SMHS MethodsHeterogeneity - Revision history

Pineaumi: /* Methods and Approaches for HTE Analytics */

2016-05-23T18:06:52Z

‎Methods and Approaches for HTE Analytics

Imoubara at 13:21, 23 May 2016

2016-05-23T13:21:20Z

Imoubara at 14:40, 19 May 2016

2016-05-19T14:40:58Z

Imoubara at 20:10, 18 May 2016

2016-05-18T20:10:40Z

Imoubara at 12:07, 17 May 2016

2016-05-17T12:07:38Z

Dinov at 13:55, 14 March 2016

2016-03-14T13:55:03Z

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Dinov at 13:08, 14 March 2016

2016-03-14T13:08:12Z

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Dinov at 12:42, 14 March 2016

2016-03-14T12:42:31Z

Imoubara at 12:32, 14 March 2016

2016-03-14T12:32:31Z

Imoubara at 17:55, 11 March 2016

2016-03-11T17:55:37Z

@@ Line 21: / Line 21: @@
 |}
 </center>
 * *CART: Classification and regression tree (CART) analysis

@@ Line 22: / Line 22: @@
 </center>
-<li> *CART: Classification and regression tree (CART) analysis</li>
+* *CART: Classification and regression tree (CART) analysis
-<li> LGM/GMM: Latent growth modeling/Growth mixture modeling.</li>
+* LGM/GMM: Latent growth modeling/Growth mixture modeling.
-<li> QTE: Quantile Treatment Effect.</li>
+* QTE: Quantile Treatment Effect.
-<li> Standard meta-analysis like fixed and random effect models, and tests of heterogeneity, together with various plots and summaries, can be found in the [http://cran.r-project.org/web/packages/rmeta R-package rmeta]. Non-parametric R approaches are included in the [http://cran.r-project.org/web/packages/np/vignettes/np.pdf np package]. </li>
+* Standard meta-analysis like fixed and random effect models, and tests of heterogeneity, together with various plots and summaries, can be found in the [http://cran.r-project.org/web/packages/rmeta R-package rmeta]. Non-parametric R approaches are included in the [http://cran.r-project.org/web/packages/np/vignettes/np.pdf np package].
 Additional details are provided in a paper entitled [http://dx.doi.org/10.1186/1471-2288-12-185 From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer].

@@ Line 29: / Line 29: @@
 Additional details are provided in a paper entitled [http://dx.doi.org/10.1186/1471-2288-12-185 From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer].
-===[[SMHS_MethodsHeterogeneity_HTE |HTE Analytics, Latent growth and growth mixture modeling (LGM/GMM)]]===
+==[[SMHS_MethodsHeterogeneity_HTE |HTE Analytics, Latent growth and growth mixture modeling (LGM/GMM)]]==
-===[[SMHS_MethodsHeterogeneity_MetaAnalysis |Meta-analysis]]===
+==[[SMHS_MethodsHeterogeneity_MetaAnalysis |Meta-analysis]]==
-===[[SMHS_MethodsHeterogeneity_CER| Comparative Effectiveness Research (CER)]]===
+==[[SMHS_MethodsHeterogeneity_CER| Comparative Effectiveness Research (CER)]]==
 <hr>

← Older revision		Revision as of 20:10, 18 May 2016
Line 1:		Line 1:
	==[[SMHS\| Scientific Methods for Health Sciences]] - Methods for Studying Heterogeneity of Treatment Effects, Case-Studies of Comparative Effectiveness Research ==		==[[SMHS\| Scientific Methods for Health Sciences]] - Methods for Studying Heterogeneity of Treatment Effects, Case-Studies of Comparative Effectiveness Research ==
		+
		+	==Methods and Approaches for HTE Analytics==

	<center>		<center>

@@ Line 20: / Line 20: @@
 </center>
-* *CART: Classification and regression tree (CART) analysis
+<li> *CART: Classification and regression tree (CART) analysis</li>
-* ** LGM/GMM: Latent growth modeling/Growth mixture modeling.
+<li> LGM/GMM: Latent growth modeling/Growth mixture modeling.</li>
-* *** QTE: Quantile Treatment Effect.
+<li> QTE: Quantile Treatment Effect.</li>
-* **** Standard meta-analysis like fixed and random effect models, and tests of heterogeneity, together with various plots and summaries, can be found in the [http://cran.r-project.org/web/packages/rmeta R-package rmeta]. Non-parametric R approaches are included in the [http://cran.r-project.org/web/packages/np/vignettes/np.pdf np package].
+<li> Standard meta-analysis like fixed and random effect models, and tests of heterogeneity, together with various plots and summaries, can be found in the [http://cran.r-project.org/web/packages/rmeta R-package rmeta]. Non-parametric R approaches are included in the [http://cran.r-project.org/web/packages/np/vignettes/np.pdf np package]. </li>
 Additional details are provided in a paper entitled [http://dx.doi.org/10.1186/1471-2288-12-185 From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer].

