瞭解本身臨床環境之資源與議題，或可先加入其它團隊，以觀摩學習，再思如何效法。
2012/4/28補充：
另外，建構團隊/聚集人才的關鍵包含：
如何滿足合作者的「需求」，尤其是剛合作者之「短期需求（通常是發表論文之壓力）」。
如何爭取研究資源，以擴大團隊資源並且合作者持續投入。
1. 團隊作戰才能截長補短，互補不足
2. 團隊/跨領域才能讓研究主題暨深又廣，確實解決盤根錯雜的臨床問題
3. 團隊才能營造持續的動能/相互支援，不因個人的心情、困擾而有所延誤
4. 態度上，自覺有需求，否則自我感覺良好，不假他人，格局小。。。。
5. 態度上，瞭解分享（付出）是營造團隊的關鍵，若吝於付出/分享，則團隊難以形成/持續
具備上述知識與心態之後，
須規劃自己中長期（至少5-8年[或10年]以上）研究方向，瞭解所需之研究專長與資源
除了自己努力培養專長與尋找資源，其它就需要他人協助或支援
那就按圖索驥
持續努力「投入」「付出」與「分享」。。。
個人的經驗，常看到的是：researchers不知或無法體會團隊之好處，或不知臨床研究需要團隊才能做好研究。if so, 一切大概已到盡頭，除非她/他改行做數理研究，自己鑽研即可。
另一種是 researchers 捨不得「付出」與「分享」，常抱怨合作者付出少又佔便宜。。。結局各位可想而知。
還有一種也很棘手，就是不知道自己的中長期的研究方向，騎驢找馬，當然就難以擘劃研究願景尋找高手加入。。。這只能再培養基本研究能力/瞭解本身臨床環境之資源與議題，或可先加入其它團隊，以觀摩學習，再思如何效法。
2012/4/28補充：
另外，建構團隊/聚集人才的關鍵包含：
如何滿足合作者的「需求」，尤其是剛合作者之「短期需求（通常是發表論文之壓力）」。
如何爭取研究資源，以擴大團隊資源並且合作者持續投入。
2012年4月27日 星期五
your comments are appreciated... invite you as a coauthor
4/27 補上 Discussion
4/19 補上 bootstrap 數據，感謝業太的高效率分析（he has been invited as a coauthor）
4/17 方法部份大致寫妥，詳下文。
同前所言：我誠摯邀請各位給建議，若有 critical comments 者，我將邀請其擔任共同作者。
下一個主要研究主題是延宕已久的 individual-level responsiveness
Introduction:
A short and psychometrically sound measure provides clinicians and researchers an efficient way to quantify patients’ outcomes. Recent studies have shown that short-form measures have similar psychometric properties, particularly responsiveness (a critical index of outcome measures), to original or long-form measures.1-4 However, Hobart et al argues that such a similar responsiveness between short forms and long forms might be because of group-level comparison.5 They used standard error generated from item response theory (IRT) for each participant to calculate individual-level responsiveness. Results show that a short measure (the 10-item, 2-4-response-option Barthel index) has less individual-level responsiveness than a long measure (the 13-item, 7-response-option Functional Independence Measure).5 Thus, individual-level responsiveness is critical for clinicians and researchers on the selection of competing measures.
Although IRT-based standard error is useful for calculating individual-level responsiveness, most measures have been developed and examined using classical testing theory (CTT) primarily because of simplicity. CTT can also generate similar index for random error (called minimal detectable change, MDC, or smallest real difference). The MDC is the smallest threshold of change scores that are beyond random error at a certain level of confidence (usually 95%).6, 7 Thus, the MDC can be used as the safest threshold for identifying statistically significant individual changes.6 Therefore, the MDC is simple, useful for estimating individual-level responsiveness of a measure.
We have shown similar group-level responsiveness of the Postural Assessment Scale for Stroke Patients (PASS) and short form PASS (SFPASS).1 The result is counterintuitive because the PASS has more items (12) and response options (4) than those of the SFPASS (5 items and 3 response options). However, the individual-level responsiveness of both measures remains unknown, which affects clinicians and researchers on the selection of a competitive measure. Thus, the purpose of this study was to compare individual-level responsiveness between the PASS and SFPASS.
Data analysis
Individual person level comparison
* Estimated by 10,000
bootstrap samples.
Discussion
The purpose of this study was to determine whether the SFPASS has equal ability to detect change with the PASS. Particularly, the PASS has more items and response categories and shows more potential to detect change than the SFPASS. However, we found similar responsiveness of the PASS and SFPASS at group level as shown by Kazis effect size and SRM. These results were confirmed by 10,000 bootstraping samples. Importantly, the PASS had better individual-level responsiveness than the SFPASS. The PASS could detect significant recovery of balance function in more patients than the SFPASS. Thus, the PASS showed better ability to detect change than the SFPASS.
Our findings also imply that studies using the PASS or SFPASS as an outcome measure should report individual-level effect (i.e., number of patients scoring beyond MDC) in addition to group-level effect (e.g., effect size) in order to comprehensively report effect of clinical trials. Such information would also help clinicians interpret their clinical observations on the basis of objective measurement properties (i.e., the change observed on each patient has to be beyond random measurement error [e.g., MDC]).
Our study raises two issues for researchers to examine responsiveness of an outcome measure. First, individual-level responsiveness is strongly recommended for examining responsiveness for outcome measures, particularly for comparing competing measures (e.g., short forms vs long forms). The results will be critical for clinicians and researchers to select for competing measures on the basis of comprehensive empirical evidences.
Second, our findings support the argument that group level indicators of responsiveness (e.g., Kazis effect size and SRM) are inappropriate or limited.5, 16 The group level indicators of responsiveness could not demonstrate different level of responsiveness between a short measure (the Barthel index) and a long measure (the Functional Independence Measure).5, 16, 17 However, the superiority of the Functional Independence Measure to detect change than the Barthel index is demonstrated by individual-level analyses.5 These observations indicate that group-based indices of responsiveness can be misleading.
There are two limitations in this study. First, our patients were followed at subacute stage. Only few patients, as expected, deteriorated during the follow-up periods. Thus the comparison of the ability of both measures to detect deterioration remains unknown. Second, the scores of SFPASS were retrieved from those of the PASS. Future studies might need to validate our findings using the PASS and SFPASS independently.
In brief, the PASS showed better individual-level responsiveness than the SFPASS and is recommended for clinical trials and clinical settings. Future studies using the PASS should report individual-level effect (i.e., number of patients scoring beyond MDC) in addition to group-level effect (e.g., effect size) in order to comprehensively report effect of clinical trials.
4/19 補上 bootstrap 數據，感謝業太的高效率分析（he has been invited as a coauthor）
4/17 方法部份大致寫妥，詳下文。
同前所言：我誠摯邀請各位給建議，若有 critical comments 者，我將邀請其擔任共同作者。
下一個主要研究主題是延宕已久的 individual-level responsiveness
Dr. Jeremy C Hobart (JNNP 2010; 81:1044-1048) 提出以IRT分析後所得之各別個案SE，計算各別個案之進步量是否超過 1.96*SE， if so, 則代表該個案達到 statistically significant improvement。所以，以此方法可比較不同量表之individual-level responsiveness。
昔日研究，包含我發表的一些文獻，已呈現短版評估工具比原始版具有類似的 group-level responsiveness (通常以 effect size 驗證)。
我可選擇Hobart 所提之IRT方法，以驗證我之前的「短版」評估工具（如 short form PASS, short form BBS等）之individual-level responsiveness，然而還需IRT分析，有些麻煩。
另外, 今天剛收到 short form PASS 的 MDC 稿件被接受刊登信函（投了3個期刊，費時約2年）。
因此就想到以 MDC 取代 1.96 SE，就毋須用到IRT了。
我找筆資料，分析結果與摘要如下：
Comparison of individual-level responsiveness between the original and short-form Postural Assessment Scale for Stroke Patients
Background and purpose: We have examined and shown that responsiveness of the Postural Assessment Scale for Stroke Patients (PASS) and short form PASS (SFPASS) at group level was similar. The result is counterintuitive because the PASS has more items (12) and response options (4) than those of the SFPASS (5, 3, respectively). However, the individual-level responsiveness of both measures remains unknown, which affects clinicians and researchers on the selection of a competitive measure. Thus, the purpose of this study was to compare individual-level responsiveness between the PASS and SFPASS.