@@ Line 2: / Line 2: @@
 <center>[[Image:SMHS_Methods1.png|500px]] </center>
 * *CART: Classification and regression tree (CART) analysis
@@ Line 9: / Line 7: @@
 * *** QTE: Quantile Treatment Effect.
 * **** Standard meta-analysis like fixed and random effect models, and tests of heterogeneity, together with various plots and summaries, can be found in the R-package <bn>rmeta</b> (http://cran.r-project.org/web/packages/rmeta). Non-parametric R approaches are included in the <b>np</b> package, http://cran.r-project.org/web/packages/np/vignettes/np.pdf.
 ===Methods Summaries===

@@ Line 686: / Line 686: @@
 {| class="wikitable" style="text-align:center; " border="1"
 |-
-| ||Estimate||Std. Error||t value||Pr(>|t|)
+| ||Estimate||Std. Error||t value||$Pr(>|t|)$
 |-
 |(Intercept)||52.0067||1.8080||28.764||< 2e-16 ***
@@ Line 704: / Line 704: @@
 |Tx:SelfEff25||1.0460||0.3118||3.354||0.000829 ***
 |}
 </center>

← Older revision		Revision as of 17:55, 11 March 2016
Line 528:		Line 528:

	<center>[[Image:SMHS_Methods11.png\|500px]] </center>		<center>[[Image:SMHS_Methods11.png\|500px]] </center>
		+
		+	<center>
		+
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\|ID\|\|Day\|\|Tx\|\|SelfEff\|\|SelfEff25\|\|WPSS\|\|SocSuppt\|\|PMss\|\|PMss3\|\|PhyAct
		+	\|-
		+	\|1\|\|1\|\|1\|\|33\|\|8\|\|0.97\|\|5.00\|\|4.03\|\|1.03\|\|53
		+	\|-
		+	\|1\|\|2\|\|1\|\|33\|\|8\|\|-0.17\|\|3.87\|\|4.03\|\|1.03\|\|73
		+	\|-
		+	\|1\|\|3\|\|0\|\|33\|\|8\|\|0.81\|\|4.84\|\|4.03\|\|1.03\|\|23
		+	\|-
		+	\|1\|\|4\|\|0\|\|33\|\|8\|\|-0.41\|\|3.62\|\|4.03\|\|1.03\|\|36
		+	\|-
		+	\|...\|\|...\|\|...\|\|...\|\|...\|\|...\|\|...\|\|...\|\|...\|\|...
		+
		+	\|}
		+	</center> Complete data is available in the <b>Appendix.</b>
		+
		+
		+
		+	<center>Data Summary
		+
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\|Intercept\|\|Constant
		+	\|-
		+	\|Physical Activity\|\|PhyAct
		+	\|-
		+	\|Intervention\|\|Tx
		+	\|-
		+	\|WP Social Support\|\|WPSS
		+	\|-
		+	\|PM Social Support (1-3)\|\|PMss3
		+	\|-
		+	\|Self Efficacy\|\|SelfEff25
		+
		+	\|}
		+	</center>
		+
		+	rm(list=ls())
		+	Nof1 <-read.table("https://umich.instructure.com/files/330385/download?download_frd=1&verifier=DwJUGSd6t24dvK7uYmzA2aDyzlmsohyaK6P7jK0Q", sep=",", header = TRUE) # 02_Nof1_Data.csv
		+	attach(Nof1)
		+	head(Nof1)
		+
		+	<center>
		+
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\| \|\|ID\|\|Day\|\|Tx\|\|SelfEff\|\|SelfEff25\|\|WPSS\|\|SocSuppt\|\|PMss\|\|PMss3\|\|PhyAct
		+	\|-
		+	\|1\|\|1\|\|1\|\|1\|\|33\|\|8\|\|0.97\|\|5.00\|\|4.03\|\|1.03\|\|53
		+	\|-
		+	\|2\|\|1\|\|2\|\|1\|\|33\|\|8\|\|-0.17\|\|3.87\|\|4.03\|\|1.03\|\|73
		+	\|-
		+	\|3\|\|1\|\|3\|\|0\|\|33\|\|8\|\|0.81\|\|4.84\|\|4.03\|\|1.03\|\|23
		+	\|-
		+	\|4\|\|1\|\|4\|\|0\|\|33\|\|8\|\|-0.41\|\|3.62\|\|4.03\|\|1.03\|\|36
		+	\|-
		+	\|5\|\|1\|\|5\|\|1\|\|33\|\|8\|\|0.59\|\|4.62\|\|4.03\|\|1.03\|\|21
		+	\|-
		+	\|6\|\|1\|\|6\|\|1\|\|33\|\|8\|\|-1.16\|\|2.87\|\|4.03\|\|1.03\|\|0