Method: A total of 179 stroke patients were assessed using the PASS at 14 days and 30 days after onset. The SFPASS scores were calculated from the patients’ responses on the PASS. We calculated individual-level responsiveness on the basis of the value of minimal detectable change (MDC). If a patient’s change score is beyond the MDC of the PASS or SFPASS, his/her improvement is significant. The MDCs of the PASS and SFPASS were obtained from previous studies. We examined the difference of the number of patients scoring beyond MDC of the PASS and SFPASS between 14 days and 30 days after onset.
Results: We found that 47.5% of the patients scored beyond the MDC of the PASS and that 36.1% of the patients scored beyond that of the SFPASS. The difference was significant (P < 0.001).
Conclusion: The PASS had better individual-level responsiveness than the SFPASS and is recommended for clinical trials. To comprehensively report effect of clinical trials, future studies used the PASS should report individual-level effect (number of patients scoring beyond MDC) in addition to group-level effect (e.g., effect size).
Introduction:
A short and psychometrically sound measure provides clinicians and researchers an efficient way to quantify patients’ outcomes. Recent studies have shown that short-form measures have similar psychometric properties, particularly responsiveness (a critical index of outcome measures), to original or long-form measures.1-4 However, Hobart et al argues that such a similar responsiveness between short forms and long forms might be because of group-level comparison.5 They used standard error generated from item response theory (IRT) for each participant to calculate individual-level responsiveness. Results show that a short measure (the 10-item, 2-4-response-option Barthel index) has less individual-level responsiveness than a long measure (the 13-item, 7-response-option Functional Independence Measure).5 Thus, individual-level responsiveness is critical for clinicians and researchers on the selection of competing measures.
Although IRT-based standard error is useful for calculating individual-level responsiveness, most measures have been developed and examined using classical testing theory (CTT) primarily because of simplicity. CTT can also generate similar index for random error (called minimal detectable change, MDC, or smallest real difference). The MDC is the smallest threshold of change scores that are beyond random error at a certain level of confidence (usually 95%).6, 7 Thus, the MDC can be used as the safest threshold for identifying statistically significant individual changes.6 Therefore, the MDC is simple, useful for estimating individual-level responsiveness of a measure.
We have shown similar group-level responsiveness of the Postural Assessment Scale for Stroke Patients (PASS) and short form PASS (SFPASS).1 The result is counterintuitive because the PASS has more items (12) and response options (4) than those of the SFPASS (5 items and 3 response options). However, the individual-level responsiveness of both measures remains unknown, which affects clinicians and researchers on the selection of a competitive measure. Thus, the purpose of this study was to compare individual-level responsiveness between the PASS and SFPASS.
Method
Data were
available from a longitudinal follow-up study. Each subject in the study was
assessed at 14 days after stroke onset and reassessed at other specific time
points (e.g., 30 days after onset) to characterize their balance ability (e.g.,
as measured by the PASS) and recovery
of neurological impairments. Subjects met the following criteria: (a) first or
recurrent onset of cerebrovascular accident without other major diseases (e.g.,
cancer, dementia, severe rheumatoid arthritis); (b) ability to follow verbal
instructions to complete the PASS; and (c) ability to provide informed consent
personally or by proxy. Subjects were excluded if they had another stroke or
other major disease/s during the follow-up period. We also excluded patients
with highest possible score (i.e., 36) of the PASS because these patients had
no room to improve on the PASS or SFPASS.
The study was approved by the institutional review board of a university
hospital.
Procedure
The PASS was administered
by an occupational therapist who was not informed of the purpose of this study.
The patients were assessed at hospital or their home. The scores of the SFPASS
were obtained from the PASS.
Measures
The PASS was
specifically developed to assess balance function in people with stroke.4 The
PASS contains 12 four-level (0-1-2-3) items assessing a person’s balance
performance in situations of varying difficulty, i.e. maintaining or changing a
lying, sitting, or standing position. Its total score ranges from 0 to 36 and the
psychometric properties of the PASS were found to be satisfactory when used to
assess people with stroke.
The SFPASS has 5
three-level items which are listed in Appendix. The 5 items are selected from
the original PASS with the best measurement properties (i.e., higher internal
consistency and greater responsiveness). The
middle level of the SFPASS is created by combining the middle two levels (1 and
2) of the original PASS. Thus, both the items and scores of the SFPASS
can be obtained from the scores of the PASS. The possible score of the SFPASS
ranges from 0 to 15. Psychometric properties (including reliability, validity
and group-level responsiveness) of the SFPASS were very similar to the original
PASS.
Data analysis
Group level comparison
Three indicators were used to examine the group level responsiveness. First, the Kazis’ effect size was calculated by dividing the mean changes by the standard deviation of the baseline scores obtained at 14 days after stroke onset. Second, the standardized response mean (SRM) (another type of effect size) was calculated by dividing the mean changes by the standard deviation of the change in scores. According to Cohen’s criteria, an effect size greater than .8 is large, .5 to .8 is moderate, and .2 to .5 is small. Third, paired t-test was performed to examine the statistical significance of the changes in scores from 14 days to 30 days after onset.
In addition, to
compare the responsiveness between the PASS and SFPASS, we estimated the 95%
confidence intervals of Kazis’ effect size and SRM to test the differences
between the above measures by 10,000 bootstrap samples.
Individual person level comparison
The MDC of both
PASS and SFPASS (3.16 and 2.16, respectively) were retrieved from a recent
study. The MDC was estimated on the basis of test-retest reliability
investigation, in which 52 patients with stable condition were tested twice,
one week apart. The MDC based on the standard error of measurement (SEM) was
calculated by the following formula:
MDC = z-score * √2 * SEM (1)
SEM
= √
(2)
The z-score (1)
represents the confidence interval (CI) from a standard normal distribution
(1.96 for 95% CI was used in this study). The SEM was calculated by the square
root of the error variance including systematic differences (2), which can be
obtained from the ANOVA table (de Vet et al., 2006a).
The
relative responsiveness of the PASS and SFPASS was compared at the individual
person level. First, we calculated the size of change score of each patient
(“score at 30 days after onset” - “score at 14 days after onset”). Second, we
calculated whether the change score was larger than the MDC. Finally, we
categorized the significance of each patient’s change into one of the three
groups according to the size and direction of the significance of change score.
The first group was significant improvement: change score ≥ MDC. The second
group was non-significant improvement: 0 ≤ change score < MDC. The third
group was others (no change and worsening).
Finally,
we counted the numbers of patients in each group. The distributions for both
balance measures were compared using a x2
test and relative risk statistics.
Results
Three-hundred-and-one patients were
assessed using the PASS at 14 days after a recent stroke onset. A total of 41
patients were not followed because of having achieved highest possible score of
the PASS, unstable condition, and unnoticeable discharge. Two-hundred-and-sixty
patients were assessed both time points and their data were used for further
analyses. These patients had a wide range of balance impairment (from bed
ridden to nearly able to stand on the affected leg for 10 seconds).
Group level comparison of both measures
Kazis effect size and SRM showed
moderate to large responsiveness (0.46 ~ 0.91) of both measures in detecting
changes from 14 days to 30 days after stroke (Table 3[暫訂]). Particularly, the 95%
CIs of the two effect size indices of PASS and SFPASS were largely overlapped
with other. The changes of the two measures were all significant (P< 0.001).
Table 3. Group level responsiveness of both balance
measures (n=251)
Measure
|
Kazis’
effect size (95% CI)*
|
Standardized
response mean (95% CI)*
|
paired
t (P)
|
PASS
|
0.46
(0.39~0.53)
|
0.91
(0.82~10.1)
|
14.4
(<0.001)
|
SFPASS
|
0.48
(0.39~0.56)
|
0.83
(0.71~0.91)
|
13.1
(<0.001)
|
Discussion
The purpose of this study was to determine whether the SFPASS has equal ability to detect change with the PASS. Particularly, the PASS has more items and response categories and shows more potential to detect change than the SFPASS. However, we found similar responsiveness of the PASS and SFPASS at group level as shown by Kazis effect size and SRM. These results were confirmed by 10,000 bootstraping samples. Importantly, the PASS had better individual-level responsiveness than the SFPASS. The PASS could detect significant recovery of balance function in more patients than the SFPASS. Thus, the PASS showed better ability to detect change than the SFPASS.
Our findings also imply that studies using the PASS or SFPASS as an outcome measure should report individual-level effect (i.e., number of patients scoring beyond MDC) in addition to group-level effect (e.g., effect size) in order to comprehensively report effect of clinical trials. Such information would also help clinicians interpret their clinical observations on the basis of objective measurement properties (i.e., the change observed on each patient has to be beyond random measurement error [e.g., MDC]).