		+
		+	\|}
		+	</center>
		+
		+	df.1 = data.frame(PhyAct, Tx, WPSS, PMss3, SelfEff25)
		+
		+	# library("lme4")
		+
		+	lm.1 = model.lmer <- lmer(PhyAct ~ Tx + SelfEff + Tx*SelfEff + (1\|Day) + (1\|ID) , data= df.1)
		+	summary(lm.1)
		+
		+	Linear mixed model fit by REML ['lmerMod']
		+	Formula: PhyAct ~ Tx + SelfEff + Tx * SelfEff + (1 \| Day) + (1 \| ID)
		+	Data: df.1
		+
		+	REML criterion at convergence: 8820
		+
		+	<center> Scaled Residuals
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\| Min\|\|1Q\|\|Median\|\|3Q\|\|Max
		+	\|-
		+	\|-2.7012\|\|-0.6833\|\|-0.0333\|\|0.6542\|\|3.9612
		+	\|}
		+	</center>
		+
		+
		+	<center> Random Effects
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\| Groups \|\|Name\|\|Variance \|\|Std.Dev.
		+	\|-
		+	\| Day\|\|(Intercept) \|\|0.0 \|\| 0.00
		+	\|-
		+
		+	\|ID\|\| (Intercept)\|\|601.5\|\|24.53
		+	\|-
		+
		+	\|Residual\|\| \|\|969.0 \|\|31.13
		+	\|}
		+	Number of obs: 900, groups: Day, 30; ID, 30
		+	</center>
		+
		+
		+	<center> Fixed Effects
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\|Estimate\|\|Std.\|\|Error\|\|t value
		+	\|-
		+	\|(Intercept)\|\|38.3772\|\|14.4738\|\|2.651
		+	\|-
		+	\|Tx\|\|4.0283\|\|6.3745\|\|0.632
		+	\|-
		+	\|SelfEff\|\|0.5818\|\|0.5942\|\|0.979
		+	\|-
		+	\|Tx:SelfEff\|\|0.9702\|\|0.2617\|\|3.708
		+	\|}
		+	</center>
		+
		+
		+	<center> Correlation of Fixed Effects
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\| \|\|(Intr)\|\|Tx \|\|SlfEff
		+	\|-
		+	\| Tx\|\| -0.220\|\| \|\|
		+	\|-
		+	\| SelfEff\|\|-0.946 \|\|0.208 \|\|
		+	\|-
		+	\| Tx:SelfEff \|\|0.208 \|\|-0.946 \|\|-0.220
		+	\|}
		+	</center>
		+
		+
		+	# Model: PhyAct = Tx + WPSS + PMss3 + TxWPSS + TxPMss3 + SelfEff25 + Tx*SelfEff25 + ε
		+	lm.2 = lm(PhyAct ~ Tx + WPSS + PMss3 + TxWPSS + TxPMss3 + SelfEff25 + Tx*SelfEff25, df.1)
		+	summary(lm.2)
		+
		+	Call:
		+	lm(formula = PhyAct ~ Tx + WPSS + PMss3 + Tx * WPSS + Tx * PMss3 +
		+	SelfEff25 + Tx * SelfEff25, data = df.1)
		+
		+	<center> Residuals
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\|Min\|\|1Q\|\|Median\|\|3Q\|\|Max
		+	\|-
		+	\| -102.39\|\|-28.24\|\|-1.47\|\|25.16\|\|122.41
		+
		+	\|}
		+	</center>
		+
		+	<center>Coefficients
		+	{\| class="wikitable" style="text-align:center; " border="1"
		+	\|-
		+	\| \|\|Estimate\|\|Std. Error\|\|t value\|\|Pr(>\|t\|)
		+	\|-
		+	\|(Intercept)\|\|52.0067\|\|1.8080\|\|28.764\|\|< 2e-16 ***
		+	\|-
		+	\|Tx\|\|27.7366\|\|2.5569\|\|10.848\|\|< 2e-16 ***
		+	\|-
		+	\|WPSS\|\|1.9631\|\|2.4272\|\|0.809\|\|0.418853
		+	\|-
		+	\|PMss3\|\|13.5110\|\|2.7853\|\|4.851\|\|1.45e-06 ***
		+	\|-
		+	\|SelfEff25\|\|0.6289\|\|0.2205\|\|2.852\|\|0.004439 **
		+	\|-
		+	\|Tx:WPSS\|\|9.9114\|\|3.4320\|\|2.888\|\|0.003971 **
		+	\|-
		+	\|Tx:PMss3\|\|8.8422\|\|3.9390\|\|2.245\|\|0.025025 *
		+	\|-
		+	\|Tx:SelfEff25\|\|1.0460\|\|0.3118\|\|3.354\|\|0.000829 ***
		+
		+
		+	\|}
		+	</center>