Our study raises two issues for researchers to examine responsiveness of an outcome measure. First, individual-level responsiveness is strongly recommended for examining responsiveness for outcome measures, particularly for comparing competing measures (e.g., short forms vs long forms). The results will be critical for clinicians and researchers to select for competing measures on the basis of comprehensive empirical evidences.
Second, our findings support the argument that group level indicators of responsiveness (e.g., Kazis effect size and SRM) are inappropriate or limited.5, 16 The group level indicators of responsiveness could not demonstrate different level of responsiveness between a short measure (the Barthel index) and a long measure (the Functional Independence Measure).5, 16, 17 However, the superiority of the Functional Independence Measure to detect change than the Barthel index is demonstrated by individual-level analyses.5 These observations indicate that group-based indices of responsiveness can be misleading.
There are two limitations in this study. First, our patients were followed at subacute stage. Only few patients, as expected, deteriorated during the follow-up periods. Thus the comparison of the ability of both measures to detect deterioration remains unknown. Second, the scores of SFPASS were retrieved from those of the PASS. Future studies might need to validate our findings using the PASS and SFPASS independently.
In brief, the PASS showed better individual-level responsiveness than the SFPASS and is recommended for clinical trials and clinical settings. Future studies using the PASS should report individual-level effect (i.e., number of patients scoring beyond MDC) in addition to group-level effect (e.g., effect size) in order to comprehensively report effect of clinical trials.
我誠摯邀請各位給建議，若有 critical comments 者，我將邀請其擔任共同作者。
2012年4月26日 星期四
也是寫作練習 -- a short-term 論文寫作練習--- 4/25 論文被接受刊登了！！
4/25 論文被接受刊登了！！
前後不到3個月。
感謝 雅珍、姿誼還有恩琦的努力！
相當順利的投稿流程！
努力＋機運 = 成果
持續努力最為重要！
中文論文主題：「影響中風病患整體健康相關生活品質之因素探討」
主角：雅珍/恩琦/姿誼
導演：ching-lin
時間：從2012 二 月初開始，利用週六或週日，每次至少6小時。
預計進度：3月底前完成投稿學會雜誌
目前第1週分工/進度：
恩琦負責前言；雅珍負責方法（除了資料分析）；姿誼：資料分析與結果
資料分析大致完成，方法與結果撰寫約完成 50-60% （第2週應可完成至 80-90%）
前言較難，努力中！！
第2週進度：雅珍/姿誼：完成方法與結果 80-90%，並討論 Discussion 之討論題材（各段落主題句）並分工之。
恩琦：寫出昔日研究不足之主段落，需與研究目的契合。
第3週進度：雅珍/姿誼：撰寫討論 80% & 中文摘要初稿
恩琦：完成前言。
第4週進度：中英文摘要 and the very first draft.
最後共5週，提早二週，於3/11完成稿件，交由通信作者投稿之！
2012年4月23日 星期一
跟奇美的合作計畫
最近跟成大老師合力擴展小兒之臨床資源
因為研究人員的主要資源之一是「收案資源」
也必須」申請「研究經費」，故擬提出約5個計畫案（成大老師寫3個小兒計畫，我寫2個成人計畫）
以利後續資源整合！！
奇美醫院之院內研究計畫案可申請人事費用，而且院內員工可領取，也就是申請到的經費，可聘請OT/PT協助收案並給予＄＄報酬。這跟台大的院內計畫實在差異大！！
奇美醫院之院內研究計畫案可申請人事費用，而且院內員工可領取，也就是申請到的經費，可聘請OT/PT協助收案並給予＄＄報酬。這跟台大的院內計畫實在差異大！！
成人的主題可能為：
1. 提升中風個案/家屬之復健參與度對於復健成效之影響（以一位復健科醫師的角度撰寫）
2. 修改SDMT以降低應用於中風病人之學習效應與測量誤差 （松德的計畫是應用於 schizo）
3. 電腦化 PDT 應用於中風病人之學習效應與測量誤差
4. 簡版平衡測驗與原版平衡測驗之個人層級反應性比較
to be continued...
4. 簡版平衡測驗與原版平衡測驗之個人層級反應性比較
to be continued...
2012年4月22日 星期日
第12屆職能治療學術研討會--心得與後續（請留意）
可說是忙碌但收穫多！
早上研究團隊之成果分享，我覺得團隊成員最大的成長為「台風」日漸穩當，跟去年比較，成長良多！
另一個收穫是來自聽眾的回饋，我想：沒有「準備」與「分享」，是不可能獲得「回饋」與「提昇」！
接下來週二的寫作課，就當成「週六報告/討論」的延伸
請各同學就自己的主題，提出2-3項值得再深入討論的議題（聽眾的回饋或自己的疑惑）加上簡要說明（您的因應）當成週二之討論議題與講義。
英文報告可免，改成3-5分鐘之討論議題口頭介紹。
亦請各位將週六的 PPT 放到「影印」的 Dropbox 資料夾，也請同學互相觀摩，若可事先（週二上課之前）對其它同學或老師的演講內容，再提出好的問題（請在此提出意見），我將給予獎勵！
下午的「認知介入與評估」，就內容範疇與講者的多元性，我想也讓大家眼界大開！
一切的辛苦，皆值得！！
早上研究團隊之成果分享，我覺得團隊成員最大的成長為「台風」日漸穩當，跟去年比較，成長良多！
另一個收穫是來自聽眾的回饋，我想：沒有「準備」與「分享」，是不可能獲得「回饋」與「提昇」！
接下來週二的寫作課，就當成「週六報告/討論」的延伸
請各同學就自己的主題，提出2-3項值得再深入討論的議題（聽眾的回饋或自己的疑惑）加上簡要說明（您的因應）當成週二之討論議題與講義。
英文報告可免，改成3-5分鐘之討論議題口頭介紹。
亦請各位將週六的 PPT 放到「影印」的 Dropbox 資料夾，也請同學互相觀摩，若可事先（週二上課之前）對其它同學或老師的演講內容，再提出好的問題（請在此提出意見），我將給予獎勵！
下午的「認知介入與評估」，就內容範疇與講者的多元性，我想也讓大家眼界大開！
一切的辛苦，皆值得！！
2012年4月19日 星期四
失而復得的經歷
昨晚不小心將皮包遺失在計程車上
懊悔不已後，立即向臺灣大車隊報備、但因沒有車牌，雖車隊有廣播，皆無司機回應
另向信用卡辦理控管（僅24小時，之後就自動作廢，連我老婆的副卡一併，這讓我一大早起來寫 e-mail像老婆報備/懺悔。。。。）。 信用卡作廢後，許多定期扣款的約定，皆須重新辦理（甚至還可能被罰錢），這太麻煩了！
另外，還好家裡還有些錢，否則一大早沒錢搭高鐵去台南成大授課。
整晚難眠
一大早跟二姊聯絡（因為姊夫是警官）她就建議去警察局調閱路口監視
上課結束後，就一直聯絡。。。。
後來請玉雲去警察局看錄影帶。。。。因為昨晚下雨視線不清
看了好久，本來已計畫要到另一個分局（我家附近），下午快6點了（24小時也快到了。。。），終於看到清楚的車牌！
直接聯絡司機，就專程送皮包過來！！
感謝上蒼還有一堆人的協助！！
2012年4月15日 星期日
義大研究計畫的進度
一： FM CAT 之反應性及預測效度，已完成收案約70位。前後約2年之收案。
未來可考慮與 STREAM CAT 一起驗證 test-retest reliability. 此主題可考慮當成今年的研究計畫案。
二：STREAM CAT 之施測順序需改，由坐姿開始（測5題），第6題再測由坐到站 (mobility)，之後就由電腦挑選題目。至多9題。修改後，再請淑茵輸入/記錄姿勢轉換次數與施測題數。
三：STREAM video 之 rating，宏嘉近期將 rerating, 並再邀一位同事（生理年資3年以上）參與 rating。
四：恩琦已完成認知評估工具之示範，請恩琦1週後追蹤進展/澄清問題。WCST-128 題目過多，除非有 64 版，否則不採用。
slow and steady wins the game!
未來可考慮與 STREAM CAT 一起驗證 test-retest reliability. 此主題可考慮當成今年的研究計畫案。
二：STREAM CAT 之施測順序需改，由坐姿開始（測5題），第6題再測由坐到站 (mobility)，之後就由電腦挑選題目。至多9題。修改後，再請淑茵輸入/記錄姿勢轉換次數與施測題數。
三：STREAM video 之 rating，宏嘉近期將 rerating, 並再邀一位同事（生理年資3年以上）參與 rating。
四：恩琦已完成認知評估工具之示範，請恩琦1週後追蹤進展/澄清問題。WCST-128 題目過多，除非有 64 版，否則不採用。
slow and steady wins the game!
2012年4月14日 星期六
以 relative risk 驗證 individual person level responsiveness
Hobart 以 relative risk (RR) 驗證 FIM 是否比 BI 能呈現較多的個案具備顯著ADL進步
用在我的數據也就是驗證 PASS 是否比 SFPASS 能呈現較多的個案具備顯著 balance 進步
RR 的算式是 [(PASS 呈現個案具備顯著進步之 probability) / (SFPASS 呈現個案具備顯著進步之 probability)] 也就是 [(133/260)/(108/260)] = 1.23。解釋上可謂：PASS呈現個案層級顯著進步的能力是SFPASS的 1.23 倍
SPSS 計算 RR & p value 有些複雜。還好網路上： http://www.medcalc.org/calc/relative_risk.php 可協助計算這些數值。
語意上， odds ratio （勝算比）是否更好！？概念上亦然！？
用在我的數據也就是驗證 PASS 是否比 SFPASS 能呈現較多的個案具備顯著 balance 進步
結果如下：
Table. Individual patient level
responsiveness of the Postural Assessment Scale for Stroke Patients (PASS) and
short form PASS (SFPASS)
|
Group
|
PASS
|
SFPASS
|
|
|
% (n)
|
% (n)
|
Relative risk (95% CI, P)
|
|
|
Significant improvement
|
51.2 (133)
|
41.5 (108)
|
1.23 (1.02 – 1.48, 0.029)
|
|
Non-significant improvement
|
40.0 (104)
|
55.4 (144)
|
0.72 (0.60 – 0.87, 0.001)
|
|
Others
|
8.8 (23)
|
3.1 (8)
|
2.88 (1.31 – 6.31, 0.008)
|
RR 的算式是 [(PASS 呈現個案具備顯著進步之 probability) / (SFPASS 呈現個案具備顯著進步之 probability)] 也就是 [(133/260)/(108/260)] = 1.23。解釋上可謂：PASS呈現個案層級顯著進步的能力是SFPASS的 1.23 倍
SPSS 計算 RR & p value 有些複雜。還好網路上： http://www.medcalc.org/calc/relative_risk.php 可協助計算這些數值。
語意上， odds ratio （勝算比）是否更好！？概念上亦然！？
2012年4月13日 星期五
time flies...
預計6/20晚上搭機前往 Brisbane， 10/7回到 Taipei.
這個暑假剛開始的2-3週，老婆也已安排好行程（親子旅遊）
我的研究生，也應更為獨立了，知道如何把握青春。。。
Have a nice time!!
這個暑假剛開始的2-3週，老婆也已安排好行程（親子旅遊）
我的研究生，也應更為獨立了，知道如何把握青春。。。
Have a nice time!!
2012年4月6日 星期五
ADL CAT 稿件被退稿了。。。
傷心了一下子（about 10 mins.）
過年後花了不少時間修改及撰寫 response letter，只能當成經驗了
決定1週內改投其它期刊
"PS" 1. 這已是第3次被退稿了。除了再接再厲，別無它法。
2. 於 April 15 週日投 Physical Therapy （考量IF較高，暫不考慮 AJOT 因為裡面太多 Rasch people 了。）
過年後花了不少時間修改及撰寫 response letter，只能當成經驗了
決定1週內改投其它期刊
"PS" 1. 這已是第3次被退稿了。除了再接再厲，別無它法。
2. 於 April 15 週日投 Physical Therapy （考量IF較高，暫不考慮 AJOT 因為裡面太多 Rasch people 了。）
2012年4月4日 星期三
有關4/21學系研討會
我的博班生，好像還沒進入情況。。。
他們不知道，老師一定會「請」他們投稿的，只是何時通知而已。
我只能以身作則：投1篇早上的研討會，投1篇下午的「認知評估與介入」。
一起養成「分享研究成果」之好習慣！
這對 research beginners 而言，也是極佳的學習/練習機會，怎可略之。
Have a nice time to prepare for the symposium!!
他們不知道，老師一定會「請」他們投稿的，只是何時通知而已。
我只能以身作則：投1篇早上的研討會，投1篇下午的「認知評估與介入」。
一起養成「分享研究成果」之好習慣！
這對 research beginners 而言，也是極佳的學習/練習機會，怎可略之。
Have a nice time to prepare for the symposium!!
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